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第43届中国控制会议

作者： Nanyan Li Hao Chen Zhigang Sun Jing Gao Dewei Yi Cunjia Liu Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University State Grid Jiangsu Integrated Energy Service Co. Ltd. Department of Computing Science University of Aberdeen Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper considers the real-time semantic segmentation of solar PhotoVoltaic(PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles(UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union(IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and96.5% IoU, with a 27.59 fps(using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights.

关键词： CNN Machine Vision Solar Inspection UAV Navigation Real-time and Embedded systems

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Key technologies for electric vehicles

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Green Energy and Intelligent Transportation 2022年第2期1卷 135-137页

作者： Rui Xiong Jonghoon Kim Weixiang Shen Chen Lv Hailong Li Xiaoyong Zhu Wanzhong Zhao Bingzhao Gao Hongyan Guo Chengming Zhang Fengchun Sun Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyNo.5 South Zhongguancun StreetHaidian DistrictBeijing 100081China Energy Storage and Conversion Laboratory Department of Electrical EngineeringCollege of EngineeringChungnam National UniversityDaejeon34134South Korea School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia School of Mechanical and Aerospace Engineering Nanyang Technological University639798Singapore School of Business Society and Engineering Mälardalen UniversitySE 72123VäteråsSweden School of Electrical and Information Engineering Jiangsu UniversityZhenjiangChina Department of Vehicle Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina Clean Energy Automotive Engineering Center Tongji UniversityShanghai 201804China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China Department of Electrical Engineering Harbin Institute of TechnologyHarbinChina

*** Electric vehicles(EVs)are playing an increasingly important role in decarbonizing the transportation *** constitute a promising solution to a set of global challenges such as climate change and air *** are an integration of a wide spectrum of techniques,such as battery monitoring,battery safety and vehicle energy *** this regard,the EV development still faces significant challenges,which necessitate innovations in EV *** this,Green Energy and Intelligent Transportation(GEITS)organizes a special issue of“Key Technologies for Electric Vehicles”that attempts to advance knowledge in the area of EVs and provides a platform for researchers and engineers to share recent research results and discuss critical challenges in this field.A wide spectrum of topics are discussed,including but not limited to the following.

关键词： battery Key challenges

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A Neurobehavioral Evaluation of the Efficacy of 1mA Longitudinal, Anodal TDCS on Multitasking and Transfer Performance

A Neurobehavioral Evaluation of the Efficacy of 1mA Longitud...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Akash K Rao Shashank Uttrani Darshil Shah Vishnu K Menon Arnav Bhavsar Shubhajit Roy Chowdhury Ramsingh Negi Varun Dutt Manipal Academy of Higher Education Applied Cognitive Science Laboratory Indian Institute of Technology Mandi School of Computing and Electrical Engineering Indian Institute of Technology Mandi Cognitive Control and Machine Learning Group at the Institute of Nuclear Medicine and Allied Sciences Defence Research and Development Organization

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS.

关键词： Training Atmospheric measurements Memory management Switches Multitasking Particle measurements Dynamic scheduling control systems Transcranial direct current stimulation Cybernetics

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Are Deep Neural Networks Robust to Named Entities? An Adversarial Attack and Defense Perspective

SSRN

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SSRN 2022年

作者： Li, Ang Leng, Xinlin Li, Shuangjiao Zhang, Fangyuan Xu, Weifeng Wang, Hongtao School of Control and Computer Engineering North China Electric Power University Baoding071051 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding071051 China

Following the pervasive applications of deep neural networks across natural language tasks, adversarial attacks against these models have proven to be a serious threat. By adding small perturbations via word substitution, word-level adversarial attacks can fool deep classifiers making incorrect decisions. Previous word-level attacks mainly focus on substituting verbs and adjectives by their synonyms. In this paper, we ask whether a deep textual classifier is robust to the replacement of Named Entities (NEs) of the same category. We propose an effective NE adversarial attack method, which proves that NEs are very sensitive to such substitutions for deep textual classifiers. To enhance the robustness of classifiers, we also propose three novel defense approaches: 1) mask replacement, 2) concept replacement, and 3) data augmentation based on NE sampling. We evaluate these approaches on text classification tasks under different victim and attack models,using a variety of standard datasets including IMDB, AGNEWS and SST2. Experimental results illustrate the performance improvements of our defense approaches against NE adversarial attacks. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Research and Exploration on Innovation and Entrepreneurship Practice Education System for Railway Transportation Specialists

Research and Exploration on Innovation and Entrepreneurship ...

