ALBERT

Description: Knightian uncertainty embedded in stock returns causes rising demand for life insurance, as the uncertainty averse agent seeks alternative investment channels. Life insurance demand of middle-aged agent is more sensitive to the uncertainty. Stock return uncertainty reduces the agent’s total wealth and subsequently the propensity of wealthy agent serving as an insurance seller. Rising demand and falling supply of life insurance imply that life insurance is more expensive in the presence of stock return uncertainty. Sensitivity of life insurance demand to the mortality rate and key stock return characteristics also changes with the uncertainty.

Description: The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average.

Description: In this study, a novel second-order prediction differential model is designed, and numerical solutions of this novel model are presented using the integrated strength of the Adams and explicit Runge–Kutta schemes. The idea of the present study comes to the mind to see the importance of delay differential equations. For verification of the novel designed model, four different examples of the designed model are numerically solved by applying the Adams and explicit Runge–Kutta schemes. These obtained numerical results have been compared with the exact solutions of each example that indicate the performance and exactness of the designed model. Moreover, the results of the designed model have been presented numerically and graphically.

Description: Experimental studies involving control against virus propagation have attracted the interest of scientists. However, most accomplishments have been constrained by the simple assumption of a single virus in various networks, but this assumption apparently conflicts with recent developments in complex network theory, which details that each node might play multiple roles in different topological connections. Multiple viruses propagate through individuals via different routes, and thus, each individual component could be located in various positions of differing importance in each virus propagation process in each network. Therefore, we propose several control strategies for establishing a multiple-virus interaction and propagation model involving multiplex networks, including a novel Multiplex PageRank target control model and a multiplex random control model. Using computer experiments and simulations derived from actual examples, we exploit several actual cases to determine the relationship of the relative infection probability with the immunization probability. The results demonstrate the differences between our multiple-virus interaction and propagation model and the single-virus propagation model and verify the effectiveness of our novel Multiplex PageRank target control strategy. Moreover, we use parallel computing for simulating and identifying the relationships of the immunization thresholds with both interaction coefficients, which is beneficial for further practical applications because it can reduce the multiple interactions between viruses and allows achieving a greater effect through the immunization of fewer nodes in the multilayer networks.

Description: With the continuous development of society, the cooperation of different dimensions is urgently needed. Analysis and modeling of team cooperation model and performance evaluation are especially important for competitive sport. In this paper, a football team’s attacking mode and the team performance were assessed using network science methodologies. The match process was analyzed by using the data of Team A (given in the form of attachment due to excessive file size) and the method of complex network science. Each player was regarded as a node in the network, and the interaction among players was considered as the connection to the network. This method directly reflected the favorable formation of the team and the interaction frequency among members. Then, a team performance evaluation model was established using the backpropagation neural network (BPNN) and the uncrossed analytic hierarchy process (U-AHP) method based on the factors including the number of passes and successful pass rate. The team performance was comprehensively rated from two levels: member and team level. Analysis from established models indicated that Team A had a higher probability of winning when using the “4-4-2” offensive strategy and performance evaluation analysis indicated that more passes and higher pass success rates were more beneficial to win the game. Following the model developed in this study, some suggestions were given from the perspectives of team strategy, attack mode, cooperation, and incentive mode.

Description: While the two-dimensional (2D) spectral peak search suffers from expensive computational burden in direction of arrival (DOA) estimation, we propose a reduced-dimensional root-MUSIC (RD-Root-MUSIC) algorithm for 2D DOA estimation with coprime planar array (CPA), which is computationally efficient and ambiguity-free. Different from the conventional 2D DOA estimation algorithms based on subarray decomposition, we exploit the received data of the two subarrays jointly by mapping CPA to the full array of the CPA (FCPA), which contributes to the enhanced degrees of freedom (DOFs) and improved estimation performance. In addition, due to the ambiguity-free characteristic of the FCPA, the extra ambiguity elimination operation can be avoided. Furthermore, we convert the 2D spectral search process into 1D polynomial rooting via reduced-dimension transformation, which substantially reduces the computational complexity while preserving the estimation accuracy. Finally, numerical simulations demonstrate the superiority of the proposed algorithm.

Description: Recently, there has been tremendous research studies in optical neural networks that could complete comparatively complex computation by optical characteristic with much more fewer dissipation than electrical networks. Existed neural networks based on the optical circuit are structured with an optical grating platform with different diffractive phases at different diffractive points (Chen and Zhu, 2019 and Mo et al., 2018). In this study, it proposed a multiwave deep diffractive network with approximately 106 synapses, and it is easy to make hardware implementation of neuromorphic networks. In the optical architecture, it can utilize optical diffractive characteristic and different wavelengths to perform different tasks. Different wavelengths and different tasks inputs are independent of each other. Moreover, we can utilize the characteristic of them to inference several tasks, simultaneously. The results of experiments were demonstrated that the network could get a comparable performance to single-wavelength single-task. Compared to the multinetwork, single network can save the cost of fabrication with lithography. We train the network on MNIST and MNIST-FASHION which are two different datasets to perform classification of 32∗32 inputs with 10 classes. Our method achieves competitive results across both of them. In particular, on the complex task MNIST_FASION, our framework obtains an excellent accuracy improvement with 3.2%. In the meanwhile, MNSIT also has the improvement with 1.15%.

Description: A spline finite point method (SFPM) based on a locking-free thin/thick plate theory, which is suitable for analysis of both thick and thin plates, is developed to study nonlinear bending behavior of functionally graded material (FGM) plates with different thickness in thermal environments. In the proposed method, one direction of the plate is discretized with a set of uniformly distributed spline nodes instead of meshes and the other direction is expressed with orthogonal functions determined by the boundary conditions. The displacements of the plate are constructed by the linear combination of orthogonal functions and cubic B-spline interpolation functions with high efficiency for modeling. The locking-free thin/thick plate theory used by the proposed method is based on the first-order shear deformation theory but takes the shear strains and displacements as basic unknowns. The material properties of the FG plate are assumed to vary along the thickness direction following the power function distribution. By comparing with several published research studies based on the finite element method (FEM), the correctness, efficiency, and generality of the new model are validated for rectangular plates. Moreover, uniform and nonlinear temperature rise conditions are discussed, respectively. The effect of the temperature distribution, in-plane temperature force, and elastic foundation on nonlinear bending under different parameters are discussed in detail.

Description: In this paper, we investigate the existence of multiple positive solutions or at least one positive solution for fractional three-point boundary value problem with p-Laplacian operator. Our approach relies on the fixed point theorem on cones. The results obtained in this paper essentially improve and generalize some well-known results.

Description: The optimization of high-dimensional functions is an important problem in both science and engineering. Particle swarm optimization is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new particle swarm optimization algorithm that can accurately compute the optimal value of a high-dimensional function. The iteration process of the algorithm is comprised of a number of large iteration steps, where a large iteration step consists of two stages. In the first stage, an expansion procedure is utilized to effectively explore the high-dimensional variable space. In the second stage, the traditional particle swarm optimization algorithm is employed to compute the global optimal value of the function. A translation step is applied to each particle in the swarm after a large iteration step is completed to start a new large iteration step. Based on this technique, the variable space of a function can be extensively explored. Our analysis and testing results on high-dimensional benchmark functions show that this algorithm can achieve optimization results with significantly improved accuracy, compared with traditional particle swarm optimization algorithms and a few other state-of-the-art optimization algorithms based on particle swarm optimization.

Description: With the continuous development of deep learning, the performance of the intelligent diagnosis system for ocular fundus diseases has been significantly improved, but during the system training process, problems like lack of fundus samples and uneven sample distribution (the number of disease samples is much smaller than the number of normal samples) have become increasingly prominent. In view of the previous issues, this paper proposes a method for generating fundus images based on “Combined GAN” (Com-GAN), which can generate both normal fundus images and fundus images with hard exudates, so that the sample distribution can be more even, while the fundus data are expanded. First, this paper uses existing images to train a Com-GAN, which consists of two subnetworks: im-WGAN and im-CGAN; then, it uses the trained model to generate fundus images, then performs qualitative and quantitative evaluation on the generated images, and adds the images to the original image set to expand the datasets; finally, based on this expanded training set, it trains the hard exudate detection system. The expanded datasets effectively improve the generalization ability of the system on the public datasets DIARETDB1 and e-ophtha EX, thereby verifying the effectiveness of the proposed method.

Description: The claim process in an insurance risk model with uncertainty is traditionally described by an uncertain renewal reward process. However, the claim process actually includes two processes, which are called the report process and the payment process, respectively. An alternative way is to describe the claim process by an uncertain alternating renewal reward process. Therefore, this paper proposes an insurance risk model under uncertain measure in which the claim process is supposed to be an alternating renewal reward process and the premium process is regarded as a renewal reward process. Then, the paper also gives the inverse uncertainty distribution of the insurance risk process. The expression of ruin index and the uncertainty distribution of the ruin time are derived which both have explicit expressions based on given uncertainty distributions. Finally, several examples are provided to illustrate the modeling ideas.

Description: We consider the problem of tracking a surface magnetic ship as it travels in a straight line path with the exertion of a magnetometer located at the seabed. Note that the initial filter parameters are prior information and the tracking performance depends on the initial filter parameters, and traditional estimation of initial filter parameters is to apply the filter bank algorithm, but there are several obvious defects in this method. In this paper, a novel algorithm based on the particle swarm optimization (PSO) algorithm is proposed to estimate initial parameters of the filter, and the model of uniformly magnetized ellipsoid is adopted to fit the magnetic field of the ship. The simulation results show that, under the condition of no prior information, the estimated ship parameters based on the observation of the single-observer are invalid, whereas the estimated ship parameters based on the observation of the double-observer are valid. Further, the estimated results of real-world recorded magnetic signals show that the ship parameters estimated by PSO based on the double-observer are also valid, as the estimated parameters are used as the initial parameters of the unscented Kalman filter (UKF), and a ship can be tracked effectively by the UKF filter. Moreover, the estimated half focal length can be used as a feature to distinguish noise environment, ships with different sizes, and mine sweepers.

Description: Deep-Argo Otarriinae profiling float is a new type of Argo profiling float that has a maximum diving depth of more than 4,000 m. It can collect ocean scientific data all-weather and uninterruptedly, which provides reliable data support for the global ocean scientific research. The working time of Deep-Argo profiling float is an important indicator of its practicality and economy, and it is clear that the energy consumption is a key factor in determining its working time. In this paper, the single profile energy consumption model with 19 parameters of Deep-Argo Otarriinae is established and the main effect indices and total effect indices of the energy consumption parameters to energy consumption are calculated using Sobol’ sensitivity analysis method, aiming to find the parameters that have the greatest impact on energy consumption. The results show that the gliding angle, the diving depth, and the gliding speed have a significant impact on energy consumption of Deep-Argo Otarriinae. The results of simulation have a good match with the actual application and have certain reference significance for the determination of the design parameters and the selection of the navigation parameters. This paper also provides a new idea of multiparameter energy consumption modeling for underwater equipment using buoyancy regulation.

