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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Zhang, Xue Xue, Junjie Zhou, Junhua Shi, Guoqiang Yu, Yan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing Simulation Center Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (纸本)9781728170862

Large-scale equipment presents the characteristics of technology-intensive, knowledge-intensive and complex, which brings high investment and high risk to equipment acquisition. This paper analyzes the various components of equipment acquisition to fully apply modeling and simulation technology to each stage of it. On the basis of multi-objective optimization theory, the objective function of the lowest risk and lowest cost of simulation-based acquisition (SBA) is proposed. Then establishing a multi-objective optimization model for simulation-based acquisition according to the corresponding constraints. The nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) algorithms are used to optimize the model, and the majority voting method is used for integration. The comparison of simulation examples proves that the multi-objective optimization model oriented to SBA designed in this paper can effectively realize the optimization of each objective of SBA under cost constraints. And the feasibility and efficiency of optimization solution are guaranteed. © 2020 IEEE.

关键词： Multiobjective optimization

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Estimate depth information from monocular infrared images based on deep learning 7

Estimate depth information from monocular infrared images ba...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Li, Xu Ding, Meng Wei, Donghui Wu, Xiaozhou Cao, Yunfeng Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing China Beijing Electro-.Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China

ISBN: (纸本)9781728170862

In the field of automatic driving, vision-based driving assistance system(V-DAS) is a research direction. Aiming at the demand of V-DAS for low visibility environment perception, we propose a depth estimation method based on monocular infrared images. Our method uses an end-to-end self-supervised deep learning framework, and uses the reprojection relationship between monocular video frames to construct the loss function, which makes it easy to collect dataset. In order to mitigate the influence of infrared noise, we adopt the minimum reprojection loss method. To ensure the neural network can learn infrared features better, we use transfer learning from RGB image dataset first. Qualitative experiments on FLIR datasets show that the proposed method can obtain pixel-lev el dense depth from monocular infrared images. Experiments on real roads show that the proposed method can effectively perceive the depth information of the target in the night, and the absolute error is 13.2% within 15m. This can meet the requirements of collision avoidance in most emergency situations during automatic driving. © 2020 IEEE.

关键词： Deep learning

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Effects of Wall Emissivity on Aerodynamic Heating in Scramjets

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Fluid Dynamics & Materials Processing 2020年第6期16卷 206-216页

作者： Yue Zhou Pengfei Ju School of Aeronautical Science and Technology Beihang UniversityBeijing100191China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education(Tianjin University of Technology) Tianjin300384China

The effects of the wall emissivity on aerodynamic heating in a scramjet are *** supersonic turbulent combustion flow including radiation is solved in the framework of a decoupled strategy where the flow field is determined first and the radiation field *** particular,a finite difference method is used for solving the flow while a DOM(iscrete ordinates method)approach combined with a WSGGM(weighted sum of gray gases)model is implemented for radiative *** nonreactive turbulent channel flows are examined for a DLR hydrogen fueled scramjet changing parametrically the wall *** results indicate that the wall radiative heating rises greatly with increasing the wall *** the wall emissivity rises,the radiative source and total absorption increase,while the incident radiation decreases ***,although the radiative heating can reach a significant level,its contribution to the total aerodynamic heating is relatively limited.

关键词： Scramjet aerodynamic heating wall emissivity radiation

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Numerical Investigation on Thermal Characteristics and Energy Efficiency of Lithium-Ion Battery Under Different Charging Stresses

SSRN

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SSRN 2024年

作者： Xie, Wenlong Gao, Xinlei Zhang, Zhengjie Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Department of Mechanical Engineering Imperial College London LondonSW7 2AZ United Kingdom School of Transportation Science and engineering Beihang University Beijing100191 China

The thermal characteristics of lithium-ion batteries during the charging process significantly impact their electrochemical performance and safety, particularly under high-rate or extreme temperature conditions. In this study, an electrochemical-thermal coupling model for a 2.5 Ah LiNi0.8Co0.1Mn0.1O2 18650 lithium-ion battery is proposed and experimentally validated. The heat generation and variation patterns of the electrodes at different charging rates and temperatures are evaluated, and the influence mechanisms of various electrode design parameters on charging energy efficiency are elucidated. The results reveal that high-rate charging increases the proportion of irreversible heat in the total heat generation, primarily due to the increase in ohmic heating at the positive electrode. Ambient temperature is less sensitive to heat generation than the C-rate, but low temperatures can also increase irreversible heat and are associated with an increase in positive polarization heat. Analyzing thermal characteristics at the electrode level can contribute to the optimization of battery design and the improvement of thermal safety. Electrode architectures, including electrode thickness, active material porosity, particle radius and negative/positive electrode capacity (N/P) ratio, are determined to analyze their effects on the heat generation of various components within the battery. Thick electrodes or active materials with high porosity induce significant ohmic heating and increase irreversible heat, with a slight effect on reversible heat, which is detrimental to energy efficiency. Large particle radii primarily increase reaction heat and polarization heat, and a high N/P ratio reduces the battery heat generation and is related to the reduction of negative reaction heat. Notably, variations in both two parameters have limited impact on to energy efficiency. Therefore, implementing appropriate thermal management across various charging scenarios is crucial for achievin

关键词： Lithium-ion batteries

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Model Free Adaptive control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Model Free Adaptive Control Algorithm based on ReOSELM for A...

