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Multi-criteria consensus sorting model with flexible linguistic preferences based on fuzzy information granulation from the perspective of preference disaggregation

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engineering applications of Artificial Intelligence 2025年 154卷

作者： Zhang, Shiji Zhang, Shitao Tian, Hao Deveci, Muhammet Liu, Xiaodi School of Microelectronics and Data Science Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Ma'anshan243032 China Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan

Multi-criteria group sorting (MCGS) that considers linguistic preferences involves multiple individuals evaluating alternatives and assigning them to pre-determined ordered categories based on specific criteria. Nevertheless, due to the limited availability of class information and the constrained cognitive capacity of decision-makers (DMs), it becomes challenging for DMs to furnish explicit preference information to reach consensus. Besides, decision parameters such as consensus threshold and class thresholds are also too hard to be assumed in advance. Therefore, this paper studies the preference disaggregation problem in MCGS, in which DMs are allowed to provide their pairwise comparisons in flexible linguistic expressions (FLEs) as preference information. To more fully utilize the preferences, semantic granulation is introduced. First, an individual consistency recognition model is proposed to identify inconsistent preferences and provide modification directions to the corresponding individuals. Next, semantic granulation and maximum entropy are combined into a granulation-driven information transformation model to convert the preference information based on FLEs into triangular fuzzy numbers (TFNs). Subsequently, in the consensus-driven preference disaggregation model, decision parameters and sorting results can be obtained at the premise of consensus by adjusting weights. Ultimately, to substantiate the effectiveness of the proposal, two numerical applications concerning the sorting of government venture capitals and information system suppliers are presented, along with comparative analysis, sensitivity analysis, and simulation analysis. © 2025 The Authors

关键词： Information granules

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Research on Intelligent Frequency Control of Permanent Magnet Synchronous Motor

Research on Intelligent Frequency Control of Permanent Magne...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Kuo-Chi Chang Zhongxin Hu Kai-Chun Chu Fu-Hsiang Chang Hsiao-Chuan Wang Tsui-Lien Hsu Yuh-Chung Lin Yuwen Zhou Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan North Borneo University College Sabah Malaysia Fujian University of Technology Fuzhou China Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

The development of PMSM is inseparable from the continuous improvement of the performance-to-price ratio of permanent magnet materials and the development of power electronic equipment. This is exactly the study of PMSM. Entered a new stage, PMSM plays an important role in military, industry and daily life. Dating vector control theory to the field of PMSM speed regulation is also a major breakthrough. Researchers combined the mathematical model of PMSM with the vector control model. It is based on this that the PMSM vector control double closed-loop model was established. Simulate in MATLAB software. There are current module, speed module and PMSM main module in the simulation diagram, and then analyze the simulation results.

关键词： Computational modeling Simulation Software Regulation Power electronics Mathematical model Machine vector control

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A regularized approach to sparse optimal policy in reinforcement learning

arXiv

引用

arXiv 2019年

作者： Li, Xiang Yang, Wenhao Zhang, Zhihua School of Mathematical Sciences Peking University Beijing China Center for Data Science Peking University Beijing China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China

We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term. The extant entropy-regularized MDPs can be cast into our framework. Moreover, under our framework, many regularization terms can bring multi-modality and sparsity, which are potentially useful in reinforcement learning. In particular, we present sufficient and necessary conditions that induce a sparse optimal policy. We also conduct a full mathematical analysis of the proposed regularized MDPs, including the optimality condition, performance error, and sparseness control. We provide a generic method to devise regularization forms and propose off-policy actor critic algorithms in complex environment settings. We empirically analyze the numerical properties of optimal policies and compare the performance of different sparse regularization forms in discrete and continuous environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

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An auto code generator for stencil on SW26010 21

An auto code generator for stencil on SW26010

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight SuperComputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

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Towards making deep transfer learning never hurt

arXiv

引用

arXiv 2019年

作者： Wan, Ruosi Xiong, Haoyi Li, Xingjian Zhu, Zhanxing Huan, Jun Big Data Laboratory Baidu Inc. Beijing China National Engineering Laboratory for Deep Learning Technology and Applications Beijing China School of Mathematical Sciences Peking University Beijing China

—Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts training procedure and may lead to even lower accuracy. In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives). Following the view, we propose a novel strategy making regularization-based Deep Transfer learning Never Hurt (DTNH) that, for each iteration of training procedure, computes the derivatives of the two terms separately, then re-estimates a new descent direction that does not hurt the empirical loss minimization while preserving the regularization affects from the pre-trained weights. Extensive experiments have been done using common transfer learning regularizers, such as L2-SP and knowledge distillation, on top of a wide range of deep transfer learning benchmarks including Caltech, MIT indoor 67, CIFAR-10 and ImageNet. The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks. All in all, DTNH strategy can improve state-of-the-art regularizers in all cases with 0.1%—7% higher accuracy in all experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Towards Making Deep Transfer Learning Never Hurt

Towards Making Deep Transfer Learning Never Hurt

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IEEE International Conference on data Mining (ICDM)

作者： Ruosi Wan Haoyi Xiong Xingjian Li Zhanxing Zhu Jun Huan Big Data Laboratory Baidu Inc. Beijing China School of Mathematical Sciences Peking University Beijing China National Engineering Laboratory for Deep Learning Technology and Applications Beijing China

Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts training procedure and may lead to even lower accuracy. In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives). Following the view, we propose a novel strategy making regularization-based Deep Transfer learning Never Hurt (DTNH) that, for each iteration of training procedure, computes the derivatives of the two terms separately, then re-estimates a new descent direction that does not hurt the empirical loss minimization while preserving the regularization affects from the pre-trained weights. Extensive experiments have been done using common transfer learning regularizers, such as L2-SP and knowledge distillation, on top of a wide range of deep transfer learning benchmarks including Caltech, MIT indoor 67, CIFAR-10 and ImageNet. The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks. All in all, DTNH strategy can improve state-of-the-art regularizers in all cases with 0.1%-7% higher accuracy in all experiments.

关键词：

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NMDA receptor inhibitor MK801 alleviated pro-inflammatory polarization of BV-2 microglia cells

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European Journal of Pharmacology 2023年第1期955卷 175927页

作者： Wu, Chih-Cheng Tzeng, Chung-Yuh Chang, Cheng-Yi Wang, Jiaan-Der Chen, Yu-Fang Chen, Wen-Ying Kuan, Yu-Hsiang Liao, Su-Lan Wang, Wen-Yi Chen, Chun-Jung Department of Anesthesiology Taichung Veterans General Hospital Taichung City 407 Taiwan Department of Financial Engineering Providence University Taichung City 433 Taiwan Department of Data Science and Big Data Analytics Providence University Taichung City 433 Taiwan Department of Orthopedics Taichung Veterans General Hospital Taichung City 407 Taiwan Department of Medicinal Botanicals and Health Applications Da-Yeh University Changhua 515 Taiwan Department of Surgery Feng Yuan Hospital Taichung City 420 Taiwan Department of Veterinary Medicine National Chung Hsing University Taichung City 402 Taiwan Children's Medical Center Taichung Veterans General Hospital Taichung City 407 Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City 407 Taiwan Department of Microbiology & Immunology National Cheng Kung University Tainan City 701 Taiwan Department of Pharmacology Chung Shan Medical University Taichung City 402 Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung City 407 Taiwan Department of Nursing HungKuang University Taichung City 433 Taiwan Department of Medical Laboratory Science and Biotechnology China Medical University Taichung City 404 Taiwan

