ALBERT

Description: A complex fuzzy set is an extension of the fuzzy set, of which membership grades take complex values in the complex unit disk. We present two complex fuzzy power aggregation operators including complex fuzzy weighted power (CFWP) and complex fuzzy ordered weighted power (CFOWP) operators. We then study two geometric properties which include rotational invariance and reflectional invariance for these complex fuzzy aggregation operators. We also apply the new proposed aggregation operators to decision making and illustrate an example to show the validity of the new approach.

Description: With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

Description: This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.

Description: When the pumping operation of pumped storage unit suffers from power outage, the hydraulic transient poses a serious threat to the safe operation of the unit and its pressure pipeline system. For high-head pumped storage power station (PSPS), the water hammer pressure (WHP) and rotational speed rise ratio (RSRR) of each hydraulic unit will be increased during the pump outage condition. In order to limit the fluctuation of rotational speed and WHP in power-off condition, optimizing and choosing a reasonable guide vane closure scheme (GVCS) is an economic and efficient means to improve the dynamic characteristics of pumped storage unit. On the basis of the calculation model of the transition process of single tube-double unit type of a high-head PSPS, an optimization model of GVCS balancing WHP and RSRR objectives is established. Furthermore, the two-stage broken line and three-stage delayed GVCSs are applied to the pump outage condition, and the nondominated sorting genetic algorithm-II (NSGA-II) is introduced to calculate the optimal solution set under different water heads and different closure schemes. For four typical water heads, the multiobjective optimization results of the closure law show that the two-stage broken line law has a better Pareto front under high water head, while the three-stage delayed law has a better performance under low water head. Furthermore, through the results of transition process of typical schemes, the adaptability of GVCS and water head is analyzed. The method proposed in this paper can make the RSRR not more than −0.89, and the three-stage delayed law can even make the RSRR only −0.01. Methods of this paper provide a theoretical basis for optimum guide vane closure mode setting of PSPS.

Description: Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

Description: A Computable Mine Safety Supervision (CMSS) model is constructed based on agent-based modeling and simulation (ABMS) technology and the conservation of resources (COR). This model aims to solve the mining safety problems involved with illegal mining operations and burnout among mining supervisors, in China. The model includes several types of agents: supervision agents, decision support agents, functional coordination agents, and miner agents, and it uses the Netlogo simulation platform to simulate the influence of reward and punishment on agent behavior. The simulation determines the decision support degree to gauge the influence of functional coordination and miner behavior on the burnout rate of supervision agents. We analyze the macroscopic emergence law of the simulation results. The results show the following: (1) Job Situation Adaptability (JSA) ∈ [−6.02, 2.64] ∪ [16.9, 21.93], which uses a reward strategy to guide miners to choose safe behavior and (2) JSA ∈ [2.64, 16.9], which uses a punishment strategy to restrict unsafe behavior. The decision support coefficient Sc has the greatest influence on the supervision agent’s job burnout. The functional coordination coefficient Fc has the second highest influence on job burnout and the processing effectiveness coefficient Ec has the least influence. According to the simulation results, suggestions for improving the mine safety supervision system are put forward and an improved safety management decision-making basis for reducing mine accidents is provided.

Description: Undisturbed loess is affected by external environmental disturbances, such as wetting and freeze-thaw cycles, which cause microstructural changes that have an important impact on the structural strength of the loess. These changes in turn affect the stability of structures such as embankments, slopes, and guards. This article takes the Q3 undisturbed loess in Lintong District, Xi’an, as an example. The effects of wetting and freeze-thaw cycles on the loess expansion ratio and pore structure were studied by wetting tests, freeze-thaw cycle tests, and scanning electron microscopy (SEM). The changes in the compression index and compression modulus were studied by a confined compression test. The loess e-lgp compression curve was obtained according to the confined compression test, and the newly defined concepts of the loess structural strength, residual structural strength, and structural strength damage variable, in addition to the e-lgp compression curve, were combined with the experimental data to calculate the damage value generated by the disturbance during the sampling and preparation of loess. The deterioration of the structural strength and damage variable of loess was analyzed. Based on the microscopic statistical damage theory and Weibull distribution, the model used the volume expansion ratio as a variable to establish a statistical damage model under wetting and freeze-thaw cycles. Finally, on the basis of the test, the model parameters were determined. The models were verified by taking loess from a foundation pit in the northern suburbs of Xi’an and were in good agreement with the results of the test. Ultimately, the models have good practicability and can provide guidance for engineering design and construction.

Description: Purpose. To use computational fluid dynamics (CFD) technology to fundamentally understand (1) the effect of surgical treatments on nasal physiology for secondary atrophic rhinitis and (2) the priority of operations. Subjects and Methods: With the aid of medical imaging and CFD modeling, three virtual operations (nasoseptal perforation repair, cavity narrowing, and a combination of both) were performed to analyze airflow, nasal resistance, and wall shear stress. Results. Compared with the cavity-narrowing virtual operation, nasal resistance was not significantly altered by septal perforation repair virtual operation. Airflow allocation changed with more air flowing through the olfactory area and less though the nasal floor after all operations, especially the cavity-narrowing operation. Wall shear stress at the original epistaxis area and the nasal floor was reduced after the cavity-narrowing operation. Conclusions. Simulation results suggest that the cavity-narrowing operation takes priority over septal perforation repair if a staged surgery approach is adopted. If only one operation can be chosen, the cavity-narrowing operation is better than the septal perforation repair. This work shows that CFD-based modeling may aid precision medicine.