ALBERT

Description: Identifying key classes can help software maintainers quickly understand software systems. The existing key class recognition algorithms consider the weight of class interaction, but the weight mechanism is single or arbitrary. In this paper, the multitype weighting mechanism is considered, and the key classes are accurately identified by using four kinds of interaction. By abstracting the software system into the directed weighted class interaction network, a novel Structure Entropy Weighted LeaderRank of identifying key classes algorithm is proposed. First, considering multiple types and directions of interactions between every pair of classes, the directed weighted class interaction software network (DWCIS-Network) is built. Second, Class Entropy of each class is initialized by the software structural entropy in DWCIS-Network; the Structure Entropy Weighted LeaderRank applies the biased random walk process to iterate Class Entropy. Finally, the iteration is completed to obtain the Final Class Entropy (FCE) of each class as the importance score of each class, top-k classes are obtained, and key classes are identified. For two sets of experiments on Ant and JHotDraw, our approach effectively identifies key classes in class-level software networks for different top-k of classes, and the recall rates of our approach are the highest, 80% and 100%, respectively. From top-15% to top-5%, the precision of our approach is improved by 13.39%, which is the highest in comparison with the precisions of the other two classical approaches. Compared with the best performance of the two classical approaches, the RankingScore of our approach is improved by 16.51% in JHotDraw.

Description: The bat algorithm (BA) is a recent heuristic optimization algorithm based on the echolocation behavior of bats. However, the bat algorithm tends to fall into local optima and its optimization results are unstable because of its low global exploration ability. To solve these problems, a novel bat algorithm based on an integration strategy (IBA) is proposed in this paper. Through the integration strategy, an appropriate operator is adaptively selected to perform global search, so that the global search ability of the IBA is improved. Furthermore, the IBA disturbs the local optimum through a linear combination of Gaussian functions with different variances to avoid becoming trapped in local optima. The IBA also updates the velocity equation with an adaptive weight to further balance the exploration and exploitation. Moreover, the global convergence of the IBA is proved based on the convergence criterion of a stochastic algorithm. The performance of the IBA is evaluated on CEC2013 benchmark functions and compared with that of the standard BA as well as several of its variants. The results show that the IBA is superior to other algorithms.

Description: Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

Description: This paper is a response to two papers. We improve the lengthy proof for the first paper by an elegant verification. For the second paper, we point out the three-sequence approach will result in different convergent rates such that when the other two sequences are converged, the ordering quantity sequence may still not converge to the optimal solution. We construct a novel iterative method to simplify the previous approach proposed by the three-sequence approach for the optimal solution. By the same numerical examples of three published papers, we demonstrate that we can control our findings to converge more accurately than previous results. Moreover, we show that there are three distinct features of our proposed approach. (i) It converges to the desired solution within the preassigned threshold value. (ii) We estimate the convergent ratio. (iii) We find the dominant factors for our proposed convergent sequence.

Description: Scientific pricing of the electric vehicle charging station is closely related to consumer behavior inevitably. Existing studies have not considered the impacts of consumer differences on the charging price, which will fail to meet the interests of various types of consumers. This paper proposes a novel pricing method based on consumer classification and comprehensive evaluation strategies. First, the basis for consumer classification is established according to a single factor sensitivity analysis of the consumer benefit model; then, the nonlinear expression of the basis is piecewise linearized. Additionally, with the principle of least fitting error to determine consumers’ classification, the initial charging price schemes for various types of consumers are formulated. Second, this paper defines evaluation indices and establishes the hierarchy model for comprehensive evaluation schemes. Finally, the integrated analytic hierarchy process and data envelopment analysis are adopted for comprehensive evaluation of schemes. Simulations results illustrate that the proposed method can formulate the comprehensive optimal charging price considering consumer differences, and the method can reflect the impacts of both subjective and objective factors conveniently and accurately.

Description: Forced vibration of the cutting system with a three-dimensional composite cutter bar is investigated. The composite cutter bar is simplified as a rotating cantilever shaft which is subjected to a cutting force including regenerative delay effects and harmonic exciting items. The nonlinear curvature and inertia of the cutter bar are taken into account based on inextensible assumption. The effects of the moment of inertia, gyroscopic effect, and internal and external damping are also considered, but shear deformation is neglected. Equation of motion is derived based on Hamiltonʼs extended principle and discretized by the Galerkin method. The analytical solutions of the steady-state response of the cutting system are constructed by using the method of multiple scales. The response of the cutting system is studied for primary and superharmonic resonances. The effects of length-to-diameter ratio, damping ratio, cutting force coefficients, ply angle, rotating speed, and internal and external damping are investigated. The results show that nonlinear curvature and inertia imposed a significant effect on the dynamic behavior of the cutting process. The equivalent nonlinearity of the cutting system shows hard spring characteristics. Multiple solutions and jumping phenomenon of typical Duffing system are found in forced response curves.

Description: Operations and maintenance (O&M) management for existing buildings is of high importance since it consumes the most cost during buildings’ lifecycle. Its effectiveness could be significantly improved through the systematic use of building information modeling (BIM). However, BIM relies on full-fledged digital models, which, for most buildings, are not available. This paper introduces a recognition algorithm aiming at the automatic generation of 3D building models from 2D drawings. The algorithm is able to generate separated wall segment 3D models with their topology relations. The algorithm is implemented and tested by several real projects. The results are very promising and show that the proposed algorithm could be a key component of future digital toolkits for O&M management.

Description: In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement.

Description: Nonlinear partial differential equations (NLPDEs) are an inevitable mathematical tool to explore a large variety of engineering and physical phenomena. Due to this importance, many mathematical approaches have been established to seek their traveling wave solutions. In this study, the researchers examine the Gardner equation via two well-known analytical approaches, namely, the improved tanΘϑ-expansion method and the wave ansatz method. We derive the exact bright, dark, singular, and W-shaped soliton solutions of the Gardner equation. One can see that the methods are relatively easy and efficient to use. To better understand the characteristics of the theoretical results, several numerical simulations are carried out.

Description: In order to improve the precision of the spaceborne Inertial Measurement Unit (IMU), this paper proposes an adhesive joint assembly of the MEMS-redundant IMU. That is the application of special redundant installation of multiple MEMS gyroscopes in the IMU, which can improve the reliability of the MEMS-redundant IMU on the basis of reducing the weight of IMU. However, with the change of working environment, the traditional mechanical assembly of MEMS-redundant IMU will produce the large packaging stress and cause the deformation of MEMS gyroscope. This change will lead to changes in installation errors, scale factor errors, and bias errors of the MEMS gyroscope, resulting in a significant reduction in measurement precision of the MEMS-redundant IMU. Therefore, this paper selects the adhesive material that matches the thermal physical parameters of the material with the circuit board by analyzing the requirements of MEMS gyroscope on working environment at first. Then, by optimizing the bonding process, the installation error of each axis of MEMS-redundant IMU under different temperatures is better than the traditional mechanical connection mode. The experiment results of thermal vacuum show that the new assembly method can reduce the influence of temperature on the bias. Compared with the traditional method, the new assembly which is based on adhesive joint assembly can improve the measurement precision of MEMS-redundant IMU by an order of magnitude.