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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Zhang, Shengnan Wang, Xingjun Jin, Wenxing Tsinghua University Shenzhen China Southeast University Nanjing China

ISBN: (纸本)9798350309034

In recent years, while blockchain has brought security to anti-counterfeiting traceability systems, its Practical Byzantine Fault Tolerance (PBFT) has always had poor scalability issues, especially as the number of nodes in the network increases, the difficulty of anti-counterfeiting traceability will increase exponentially. To address this issue, this article first generates random numbers through the verifiable random function RandHound, and nodes are partitioned based on the random numbers. Conduct anti-counterfeiting measures. Secondly, a traceability strategy based on verifiable random functions was designed using the (Artificial Fish Swarm algorithm, afsa), and the main node was ultimately obtained through iteration using the afsa algorithm. Finally, the final consensus committee is selected through a verifiable random function, which is responsible for authenticating the client's request. Compared with traditional methods, the improved afsa algorithm has the characteristics of more acceptable nodes and low latency in anti-counterfeiting traceability problems. © 2023 IEEE.

关键词： afsa algorithm Anti counterfeiting traceability Blockchain Random number

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SOC estimation algorithm of power lithium battery based on afsa-BP neural network

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 535-539页

作者： Wang, Qiuxia Wu, Peizhou Lian, Jialing Fujian Chuanzheng Commun Coll Mech Engn Dept Fuzhou Fujian Peoples R China South East Fujian Motor Corp Ltd Purchasing Dept Fuzhou Fujian Peoples R China

The non-linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (SOC) more accurately, the artificial fish swarm algorithm-back propagation (afsa-BP) neural network structure was designed based on afsa and BP neural network theory. According to the test parameters of power lithium battery, the related mathematical model was established. The flow charts of optimising BP neural network with afsa algorithm and estimating SOC value by afsa-BP algorithm are given. The specific implementation steps are elaborated. Using the 48 V, 50 Ah lithium iron phosphate (LiFePO4) power battery as experimental object, through the periodic charging and discharging experiments and software simulation, the correctness, validity and accuracy of the application of afsa-BP neural network in estimating SOC value of the power lithium battery are verified.

关键词： neural nets secondary cells backpropagation power engineering computing SOC estimation algorithm power lithium battery afsa-BP neural network accurate battery models artificial fish swarm algorithm-back propagation neural network structure optimising BP neural network afsa algorithm afsa-BP algorithm iron phosphate power battery voltage 48 0 V

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A hybrid artificial fish swam algorithm for disassembly sequence planning considering setup time

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ASSEMBLY AUTOMATION 2019年第1期39卷 140-153页

作者： Guo, Jun Zhong, Jingcheng Li, Yibing Du, Baigang Guo, Shunsheng Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Hubei Peoples R China

Purpose To improve the efficiency of end-of-life product's disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing parts when considering the changes of disassembly directions and tools. Design/methodology/approach The methodology has three parts. First, a disassembly hybrid graph model (DHGM) was adopted to represent disassembly operations and their precedence relations. After representing the problem as DHGM, a new integer programming model was suggested for the objective of minimizing the total disassembly time. The objective takes into account several criteria such as disassembly tools change and the change of disassembly directions. Finally, a novel hybrid approach with a chaotic mapping-based hybrid algorithm of artificial fish swarm algorithm (afsa) and genetic algorithm (GA) was developed to find an optimal or near-optimal disassembly sequence. Findings Numerical experiment with case study on end-of-life product disassembly planning has been carried out to demonstrate the effectiveness of the designed criteria and the results exhibited that the developed algorithm performs better than other relevant algorithms. Originality/value This paper proposes a novel CAAFG algorithm for solving DSP problems. The implemented tool generates a feasible optimal solution and the considered criteria can help the planer obtain satisfactory results.

关键词： afsa algorithm Disassembly sequence planning Product representation Sequence optimization

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ATC Estimation Approach Applying Co-Evolutionary Strategy

ATC Estimation Approach Applying Co-Evolutionary Strategy

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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Chai, Aiping Wuwei Occupat Coll Dept Automat & Elect Engn Wuwei 733000 Peoples R China

ISBN: (纸本)9783037858882

Under electricity market environment, it is very important how to calculate available transfer capacity(ATC) of power grids with high speed and precision, and how to ensure its safety and stability. In order to resolve the issue more effectively and operate the power systems more efficiently, in this paper an co-evolutionary strategy is proposed for ATC estimation based on particle swarm optimization(PSO) and artificial fish swarm algorithm(afsa), where PSO and afsa are respectively improved to boost the efficiency. In the end, simulation studies show that the proposed method is capable of estimating ATC with high speed and sufficient precision.

关键词： transmission capacity co-evolutionary strategy PSO afsa algorithm

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QPSO and FISH algorithm Apply in the Wireless Sensor Network Coverage

QPSO and FISH Algorithm Apply in the Wireless Sensor Network...

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10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Wang Zhi Xu Wen-bo So Yangtze Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9780769544151

The applying of QPSO-FISH algorithm in the wireless senor network are mainly studied in this paper. Wireless sensor network is an energy-constrain network, coverage efficiency and power consumption are two important performance indicators. Maximizing the network coverage and minimizing the number of working nodes are network optimization goals. Establish an optimal model for coverage in wireless sensor network. Utilize the characteristics of parallel, fast coveragence and fast local search speed, propose a coverage optimization strategy based on QPSO-FISH algorithm. The results of simulation show that the algorithm can get an optimal set of nodes and improve the real-time capability of nodes scheduling.

关键词： wireless sensor networks afsa algorithm QPSO algorithm optimal coverage

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QPSO and FISH algorithm Apply in the Wireless Sensor Network Coverage

QPSO and FISH Algorithm Apply in the Wireless Sensor Network...

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第十届电子商务、工程及科学领域的分布式计算和应用（DCABES）国际学术研讨会

作者： WANG Zhi School of Internet of Things Engineering,Southern Yangtze University,Wuxi,Jiangsu,China XU Wen-bo School of Internet of Things Engineering,Southern Yangtze University,Wuxi,Jiangsu,China

The applying of QPSO-FISH algorithm in the wireless senor network are mainly studied in this *** sensor network is an energy-constrain network,coverage efficiency and power consumption are two important performance *** the network coverage and minimizing the number of working nodes are network optimization *** an optimal model for coverage in wireless sensor *** the characteristics of parallel,fast coveragence and fast local search speed,propose a coverage optimization strategy based on QPSO-FISH *** results of simulation show that the algorithm can get an optimal set of nodes and improve the real-time capability of nodes scheduling.

关键词： wireless sensor networks afsa algorithm QPSO algorithm optimal coverage

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