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arXiv 2020年

作者： Liu, Teng Yang, Xing Wang, Hong Tang, Xiaolin Chen, Long Yu, Huilong Wang, Fei-Yue State Key Laboratory of Mechanical Transmissions College of Automotive Engineering Chongqing University Chongqing400044 China Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Vehicle and Mobility Tsinghua University Beijing China Waytous Inc. Qingdao Shandong266109 China Department of Mechanical and Mechatronics Engineering University of Waterloo N2L 3G1 Canada State Key Laboratory for Management and Control of Complex System Institute of Automation Beijing China

Digital quadruplets aiming to improve road safety, traffic efficiency and driving cooperation for future connected automated vehicles are proposed with the enlightenment of ACP based parallel driving. The ACP method denotes Artificial societies, Computational experiments and Parallel execution modules for cyber-physical-social systems. Four agents are designed in the framework of digital quadruplets: descriptive vehicles, predictive vehicles, prescriptive vehicles and real vehicles. The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle. The details of the three virtual vehicles in the digital quadruplets are described. Then, the interactions between the virtual and real vehicles are presented. The experimental results of the digital quadruplets demonstrate the effectiveness of the proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2023年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Modulated-Virtual-Vector-Based Predictive Current control for Dual Three-Phase PMSM With Enhanced Steady-State Performance

Modulated-Virtual-Vector-Based Predictive Current Control fo...

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Annual Conference of Industrial Electronics Society

作者： Ze Li Xingyu Zheng Jinhui Xia Liling Wang Yuanbo Guo Xiaonan Gao Zhiheng Liu Xiaohua Zhang College of Electronic and Information Engineering Hebei University Baoding China School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Southeast University Nanjing China School of Electrical Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The School of Electrical Engineering Dalian University of Technology Dalian China

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.

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Aadaptive genetic algorithm based on real coded and its application

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Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology 2007年第2期33卷 144-149页

作者： Gao, Xue-Jin Wang, Pu Sun, Chong-Zheng Yi, Jian-Qiang Zhang, Ya-Ting Zhang, Hui-Qing College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Key Laboratory of Complex System and Intelligence Science Chinese Acad. of Sci. Beijing 100033 China

To solve the problem of the premature convergence and the slow convergence of Genetic Algorithms, an adaptive Genetic Algorithms based on real coded (RAGA) was proposed to improve the coding modes, genetic operators, etc. Optimization simulation results based on typical complex functions show that the optimal convergence rate and global hitting probability of the proposed algorithm are much better than those of simple Genetic algorithms.

关键词： Genetic algorithms

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Concept, Principle, and Modeling of Driving Risk Entropy Based on Human-Vehicle-Road Coupling Model for Autonomous Vehicle

SSRN

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SSRN 2024年

作者： Gao, Hongbo Yang, Hanqing Zhu, Juping Su, Huiping Wang, Ning Xie, Liping Li, Keqiang Department of Automation University of Science and Technology of China Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Chongqing College of Mobile Communication Chongqing400044 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

Driving risk entropy, based on entropy law, is an innovative concept proposed for intelligent driving systems. The concept deals with the driving risks caused by the human-vehicle-road system from the driving information obtained;however, driving risks are difficult to assess due to many influencing factors and great uncertainty. The risk entropy model of vehicles is visualized by introducing the concept of basic isotropic risk elliptic line (BIREL). Further incorporating the concept of information entropy, risk entropy models for vehicle interaction and vulnerable participants are established, and the risk entropy model for roads is developed using raster coordinates. Ultimately, a three-dimensional driving risk entropy model is constructed based on the human-vehicle-road coupling model, with appropriate weights assigned to each parameter through the DDPG algorithm. Experimental results demonstrate the effectiveness of the driving risk entropy model. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 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 (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP 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. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP 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 S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP 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%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

Copy and Paste GAN: Face Hallucination From Shaded Thumbnail...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang Zhang Ivor W. Tsang Yawei Luo Chang-Hui Hu Xiaobo Lu Xin Yu School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China Southeast University Nanjing China Australian Centre for Robotic Vision Australian National University Australia

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternatively in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.