Description: In this paper, we investigate the code design problem of improving the detection performance of a moving target in the presence of nonhomogeneous signal-dependent clutter for moving target-detecting (MTD) radar systems. The optimization metric is constructed based on the signal to clutter and noise ratio (SCNR) of interpulse matched filtering. Under the frameworks of cyclic and majorization-minimization algorithms, we propose a novel algorithm, named CMMCODE, to tackle the code design optimization problem in the case of unknown precise target Doppler information and nonhomogeneous clutter. In the white-noise case, the simplified algorithm is also given based on CMMCODE algorithm. The presented algorithm is computationally efficient and convergent. Numerical examples show the effectiveness of the proposed algorithms.

Description: In this study, the Tiwari and Das model is numerically studied, in case of a moving plate containing both single-walled and multiwalled carbon nanotubes (SWCNTs and MWCNTs, respectively), in the presence of thermal radiation and the slip effect. Employing the similarity transformation, a set of 2nd-order partial differential equations (which are used to model the flow and heat transfer) are solved numerically using the boundary value problem with 4th-order accuracy (BVP4C) method. The effects of related parameters, such as the volume fraction of nanoparticles, moving, slip, and radiation parameter on the heat transfer performance are analysed and discussed. Results indicate that a unique solution was placed when the plate travels in assisting flow conditions. Additionally, as the nanoparticle volume fraction (φ) rises at φ = 0.2, the skin friction and heat transfer rate decrease. It is also observed that when the slip parameter (β) increases at β = 0.4, the skin friction decreases, whereas the heat transfer rate increases. Meanwhile, the heat transfer rate decreases when the thermal radiation (NR) increases to 0.7. Moreover, it is found that the SWCNTs are more efficient when the skin friction coefficient and the Nusselt number are considered. It is found that the Weibull distribution is more suitable in fitting the skin friction data.

Description: Since the tailing dam fails catastrophically with substantial instantaneous deformation, it is difficult to measure the migration of debris flow caused by the failure of the tailings dam. A simulation model of tailing debris flow based on Smoothed Particle Hydrodynamics (SPH) theory of elastic-plastic constitutive equation has been established by considering the viscoplasticity of mud and the elastic-plastic characteristics of tailing sand to investigate the impact effect of tailing flow on the downstream structures. By comparing the experimental and two different simulation results obtained, it can be concluded that SPH elastic-plastic constitutive model can effectively simulate the accumulation and migration processes of the tailing debris flow, which indicates that the SPH model has good applicability to solve geotechnical large deformation problems of similar tailings flow slide. Then, the verified simulation model developed based on a series of simulations of tailing debris flow propagations was used to determine the momentum reduction on the downstream structure resulting from the presence of a simple checking dam perpendicular to the direction of propagation and to determine the characteristics of stresses applied to this structure in terms of peak impact force and evolution over time to the main flow direction.

Description: The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China’s hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident.

Description: Geological strength index GSI, disturbance factor (D), material constant mi, and uniaxial compressive strength σci of the intact rock are essential input parameters IPs of the Hoek–Brown H−B criterion. Mechanical parameters MPs of the engineering rock mass, including elastic modulus E, cohesion c, and internal friction angle φ estimated by the H–B criterion, and the predicted excavation response of surrounding rock, including the displacement and excavation damage zone EDZ based on the MPs, are of high relevance with the four IPs of the H–B criterion. In this paper, the deep and huge underground cavern excavated in basalt from a hydropower station under construction in the southwest of China is used to analyse the sensitivity of the IPs on the MPs, the displacement, and EDZ of the surrounding rock mass. Firstly, the H–B criterion is applied to estimate the MPs, among which the IPs are obtained from a series of in situ and laboratory tests, including borehole camera observation, wave velocity test, uniaxial and triaxial compression tests, and so on. Secondly, the sensitivity relationships between IPs, MPs, and prediction results of displacement and EDZ are established and described quantitatively by the sensitivity factor (si). Results show that the MPs of the rock mass are more sensitive to GSI and D⋅GSI and σci are high-sensitivity parameters affecting the displacement and EDZ. Finally, the variations in the estimated MPs and associated prediction results concerning excavation response, which are caused by the uncertainties in the determination of the IPs, are further quantified. This study provides a straightforward assessment for the variability of the rock mass parameters estimated by the H–B criterion. It also gives a valuable reference to similar geotechnical engineering for the determination of rock mass parameters in the preliminary design.

Description: Seismic data denoising and interpolation are generally essential steps for reflection processing and imaging workflow especially for the complex surface geologic conditions and the irregular acquisition field area. The rank-reduction method is a valid way for the attenuation of random noise and data interpolation by selecting the suitable threshold, i.e., the rank of the useful signals. However, it is difficult for the traditional rank-reduction method to select an appropriate threshold. In this paper, we propose an adaptive rank-reduction method based on the energy entropy to automatically estimate the rank as the threshold for seismic data processing and interpolation. This method considers the energy entropy into the traditional rank-reduction method. The energy entropy of signals can be used to indicate the energy intensity of a signal component in the total energy. The difference of the energy entropy between the useful signals and random noise is perceived as a measurement for selecting the appropriate threshold. Synthetic and field examples indicate that the proposed method can well achieve the attenuation of random noise and interpolation automatically without the estimation of the ranks and demonstrate the feasibility of the new adaptive method in seismic data denoising and interpolation.

Description: In order to better track the planned trajectory of Delta high-speed parallel robot, this paper proposes a dynamics control strategy for Delta high-speed parallel robots based on the linear active disturbance rejection control (LADRC) strategy which realizes decoupling control through observing and compensating the coupling and internal and external disturbances between the three joints. Firstly, the structure and dynamics model of the Delta high-speed parallel robot are analyzed, respectively. Secondly, the control scheme of the Delta high-speed parallel robot dynamic LADRC strategy is constructed, and then, the system stability is analyzed. Taking a representative 8-shaped space helical variance trajectory as a given input of the system and a triangular wave as an external disturbance as given disturbance input of the system, simulations are carried out to demonstrate the effectiveness of the proposed LADRC strategy; results indicate that the system with the LADRC strategy has a good quick and precise real-time trajectory tracking and strong robustness.

Description: In this study, a piezoelectric laminate is analyzed by applying the Laplace transform and its numerical inversion, Fourier transform, differential quadrature method (DQM), and state space method. Based on the modified variation principle for the piezoelectric laminate, the fundamental equations for dynamic problems are derived. The solutions for the displacement, stress, electric potential, and dielectric displacement are obtained using the proposed method. Durbin’s inversion method for the Laplace transform is adopted. Four boundary conditions are discussed through the DQM. The proposed method is validated by comparing the results with those of the finite element method (FEM). Moreover, this semianalytical method is further extended to describe the dynamic response of piezoelectric laminated plates subjected to underwater shocks by introducing Taylor’s fluid-structure interaction algorithm. Both air-backed and water-backed laminated plates are investigated, and the effect of the fluid is examined. In the time domain, the electric potential and displacements of sample points are calculated under four boundary conditions. The present method is shown to be accurate and can be a useful method to calculate the dynamic response of underwater laminated sensors.

Description: The output values for agriculture, forestry, animal husbandry, and fishery are important indicators of agricultural economic development. Therefore, accurately predicting the output values for agriculture, forestry, animal husbandry, and fishery can capture the developmental trend and the optimize the structure. Agriculture, forestry, animal husbandry, and fishery are typical seasonal industries, and thus their output values vary greatly among different seasons. To accurately estimate the seasonal variations in the observed sequence and obtain better prediction results, the output values for agriculture, forestry, animal husbandry, and fishery in different quarters from 2018 to 2021 are predicted and analyzed by using the grey seasonal model (GSM). The results indicated that the prediction accuracy of GSM is relatively high. The output values for the agriculture, forestry, animal husbandry, and fishery as well as their total output value will increase gradually. It is an important achievement of structural reform under the new normal economic situation. In addition, the GSM provides a new method for predicting seasonal data.

Description: Morphing aircraft are the flight vehicles that can reconfigure their shape during the flight in order to achieve superior flight performance. However, this promising technology poses cross-disciplinary challenges that encourage widespread design possibilities. This research aims to investigate the flight dynamic characteristics of various morphed wing configurations that can be incorporated in small-scale UAVs. The objective of this study was to analyze the effects of in-flight wing sweep and wingspan morphing on aerodynamic and flight stability characteristics. Longitudinal, lateral, and directional characteristics were evaluated using linearized equations of motion. An open-source code based on Vortex Lattice Method (VLM) assuming quasi-steady flow was used for this purpose. Trim points were identified for a range of angles of attack in prestall regime. The aerodynamic coefficients and flight stability derivatives were compared for the aforementioned morphing schemes with a fixed-wing counterpart. The results indicated that wingspan morphing is better than wing sweep morphing to harness better aerodynamic advantages with favorable flight stability characteristics. However, extension in wingspan beyond certain limits jeopardizes the advantages. Dynamically, wingspan and sweep morphing schemes behave in an exactly opposite way for longitudinal modes, whereas lateral-directional dynamics act in the same fashion for both morphing schemes. The current study provided a baseline to explore the advanced flight dynamic aspects of employed wing morphing schemes.

Description: The purpose of this paper is to introduce a new four-step iteration scheme for approximation of fixed point of the nonexpansive mappings named as S∗-iteration scheme which is faster than Picard, Mann, Ishikawa, Noor, Agarwal, Abbas, Thakur, and Ullah iteration schemes. We show the stability of our proposed scheme. We present a numerical example to show that our iteration scheme is faster than the aforementioned schemes. Moreover, we present some weak and strong convergence theorems for Suzuki’s generalized nonexpansive mappings in the framework of uniformly convex Banach spaces. Our results extend, improve, and unify many existing results in the literature.

Description: In view of the good properties of copulas and their effective use in various fuzzy environments, the goal of the current study is to develop a series of aggregation operators for hesitant fuzzy information based on Archimedean copula and cocopula, which are applied to the MADM problems. Firstly, operational laws of hesitant fuzzy elements on the basis of copulas and cocopulas are defined which can show the relevance between hesitant fuzzy values. Secondly, four aggregation operators (AC-HFWA, AC-GHFWA, AC-HFWG, and AC-GHFWG) under hesitant fuzzy environment are developed according to the proposed operational laws. The properties of these operators are also studied in detail, including idempotence, monotonicity, boundedness, etc. Subsequently, five special cases of copula are also given and the special forms of aggregation operator are obtained. In the end, an example is used to illustrate the application of the proposed approach in MADM problems. The influences of different generated functions and parameters are shown, and the feasibility of the proposed method is validated through comparative analyses.

