Shandong Institute of Business and Technology

About: Shandong Institute of Business and Technology is a education organization based out in Yantai, China. It is known for research contribution in the topics: Artificial neural network & Fuzzy logic. The organization has 1487 authors who have published 1433 publications receiving 8915 citations.

An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network

Abstract: Deep belief network (DBN) is one of the most representative deep learning models. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this article, an improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability. Then, the MSIQDE with global optimization ability is used to optimize the parameters of the DBN to construct an optimal DBN model, which is further applied to propose a new fault classification, namely MSIQDE-DBN method. Finally, the vibration data of rolling bearings from the Case Western Reserve University and a real-world engineering application are carried out to verify the performance of the MSIQDE-DBN method. The experimental results show that the MSIQDE takes on better optimization performance, and the MSIQDE-DBN can obtain higher classification accuracy than the other comparison methods.

An Enhanced MSIQDE Algorithm With Novel Multiple Strategies for Global Optimization Problems

Abstract: Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability.

Understanding perceived risks in mobile payment acceptance

Abstract: Purpose – The purpose of this paper is to examine and quantify how various uncertainties result in different perceived risk dimensions that hinder mobile payment (m-payment) acceptance. Design/methodology/approach – An uncertainty-risk-value framework was proposed based on perceived risk theory, prospect theory, and perceived value theory. Structural equation modeling method was used to test the research model. Findings – Perceived information asymmetry, perceived technology uncertainty, perceived regulatory uncertainty, and perceived service intangibility are confirmed as the main determinants of perceived risk, while perceived performance risk, perceived financial risk, and perceived privacy risk were found to have strong negative effects on perceived value and acceptance intention. Practical implications – The findings may help businesses and policy makers better understand the sources of perceived risk and help support the development of appropriate strategies to mitigate the risk concerns consumers h...

A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA

Abstract: With the continuous and rapid growth of air traffic demand, gate resource becomes a major bottleneck restricting airport development. Rational gate allocation is regarded as one of the most important means to solve this bottleneck. In this paper, in order to comprehensively considere different stakeholders, a three-objective gate allocation model is to consider a wider scope, in which the minimizing passenger walking distances, the most balanced idle time of each gate and the best full use of large gate are optimized simultaneously to improve the practical efficiency. To efficiently solve this model, an improved quantum evolutionary algorithm (QEA) based on the niche co-evolution strategy and enhanced particle swarm optimization (PSO), namely IPOQEA is designed. An IPOQEA-based gate allocation method is proposed to allocate the flights to suitable gates within different periods. Finally, the actual operation data of Baiyun Airport is used to validate the effectiveness of the proposed method. Comparison results show that the constructed model can address the passenger walking distances, robustness and costs in airport management. Moreover, the IPOQEA has better optimization ability in solving gate allocation problem. Therefore, the proposed gate allocation method has great potential for practical engineering since it can easily make decisions for airport managers.