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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Liu, Hao Zong, Xiju Ma, Guoyao University of Jinan School of Electrical Engineering Jinan China University of Jinan School of Information Science and Engineering Jinan China Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9798350303636

In this paper, we study the problem of parameter identification for linear wave equations with dynamic boundary conditions. Using the finite difference method to discretize the time and space variables of the wave equation, a system of ordinary differential equations is obtained. With reasonable discretization, finite dimensional systems can approximate infinite dimensional systems. The ordinary differential equations are further organized to obtain a more recognizable expression form and the unknown parameters are hidden in the coefficient matrix in the form of linear combinations. Each element in the coefficient matrix is identified by the least square method, the discrete unknown parameters can be obtained by the inverse solution, and the general expression of the unknown parameters can be obtained by selecting the appropriate fitting method. Finally, simulation examples are given, and the results confirm the effectiveness of the algorithm. © 2023 IEEE.

关键词： finite difference method least square method parameter identification wave equation

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Single-atomic tungsten-doped Co_(3)O_(4) nanosheets for enhanced electrochemical kinetics in lithium–sulfur batteries

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Carbon Energy 2023年第8期5卷 31-41页

作者： Sangni Wang Riming Hu Ding Yuan Lei Zhang Chao Wu Tianyi Ma Wei Yan Rui Wang Liang Liu Xuchuan Jiang Hua Kun Liu Shi Xue Dou Yuhai Dou Jiantie Xu National Engineering Laboratory for VOCs Pollution Control Technology and Equipment Guangdong Provincial Key Laboratory of Atmospheric EnvironmentPollution Control School of Environment and EnergySouth China University of TechnologyGuangzhouChina Shandong Institute of Advanced Technology JinanChina School of Materials Science and Engineering Institute for Smart Materials&EngineeringUniversity of JinanJinanChina Institute of Energy Materials Science University of Shanghai for Science and TechnologyShanghaiChina Institute for Superconducting and Electronic Materials University of WollongongWollongongAustralia School of Science RMIT UniversityMelbourneVictoriaAustralia State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghaiChina

The practical application of lithium–sulfur batteries(LSBs)is severely hindered by the undesirable shuttling of lithium polysulfides(LiPSs)and sluggish redox kinetics of sulfur ***,a series of ultrathin singleatomic tungsten-doped Co_(3)O_(4)(Wx-Co_(3)O_(4))nanosheets as catalytic additives in the sulfur cathode for LSBs are rationally designed and *** from the enhanced catalytic activity and optimized electronic structure by W doping,the Wx-Co_(3)O_(4) not only reduces the shuttling of LiPSs but also decreases the energy barrier of sulfur redox reactions of sulfur species,leading to accelerated electrode *** a result,LSB cathodes with the use of 5.0 wt%W0.02-Co_(3)O_(4) as the electrocatalyst show the high reversible capacities of 1217.0 and 558.6 mAh g^(-1) at 0.2 and 5.0 C,respectively,and maintain a high reversible capacity of 644.6 mAh g^(-1) at 1.0 C(1.0 C=1675 mA g^(-1))after 500 *** a high sulfur loading of 5.5 mg cm^(-2) and electrolyte–electrode ratio of 8μL_(electrolyte) mg_(sulfur)^(-1),the 5.0 wt%W_(0.02)-Co_(3)O_(4)-based sulfur cathode also retains a high reversible areal capacity of 3.86 mAh cm^(-2) at 0.1 C after 50 cycles with an initial capacity retention of 84.7%.

关键词： catalytic additives lithium-sulfur batteries single-atomic dopant sluggish redox kinetics

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Dual-selective silver recovery strategy by simultaneous adsorption-reduction boosted by in-situ magnetic field

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Green Energy & Environment 2025年第2期10卷 433-440页

作者： Jianran Ren Zhiliang Zhu Yanling Qiu Fei Yu Tao Zhou Jie Ma Jianfu Zhao Research Center for Environmental Functional Materials State Key Laboratory of Pollution Control and Resource ReuseCollege of Environmental Science and EngineeringTongji UniversityShanghai200092China School of Civil Engineering Kashi UniversityKashi844000China College of Marine Ecology and Environment Shanghai Ocean UniversityShanghai201306China