Description: We investigate how to correct exposure of underexposed images. The bottleneck of previous methods mainly lies in their naturalness and robustness when dealing with images with various exposure levels. When facing well-exposed or extremely underexposed images, they may produce over- or underenhanced outputs. In this paper, we propose a novel retinex-based approach, namely, LiAR (short for lightness-aware restorer). The word “lightness-aware” refers to that the estimated illumination not only is a component to be adjusted but also serves as a measure that reflects the brightness of the scene, determining the degree of adjustment. In this way, underexposed images can be restored adaptively according to their own brightness. Given an image, LiAR first estimates its illumination map using a specially designed loss function which can ensure the result’s color consistency and texture richness. Then adaptive correction is performed to get properly exposed output. LiAR is based on internal optimization of the single test image and does not need any prior training, implying that it can adapt itself to different settings per image. Additionally, LiAR can be easily extended to the video case due to its simplicity and stability. Experiments demonstrate that facing images/videos with various exposure levels, LiAR can achieve robust and real-time correction with high contrast and naturalness. The relevant code and collected data are publicly available at https://cslinzhang.github.io/LiAR-Homepage/.

Description: Hybrid excitation synchronous motor (HESM) offers the advantages of a convenient flux regulation and a wide range of available speeds. As such, it may be conveniently employed in the fields of electric vehicle and aerospace. In this paper, based on a suitable control strategy for vector and speed partition, we put forward a speed control method for HESM which shows optimal efficiency. On the one hand, our method aims at minimizing the copper loss by an optimal configuration of the q-axis current, the d-axis current, and the excitation current under the constraint voltage at the armature end. On the other hand, we conduct a comprehensive analysis of the effects of the flux-weakening speed coefficient on the operating performance of the motors and suggest a self-adaptive control method to regulate that coefficient and further improve the overall performance of the motors, including their speed-range regulation, their efficiency, and their dynamical properties. The proposed flux-weakening control method has been assessed by simulations and HESM-driven experiments. Results have confirmed the feasibility and optimality of our method.

Description: A WAcrobot is an underactuated nonlinear system that has three degrees of freedom (DOF) and two inputs. This paper discusses the global stabilization control problem for this 3-DOF underactuated system. A new control strategy is developed to solve this problem. The strategy first changes the 3-DOF WAcrobot system to be a 2-DOF reduced-order model in finite time. This transforms the stabilizing control of the WAcrobot system into that of the reduced-order model. After that, nonsingular control laws that globally stabilize the reduced-order model at the origin are designed. It guarantees the stabilizing control objective of the WAcrobot to be achieved. Finally, a simulation experimental example demonstrates the validity of the presented theoretical results. Simulation results show the advantage of our strategy over others.

Description: This study tests and verifies the main factors influencing consumers’ intention to purchase and the marketing strategy of remanufactured new-energy automobile parts in China. The revised model of Theory of Reasoned Action is used to study the factors influencing consumers’ intention to purchase. The main factors influencing consumer purchase intention of remanufactured new-energy automobile parts in China are identified through correlation analysis of factors including subjective norms, attitudes, face consciousness, brand extension, and perceived risk. Through market segmentation, consumers in China are divided into three types: passive-accepted, brand-driven, and green-consuming; the marketing strategy is developed accordingly. This study provides theory and decision-making basis for remanufactured new-energy automobile parts markets.

Description: As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm is frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some drawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or lacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these shortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is proposed in this paper. Different from the above mentioned algorithms which take clustering as a continuous optimization problem, COCA solves the clustering problem from the perspective of combinatorial optimization. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem. Then, the binary particle coding scheme of cluster head is proposed, which is based on the corresponding relationship between nodes and particle position vectors, and the fitness function is designed according to the parameters used in the process of cluster formation. Finally, the binary particle swarm optimization algorithm is applied to implement the clustering. COCA is validated under different scenarios compared with three other clustering algorithms. The simulation results show that COCA has better performance than its comparable algorithms.

Description: The operation and maintenance sectors of electric multiple unit (EMU) are considered as game participants to optimize the preventive maintenance (PM) schedule of EMU components. The total cost of a component over a life cycle includes failure risk and maintenance cost. The failure risk of EMU components is assessed quantitatively by using an analytic hierarchy process and scoring and weighting the factors that affect the failure risk of such components. The operation sector expects failure risk to be minimized, whereas the maintenance sector expects maintenance costs to be low, and their interests interfere with each other to some extent. This study establishes a tristage bargaining dynamic game model of the operation sector priority bid and maintenance sector priority bid considering the PM reliability threshold R as the bargaining object. A numerical example demonstrates that the result is more beneficial to the sector that bids first, and the operation priority bid allows the component to maintain a higher reliability level over a life cycle, especially in the latter half of the component life cycle.

Description: In contrast to the econometric models that have been commonly used throughout a large portion of the literature, we develop six game-theoretic models to analyze governmental subsidy strategies in different market environments and to investigate the question of whether government subsidies crowd in or crowd out private investment in R&D activities. Based on realistic situations, we classify governmental subsidy strategies into three types, namely, no subsidy provided, subsidies provided based on the price of the end products, and subsidies provided based on the cost of R&D. In addition, according to whether competition exists in the market, we classify markets into monopoly markets and duopoly markets. Our research shows (a) that the relationship between government subsidies and private R&D investment is deeply impacted by the form of the subsidies used; (b) that the characteristic value of the R&D project and the competitive environment of the market are the two key factors that should be considered when governments decide which form of subsidy to employ; and (c) the optimal amount for each type of subsidy.

Description: Recent research has shown that, during integrated piercing, the yarn tension can displace the needles from the centers of the holes in the piercing template. To reduce the damage done to the needle tips when the needles and the hole walls collide when the needle displacement is too large, this paper proposes a method for optimizing the needle shape that satisfies the strength constraint while targeting minimum needle displacement. First, the optimal objective function and strength constraint conditions for the tip displacement of the integrated puncture needle are established, which are affected by many factors. Then, the feature selection method of machine learning is used to reduce the dimensionality of the objective function after data reduction, and the feasible region of key features is reduced to avoid falling into the local best point in the optimization iteration. Finally, machine vision is used to measure experimentally the tip displacement of a needle array. The results show that the average tip displacement is reduced by 18.16–31.42% and the collision risk is reduced from 43.14% to 5.54%. It shows that the optimization method of needle shape based on feature selection is effective for reducing needle tip impact damage during integrated piercing.

Description: The nozzle flapper pressure servo valve is a kind of high-precision hydraulic component that can be widely used in the aircraft brake system. In actual service, the dynamic erosion wear behavior will occur at the pilot stage because of the gradual contamination of oil and the variable distance between nozzle and flapper. For this purpose, the paper proposes a dynamic erosion wear characteristics analysis and service life prediction method in which firstly the structural feature and working principle of the nozzle flapper pressure valve are analyzed using the brake cavity as the load blind cavity. Secondly, the dynamics simulation model and the performance experiment system of the pressure valve are separately constructed, and then the validation of the constructed model is conducted by contrasting the results between simulation and experiment. Finally, the mathematical models of the degradation process induced by the dynamic erosion wear are established, and then the dynamic erosion wear characteristics under dynamic structural distance and contamination conditions are analyzed, which are combined with the failure threshold value determined by the dynamics simulation to finish the service life prediction of the nozzle flapper pressure servo valve.

Description: Sparse recovery is one of the most important methods for single snapshot DOA estimation. Due to fact that the original l0-minimization problem is a NP-hard problem, we design a new alternative fraction function to solve DOA estimation problem. First, we discuss the theoretical guarantee about the new alternative model for solving DOA estimation problem. The equivalence between the alternative model and the original model is proved. Second, we present the optimal property about this new model and a fixed point algorithm with convergence conclusion are given. Finally, some simulation experiments are provided to demonstrate the effectiveness of the new algorithm compared with the classic sparse recovery method.

Description: The applications of finite commutative ring are useful substances in robotics and programmed geometric, communication theory, and cryptography. In this paper, we study the vertex-based eccentric topological indices of a zero-divisor graphs of commutative ring ℤp2×ℤq, where p and q are primes.

Description: With comprehensive application of the theories of coating shear failure mechanism and fatigue cumulative damage, a life prediction method of a gun barrel is proposed based on the shear fatigue damage accumulation at the coating-substrate interface. The life of a small-caliber gun barrel is predicted by use of constant tensile strength at normal temperature and temperature-dependent tensile strength, respectively. The influence of the two kinds of tensile strength on barrel life prediction results is analyzed. Life test proves that the prediction method proposed here is credible and practical. The research results show that the reduction of interface tensile strength due to temperature rise in the firing process is an important inducement of interface damage and gun barrel failure. When the temperature-dependent tensile strength is considered in life prediction model, the prediction results are smaller than that predicted by use of constant tensile strength and well matched with the life test results. Therefore, the temperature-dependent tensile strength should be incorporated in the model of gun barrel life prediction.

Description: Grid-based Generalized Voronoi Diagrams (GVDs) are widely used to represent the surrounding environment of intelligent agents in the fields of robotics, computer games, and military simulations, which improve the efficiency of path planning of intelligent agents. Current studies mainly focus on square-grid-based GVD construction approaches, and little attention has been paid to constructing GVDs from hexagonal grids. In this paper, an algorithm named hexagon-based crystal growth (HCG) is presented to extract GVDs from hexagonal grids. In addition, two thinning patterns for obtaining one-cell-wide GVDs from rough hexagon-based GVDs are proposed. On the basis of the principles of a leading square-grid-based algorithm named Brushfire, a hexagon-based Brushfire algorithm is realized. A comparison of the HCG and the hexagon-based Brushfire algorithm shows that HCG is much more efficient. Further, the usefulness of hexagon-based GVDs for the path planning tasks of intelligent agents is demonstrated using several representative simulation experiments.