关键词： Face Lighting Gallium nitride Image resolution Feature extraction Generative adversarial networks Automation

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Single-shot fringe projection profilometry based on deep learning and computer graphics

arXiv

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arXiv 2021年

作者： Wang, Fanzhou Wang, Chenxing Guan, Qingze School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science University College London Gower St Bloomsbury LondonWC1E 6BT United Kingdom

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer graphics

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Role of Black Carbon in Modulating Aerosol Direct Effects Driven by Air Pollution controls During 2013-2017 in China

SSRN

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SSRN 2022年

作者： Zhang, Fenfen Xing, Jia Ding, Dian Wang, Jiandong Zheng, Haotian Zhao, Bin Qi, Ling Wang, Shuxiao State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing100084 China Institute for Atmospheric and Earth System Research Faculty of Science University of Helsinki Helsinki00014 Finland School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China School of Energy and Environmental Engineering University of Science and Technology Beijing Beijing100083 China

Aerosol direct effects (ADEs) can modulate shortwave radiation as well as atmospheric dynamics and air quality. As the key absorbing component of aerosol, the black carbon (BC) largely determines the aerosol optical properties. Therefore, it is expected that BC emission controls might gain co-benefits from the simultaneous reduction of ADEs. To demonstrate such synergy, we quantified the ADEs changes and role of BC controls in China during 2013-2017 using a regional two-way coupled meteorology chemistry transport model. Results suggest that the control action effectively reduced wintertime PM2.5 concentration (-26.0 μg m-3) and associated ADEs. The influence of ADEs on surface shortwave radiation, 2-meter temperature, and planetary boundary layer height was weakened from -16.7 W m-2, -0.20°C, and -15.4 m in 2013 to -11.3 W m-2, -0.06°C, and -10.7 m in 2017, respectively. The enhancement of SO2, NO2, and PM2.5 concentrations due to ADEs was reduced from +3.1%, +5.2%, and +5.4% in 2013, to +2.6%, +4.5%, and +3.3% in 2017, respectively, demonstrating the extra benefit of air pollution controls by reducing ADEs. The BC emission reduced by 12.5% simultaneously along with the effective controls during 2013-2017. Such BC controls contributes 15.6-60.2% of such changes in the ADEs influence on meteorological variables, and 32.6-41.1% on air pollutants. More specially, the effectiveness of collaborative reduction of BC further reduced surface shortwave radiation in China by 3.6 W m-2 in January and 1.0 W m-2 in July, leading to a more weakened ADEs that bring extra benefits in reducing PM2.5 concentrations by 1.8 μg m-3 in January and 0.3 μg m-3 in July. Apparently, BC played a dominant role in modulating the ADEs and associated influences on meteorology and air quality, suggesting a wise control strategy by targeting absorbing component of PM2.5 reduction to address both air pollution and climate change in the future. © 2022, The Authors. All rights reserved.

关键词： Air quality

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Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Cluster-based Relevance Analysis Between Construction Mode a...

引用

Data Driven control and Learning systems (DDCLS)

作者： Jiran Zhu Hua Leng Haiguo Tang Min Fan Yaling Liu Qing Yang College of Electronic and Information Engineering Tongji University Jiading Shanghai China State Grid Corporation of Hunan Electric Power corporation research institute Changsha Hunan China College of Electrical and Information Engineering Hunan University Changsha Hunan China School of Automation Chongqing University Chongqing Key Laboratory of Complex System Safety and Control (Ministry of Education) Chongqing University Chongqing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. This study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. This method clusters the DASs of different cities, and analyzes the clustering results. Then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. The case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.

关键词： Urban areas Principal component analysis Investment Optimization Sensitivity analysis Power distribution Automation

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