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Adaptive bat algorithm for toy model of protein folding

Journal of Bionanoscience 2014年第5期8卷 360-363页

作者： Cai, Xingjuan Li, Wuzhao Kang, Qi Wang, Lei Wu, Qidi Department of Control Science and Engineering Tongji University Shanghai201804 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi030024 China Department of Electrical and Mechanical Engineering Jiaxing Vocational Technical College Jiaxing314036 China Shanghai Key Laboratory of Financial Information Technology Shanghai200433 China

Bat algorithm is a novel swarm intelligent algorithm inspired by the echolocation behavior of bats with varying pulse rates of emission and loudness. In this paper, a new variant which is called adaptive bat algorithm is designed and applied to solve the toy model prediction problem. In this algorithm, a linearly dynamic pulse rate selection strategy is designed. To test the performance, short sequences, Fibonacci sequence and real protein sequences are selected to compare, simulation results show adaptive bat algorithm is validity. Copyright © 2014 American Scientific Publishers.

关键词： Proteins

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Multi-agent evaluation for energy management by practically scalingα-rank

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Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 1003-1016页

作者： Yiyun SUN Senlin ZHANG Meiqin LIU Ronghao ZHENG Shanling DONG Xuguang LAN National Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China National Key Laboratory of Human–Machine Hybrid Augmented Intelligence Xi’an Jiaotong UniversityXi’an 710049China

Currently,decarbonization has become an emerging trend in the power system ***,the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues,providing challenges for voltage regulation across a large-scale power grid *** learning based intelligent control of smart inverters and other smart building energy management(EM)systems can be leveraged to alleviate these *** achieve the best EM strategy for building microgrids in a power system,this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’comfort while pursuing systemlevel *** EM problem is formulated as a general-sum game to optimize the benefits at both the system and building ***α-rank algorithm can solve the general-sum game and guarantee the ranking theoretically,but it is limited by the interaction complexity and hardly applies to the practical power system.A new evaluation algorithm(TcEval)is proposed by practically scaling theα-rank algorithm through a tensor complement to reduce the interaction ***,considering the noise prevalent in practice,a noise processing model with domain knowledge is built to calculate the strategy payoffs,and thus the TcEval-AS algorithm is proposed when noise *** evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches,including ResponseGraphUCB(RG-UCB)andαInformationGain(α-IG).Finally,the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

关键词： Energy management Multi-agent deep reinforcement learning Strategy evaluation Power grid system

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Euler-precision formula ZD3NgyP-Z applied to discrete control simulation of agitation tank

Euler-precision formula ZD3NgyP-Z applied to discrete contro...

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2017 Chinese Automation Congress, CAC 2017

作者： Zhang, Yunong Zhang, Jianxin Ding, Yaqiong Gong, Huihui Li, Jian Guangzhou510006 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou510640 China SYSU-CMU Shunde International Joint Reseach Institute Foshan528300 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

ISBN: (纸本)9781538635247

In this paper, a new type of Euler-precision 3-node g-proportional (3NgP) formula is proposed and investigated in the general framework of Zhang discretization (ZD), which is named formula ZD3NgPZ with subtype Z corresponding to the second (as well as student) coauthor. By Taylor expansion, we prove that formula ZD3NgPZ approximating the fst-order derivative can achieve an error pattern of O(g). Numerical experiments of approximation are conducted to show the efficacy of the formula. In the meanwhile, formula ZD3NgPZ is used in the discrete control and simulation of agitation tank system. Finally, numerical experiments of discretization are conducted and analyzed to illustrate the efficacy and accuracy of formula ZD3NgPZ applied to agitation tank system. © 2017 IEEE.

关键词： Tanks (containers)

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Nash–Minmax Strategies for Multiagent Pursuit–Evasion Games With Reinforcement Learning

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IEEE Transactions on Cybernetics 2025年 PP卷 PP页

作者： Wang, Yuhan Zhang, Hao Wang, Zhuping Yan, Huaicheng Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Shanghai Key Laboratory of Wearable Robotics and Human–Machine Interaction College of Electronic and Information Engineering The Department of Control Science and Engineering Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Tongji University Frontiers Science Center for Intelligent Autonomous System of Ministry of Education Shanghai201210 China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