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第40届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhichao Wang Mingyu Fan Bolin Zhao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology The College of Computer Science and Artificial Intelligence Wenzhou University

Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle（ADV）. As is known, historical data collected from ADV contains valuable information about control systems,therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine（ReOSELM） is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.

关键词： data-driven control autonomous driving vehicle model free adaptive control regularized online sequential extreme learning machine

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Understanding the Limitations of Thick Electrodes on the Rate Capability of High-Energy Density Lithium-Ion Batteries

SSRN

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SSRN 2024年

作者： Xie, Wenlong Zhang, Zhengjie Gao, Xinlei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China School of Transportation Science and engineering Beihang University Beijing100191 China Department of Mechanical Engineering Imperial College London LondonSW7 2AZ United Kingdom

As a core component of transportation electrification, lithium-ion batteries need to address two critical challenges: achieving high energy density and enabling fast charging. However, changes in electrode thickness could result in a trade-off between these two characteristics. Herein, the impact of electrode thickness on the electrochemical performance, thermal behavior, as well as energy and power density of LiNi0.8Co0.1Mn0.1O2(NCM)/graphite batteries are investigated through mathematical modeling. Underutilization of active materials of the thick electrode is identified as the primary limitation on rate capability, which can be attributed to the slow solid-state diffusion and poor electrolyte transport within the electrode, especially the enlarged concentration gradients in the solid phase and the lithium depletion in the electrolyte phase during high-rate charging. Evolutions of local current density and heat generation rate along the electrode thickness are compared, identifying the underlying mechanism of inducing uneven reaction kinetics inside the battery. The trade-off between the energy and power density as a function of electrode thickness is investigated based on the limitations of charge transfer and mass transport. Furthermore, the rate-limiting factors under various application requirements are elucidated. This study will provide guidance for the electrode design of high-energy-density batteries with fast-charging capabilities. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Depth Estimation for Small Obstacles Based on Monocular Vision

Depth Estimation for Small Obstacles Based on Monocular Visi...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Wang Gao Changqing Wang Lei Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.

关键词： intelligent agents Estimation control systems Training Deep learning Computer vision complex systems

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Channel Estimation Based on Lorentzian Adaptive Algorithm in α-stable Noise Environment

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Aleksey Cherednichenko Xinqi Huang Yanyan Wang Yingsong Li Harbin Engineering University Harbin China Intelligent Agent Cooperation Laboratory Science and Technology on Complex System Control Beijing China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198064

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel inα-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons.

关键词： Computer simulation Adaptive algorithms Channel estimation Adaptive arrays

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Observability Enhancement of the Target Tracking Model Based on Infrared Semi-strapdown Seeker

Observability Enhancement of the Target Tracking Model Based...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Siting, Peng Qian, Zhao Yichao, Ma Cheng, Xu Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

As an important device for target interception, The seeker is used to search, recognize and track the target autonomously. Infrared semi-strapdown seekers are widely used in applications due to their low cost and small size. However, one non-negligible disadvantage of semi-strapdown infrared seekers is that the observability of target tracking model is poor, especially for the interception of high-speed maneuvering weapons. In this paper, the target tracking model ( i.e., the filtering estimation model) based on the infrared simi-strapdown seeker is established. The observability of the target tracking model is analyzed and the evaluation method of the observability is studied. Simulation results show that the observability of the filtering estimation model can be improved by increasing the relative motion between missile and target which results in faster changes of the line-of-sight (LOS) angle. According to the results on the observability analysis, the desired trajectory is obtained through the design of proper guidance law. Nonlinear filtering algorithm is also introduced for the trajectory design. Simulation experiments are conducted for the integrated filtering and guidance, and simulation results illustrate the effectiveness of the proposed observability enhancement method. © 2019 IEEE.

关键词： Observability

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Failure mode and effects analysis based on intuitionistic fuzzy sets and evidential correlation coefficient

Failure mode and effects analysis based on intuitionistic fu...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Hang Huang, Chan Lu, Mingsheng Dong, Xiaofei School of Software Northwestern Polytechnical University Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Xi'an Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Agent Cooperation Laboratory Science and Technology on Complex System Control and Intelligent Beijing China

ISBN: (纸本)9781728137926

As an effective implement, failure mode and effects analysis (FMEA) is widely applied in the security of system for practical application. Nowadays, many methods determine the order of fault mode by a crisp risk priority number (RPN). However, these methods exist several shortcomings, for instance, the correlation of the assessments given by team members are not fully considered. In this article, a new method for risk assessment and sequence for failure modes in FMEA is proposed on account of the D-S evidence theory and the evidential correlation coefficient. By using the proposed approach, the weights of team members for each failure mode and risk factor is obtained. Then the weighted assessments are used to perform the aggregation process by Intuitionistic fuzzy weighted averaging (IFWA) operator. A classic application regarding risk assessment is used to verify the effectiveness of the proposed method. © 2019 IEEE.

关键词： Failure modes

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