Description: To improve the efficient and precise seeding of potatoes, a novel combination vacuum and spoon belt metering device was designed. The overall structure and working principle of vacuum and spoon belt metering devices were illustrated and analyzed. The stress of the potato seed situated on the vacuum and spoon belt metering device was analyzed theoretically. The structure and parameters of the key parts of the seed metering device were studied and calculated. Three-factor and three-level response surface experiments were designed based on the Box–Behnken central composite experimental design principle. After selecting seeding speed, spoon aperture, and cleaning-seed air amount as the experimental factors and selecting missing seed index, multiple seed index, and qualified seed index as the experimental indexes, the performance of the seed metering device with high efficiency and precision was verified by the experiments. The mathematical model of the response surface was established, and the influence of each factor on the performance of the seed metering device was analyzed using Design-Expert 10.0.4 software. To improve the efficient and precise seeding of potatoes, the three experimental factors were optimized. Experimental results show that the order of the factors affecting the missing seed index was seeding belt speed 〉 cleaning air pressure 〉 spoon aperture; the order of the factors affecting the multiple seed index was spoon aperture 〉 cleaning air pressure 〉 seeding belt speed; the order of the factors affecting the qualified seed index was seeding belt speed 〉 cleaning air pressure 〉 spoon aperture; when the seeding belt speed was 0.43 m·s−1, the spoon aperture was 15.72 mm, and the cleaning air pressure was 2.94 kPa, the experiment had realized potato’s highly efficient and precise seeding and the missing seed index was 3.97%, the multiple seed index was 4.65%, and the qualified seed index was 91.38%. This paper can provide a theoretical and technical reference for improving the efficient and precise seeding of potatoes.

Description: Roll-to-Roll (R2R) processing is a common processing method for flexible photoelectric film materials. Due to the physical properties of the materials, the change in the performance of the R2R processing equipment can easily cause deformation of the flexible film material, it is particularly important to predict the performance degradation of the processing equipment. Based on the accuracy and real-time requirements of performance degradation prediction, a PTS-FNN model for performance degradation prediction was proposed in this paper, which combines the Possibilistic C-Means (PCM) fuzzy clustering and Takagi–Sugeno Fuzzy Neural Network (TS-FNN). We also studied the PCM classification algorithm of input data of PTS-FNN model, the predecessor network of TS-FNN prediction model and the construction method of post-component network. Finally, the implementation process of PCM classification algorithm and TS-FNN prediction model were given. The R2R processing equipment health prediction experiment system was built and the PTS-FNN model experiment was carried out. The experimental results showed that the training time of PTS-FNN model was 50.37% less than the standard TS-FNN prediction model. The prediction accuracy increased by 5.48%, and the PTS-FNN had no error in the judgment of state 1 and state 4.

Description: This study constructs an optimization model to address ski-jump energy dissipation problems in different reservoir environments. The multiobjective genetic algorithm is herein applied as the calculation method. In the process of changing the ski-jump flow model, the runoff of ski-jump flow is changed (especially the abnormal flip bucket). Water flow might go from having a single falling point to multiple falling points. Optimization is only performed after the stabilization of the fall of the water because the fall of the water from the starting point to the ground is not stable over a short period. Only a stable flow can reduce the computation time for optimization. The optimal overflow width and height of the flip bucket were calculated through optimal computation, which can minimize the scouring force of the water flow as it falls to the ground. The results obtained provide theoretical references for practical engineering and reduce the potential safety hazards. The energy in flip buckets following the optimization of the water flow can be fully dissipated, creating an ecosystem where water can flow unobstructed. Guiding a water flow to water-deficient areas is of great significance in ensuring the long-term protection of environments and ecosystems.

Description: Boundary behavior of important functions has been an object of intensive research since the time of Riemann. Kurokawa, Kurokawa-Koyama, and Chapman studied the boundary behavior of generalized Eisenstein series which falls into this category. The underlying principle is the use of the Lipschitz summation formula. Our purpose is to show that it is a form of the functional equation for the Lipschitz–Lerch transcendent (and in the long run, it is equivalent to that for the Riemann zeta-function) and that this being indeed a boundary function of the Hurwitz–Lerch zeta-function, one can extract essential information. We also elucidate the relation between Ramanujan’s formula and automorphy of Eisenstein series.

Description: In the structural earthquake engineering, a single parameter is often not sufficient enough to depict the severity of ground motions, and it is thus necessary to use multiple ones. In this sense, the correlation among multiple parameters is generally considered as an importance issue. The conventional approach for developing the correlation is based on regression analysis, along with simple pair copula approaches proposed in recent years. In this study, an innovative mathematical technique—vine copula—is firstly introduced to develop the empirical model for the multivariate dependence of pseudospectral accelerations (PSAs), which are the most commonly used earthquake ground motion parameters. This advancement not only offers a more flexible way of describing nonlinear dependence among multivariate PSAs from the marginal distribution functions but also highlights the extreme dependence. The results can be conventionally acquired in the ground motion selection and seismic risk and loss assessment based on multivariate parameters.

Description: Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value.

Description: Atmospheric pressure loading (APL) deformation is one component of nontectonic deformation for Global Navigation Satellite System (GNSS) time series and is a kind of deformation response caused by a redistribution of atmospheric pressure. In this paper, we present an atmospheric data processing strategy to compute the APL based on a spherical harmonic expansion of the global atmosphere pressure changes. We also provide a sample model to describe the relativity between the global atmosphere pressure changes and APL vertical deformation. The results show that the variation of air mass has a major impact on the north-eastern area of East China, the eastern area of North China, and Northeast China, and the vertical crustal displacement caused by the atmosphere changes in these regions can reach about 20 mm. The correction of APL for vertical time series of GNSS reference stations in different regions indicates that the arid area of the Northwest China, Northeast China, Central China, and North China are greatly affected by APL. While for the station located in Sichuan-Yunnan region, the amplitude and period change are small after correction of APL for vertical time series of GNSS reference stations, which reveals that the area is seriously affected by tectonic movement and water migration loading. The correlation between atmospheric pressure changes and crustal deformation is analyzed, which shows that APL has a serious impact on the north-eastern area of North China, the Northeast China, and the eastern area of Central China when the variations in atmospheric pressure in mainland China are the same. The research results of this paper will provide some reference value for the study of crustal structural deformation and the establishment of geodetic datum.

Description: Suppliers offering trade credit to the downstream retailers have to face many problems, such as receivables management, capital occupancy, and buyer’s credit risk. Many of them choose factoring finance to solve those problems simultaneously. This paper develops several supply chain decision models to show the benefits a supplier can obtain from the main functions of factoring and how he should choose between recourse factoring and nonrecourse factoring. In particular, we identify the conditions on which factoring may bring benefits (including financial benefit, guarantee benefit, and receivables management benefit) to the supplier. The supplier’s choice between recourse and nonrecourse factoring relies on his risk attitude. Given that the supplier is risk-neutral and the factoring fees are acceptable, recourse factoring is preferred when the factoring finance ratio is relatively high; otherwise, nonrecourse factoring is preferred. However, if the supplier is risk-averse, his preference for the two factoring schemes under different finance ratios may change when the risk constraints become stricter. If the target profit is lower than a certain level, the supplier’s financial choice will switch from recourse factoring to nonrecourse factoring in the case finance ratio is relatively low; otherwise, his financial choice switches from nonrecourse factoring to recourse factoring in the case finance ratio is relatively high.

Description: A numerical analysis method for block failure is proposed that is based on continuum mechanics. First, a mesh model that includes marked blocks was established based on the grid-based block identification method. Then, expressions of the contact force under various contact states were derived based on the explicit contact force algorithm, and a contact simulation method between blocks and the surrounding rock was proposed. The safety factors of the blocks were calculated based on the strength reduction method. This numerical analysis method can simulate both the continuous deformation of the surrounding rock and the discontinuous failure processes of the blocks. A simple example of a sliding block was used to evaluate the accuracy and rationality of the numerical method. Finally, combined with a deep underground excavation project under complex geological conditions, the stability of the blocks and rock were analyzed. The results indicate that the key blocks are damaged after excavation, the potentially dangerous blocks loosen and undergo large deformations, and the cracks between the blocks and the rock gradually increase as the excavation proceeds. The safety factors of the blocks change during the excavation. The numerical results demonstrate the influence of the surrounding rock on the failure process and on the stability of the blocks, and an effective analysis method is provided for the stability analysis of blocks under complex geological conditions.

Description: Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

Description: Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver’s turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle.

Description: Image caption enables computers to generate a text description of images automatically. However, the generated description is not good enough recently. Computers can describe what objects are in the image but cannot give more details about these objects. In this study, we present a novel image caption approach to give more details when describing objects. In detail, a visual attention-based LSTM is used to find the objects, as well as a semantic attention-based LSTM is used for giving semantic attributes. At last, a gated object-attribute matching network is used to match the objects to their semantic attributes. The experiments on the public datasets of Flickr30k and MSCOCO demonstrate that the proposed approach improved the quality of the image caption, compared with the most advanced methods at present.

Description: Specific emitter identification is a technique that distinguishes different emitters using radio fingerprints. Feature extraction and classifier selection are critical factors affecting SEI performance. In this paper, we propose an SEI method using the Bispectrum-Radon transform (BRT) and a hybrid deep model. We propose BRT to characterize the unintentional modulation of pulses due to the superiority of bispectrum distributions in characterizing nonlinear features of signals. We then apply a hybrid deep model based on denoising autoencoders and a deep belief network to perform further deep feature extraction and discriminative identification. We design an automatic dependent surveillance-broadcast signal acquisition system to capture signals and to build dataset for validating our proposed SEI method. Theoretical analysis and experimental results show that the BRT feature outperformed traditional features in characterizing UMOP, and our proposed SEI method outperformed other feature and classifier combination methods.

Description: This paper presents the development of a model of homogeneous, moderately thick shells for elastodynamic problems. The model is obtained by adapting and modifying SAM-H model (stress approach model of homogeneous shells) developed by Domínguez Alvarado and Díaz in (2018) for static problems. In the dynamic version of SAM-H presented herein, displacements and stresses are approximated by polynomials of the out-of-plane coordinate. The stress approximation coincides with the static version of SAM-H when dynamic effects are neglected. The generalized forces and displacements appearing in the approximations are the same as those involved in a classical, moderately thick shell model (CS model) but the stress approximation adopted herein is more complex: the 3D motion equations and the stress boundary conditions at the faces of the shell are verified. The generalized motion and constitutive equations of dynamic SAM-H model are obtained by applying a variant of Euler–Lagrange equation which includes pertinently Hellinger–Reissner functional. In the constitutive equations, Poisson’s effect of out-of-plane normal stresses on in-plane strains is not ignored; this is one important feature of SAM-H. To test the accuracy of dynamic SAM-H model, the following structures were considered: a hollow sphere and a catenoid. In each case, eigenfrequencies are first calculated and then a frequency analysis is performed applying a harmonic load. The results are compared to those of a CS model, MITC6 (mixed interpolation of tensorial components with 6 nodes per element) shell element calculations, and solid finite element computations. In the two problems, CS, MITC6, and dynamic SAM-H models yield accurate eigenfrequencies and eigenmodes. Nevertheless, the frequency analysis performed in each case showed that dynamic SAM-H provides much more accurate amplitudes of stresses and displacements than the CS model and the MITC6 shell finite element technique.