The mainstream silver recovery has problems such as resource waste,weak silver selectivity,and complicated ***,self-propelled magnetic enhanced capture hydrogel(magnetic NbFeB/MXene/GO,MNMGH)was prepared by self-crosslinking encapsulation *** achieved high selectivity(K_(d)=23.31 mL/g)in the acidic range,and exhibited ultrahigh silver recovery capacity(1604.8 mg/g),which greatly improved by 66%with the assistance of in-situ magnetic *** recovered silver crystals could be directly physically exfoliated,without acid/base *** selective sieving effect of adsorption,MNMGH preferentially adsorbed Ag(I),and then selectively reduced to Ag(0),realizing dual-selective *** in-situ magnetic field enhanced selective adsorption by enhancing mass transfer,reactivity of oxygen-containing functional ***,density function theory simulations demonstrated that the in-situ magnetic field could lower the silver reduction reaction energy barrier to enhance the selective ***-drive synergy system(reduction drive,adsorption drive and magnetic drive)achieved ultrahigh silver recovery *** study pioneered an in-situ magnetic field assisted enhancement strategy for dual-selective(adsorption/reduction)recovery of precious metal silver,which provided new idea for low-carbon recovery of noble metal from industrial waste liquids.

关键词： In-situ magnetic field Silver recovery Adsorption-reduction Selectivity Physical separation

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An Optimization Method for the Swing of a Quadrotor Flight Suspension System 25

An Optimization Method for the Swing of a Quadrotor Flight S...

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Yang, Sen Zang, Xinlian Tong, Jigang Xie, Tian Jin, Xin Tianjin University of Technology The School of Electrical Engineering and Automation Tianjin China AVIC Xi'an Flight Automatic Control Research Institute National key laboratory of science and technology on aircraft control Xi'an China

ISBN: (纸本)9798350366600

The aim of this study is to explore optimization control methods for suspension systems during the flight of quadcopters. The traditional control method of suspension system has some problems such as oscillation and instability, so an optimization scheme based on Gaussian pseudo-general algorithm is proposed in this paper. Through numerical simulation and actual flight test, the effectiveness of the optimization algorithm in improving system stability and reducing oscillation amplitude is verified. At the same time, this study conducted a comparative analysis of the suspension system of the quadrotor vehicle and the free fall body, and the optimization algorithm is found to be feasible and can well improve the control accuracy and stability of the system, and provide strong support for the safety of the quadrotor vehicle in various application scenarios. © 2024 IEEE.

关键词： Flight testing

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Steady-state model for predicting size-resolved gas-particle partitioning of semi-volatile organic compounds (SVOCs) in indoor environments

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Building Simulation 2023年第3期16卷 443-460页

作者： Jianping Cao Yu Han Yujie Zhu Xingyu Duan Luyang Wang Haibao Huang School of Environmental Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology Guangzhou 510006China Department of Building Science Tsinghua UniversityBeijing 100084China

Semi-volatile organic compounds(SVOCs)are ubiquitous and important pollutants in indoor *** strong partition between gas phase and suspended particles has significant effects on the transport,human exposure via inhalation,and control strategies of indoor *** models have been developed to simulate the gas–particle partitioning of indoor SVOCs,including a steady-state model by expanding the steady-state model suitable for the outdoor environment to indoor ***,the effects of two important indoor environment-specific parameters,i.e.,the particle size distribution(PSD)and the air-change rate(ACH),were not considered in the existing steady-state model,leading to the inaccurate predictions among *** solve this problem,this study developed a novel steady-state model to more comprehensively simulate the gas-particle partitioning of indoor SVOCs by incorporating the effects of PSD and *** agreement between the predictions of the novel model and the results collected via both field tests and laboratory tests(retrieved from two different studies)supported the effectiveness of the improvements in the novel *** analysis further supported the necessity of involving PSD and *** implications of the novel model were also *** study should be helpful for deepening the understanding and accurate simulation of the gas-particle partitioning,as well as the transport and human exposure via inhalation,of indoor SVOCs.

关键词： indoor air quality airborne particles particle size distribution ventilation mass transfer analysis

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An Early Multi-Fault Diagnosis Method of Lithium-ion Battery Based on Data-Driven 41

An Early Multi-Fault Diagnosis Method of Lithium-ion Battery...