This article investigates the pursuit–evasion games for target capture in multiagent systems. To address this challenge, a novel data-driven optimal control policy is proposed, leveraging off-policy reinforcement learning and Nash–minmax strategies. First, a comprehensive framework for multiagent pursuit–evasion games is developed, modeled as a two-layer game structure. In this framework, interactions among agents within the same team are characterized as nonzero-sum games, while interactions between opposing teams are adversarial and treated as zero-sum games. Second, Nash–minmax strategies are introduced to solve the formulated multiagent pursuit–evasion games. These strategies effectively derive distributed Nash solutions for agents within the same team and adversarial worst-case policies for agents in opposing teams. Furthermore, to eliminate the reliance on prior knowledge of agent dynamics and initial stabilizing control gains, a data-driven optimal control policy is designed, ensuring the achievement of target capture. Finally, a numerical example is provided to demonstrate the effectiveness and practical applicability of the proposed approach. © 2013 IEEE.

关键词： Reinforcement learning

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Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data

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Journal of Environmental Sciences 2023年第8期130卷 126-138页

作者： Hui Ding Yongming Zhao Shenhua Miao Tong Chen Yonghong Liu College of Automation&College of Artifical Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Intelligent Systems Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Provincial Key Laboratory of Intelligent Transport System Guangzhou 510275China Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control Guangzhou 510275China

Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple *** the variability of emissions caused by vehicles,roads,and traffic,the 24-hour change characteristics of air pollutants(CO,HC,NO_(X),PM_(2.5))on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July *** results showed that the spatial diversity of emissions caused by the unbalanced economywas *** vehicle emissions in the Pearl River Delta region(PRD)with a higher economic level were approximately 1–2 times those in the non-Pearl RiverDelta region(non-PRD).Provincial roads with high loads became potential sources of high ***,emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater *** passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions,contributing more than 50%in the daytime(7:00–23:00)and higher than 26%at night(0:00–6:00).Diesel trucks made up 10%of traffic,but were the dominant player at night,contributed 50%–90%to NO_(X) and PM_(2.5) emissions,with amarked 24-hour change rule of more than 80%at night(23:00–5:00)and less than 60%during ***,targeted control measures by time-section should be set up on collaborative *** findings provide time-varying decision support for variable vehicle emission control on a large scale.

关键词： Intercity roads Dynamic vehicle emissions Vehicle identity detection data Diesel trucks

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A fast growing cascade neural network for BOD estimation 34

A fast growing cascade neural network for BOD estimation

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34th Chinese control Conference, CCC 2015

作者： Fanjun, Li Junfei, Qiao Wei, Zhang College of Electronic Information and Control Engineering Beijing University of Technology Beijing China School of Mathematical Science University of Jinan Shandong China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9789881563897

In this paper, a fast growing cascade neural network (FGCNN) is proposed, as a software sensor, to rapidly estimate the biochemical oxygen demand (BOD) in wastewater treatment plants (WWTPs). Firstly, a novel method, based on the orthogonal least squares (OLS), is put forward to add input and hidden units to the existing network one by one. Every unit added to the network affords the maximal reduction of the sum of squared errors (SSE). Then, the FGCNN incrementally updates its output weights by iterations without gradients and generalized inverses, while the other weights remain unchanged during the growth of the network. The simple and effective training method make the FGCNN learn extremely fast. Finally, the proposed FGCNN is applied to estimate the BOD in WWTPs using other easy-to-measure or secondary variables. The experiment results show that the FGCNN has better performance on real-time estimation of BOD than other similar methods. © 2015 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Artificial neural network biochemical oxygen demand software sensor wastewater treatment plant

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A Review of Rotation Mechanical Fault Diagnosis Research Based on Deep Domain Adaptation 9

A Review of Rotation Mechanical Fault Diagnosis Research Bas...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Zhang, Shun Xie, Gang Tian, Juan Shi, Hui Zhang, Xiaohong Nie, Xiaoyin Xie, Xinlin Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China Taiyuan University of Science and Technology Dept. of Electronic and Information Engineering Taiyuan China Division of Industrial and System Engineering Taiyuan China

ISBN: (纸本)9798350333794

With the rapid development of computer technology and the Internet of Things (IoT), a large amount of data containing both healthy and faulty states can be collected from existing devices in factories. However, the usable faulty state data is scarce. Transfer learning makes it possible to train efficient and reliable neural networks with a small number of samples. Domain adaptation, as a branch of transfer learning, can effectively realize knowledge transfer between small sample data. Starting from three subcategories of deep learning-based domain adaptation methods, this article summarizes the latest research achievements and provides insights into future research directions. © 2023 IEEE.

