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parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism

Frontiers of Information Technology & Electronic Engineering 2025年第1期26卷 93-108页

作者： Yinhong XIANG Kaiqing ZHOU Arezoo SARKHEYLI-HÄGELE Yusliza YUSOFF Diwen KANG Azlan Mohd ZAIN School of Communication and Electronics Engineering Jishou UniversityJishou 416000China Internet of Things and People Research Center Department of Computer Science and Media TechnologyMalmöUniversityMalmö20506Sweden Faculty of Computing Universiti Teknologi MalaysiaSkudai 81310Malaysia

The state space explosion,a challenge analogous to that encountered in a Petri net(PN),has constrained the extensivestudy of fuzzy Petri nets(FPNs).Current reasoning algorithms employing FPNs,which operate through forward,backward,andbidirectional mechanisms,are *** algorithms streamline the inference process by eliminating irrelevant componentsof the ***,as the scale of the FPN grows,the complexity of these algorithms escalates sharply,posing a significantchallenge for practical *** address the state explosion issue,this work introduces a parallel bidirectional reasoningalgorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation *** algorithminvolves hierarchically dividing a large-scale FPN into two sub-FPNs,followed by a converse operation to generate the reversalsub-FPN for the *** detailed mapping between the original and reversed FPNs is thoroughly *** operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN,with the final resultderived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs.A case study ispresented to illustrate the implementation process,demonstrating the algorithm’s significant enhancement of inference efficiencyand substantial reduction in execution time.

关键词： Fuzzy Petri net(FPN) State explosion Decomposition parallel Bidirectional reasoning

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parallel ADR Detection Based on Spark and BCPNN

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Tsinghua Science and Technology 2019年第2期24卷 195-206页

作者： Li Sun Shan Sun Tianlei Wang Jiyun Li Jingsheng Lin School of Computer Science and Technology Donghua University Ruijin Hospital Affiliated to Shanghai Jiao Tong University

Adverse Drug Reaction(ADR) is one of the major challenges to the evaluation of drug safety in the medical field. The Bayesian Confidence Propagation Neural Network(BCPNN) algorithm is the main algorithm used by the World Health Organization to monitor ADRs. Currently, ADR reports are collected through the spontaneous reporting system. However, with the continuous increase in ADR reports and possible use scenarios, the efficiency of the stand-alone ADR detection algorithm will encounter considerable challenges. Meanwhile, the BCPNN algorithm requires a certain number of disk I/O, which leads to considerable time consumption. In this study,we propose a Spark-based parallel BCPNN algorithm, which speeds up data processing and reduces the number of disk I/O in BCPNN, and two optimization strategies. Then, the ADR data collected from the FDA Adverse Event Reporting System are used to verify the performance of the proposed algorithm and its optimization *** show that the parallel BCPNN can significantly accelerate data processing and the optimized algorithm has a high acceleration rate and can effectively prevent memory overflow. Finally, we apply the proposed algorithm to a dataset provided by a real medical consortium. Experiments further prove the performance and practical value of the proposed algorithm.

关键词： Adverse Drug Reaction (ADR) Bayesian Confidence Propagation Neural Network (BCPNN) parallel Spark

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parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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parallel Switch System with QoS Guarantee for Real-Time Traffic

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Journal of Computer Science & Technology 2006年第6期21卷 1012-1021页

作者：李文杰刘斌徐扬廖恒 Department of Computer Science and Technology Tsinghua University Beijing 100084 P.R. China PMC-SIERRA 8555 Baxter Place Burnaby BC V5A4V7 Canada

This paper studies the load-balancing algorithm and quality of service （QoS） control mechanism in a 320Gb/s switch system, which incorporates four packet-level parallel switch planes. Eight priorities for both unicast and multicast traffic are implemented, and the highest priority with strict QoS guarantee is designed for real-time traffic. Through performance analysis under multi-prlorlty burst traffic, we demonstrate that the load-balancing algorithm is efficient, and the switch system not only provides excellent performance to real-time traffic, but also efficiently allocates bandwidth among other traffic of lower priorities. As a result, this parallel switch system is more scalable towards next generation core routers with QoS guarantee, as well as ensures in-order delivery of IP packets.

关键词： load-balancing parallel priority QoS switch system

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parallel SELF-ORGANIZING MAP

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中国有色金属学会会刊：英文版 1999年第1期9卷 174-182页

作者： Li Weigang Department of Computer Science CIC, University of Brasilia UnB, C. P. 4466, CEP: 70919-970, Brasilia DF, Brazil, E mail: Weigang@cic.unb.br University of Brasilia-UnB

１ＩＮＴＲＯＤＵＣＴＩＯＮ“Ｏｎｃｅｓａｗ，ｎｅｖｅｒｆｏｒｇｏｔｅｎ”ｉｓａｓｅｎｔｅｎｃｅｗｈｉｃｈｕｓｅｄｔｏｄｅｓｃｒｉｂｅａｈｕｍａｎｓｅｎｓｅａｎｄｌｅａｒｎｉｎｇｓｅｑｕｅｎｃｅ．Ｆｏｒｅｘａｍｐｌｅ... 详细信息

１ＩＮＴＲＯＤＵＣＴＩＯＮ“Ｏｎｃｅｓａｗ，ｎｅｖｅｒｆｏｒｇｏｔｅｎ”ｉｓａｓｅｎｔｅｎｃｅｗｈｉｃｈｕｓｅｄｔｏｄｅｓｃｒｉｂｅａｈｕｍａｎｓｅｎｓｅａｎｄｌｅａｒｎｉｎｇｓｅｑｕｅｎｃｅ．Ｆｏｒｅｘａｍｐｌｅ，ａｂｏｙｇｌａｎｃｅｄａｔａｌｏ...