Microglia have both protective and pathogenic properties, while polarization plays a decisive role in their functional diversity. Apart from being an energetic organelle, mitochondria possess biological capabilities of signaling and immunity involving mitochondrial dynamics. The N-methyl-D-aspartate (NMDA)-type glutamate receptor displays excitatory neurotransmission, excitatory neurotoxicity and pro-inflammatory properties in a membrane location- and cell context-dependent manner. In this study, we have provided experimental evidence showing that by acting on mitochondrial dynamics, NMDA receptors displayed pro-inflammatory properties, while its non-competitive inhibitor MK801 exhibited anti-inflammatory potential in Lipopolysaccharide (LPS)-challenged BV-2 microglia cells. LPS stimulation increased the protein phosphorylation of cells regarding their NMDA receptor component subunits and Calcium/Calmodulin-dependent Protein Kinase II (CaMKII), along with mobilizing intracellular calcium. Additionally, parallel changes occurred in the activation of Transforming Growth Factor-β (TGF-β)-Activated Kinase 1 (TAK1), NF-κB p65 and NF-κB DNA binding activity, acquisition of pro-inflammatory M1 polarization and expression of pro-inflammatory cytokines. LPS-treated cells further displayed signs of mitochondrial dysfunction with higher expressions of the active form of Dynamin-Related Protein 1 (Drp1), NADPH Oxidase-2 (NOX2) expression and the generation of DCFDA-/MitoSOX-sensitive Reactive Oxygen Species (ROS). NMDA receptor blockade by MK801, along with CaMKII inhibitor KN93, Drp1 inhibitor Mdivi-1 and antioxidant apocynin alleviated LPS-induced pro-inflammatory changes. Other than the reported CaMKII/TAK1/NF-κB axis, our in vitro study revealed the CaMKII/Drp1/ROS/NF-κB axis being an alternative cascade for shaping pro-inflammatory phenotypes of microglia upon LPS stimulation, and MK801 having the potential for inhibiting microglia activation and any associated inflammator

关键词： Inflammation Mitochondrial dynamics NMDA receptor Polarization

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Ball k-means

arXiv

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arXiv 2020年

作者： Xia, Shuyin Peng, Daowan Meng, Deyu Zhang, Changqing Wang, Guoyin Chen, Zizhong Wei, Wei Department of Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China National Engineering Laboratory for Algorithm and Analysis Technologiy on Big Data Xian Jiaotong University Xi'an710049 China College of Intelligence and Computing Tianjin University 300072 China Department of Computer Science and Engineering University of California Riverside 900 University Avenue RiversideCA92521 United States School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China

This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid distance computation. The Ball k-means can accurately find the neighbor clusters for each cluster resulting distance computations only between a point and its neighbor clusters centroids instead of all centroids. Moreover, each cluster can be divided into a stable area and an active area, and the later one can be further divided into annulus areas. The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration. Also, there are no upper or lower bounds in the proposed Ball k-means. Furthermore, reducing centroid-centroid distance computation between iterations makes it efficient for large k clustering. The fast speed, no extra parameters and simple design of the Ball k-means make it an all-around replacement of the naive k-means algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： K-means clustering

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Magic Cube Bloom Filter: Answering Membership Queries for Multiple Sets

Magic Cube Bloom Filter: Answering Membership Queries for Mu...

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International Conference on big data and Smart Computing (bigCOMP)

作者： Zhouyi Sun Siang Gao Bingqing Liu Yufei Wang Tong Yang Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application (PKU) China Department of Computer Science Peking University China International School Beijing University of Posts and Telecommunications China

Given d sets without intersection, the multi-set membership query problem is to decide which set an incoming item e belongs to. Multi-set membership query is a fundamental problem in a variety of fields of computer science, especially computer networking. Although several approaches have been proposed in the literature to address this problem, they cannot achieve a high processing speed, a high accuracy, and a small memory usage at the same time. In this paper, we propose a novel data structure, namely Magic Cube Bloom Filter (MCBF), which outperforms the state-of-the-art in terms of accuracy and query speed with a limited memory usage. The key idea of the Magic Cube Bloom filter is that items from the same set are stored in different Bloom filters, improving the accuracy by the redistribution of items. The MC-BF also improves the query speed by utilizing spatial locality. Experimental results show that the MC-BF outperforms the state-of-the-art BUFFALO by up to 148.5 times in terms of accuracy, and up to 3.16 times in terms of query speed. We have made the source code of our MC-BF available on Github [1].

关键词： Random access memory Hash functions Servers Memory management Switches Arrays

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Relaxed 2-D principal component analysis by Lpnorm for face recognition

arXiv

引用

arXiv 2019年

作者： Chen, Xiao Jia, Zhi-Gang Cai, Yunfeng Zhao, Mei-Xiang School of Mathematics and Statistics Jiangsu Key Laboratory of Education Big Data Science and Engineering Jiangsu Normal University Xuzhou221116 China Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications Beijing100193 China

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-L1 and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal Lp-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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