关键词： Failure analysis

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Fan Fault Diagnosis Based on Wavelet Spectral Analysis

Fan Fault Diagnosis Based on Wavelet Spectral Analysis

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第三十四届中国控制会议

作者： Wen Zheng Wang Pu Gao Xuejin Zhang Yachao College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory For Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467374439

Fan plays an irreplaceable role in the ventilation of underground environment. It will cause serious consequences once the fan fails. So it is essential to do fault diagnosis of fan to prevent the emergence of break down. Spectral analysis method is a simple and effective method for signal analysis and it has been widely used in fault diagnosis of rotating machinery. This paper has researched the bearing fault of fan on the base of in-depth study of the frequency characteristics of typical faults fan. High-frequency noise of vibration signal has been removed through wavelet soft thresholding method. Get its spectrum characteristic of the reconstructed signal, and then the failure category of signal is discriminated by comparing its characteristic frequency to typical faults frequency in theory. Combining the advantage of Lab VIEW and MATLAB, we implement the algorithm through two of them, accomplishing denoising and spectral analysis of vibration signals, completing fault diagnosis of fan.

关键词： Fan Fault Diagnosis,Wavelet Denoising,Spectral Analysis

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Robust Platoon control of Industrial Heavy-Load Autonomous Vehicles 12

Robust Platoon Control of Industrial Heavy-Load Autonomous V...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Liu, Zhe Li, Xiang Wang, Hesheng MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China School of Mechanical Engineering and Electronic Information China University of Geosciences China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Platoon control of autonomous industrial vehicles contributes to improving the safety and reliability of cargo transportation in complex industrial scenarios, increasing traffic efficiency and saving energy. The platoon control of autonomous heavy-load industrial vehicles is much harder, as the high robustness requirements, low hardware ability and complex un-structural environments. This paper considers the platoon control task from the view-point of practical industrial applications. Firstly, the longitudinal dynamic model of the heavy-load industrial vehicle is transferred into a first-order damping acceleration response model by introducing the low-level feedback linearization control and compensations. Secondly, the synchronization control concept is introduced and a fully distributed control law is presented, which only requires the coupling information of the two nearest neighbors. In particular, in order to ensure the practical applicability, the information processing delays, data samplings and external control disturbances are explicitly modelled and the global stability analysis approach is presented. Finally, we provide real experiments using full-scale industrial heavy-load vehicles to demonstrate our effectiveness and robustness. © 2022 IEEE.

关键词： Accident prevention

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Adsorption and Sensing Characteristics of Transition Metal (AU, Fe, Pt, Rh and Cu) Doped Ws2 on Dissolved Gases in Transformer Oil: A Dft Perspective

SSRN

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SSRN 2025年

作者： Jiang, Wenwen Zeng, Qingbin Zhang, Yiyi Wang, Guishan Sun, Kuoteng Xu, Min Chen, Dachang Jia, Pengfei School of Electrical Engineering Guangxi University Nanning China School of Artificial Intelligence Guangxi University Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi University Nanning China China Southern Power Grid Extra High Voltage Power Transmission Company Liuzhou Branch Liuzhou China College of Food Science and Bioengineering Xihua University Chengdu China School of Electrical and Electronic Engineering Wuhan Polytechnic University Wuhan China Guangxi Key Laboratory of Power System Optimization and Energy Technology China

In this work, the sensing characteristics of transition metal (Au, Pt, Fe, Rh, Cu) doped WS2 for faulty decomposition gases (CO, CH4, C2H2, C2H4) in oil-immersed transformers were studied using density functional theory (DFT). The analysis showed that pristine WS2 had limited adsorption capacity for these gases. After comparing two doping sites, the more stable TM-WS2 structure was selected. Adsorption structures were optimized, and interactions between the gas molecules and TM-WS2 were analyzed through binding energies, charge transfer, band gaps, and density of states. Results showed that doped WS2 exhibited enhanced gas adsorption compared to pristine WS2. Pt-WS2 showed excellent performance for CH4, Rh-WS2 for C2H4, and Cu-WS2 for C2H2, with short response times and high detection efficiency. This work provides insights into the design of gas-sensing materials based on TM-WS2 © 2025, The Authors. All rights reserved.

关键词： Chemical sensors

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