Description: In this paper, we evaluate several families of Toeplitz–Hessenberg determinants whose entries are the Boubaker polynomials. Equivalently, these determinant formulas may be also rewritten as combinatorial identities involving sum of products of Boubaker polynomials and multinomial coefficients. We also present new formulas for Boubaker polynomials via recurrent three-diagonal determinants.

Description: In order to study the influence of joint fissures and rock parameters with random characteristics on the safety of underground caverns, several parameters affecting the stability of surrounding rock of underground caverns are selected. According to the Monte Carlo method, random numbers satisfying normal distribution characteristics are established. A three-dimensional model of underground caverns with random characteristics is established by discontinuous analysis software 3DEC and excavation simulations are carried out. The maximum displacement at the numerical monitoring points of arch and floor is the safety evaluation index of the cavern. The probability distribution and cumulative distribution function of the displacement at the top arch and floor are obtained, and the safety of a project is evaluated.

Description: The objective of this paper is to establish an accurate nonlinear mathematical model of the hydraulic damper during the orifice-working stage. A new mathematical model including the submodels of the orifices, hydraulic fluids, pressure chambers, and reservoir chambers is established based on theories of the fluid mechanics, hydropneumatics, and mechanics. Subsequently, a force element based on the established model of the hydraulic damper which contains 56 inputs, 6 force states, and 47 outputs is developed with the FORTRAN language in the secondary development environment of the multibody dynamics software SIMPACK. Using the force element, the damping characteristics of the modified yaw damper with different diameters of the base orifice are calculated under different amplitudes and frequencies of the sine excitation, and then the simulation results are compared with the experimental results which are obtained under the same conditions. Results show that during the orifice-working stage, the new established mathematical model can accurately reproduce the nonlinear static and dynamic characteristics of hydraulic dampers such as the force-displacement characteristic, force-velocity characteristic, fluid shortage, hysteresis effect, and pressure limited effect. Furthermore, it also shows that the nonlinear characteristics of the orifice, air release, cavitation, leakage for high frequencies, and dynamic characteristics of fluid (i.e., the density, bulk modulus, and air/gas content) should be taken seriously during the modelling of the hydraulic damper at the orifice-working stage. The mathematical model proposed in this paper is more applicable to the railway vehicle system dynamics and individual system description of the hydraulic damper.

Description: A jumping leg with one degree of freedom (DOF) is characterized by high rigidity and simple control. However, robots are prone to motion failure because they might tip over during the jumping process due to reduced mechanism flexibility. Mechanism design, configuration optimization, and experimentation were conducted in this study to achieve jumping stability for a bioinspired robot. With locusts as the imitated object, a one-DOF jumping leg mechanism was designed taking Stephenson-type six-bar mechanism as reference, and kinematic and dynamic models were established. The rotation angle of the trunk and the total inertia moment were used as stability criteria, and the sensitivity of different links to the target was analyzed in detail. With high-sensitivity link lengths as the optimization parameters, a configuration optimization method based on the particle swarm optimization algorithm was proposed in consideration of the different constraint conditions of the jumping leg mechanism. Optimization results show that this method can considerably improve optimization efficiency. A prototype of the robot was developed, and the experiment showed that the optimized trunk rotation angle and total inertia moment were within a small range and can thus meet the requirements of jumping stability. This work provides a reference for the design of jumping and legged robots.

Description: In this paper, a new approach to the decentralized control design for vehicle platooning for uncertain automated highway systems is proposed. The uncertainty in the system, which is nonlinear and (possibly) fast time-varying, is bounded. The bound is assumed to be within a prescribed fuzzy set. A creative transformation is made to the system, which converts a local problem to a global problem. Based on the fuzzy description of the uncertainty bound and the transformation, a class of decentralized control is proposed in which each vehicle only needs the knowledge of its preceding vehicle in the platoon. No acceleration feedback or the information of the leading vehicle is required. Both the vehicle platooning system and the control are deterministic, hence not if-then fuzzy rule-based. The performance of the resulting controlled system is twofold. First, the collision avoidance performance is guaranteed under any safe initial conditions regardless of the value of the uncertainty. Second, the minimization of a fuzzy-based performance index is guaranteed based on an optimal choice of a control design parameter. Numerical simulations are conducted to validate the efficiency of the proposed algorithm.

Description: With the extensive application of power electronics interfaced nonsynchronous energy sources (NESs) in modern power systems, the system stability especially the transient stability is prominently deteriorated, and it is crucial to find a comprehensive and reasonably simple solution. This paper proposes a direct method-based transient stability analysis (DMTSA) method which concludes the key steps as follows: (1) the system modeling of Lyapunov functions using mixed potential function theory and (2) the stability evaluation of critical energy estimation. A voltage source converter- (VSC-) based HVDC transmission system is simulated in a weak power grid to validate the proposed DMTSA method under various disturbances. The simulation results verify that the proposed method can effectively estimate the transient stability with significant simplicity and generality, which is practically useful to secure the operation and control for power electronics-dominated power systems.

Description: Carbon tax is an emission regulation, which widely used to curb the carbon emissions generated from firms. In the context of carbon tax policy, firms need to determine an optimal carbon reduction level and optimal product prices. To address firms’ decision-making challenges, this paper considers a two-echelon supply chain consisting of a single manufacturer and a single retailer under carbon tax policy; it establishes a Stackelberg game model with a risk-averse retailer and a risk-neutral manufacturer who is the leader of the game. The paper studies the influence of the government’s carbon tax policy and retailer’s risk-averse attitude on the optimal decision of the supply chain. The result shows that when the retailer is risk aversion, the degree of risk aversion of the retailer is positively correlated with the wholesale price of the manufacturer and unit carbon emission reduction, and within a certain range of carbon emission reduction cost coefficient, it is positively correlated with the price of products; with the increase of the carbon tax rate imposed by the government, the retail price of unit products, the wholesale price of the manufacturer, and the carbon emission reduction of unit products also increase. Finally, the results are verified by numerical examples.

Description: The inverse problem is always one of the important issues in the field of fluid machinery for the complex relationship among the blade shape, the hydraulic performance, and the inner flow structure. Based on Bayesian theory of posterior probability obtained from known prior probability, the inverse methods for the centrifugal pump blade based on the single-output Gaussian process regression (SOGPR) and the multioutput Gaussian process regression (MOGPR) were proposed, respectively. The training sample set consists of the blade shape parameters and the distribution of flow parameters. The hyperparameters in the inverse problem models were trained by using the maximum likelihood estimation and the gradient descent algorithm. The blade shape corresponding to the objective blade load can be achieved by the trained inverse problem models. The MH48-12.5 low specific speed centrifugal pump was selected to verify the proposed inverse methods. The reliability and accuracy of both inverse problem models were confirmed and compared by implementing leave-one-out (LOO) cross-validation and extrapolation characteristic analysis. The results show that the blade shapes within the sample space can be reconstructed exactly by both models. The root mean square errors of the MOGPR inverse problem model for the pump blade are generally lower than those of the SOGPR inverse problem model in the LOO cross-validation. The extrapolation characteristic of the MOGPR inverse problem model is better than that of the SOGPR inverse problem model for the correlation between the blade shape parameters can be fully considered by the correlation matrix of the MOGPR model. The proposed inverse methods can efficiently solve the inverse problem of centrifugal pump blade with sufficient accuracy.

Description: In this study, a novel mathematical model based on third-order nonlinear multisingular functional differential equations (MS-FDEs) is presented. The designed model is solved by using a well-known differential transformation (DT) scheme that is a very credible tool for solving the nonlinear third-order nonlinear MS-FDEs. In order to check the exactness, efficacy, and convergence of the scheme, some numerical examples are presented based on nonlinear third-order MS-FDEs and numerically solved by using DT scheme. The scheme of differential transformation allows us to find a complete solution and a closed approximate solution of the differential equation. The distinctive advantage of the computational technique is to deal with the complex and monotonous physical problems that are obtained in various branches of engineering and natural sciences. Moreover, a comparison of the obtained numerical outcomes from the exact solutions shows the correctness, accurateness, and exactness of the designed model as well as the presented scheme.

Description: The knowledge on strength properties of porous metals in compression is essential in tailored application design, as well as in elaboration of general material models. In this article, the authors propose specification details of the ANN architecture for adequate modelling of the phenomenon of compressive behaviour of open-cell aluminum. In the presented research, an algorithm was used to build different structures of artificial neural networks (ANNs), which approximated stress-strain relations of an aluminum sponge subjected to compression. Next, the quality of the built approximations was appraised. The mean absolute relative error (MARE), coefficient of determination between outputs and targets , root mean square error (RMSE), and mean square error (MSE) were assumed as criterial measures for the assessment of the fitting quality. The studied neural networks (NNs) were two-layer feedforward networks with different numbers of neurons in the hidden layer. A set of experimental stress-strain data from quasistatic uniaxial compression tests of open-cell aluminum of various apparent densities was used as data for training of neural networks. Analysis was performed in two modes: in the first one, all samples were taken for training, and in the second case, one sample was left out during training in order to play the role of external data for testing the trained network later. The taken out samples were maximum and minimum density samples (for extrapolation) and one random from within the density interval. The results showed that good approximation on the engineering level was reached for teaching networks with ≥7 neurons in the hidden layer for the first studied case and with ≥8 neurons for the second. Calculations on external data proved that 8 neurons are enough to actually obtain . Moreover, it was shown that the quality of approximation can be significantly improved to (tested on external data) if the initial region of the stress-strain relation is modelled by an additional network.

Description: In the process of tunnel construction, the problems of strong sealing, inconvenient communication, and harsh environment pose a serious threat to the personal safety of construction workers. Therefore, personnel positioning technology has important application value in tunnel safety construction. A special environment for tunnel personnel positioning and the ultrawideband (UWB) positioning system are affected by personnel movement, which leads to the problem of lowering positioning accuracy. A wavelet threshold denoising method for motion positioning of people in tunnels is proposed. The positioning algorithm of the method adopts a three-sided positioning algorithm based on symmetric double-sided two-way ranging. The wavelet analysis is used to decompose the motion signal of the personnel in the tunnel, and the low frequency coefficient and high frequency coefficient of the signal are decomposed to determine the influence of the motion noise of the personnel on the UWB positioning. The soft threshold function and the hard threshold function are, respectively, selected to perform wavelet threshold denoising on the motion positioning result in the tunnel. According to the denoising effect, the db5 wavelet 5-layer decomposition, under the heuristic threshold estimation criterion, the soft threshold function denoising is the best denoising method. The verification by the positioning experiment shows that the method is suitable for tunnel personnel positioning. The wavelet threshold denoising method can weaken the influence of outliers in the motion positioning of UWB personnel and improve the positioning accuracy.