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第41届中国控制会议

作者： Xin Gu Yunlong Shang Chijun Li Yuhao Zhu Bin Duan Jinglun Li Wenyuan Zhao School of Control Science and Engineering Shandong University School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Lithium-ion power battery is the "heart" of electric *** is significant to diagnose the battery fault quickly and accurately.A real-time data driven diagnosis approach for early battery faults based on improved principal component analysis is proposed in this *** technique rotates the battery pack voltage sequence into a new coordinate space through linear combination,while the detection metrics of square prediction errors and modified contribution plots are employed to achieve minor fault *** addition,the training sample of this method relies on the voltage sequence of the battery health state instead of the battery fault data which is difficult to *** experimental results demonstrate that this approach can not only locate the battery cell where the fault occurs,but also diagnose the open-circuit and short-circuit faults of the battery as well as the occurrence and duration of the fault in ***,the feasibility and robustness of the proposed method are verified by applying different experimental *** summary,the presented approach provides an easy-to-implement option that does not require accurate mathematical modeling,expert understanding,and complex computational processes.

关键词： Early fault diagnosis Lithium-ion battery pack Principal component analysis Data driven

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Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

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Chinese Journal of Aeronautics 2022年第9期35卷 314-332页

作者： Yanhua MA Xian DU Ximing SUN School of Microelectronics Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.

关键词： Hybrid optimization Long short-term memory Model modification and testing Sensitivity analysis Turbofan engines

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Anomaly Monitoring of Hydropower Units under Complex Conditions 5

Anomaly Monitoring of Hydropower Units under Complex Conditi...

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5th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Chen, Siqi Wang, Hongteng Ma, Liyong School of Control Science and Engineering Qingdao China Huadian Electric Power Research Institute Co. Ltd Hangzhou China Harbin Institute of Technology School of Information Science and Engineering Weihai China

ISBN: (纸本)9798350352719

Hydropower has now become a fundamental industry and infrastructure for the social and economic development of our country. While hydropower brings convenience and benefits, it also comes with significant safety hazards. Therefore, real-time condition monitoring of hydropower units is necessary. This paper addressed the complex operational conditions of hydropower units by first dividing the data into mixed datasets containing both normal and abnormal data, and independent datasets where normal and abnormal data were separated. For the mixed datasets with both normal and abnormal data, the upper and lower bounds for anomaly detection were obtained using the standard deviation criterion based on normalized weighting and Monte Carlo simulation. For the independent datasets that follow a normal distribution, the dynamic upper and lower bounds for anomaly detection were determined based on the standard deviation criterion. For the independent datasets that do not follow a normal distribution, anomaly monitoring and calculation were performed based on multi-factor indicators. Experimental results show that when the proposed methods were applied to actual data from hydropower units under complex conditions, the accuracy of anomaly detection was relatively high, resulting in better monitoring of abnormal conditions. © 2024 IEEE.

关键词： complex conditions condition monitoring hydroelectric units generating set normal distribution standard deviation criterion

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Diminishing ether-oxygen content of electrolytes enables temperature-immune lithium metal batteries

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science China Chemistry 2023年第9期66卷 2700-2710页

作者： Tao Liu Jingnan Feng Zhe Shi Huajun Li Weina Zhao Minglei Mao Xiangzhen Zhu Yong-Sheng Hu Hong Li Xuejie Huang Liquan Chen Liumin Suo Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China Department of Nuclear Science and Engineering and Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control Guangdong Key Laboratory of Environmental Catalysis and Health Risk ControlSchool of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China

The reversibility of lithium(Li) metal anodes is highly susceptible to temperature,owing to the aggravated side reactions at high temperatures and serious Li dendrite growth at low *** it is extremely challenging to simultaneously realize the high Li reversibility in both low and high temperature ***,an oxygen-free solvent(n-hexane,HEX) assisted with the hexyl methyl ether and 1 mol L^(-1)lithium bis(fluorosulfonyl)imide is proposed to constitute an electrolyte for temperatureimmune lithium metal *** demonstrates that the HEX not only greatly suppresses the solvent reduction even at high temperatures but also weaken the Li~+-solvent interaction for the facile Li-ion desolvation,leading to high Li Coulombic efficiencies(99.59% at 25℃,99.30%at 60℃ and 98.75% at -30℃) and the dendrite-free Li plating from -30℃ to 60℃.Benefitting from the low density and temperature-immune properties of our electrolyte,the sulfurized polyacrylonitrile(3.8mAh cm^(-2))||Li(60 μm) pouch-cells deliver 278 Wh kg^(-1)energy density and maintain the stable performance over 50 cycles,and retain 248 and 320 Wh kg^(-1) energy density at -30℃ and 60℃,*** work provides a new perspective on the electrolyte design for wide-temperature Li metal batteries.

关键词： wide temperature range oxygen-free solvent dendrite-free Li plating high Coulombic efficiencies lithium metal batteries

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