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Deep mining of temporal information for lithium-ion battery health status assessment based on wavelet-time-frequency fusion model and multi-attention mechanism

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Journal of Energy Storage 2025年 130卷

作者： Tian-E Fan Han-Yu Li Fei Feng College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 PR China Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Automation Chongqing University Chongqing 400044 China Key Laboratory of Complex System Safety and Control Ministry of EducationChongqing University Chongqing 400044 China

State-of-health (SOH) of lithium-ion batteries plays an important role in battery management system, because accurate SOH estimation can ensure the safety and reliability of electric vehicles. However, because the measured voltage and current data contains abundant temporal periodic information and frequency components, which leads to the current SOH estimation methods either fail to fully display the inherent information hide in the data or adequately integrate time -frequency domain information for accurate SOH estimation. Therefore, to address this issue, an improved wavelet time-frequency fusion model (IWTFFM) and multi-attention mechanism is proposed for SOH estimation of lithium-ion batteries. Also, the noise interference in the measured data is considered. Firstly, the voltage/current data in frequency domain is extracted from charging stage by employing wavelet decomposition and threshold denoising techniques. Then, multi-attention mechanisms are applied to effectively enhance the information fusion and improve the model's ability in capture essential features. Finally, the effectiveness and accuracy of our proposed method is verified on NASA dataset, the results show that, compared to traditional machine learning methods, our approach improves the RMSE by 2 %–3 % when considering the interference of Gaussian noise. The proposed IWTFFM method offers a robust solution for accurate SOH estimation of lithium-ion batteries.

关键词： Wavelet decomposition

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Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains

Evaluating Image Quality of DIBR-synthesized Views in Textur...

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Chinese Automation Congress (CAC)

作者： Guangcheng Wang Nan Guo Hongyan Liu Yonghui Zhang Yazhen Zhou Ke Gu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute

ISBN: (纸本)9781665426480

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, Many DIBR-synthesized image quality assessment (IQA) algorithms based on quantifying the distortion existed in texture images have been proposed. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to evaluate the visual quality of DIBR-synthesized images accurately. To this end, we present a DIBR-synthesized image quality assessment metric with multi-modal information (i.e. integrating texture and depth information), dubbed as MMI. MMI predicts the quality of DIBR-synthesized images by jointly measuring the texture structure and depth structure of the synthesized image. The design principle of our MMI is that the local geometric distortion, introduced by DIBR techniques in the hole-filling process, destroys the texture structure and the depth structure of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality by a joint representation of texture structure and depth structure. Experiments show that our MMI is better than the competing state-of-the-art IQA algorithms in predicting DIBR-synthesized image quality.

关键词： Image quality Visualization Wavelet domain Image color analysis Fuses Databases Predictive models

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： autonomous vehicles

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Remote Sensing Inversion for River Turbidity Estimation Based on Noise Injection and Ensemble Learning

Remote Sensing Inversion for River Turbidity Estimation Base...

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Chinese Automation Congress (CAC)

作者： Yazhen Zhou Shuang Shi Hongyan Liu Yonghui Zhang Ke Gu Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute

ISBN: (纸本)9781665426480

Turbidity is one main indicator of quality typically used for monitoring water pollution and water eutrophication. The monitoring technology of river water quality can be divided into contact monitoring technology based on physical sensors and non-contact monitoring technology based on remote sensing satellites. The traditional contact monitoring technology has limited monitoring spatial resolution. As a new technology, the remote sensing monitoring technology of the river can conveniently solve the above-mentioned shortcomings and realize the coverage of the whole river basin. However, due to the scarcity of remote sensing data and the measurement errors of remote sensing satellites themselves, learning-based turbidity measurement methods have weak generalization ability and thus are easily prone to produce large deviations. To effectively address such difficulty, a noise injection method is introduced into a river turbidity measurement model based on ensemble learning. To be specific, we randomly generate many noise sets and add them to the remote sensing data to make it closer to the real application scene, and then adopt the ensemble learning model to estimate river turbidity. Experiments validate that the random noise injection strategy can effectively improve the generalization performance of river turbidity measurement.

关键词： Satellites Water quality Predictive models Water pollution Rivers Pollution measurement Sensors

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UAV-based Odor Source Localization in Multi-Building Environments Using Simulated Annealing Algorithm

UAV-based Odor Source Localization in Multi-Building Environ...

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第三十九届中国控制会议

作者： Zi-Zhen Yang Tao Jing Qing-Hao Meng Institute of Robotics and Autonomous System Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

A single unmanned-aerial-vehicle(UAV) based odor source localization(OSL) method for outdoor multi-building environments was proposed. The simulated annealing(SA) algorithm was introduced in this problem in consideration of the difficulties of relatively large search space, turbulent airflow in the building environment, and local concentration extrema near the building walls. To avoid the effect of rotating UAV rotors on airflow, a tunable diode laser absorption spectroscopy(TDLAS) rather than the contact gas sensor was used to collect odor information remotely. Moreover, a three-mode(plume finding, tracking, and escaping local extremum) searching framework was designed in the SA-based OSL method to equip the UAV with abilities of traversing the searching area and escaping local extrema rapidly. Simulation results on a robot active olfaction simulator show that, the UAV could effectively escape the local extremum, track the plume and localize the odor source in the area of 30×30×10 m with an average time of about 119 steps and an average error of 1.56 m.