关键词： artificial neural networks competitive learning parallel computing quantum computing

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parallel droplet microfluidics for high throughput cell encapsulation and synthetic microgel generation

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Microsystems & Nanoengineering 2018年第1期4卷 525-533页

作者： Devon M.Headen JoséR.García Andrés J.García Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Petit Institute for Bioengineering and Biosciences Georgia Institute of TechnologyAtlantaGA 30332USA

Cells can be microencapsulated in synthetic hydrogel microspheres(microgels)using droplet microfluidics,but microfluidic devices with a single droplet generating geometry have limited throughput,especially as microgel diameter *** we demonstrate microencapsulation of human mesenchymal stem cells(hMSCs)in small(o100μm diameter)microgels utilizing parallel droplet generators on a two-layer elastomer device,which has 600% increased throughput ***-nozzle *** of microgel diameters were compared between products of parallel ***-nozzle configurations for two square nozzle widths,35 and 100μ*** produced on parallel nozzles were equivalent to those produced on single nozzles,with substantially the same *** of hMSCs was compared for parallel nozzle devices of each *** five micrometer wide nozzle devices could be operated at twice the cell concentration of 100μm wide nozzle devices but produced more empty microgels than predicted by a Poisson *** micrometer wide nozzle devices produced microgels as predicted by a Poisson *** of microgels did not increase with the addition of cells for either nozzle *** encapsulated on 35μm wide nozzle devices had reduced viability(~70%)and a corresponding decrease in vascular endothelial growth factor(VEGF)secretion compared to hMSCs cultured on tissue culture(TC)*** hMSCs using 100μm wide nozzle devices mitigated loss of viability and function,as measured by VEGF secretion.

关键词： droplets microencapsulation microgel multilayer parallel

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parallel Seismic Modeling Based on OpenMP+AVX and Optimization Strategy

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Journal of Earth Science 2019年第4期30卷 843-848页

作者： Wenge Liu Fan Wang Huawei Zhou School of Geoscience and Technology Southwest Petroleum UniversityChengdu 610500China Department of Earth and Atmospheric Sciences University of HoustonHouston TX 77004USA

This paper describes parallel simulation of the memory/computation-intensive acoustic wave equation with CPU template buffer optimization. Considering the 8-core CPU shared storage platform as an example,we obtain a one-time speed-up ratio of 6.7× compared with the serial program by using a coarse-grained OpenMP parallel scheme. Then,data is vectorized on the template buffer using the single instruction-multiple data(SIMD) technique to further exploit the computing potential of the CPUs. We apply an 8-channel parallel vector to simulate seismic wavefields with the 256-bit advanced vector extensions(AVX) instruction set. This increases the computing bandwidth,thus eliminating a significant volume of the computing instructions and obtaining a secondary speed-up ratio of 3–7×. In addition,we use 32-byte data alignment,shortest data direction vectorization,and loop tiling optimization algorithm to achieve faster program execution. Finally,we analyze the factors affecting the secondary speed-up of AVX through three-dimensional modeling experiments with the salt *** results indicate that the memory,cache,and register can better cooperate with each other and the speed-up is increased by optimizing the AVX algorithm.

关键词： forward modeling OpenMP AVX parallel optimization

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parallel computing strategy for the simulation of particulate flows with immersed boundary method

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Science China(Technological Sciences) 2008年第8期51卷 1169-1176页

作者： WANG ZeLi FAN JianRen LUO Kun State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310027China

A parallel computing strategy for the simulation of particulate flows with immersed boundary technique is proposed. This strategy can deal with the coupling between fluid and particle easily when particle crosses the boundaries of sub-domains which are decomposed from original computational domain. And a two- dimen- sional circular particle settling in a closed rectangular domain is simulated with the parallel technique and immersed boundary method to validate the parallel effi- ciency.

关键词： parallel computing immersed boundary method particulate flows

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Heuristics for Online Scheduling on Identical parallel Machines with Two GoS Levels

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Journal of Systems Science & Complexity 2019年第4期32卷 1180-1193页

作者： CAI Shuang LIU Ke Logistics R&D Department Beijing Jingdong Zhenshi Information Technology Co.Ltd.Beijing 100176China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

This paper considers the online scheduling problem on m(m≥3)parallel machines(the first k machines with grade 1 and the remaining m-k machines with grade 2)with two Go S levels and makespan as the objective *** jobs arrive over time with grade 1 or 2 and an arrival job can be assigned to a machine only when the grade of the job is no less than the grade of the *** cases are considered:(i)For k=1,the authors present an online algorithm with competitive ratio of9/5.(ii)For 1

关键词： Grade of Service(GoS)constraints heuristic algorithm online scheduling parallel machine scheduling

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