Description: Return services are increasingly valued by manufacturers, retailers, and customers. In many countries, an offer of money-back guarantee (MBG) is legally binding. In this paper, we discuss how a retailer and manufacturer with a direct channel should choose single or dual MBG and pricing strategies. We identify the conditions under which a retailer or manufacturer should choose a return strategy and show that the handling return loss and the customer return cost in each channel are critical factors that should be considered by the retailer and manufacturer when choosing a return strategy. In addition, the retailer should cooperate with the manufacturer to establish an offline-to-online (O2O) omnichannel to fully benefit from the convenience and advantages of the retail channel. We find that the adoption of the O2O strategy by the retailer in its retail channel always generates profits, while the manufacturer may implement the O2O strategy in its direct channel when the profit from the Internet service is higher than a threshold. Additionally, the impact of various strategies on pricing, market share, and profits is discussed.

Description: To analyze the distribution characteristics of voltage and current along half-wavelength transmission lines (HWTLs) in the cases with or without short circuit in the steady state, the method based on the frequency-length factor (FLF) for lossy lines is proposed. Firstly, according to the pole condition of the FLF, the distribution characteristics of power-frequency waves along HWTLs are analyzed. Then, the comprehensive effects of the system parameters and fault resistance are explored, revealing the mechanism of the power-frequency resonance caused by nonmetallic short circuit. Meanwhile, unbalanced short-circuit fault is studied by exploiting additional impedance. The results show that the distribution of the maximum value of power-frequency resonance voltage is related to the system parameters but not to the fault impedance. When a HWTL is short circuited at 2640 km∼2930 km, the resonance voltage can reach to 21 p.u. In relation to symmetrical short circuit, the resonance voltage appears at 1469 km from the short-circuit point, while the position moves towards the short-circuit point with the increase of additional impedance in asymmetrical short-circuit conditions. Additionally, the model theoretically proves that the power-frequency overvoltage induced by short circuit does not appear on a line whose length is less than 1469 km. Finally, cases are studied on PSCAD to verify the accuracy of the model.

Description: Managing the distribution of goods is a vital operation for many companies. A successful distribution system requires an effective distribution strategy selection and optimum route planning at the right time and minimum cost. Furthermore, customer’s demand and location can vary from order to order. In this situation, a mixed delivery system is a good solution for it and allows the use of different strategies together to decrease delivery costs. Although the “distribution strategy selection” is a critical issue for companies, there are only a few studies that focus on the mixed delivery network problem. There is a need to propose an efficient solution for the mixed delivery problem to guide researchers and practitioners. This paper develops a new “modified” savings-based genetic algorithm which is named “distribution strategy selection and vehicle routing hybrid algorithm (DSSVRHA).” Our new algorithm aims to contribute to the literature a new hybrid solution to solve a mixed delivery network problem that includes three delivery modes: “direct shipment,” “milk run,” and “cross-docking” efficiently. It decides the appropriate distribution strategy and also optimal routes using a heterogeneous fleet of vehicles at minimum cost. The results of the hybrid algorithm are compared with the results of the optimization model. And the performance of the hybrid algorithm is validated with statistical analysis. The computational results reveal that our developed algorithm provides a good solution for reducing the supply chain distribution costs and computational time.

Description: As passengers are proved to be preference heterogeneous in air travel, this paper tries to model the air ticket purchase behavior incorporating market segmentation. In the research, a latent-NL model, established on the latent class structure and the nested logit model, integrates the personal features as well as the purchase preferences into the forecast of segment-specific purchase probability. In order to calibrate the model, a stated preference survey is designed with the choice profiles using real service information, and the survey is conducted in four cities in China for data collection. The results show that the proposed model provides an effective approach for predicting the air travel demand in particular for air ticket pricing, and the estimation results outperforms the traditional-nested logit model with higher goodness-of-fit. Besides, the model is then adopted to test the efficiency of different pricing strategies, showing its advantages in improving the flight revenues.

Description: This paper presents an effective approach for uncertain aerodynamic analysis of airfoils via the polynomial chaos expansion (PCE). To achieve this, the multivariate polynomial is first setup to represent random factors within the aerodynamic model, whereas the expansion coefficient is expressed as the multivariate stochastic integral of the input random vector. In this regard, the statistical regression in conjunction with a small number of representative samples is employed to determine the expansion coefficient. Then, a combination of the PCE surrogate model with brutal-force Monte Carlo simulation allows to determine numerical results for the uncertain aerodynamic analysis. Potential applications of this approach are first illustrated by the uncertainty analysis of the Helmholtz equation with spatially varied wave-number random field, and its effectiveness is further examined by the uncertain aerodynamic analysis of the NACA 63-215 airfoil. Results for the small regression error and a close agreement between simulated and benchmark results have confirmed numerical accuracy and efficiency of this approach. It, therefore, has a potential to deal with computationally demanding aerodynamical models for the uncertainty analysis.

Description: Seabed fishing depends on humans in common, for instance, the sea cucumber, sea urchin, and scallop fishing, which is always a very dangerous task. Considering the underwater complex environment conditions such as low temperature, dim vision, and high pressure, collecting the marine products using underwater robots is commonly regarded as a feasible solution. The key technique of the underwater robot development is to detect and locate the main target from underwater vision. This research is based on the deep convolutional neural network (CNN) to realize the target recognition from underwater vision. The RPN (Region Proposal Network) is used to optimize the feature extraction capability. Deep learning dataset is prepared using an underwater video obtained from a sea cucumber fishing ROV (Remote Operated Vehicle). The inspiration of the network structure and the improvements come from the Faster RCNN and Hypernet method, and for the underwater dataset, the method proposed in this paper shows a good performance of recall and object detection accuracy. The detection runs with a speed of 17 fps on a GPU, which is applicable to be used for real-time processing.

Description: A joint contract is proposed to coordinate the time-varying supply chain of risk-averse manufacturers and retailers. The joint contract uses price reduction subsidies and revenue-sharing strategies to enable manufacturers and retailers to share risks and achieve overall coordination of the supply chain. Firstly, a centralized and a decentralized decision-making model of the risk-averse supply chain are established. On this basis, reasons for the supply chain failure to coordinate are analyzed, and a joint contract is designed. Then, the specific form of the joint contract is given. Finally, the coordination effect of the joint contract is quantitatively analyzed through numerical analysis.

Description: The double singularities including singular stress field and singular electric displacement field, in the tips of piezoelectric composite junctions, are analyzed by the interpolation matrix method (IMM). The double singularity analysis problem of piezoelectric composite junctions is converted into eigenvalue solution problem of ordinary differential equations with variable coefficients under corresponding boundary conditions. In numerical examples, the first couple of singularity orders and the corresponding characteristic angular functions of displacement and electric potential for the electromechanical coupling field are obtained and comparisons are presented to validate the accuracy of the proposed method. The singularity of the electromechanical coupling field at the tip of piezoelectric composite material junctions is closely related to the bonding angle and fiber direction. According to the numerical results, the best scheme can be configured for the combination of dissimilar materials.

Description: In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

Description: A three-dimensional (3D) corner-point grid model gives a relatively accurate description of the structural properties and spatial distribution of oil and gas reservoirs than Cartesian grids. The finite element simulation of the stress field provides a relatively probable presentation of the in situ stress distribution. Both methods are of great importance to the exploration and development of oil and gas fields. Implementing the finite element simulation of in situ stress on a 3D corner-point grid model not only retains the structural attributes of a reservoir but also allows the accurate simulation of the 3D stress distribution. In this paper, we present a method for implementing the finite element simulation of in situ stress based on a 3D corner-point grid model. We first established a fine 3D reservoir model with corner-point grids and then converted the grids into corresponding 3D finite element grid models using a grid conversion algorithm. Next, we simulated the in situ stress distribution with the finite element method. The stress model is then resampled to corresponding corner-point grid geological models using the reverse algorithm. The grid conversion algorithm is to provide data support for the subsequent numerical simulation and other research efforts, thereby guaranteeing procedure continuity and data consistency. Finally, we simulated the stress distribution of a real oil field, the X region. Comparing the simulated result with the measured result, the high agreement validated the effectiveness and accuracy of the proposed method.

Description: The present research studies about sealability of premium connection almost focus on qualitative description by sealing contact stress distribution, which is not convenient for sealing capacity evaluation and sealing parameter design. This paper proposes a quantitative model to calculate directly gas sealing capacity of cone to cone premium connection, different from the usual finite element method with testing, which combines the elastic thick wall cylinder interference fit theory for calculating sealing stress with the gas sealing criterion obtained from Murtagian’s experimental results. With the proposed model, the effects of pipe wall thickness, seal cone taper, radial sealing interference, and axial sealing length on the gas sealing capacity have been investigated. Furthermore, the gas sealing capacity envelope curve based on radial sealing interference and axial sealing length is also calculated and a new sealing parameter design method is proposed for cone to cone premium connection. The results show that the internal upset pipe is good for sealability, and increasing both radial sealing interference and axial sealing length can significantly enhance gas sealability while seal cone taper has an unobvious effect on it. To meet sealing capacity, the designed sealing parameter combination (δd, Ld) should be located in the upper right region of the gas sealing capacity envelope curve.

Description: The reliable operating region, which is the set of all possible points in the design space that satisfy the reliability requirement, is capable to improve the reliability of products in the design stage. However, the reliable operating region has an irregular geometry shape and it is hard to derive an explicit expression; therefore, its practicality is poor. In order to obtain a more convenient approach, this paper proposes a reliable hyperrectangle operating region, which is expressed by permissible intervals for each design parameter and has the advantage that design parameters are decoupled. An iterative algorithm that seeks an axis-parallel reliable hyperrectangle with maximum volume is proposed. Starting from a design point with target performance, the lengths of the sides of the reliable hyperrectangle are iteratively updated. Theoretical analysis shows that the proposed algorithm is convergent. Furthermore, we extend the proposed methodology to deal with design space constraints. Some numerical examples and engineering cases demonstrate that the proposed algorithm can achieve the requirement of reliability efficiently.