关键词： Odor source localization active olfaction UAV multi-building environments simulated annealing

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Coordinate dispatch of combined heat and power system based on approximate dynamic programming 4

Coordinate dispatch of combined heat and power system based ...

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4th IEEE Conference on Energy Internet and Energy system Integration, EI2 2020

作者： Hu, Bo Liu, Ding Zang, Chuanzhi Wang, Yue Li, Wanting Liu, Yuqi Zeng, Peng State Grid Liaoning Electric Power Co. Ltd. Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control System Liaoning Shenyang China Chinese Academy of Sciences University Beijing China Technical Service Center Shenyang Liaoning Lightning Protection Liaoning China

ISBN: (纸本)9781728196060

Combined heat and power (CHP) can simultaneously produce electrical and thermal energy. CHP is an important and main component of the power system in areas with heat demand. However, its operating mode limits the flexibility of the system and aggravates the waste of renewable energy. This paper presents a coordinate dispatch algorithm for electric heating integrated energy system (EHIES), a combined heat and power system with wind power. An efficient optimization algorithm based on dynamic programming (DP) is developed, which overcomes the challenges of solving high dimensional optimization problems for the EHIES. The results of simulation experiments show that the proposed coordinate dispatch algorithm can meet the scheduling time requirements, reduce the operating cost of the system and realize the full consumption of wind power. © 2020 IEEE.

关键词： Wind power

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The Heterogeneous Deployment Tool for Hardware and Software Co-design

The Heterogeneous Deployment Tool for Hardware and Software ...

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International Conference on Computer, Information and Telecommunication systems (CITS)

作者： Bingjie Zhao Ziyang Li Tao Zhang Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Acsdemy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

In order to solve the shortcomings of manually writing code that cannot meet the requirements of rapid development and product verification, we designed a heterogeneous deployment tool based on Simulink model development. This heterogeneous deployment tool can deploy the algorithm designed by Simulink model to the CPU and FPGA platforms and can communicate between CPU and FPGA via PCI Express. Users do not need to concern the underlying hardware and drivers and but only need to build an algorithm, they can quickly deploy the algorithm on a heterogeneous hardware platform to verify the performance of the algorithm.

关键词： Field programmable gate arrays Tools Mathematical model Software packages Hardware design languages Matlab Load modeling

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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Research on the corrosive effect of inactive sulfur on oil-paper insulation 7

Research on the corrosive effect of inactive sulfur on oil-p...

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7th IEEE International Conference on High Voltage Engineering and Application, ICHVE 2020

作者： Gao, Sihang Li, Jiajun Ke, Tingjing Liu, Yang Ding, Dong Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China State Grid Chongqing Electric Power Company Chongqing Chongqing China Baoding Electric Power Corporation Baoding China State Key Lab. of Transmission Distribution Equipment and Power System Safety and New Technology Chongqing University Chongqing China

ISBN: (纸本)9781728155111

Sulfur corrosion induced by active sulfur has been proved to significantly decrease the insulation performance of oil-immersed equipment. In order to protect the oil-paper insulation from sulfur corrosion, active sulfur is cleared in the refining process of crude oil. Some inactive sulfur with high-efficiency antioxidant is still retained in the mineral oil to improve the oxidation stability of oil, such as thiophenic compounds. However, the corrosive effect of inactive sulfur on the oil-paper insulation has not yet clear. Therefore, thiophene that is the largest content of sulfide in the insulating oil was selected as the research object, a series of accelerated thermal ageing experiments of oil-paper without and with thiophene under the different high temperature were conducted, scanning electron microscope equipped with an energy-dispersive X-ray spectrometer (SEM/EDX) and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used to characterize the corrosion degree of oil-paper insulation. The results show that no corrosive deposit was observed on the surface of oilpaper insulation without thiophene even at very high temperature, while a few corrosive deposit was accumulated on the surface of oil-paper insulation with 500 mg/kg thiophene. The corrosion degree of oil-paper insulation with thiophene had an uptrend as the increase of temperature (130°C-180°C), preliminarily indicating that thiophene may be activated in the oil-paper system under the action of overheating fault of oil-immersed transformer and posed a corrosion threat to oil-paper insulation. © 2020 IEEE.

关键词： Scanning electron microscopy

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Deep samplable observation model for global localization and kidnapping

arXiv

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arXiv 2020年

作者： Chen, Runjian Yin, Huan Jiao, Yanmei Dissanayake, Gamini Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China The Center for Autonomous System University of Technology Sydney Australia

Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL [1]. However, traditional MCL can only sample from a uniform distribution over the state space. Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes. To better deal with these problems, we present a distribution proposal model named Deep Samplable Observation Model (DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be more effective and efficient. Considering that the learning-based sampling model may fail to capture the accurate pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation. Equipped with DSOM, AdaM MCL can achieve more accurate estimation, faster convergence and better scalability than previous methods in both synthetic and real scenes. Even in real environments with long-term changes, AdaM MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map or a blueprint map. Source code for this paper is available here. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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