Description: Carbonate reservoirs usually have strong anisotropy. Oil and gas recovery from fractured reservoirs is highly challenging due to complicated mechanisms involved in production from these reservoirs. A horizontal well completed in these reservoirs may extend through multiple zones, including homogeneous, dual-porosity, and triple-porosity formations. Traditional well test models assume that the entire length of a horizontal or multilateral well remains in the same formation with uniform properties. A well test model for pressure transient analysis of horizontal wells extending through a carbonate reservoir consisting of natural fractures, rock matrix, and vugs with different properties is presented in this study. The focus of this study is on dual-porosity (fracture-matrix) and triple-porosity (fracture-matrix-vug) reservoirs, considering the pseudosteady interporosity flows from rock matrix and vugs into fractures. A multizone triple-porosity model was established and solved by using the point source function, Green’s function, and coupling of multiple reservoir sections. The corresponding type curves were developed, and sensitivity analysis was carried out. The type curves of flow stage division reveal that a horizontal well traversing a three-section reservoir including homogeneous, dual-porosity (fracture-matrix)/triple-porosity (fracture-vug-matrix), and homogeneous sections identifies the stages of pseudosteady interporosity flow from matrix and vug into fracture, fracture pseudoradial flow, system linear flow, system pseudoradial flow, and pseudosteady flow occur in sequence. The greater the difference of permeability between the dual-porosity/triple-porosity section and the two homogeneous sections, the more obvious the interporosity flow on the pressure derivative curve. This approach satisfies the need for pressure transient analysis for a horizontal well that traverses two or more regions with distinct properties in heterogeneous carbonate reservoirs.

Description: Diver target automatic detection is indispensable for underwater defense systems, such as the unmanned harbor surveillance system. It is a very challenging task due to various poses and intensity features of diver target. In addition, the background noise in sonar images is complex, which also makes the task more difficult. In this paper, we propose a diver detection method based on saliency detection for sonar images. On the basis of studying the characteristics of diver sonar images, we first decompose the original sonar image and perform median filtering on it, which can significantly improve the quality of the sonar image saliency map. We employ saliency detection technique based on frequency analysis to segment the acoustic highlight region from its surroundings. This segmentation region roughly locates the diver target and generates a region of interest (ROI). We then extract the acoustic shadow region in ROI, which contributes to furtherly improve the localization accuracy. Finally, we merge the segmented highlight region and the extracted acoustic shadow region and compute the minimum outer rectangle of the merged region. Experimental results validate that the proposed method can well detect and locate the diver target, and it can also satisfy the demands of real-time application, and there is almost no false alarm in this method.

Description: The total variation (TV) regularized reconstruction methods for computed tomography (CT) may lead to staircase effects in the reconstructed images because of using the TV regularization. This paper develops a total fractional-order variation regularized CT reconstruction method, aiming at overcoming the weakness of the reconstruction methods based on the TV. Specifically, we propose an optimization model for CT reconstruction, including a fidelity term, a regularization term, and a constraint term. Here, the regularization is a total fractional-order variation arising from the fractional derivative of the underlying solution. To address the nondifferentiability of the resulting model, we introduce a fixed-point characterization for its solution through the proximity operators of the nondifferentiable functions. Based on the characterization, we further develop a fixed-point iterative scheme to solve the resulting model and provide convergence analysis of the developed scheme. Numerical experiments are presented to demonstrate that the developed method outperforms the TV regularized reconstruction method in terms of suppressing noise for CT reconstruction.

Description: The torsional effect of the tracks on curved segment intensifies the ambient vibration response induced by Metro operation. This paper studies the ambient vibration responses induced by the operation of the Metro train on curved rail segments. By taking the curved segment of Hangzhou Metro Line 1 of China as an example, the wheel-rail model employing the multibody dynamics is demonstrated; the dynamic wheel-rail force of a B-type vehicle passing through a 350 m radius curved segment is also calculated. A finite element method model of the track-tunnel-soil-building is developed and verified by comparing the measured results with the simulated ones. Then, we analyzed the ambient vibration responses induced by the Metro operation. The results show that the horizontal vibration induced by the Metro train, on curved segments, cannot be ignored. When propagating on the ground, the variation trend of the horizontal vibration acceleration is greater than that of the vertical vibration. The horizontal vibration attenuates faster than the vertical vibration. The secondary vibration along the horizontal direction yields a significant amplifying effect upon the building. The vibration level increases with the height of the building, along the horizontal direction, but the vertical vibration level changes negligibly. The insight exhibits the influence level of the horizontal action of the Metro train wheels to the tracks, which can be referred by the practitioners for the planning and operation.

Description: In this paper, in order to handle the nonlinear system and the sophisticated disturbance in the marine engine, a finite-time convergence control method is proposed for the diesel engine rotating speed control. First, the mean value model is established for the diesel engine, which can represent response of engine fuel injection to engine speed. Then, in order to deal with parameter perturbation and load disturbance of the marine diesel engine, a finite-time convergence active disturbance rejection control (ADRC) is proposed. At the last, simulation experiments are conducted to verify the effectiveness of the proposed controller under the different load disturbances for the 7RT-Flex60C marine diesel engine. The simulation results demonstrate that the proposed control scheme has better control effect and stronger anti-interference ability than the linear ADRC.

Description: This paper has proposed a fractional K-BKZ numerical model by adopting the framework of the classical K-BKZ model and the relaxation modulus of the fractional Maxwell model with quasiproperties to study the start-up flow of a viscoelastic shock absorber. The start-up flows in both the orifice and the gap of a shock absorber were simplified to unidirectional accelerated flows in a pipe and between two parallel plates where one plate is accelerating and the other is at rest. The fractional K-BKZ numerical model was then developed using the finite difference method with real-world initial and boundary conditions. Numerical simulation was then performed, and the results were validated through laboratory testing, based on a comparison of the maximum fluid level and the contact angle. The proposed fractional K-BKZ numerical model successfully simulated the characteristics of the viscoelastic material passing through the orifice or the gap of a shock absorber, as demonstrated by accurately capturing the change of the shape of the flow. This fractional K-BKZ numerical model provided better accuracy for the fluid’s viscoelasticity and can be used for shock absorber design.

Description: Multicriteria group decision-making (MCGDM) problems have been a research hotspot in recent years, and prospect theory is introduced to cope with the risk and imprecision in the process of decision-making. To guarantee the effectiveness of information aggregation and extend the feasibility of prospect theory, this paper proposes a novel decision-making approach based on rough numbers and prospect theory to solve risky and uncertain MCGDM problems. Firstly by combining rough numbers and the best-worst method (BWM), we construct a linear programming model to calculate rough criteria weights, which are defined by lower limitations and upper limitations. Then for the imprecision of value function and weighting function in prospect theory, we propose a novel method with the aid of combining rough numbers and prospect theory to handle the risk in decision-making problems. Finally, a numerical example involving investment is introduced to illustrate the application and validity of the proposed method.

Description: The augmented Lagrangian method (ALM) is one of the most successful first-order methods for convex programming with linear equality constraints. To solve the two-block separable convex minimization problem, we always use the parallel splitting ALM method. In this paper, we will show that no matter how small the step size and the penalty parameter are, the convergence of the parallel splitting ALM is not guaranteed. We propose a new convergent parallel splitting ALM (PSALM), which is the regularizing ALM’s minimization subproblem by some simple proximal terms. In application this new PSALM is used to solve video background extraction problems and our numerical results indicate that this new PSALM is efficient.

Description: This paper proposed a cubic spline interpolation-based path planning method to maintain the smoothness of moving the robot’s path. Several path nodes were selected as control points for cubic spline interpolation. A full path was formed by interpolating on the path of the starting point, control points, and target point. In this paper, a novel chaotic adaptive particle swarm optimization (CAPSO) algorithm has been proposed to optimize the control points in cubic spline interpolation. In order to improve the global search ability of the algorithm, the position updating equation of the particle swarm optimization (PSO) is modified by the beetle foraging strategy. Then, the trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to weigh global and local search capabilities. At the beginning of the algorithm, particles can explore better regions in the global scope with a larger speed step to improve the searchability of the algorithm. At the later stage of the search, particles do fine search around the extremum points to accelerate the convergence speed of the algorithm. The chaotic map is also used to replace the random parameter of the PSO to improve the diversity of particle swarm and maintain the original random characteristics. Since all chaotic maps are different, the performance of six benchmark functions was tested to choose the most suitable one. The CAPSO algorithm was tested for different number of control points and various obstacles. The simulation results verified the effectiveness of the proposed algorithm compared with other algorithms. And experiments proved the feasibility of the proposed model in different dynamic environments.

Description: Modified 3D Moving Particle Semi-Implicit (MPS) method is used to complete the numerical simulation of the fluid sloshing in LNG tank under multidegree excitation motion, which is compared with the results of experiments and 2D calculations obtained by other scholars to verify the reliability. The cubic spline kernel functions used in Smoothed Particle Hydrodynamics (SPH) method are adopted to reduce the deviation caused by consecutive two times weighted average calculations; the boundary conditions and the determination of free surface particles are modified to improve the computational stability and accuracy of 3D calculation. The tank is under forced multidegree excitation motion to simulate the real conditions of LNG ships, the pressures and the free surfaces at different times are given to verify the accuracy of 3D simulation, and the free surface and the splashed particles can be simulated more exactly.

Description: This paper analyzes the necessity that multigeneral quality characteristic parameters should be considered in availability modelling and optimization and then constructs an availability model including the indicators of reliability, maintainability, supportability, testability, and environmental factor. In the research process, it is assumed that the system has soft and hard failure mode, adopting mixed maintenance strategies, system delay time, and repair time, and test time are independent. On the basis of this, the single parameter, binary parameters, and multiparameters optimization were carried out with the maximum availability as the optimization function. Especially, the multiparameters availability optimization model in time dimension was designed based on particle swarm optimization. The design method was proved feasible through a numerical example, which can provide method support for the design and evaluation of general quality characteristic thresholds in the life cycle of a repairable system.

Description: The purpose of the research is to inquire the outcomes of viscous and ohmic dissipation on the MHD flow in porous media in the region of suction and injection. A flow model of nonlinear ODEs with assisting boundary conditions is tackled with the help of computational software by using various standard techniques. The effects of relevant parameters on the concentration, thermal, and velocity distribution are illustrated graphically; also, the skin friction coefficient and flow rates of heat and mass transfer are calculated and shown in a tabular way. An analysis of the consequences proves that the flow field is effectively appreciable by injection and suction. Comparison with the already published work is made and found to be in good agreement.

Description: A novel variable stiffness 3D virtual brush model and haptic decoration technique suitable for the surface of the three-dimensional objects for the automobile industry are introduced based on real-time haptic feedback mechanism using a 6 DOFs input device, and the haptic behavior of an expressive virtual 3D brush with variable stiffness is studied in detail for the first time. First, the intrinsic mechanism between the deformation of real hair brush and the applied external forces (such as the bending moment) is analyzed and studied in detail by introducing a bending spring to express the basic mechanical behavior for the 3D hair brush. Based on this brush model, many important painting features can be simulated, such as the softer brush tip, brush flattening, and bristle spreading. And a useful algorithm (named the weighted-average distance) for dealing with collision checking among the two objects (3D clay and the 3D brush) is presented. As long as the brush head is close to the 3D object, within a tolerance range, the computational tactile sensation force will be emerged, and the interactive painting process is implemented actually on the outer surface of the virtual object. We then calculate the related bounding ball for deformed 3D brush using a fast ball-expanding search algorithm to determine the virtual projection plane. Based on the real-time deformation about the virtual brush head at a sampling point, the 2D painting footprints, which is produced between the brush head and virtual projection painting plane, is calculated and rendered. Next, the 3D painting footprint could be easily produced via mapping the 2D painting footprints onto the surface of the 3D model in real time. Finally, the 3D painting strokes are formed via controlling the exerted force and overlapping the virtual 3D painting footprints with different shape, size, and following the moving direction of the 3D brush. Experiment result shows that the adopted method can effectively enhance reality to users, with high performance.

Description: Theoretical calculation, numerical simulation, and field measurement were combined to study the recovery of coal resources in the 5th pan area of Dianping Coal Mine without damaging surface buildings. A mathematical model was established to predict the settlement range and displacement of the surface after coal mining. The FLAC3D5.0 program was used to simulate the evolution law of the overburden strata under the coal mining conditions of the study area. The lateral influence range was 45.8 m and 42.4 m, and the maximum surface subsidence was 2.604 m and 2.78 m, respectively. The mining boundary of the 5-210 working face was designed using the result of the simulation program. Real-time dynamic observation data collected from 18 surface displacement monitoring points measured surface subsidence as 2.69 m. The civil construction protection area was not deformed. The working face provided safe recovery of 1,471,446,000 kg of coal, resulting in significant economic and social benefits.

Description: Linguistic aggregation operator is a paramount appliance to fix linguistic multiple attribute decision-making (MADM) issues. In the article, the Hamy mean (HM) operator is utilized to fuse hesitant fuzzy linguistic (HFL) information and several novel HFL aggregation operators including the hesitant fuzzy linguistic Hamy mean (HFLHM) operator, weighted hesitant fuzzy linguistic Hamy mean (WHFLHM) operator, hesitant fuzzy linguistic dual Hamy mean (HFLDHM) operator, and weighted hesitant fuzzy linguistic dual Hamy mean (WHFLDHM) operator are proposed. Besides, several paramount theorems and particular cases of these aggregation operators are investigated in detail, and then a novel MADM approach is presented by using the proposed aggregation operators. Ultimately, a practical example is utilized to manifest the effectiveness and practicability of the propounded method.

Description: In this paper, the transmission process of the heald frame driven by the dobby is analyzed. The equivalent motion model of the dobby modulator, the eccentric mechanism, and the motion transmission unit are constructed. Then, based on the given movement characteristics of the heald frame, the mathematical model is built to achieve the cam pitch curve and the cam profile of the modulator. The numerical solution method for this is developed. The preparation of a mathematical model for the new concept of the solving cam profile based on the motion characteristics of heald frames is explained in this study. By setting a 11th polynomial motion law of the heald frame, due to the inconsistency between the outward and return motion laws of the crank-rocker mechanism, an asymmetrical cam profile is obtained under the premise of ensuring that the heald frame’s ascending and descending motions are consistent. Through the kinematics simulation analysis, the correctness of the reverse process is verified.

Description: For the sale of a product with network externalities, a Stackelberg model involving an incumbent and an entrant is developed considering the impact of three strategic decision-making modes of the incumbent and consumers on the pricing, market share, and profit of firms. In addition, the impact of consumers’ strategic behaviours on firms’ pricing decisions and how firms respond to strategic customers is discussed. The results show that, in the (strategic firm facing strategic consumers) decision-making mode, the incumbent will implement long-term pricing and finally obtain the maximum profit, while as a follower of the incumbent, the entrant will also obtain the maximum profit in the mode. In the (nonstrategic firm facing strategic consumers) decision-making mode, the strategy of consumers seriously weakens the decision-making behaviour of the incumbent and causes the incumbent to obtain the lowest profit, but at the same time, the competitiveness of the entrant is enhanced to a certain extent, thereby rendering its profit higher than that in the (nonstrategic firm facing nonstrategic consumers) decision-making mode.

Description: The degraded aeolian sandy soil in China’s Mu Us Sandy Land requires amendment before it can be suitable for maize or other agricultural production. The addition of material from the local “soft” bedrock can create a new compound soil whose particle composition and structural stability are key issues for sustainable soil development in the region. We used field data from 2010 to 2018 to study the variations in fractal characteristics of compound soil particles at soft rock to sand volume ratios of 1 : 1, 1 : 2, and 1 : 5, along with changes in soil organic matter. Over the study period, all three compound soils showed gradual increases in clay and silt content with corresponding decreasing sand content. The fractal dimension (FD) of particles at ratio 1 : 2 increased by 8.8%, higher than those at 1 : 1 (8.6%) and 1 : 5 (7.7%). The organic matter content (OMC) of particles at ratio 1 : 2 reached a maximum (6.24 ± 0.30 g/kg), an increase of 12 times over the original value. The FD and OMC of particles at ratios 1 : 1 and 1 : 5 were less stable but showed overall increase. The 1 : 2 ratio compound soil was most suitable for maize growth as its clear increase in silt and clay content most improved the texture and OMC of the original sandy soil. Such research has important theoretical and practical significance for understanding the evolutionary mechanism and sustainable use of the compound soil in agriculture within the Mu Us Sandy Land.

Description: Oxygen lances (OLs) are important devices used in converter steel making. However, the occurrence of OL burning loss (OLBL) failure may lead to explosion accidents. To better prevent OLBL failure, it is necessary to perform a probabilistic assessment. Bow-tie analysis based on fuzzy theory was proposed to assess OLBL, which represents a hazardous event. In this paper, fuzzy theory based on triangular fuzzy numbers (TFNs) was applied to calculate the failure data. Fuzzy fault tree analysis (FFTA) in combination with the improved similarity aggregation method (ISAM) was employed to reduce the error generated due to the subjective judgement of experts. Furthermore, a comprehensive importance analysis method was developed to rank the importance of basic events (BEs), facilitating the adoption of the corresponding safety decisions. When performing fuzzy event tree analysis (FETA), the occurrence probability of outcome events (OEs) was determined by conducting a layer of protection analysis (LOPA). Finally, safety measures were proposed based on the assessment results to achieve safe production. The results indicated that the use of bow-tie analysis is appropriate to perform qualitative and quantitative assessment. Through bow-tie analysis based on fuzzy theory, the occurrence probability of OLBL was determined to be in the interval (5.34E − 02, 2.69E − 01). By adding independent protective layers (IPLs), the occurrence probability of OEs caused by OLBL can be effectively reduced.

Description: Since a large number of bug reports are submitted to the bug repository every day, efficiently assigning bug reports to the correct developer is a considerable challenge. Because of the large differences between the different components of different projects, the current bug classification mainly relies on the components of the bug report to dispatch bug reports to the designated developer or developer community. Unfortunately, the component information of the bug report is filled in by default according to the bug submitter and the result is often incorrect. Thus, an automatic technology that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. In this paper, we propose a method based on the combination of imbalanced learning strategies such as random undersampling (RUS), random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and AdaCost algorithms with multiclass classification methods, OVO and OVA, to solve bug reports component classification problem. We investigate the effectiveness of different combinations, i.e., variants, each of which includes a specific imbalance learning strategy and a specific classification algorithm. We mainly perform an analytical study on five open bug repositories (Eclipse, Mozilla, GCC, OpenOffice, and NetBeans). The results show that different variants have different performance for bug reports component identification and the best performance variants are combined with the imbalanced learning strategy RUS and the OVA method based on the SVM classifier.

Description: The manufacturing process of large-area, high-precision gratings is a very complicated and time-consuming process. The hardness testing of grating films is an important step in the entire process. In order to simplify the manufacturing process of gratings, we have proposed a new method for testing microhardness based on tool edge indentation. Also, it unified tool adjustment and microhardness testing steps in the grating manufacturing process. First, a mathematical model of the relationship between tool load and indentation contour length is established. The model parameters were then modified using tool indentation experiments with different loads. When measured with a nanoindenter, the average hardness of the grating film was 447 MPa. The hardness value of the grating film obtained by our proposed method is almost the same as that measured by the nanoindenter, and the maximum deviation is about 2.2% of the average hardness value. The experimental results show that our proposed method can replace the microhardness test method of using a nanoindenter. Therefore, the disadvantages of using a nanoindenter to test the hardness of a grating film are avoided, such as the limited sample size, the sensitivity of the indenter to the roughness of the film and the depth of the indentation, and the accuracy of film testing, and the efficiency of grating ruling can be improved.

Description: Geometric errors are inevitably introduced into any multiaxis measuring system, and the geometric error is one of the main factors that seriously affects the measurement accuracy. The present work investigates the error distribution of the prototype of a 5-axis measuring machine based on sensitivity analysis of geometric errors. The measurement error modeling of the 5-axis measuring machine is first established via the homogeneous coordinate transformation, and the Sobol global sensitivity analysis method is then employed to quantify the influence of geometric errors on the measurement result with the sensitivity index. The result shows that most of the angular errors are the crucial geometric errors seriously affecting the measurement result. These errors are supposed to be fully considered in the accuracy design and manufacturing stages. The error levels of the crucial geometric errors were distributed and readjusted according to the sensitivity analysis result. Some practical approaches to distribute and improve the crucial geometric errors have been given in detail. The error distribution method is effective to equalize the influence of the crucial geometric errors on the measurement result as possible. The findings of this study provide significant meanings for the optimal design and accurate manufacturing of the 5-axis measuring machine, and the proposed method can be used to improve the measurement accuracy of the 5-axis measuring machine.

Description: Satellite images are always with complex background and shadow areas. These factors can lead to target segmentation break up and recognition with a low accuracy. Aiming at solving these problems, we proposed an aircraft recognition method based on superpixel segmentation and reconstruction. First, we need to estimate the orientation of an aircraft by using histograms of oriented gradients. And then, an improved Simple Linear Iterative Cluster (SLIC) superpixel segmentation algorithm is provided. By comparing texture feature instead of color feature space, we cluster the pixels that are with the same features. Last, through target template images and orientation, we reconstruct the superpixels. Also, the lowest error matching ratio is the recognized target. The test results show that the algorithm is robust to noise and recognize more aircrafts. Especially, when the satellite images with complex background and shadow areas, our method recognizes accuracy better than other methods. It can satisfy the demand of satellite image aircraft recognition.