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作者： Jiu Yong Jianwu Dang Yangping Wang Jianguo Wei Yan Lu The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University College of Intelligence and Computing Tianjin University The School of Electronic and Information Engineering Lanzhou Jiaotong University Key Laboratory of Four Electric BIM Engineering and Intelligent Application Railway Industry

With the background of concept of rail transit major engineering,guided by industry demand, and based on the cultivation of practical abilities for outstanding engineering and technology talents in rail transit characteristic majors,we are committed to creating a national strategic innovation and entrepreneurship practical education system for rail transit characteristic professionals, continuously improving the practical teaching mode. We will carry out the construction of a "matrix style" practical teaching system that integrates the four modernizations,the construction of a virtual simulation practical teaching platform that integrates expertise and innovation, and open sharing, as well as the reform and exploration of the entire chain of integrated practical education. The scheme focuses on improving students' ability to solve complex engineering problems and their ability to apply composite knowledge and innovate and start businesses, cultivating a large number of outstanding rail transit engineering talents with solid professional foundations, outstanding practical abilities, and innovative and entrepreneurial qualities.

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Federated Linear Bandit Learning via Over-the-air Computation

arXiv

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arXiv 2023年

作者： Wang, Jiali Jiang, Yuning Liu, Xin Wang, Ting Shi, Yuanming MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Automatic Control Laboratory EPFL Switzerland ShanghaiTech University China

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings. © 2023, CC BY-NC-SA.

关键词： Fading channels

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Rational Modulation of Redox Equilibrium Over Mn Modified Ce-Sn Catalyst for Low-Temperature Elimination of No and Hg0

SSRN

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SSRN 2024年

作者： Shi, Qiqi Lu, Litong Kang, Dongrui Shen, Boxiong Bian, Yao Zhang, Xiao Lyu, Honghong Li, Shuhao Tianjin Key Laboratory of Clean Energy and Pollution Control School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China Department of Chemistry and Biotechnology School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia CSIRO Manufacturing Private Bag 10 Clayton SouthVIC3169 Australia

The synergistic catalytic removal of NO and Hg0 under low temperatures is still a great challenge. Here, we report a Mn-modified Ce-Sn catalyst for simultaneous removal of NO and Hg0 at low temperature. The optimum Mn0.2/Ce0.33Sn0.67 exhibited more than 95% NO conversion and N2 selectivity above 86% at 150-250 °C as well as Hg0 removal of nearly 100% within 150-300 °C. SnO2-promoted Ce-Sn solid solution enhanced the surface proportion of Ce3+, the number of surface chemisorbed oxygen and the electronic interaction within Ce-Sn linkage. Mn modification further reasonably promoted redox cycling at low temperatures and improved acidic distribution of catalyst. Under the cooperation of Ce-Sn and Mn sites, the presence of Hg0 could substantially facilitate the reduction of NO at 250-300 °C and the SCR atmosphere had a weak inhibitory effect on Hg0 removal. During the SCR process, the NH4+ and NH2 species that directly reacted with gaseous NO following the Eley−Rideal mechanism and the improved reactivity of NH2 with nitrates following the Langmuir−Hinshelwood mechanism enhanced the NO conversion efficiency at low temperature. Meanwhile, Mn modification could alleviate competitive adsorption of NH3 on Hg0 and promote the formation of more chemically adsorbed mercury species. This work will provide guidance for the design of low-temperature catalysts for the simultaneous removal of NO and Hg0 in flue gas. © 2024, The Authors. All rights reserved.

关键词： Temperature

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared Images 22

A Comparative Study of the Performance of Personnel Detectio...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Futao Liu Zhenling Li Yuezhong Wan Weida Cao Duanwei Ma Xiaolei Li Peng Yan Maintenance Departments Liaocheng Power Supply Company State Grid Corporation of China China School of Control Science and Engineering Shandong University China Trusted Cloud Computing Lab Shandong TCCA Information Technology Research Institute China

ISBN: (纸本)9781450397810

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the [email protected] can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%.

关键词： Visible images Infrared images Different light conditions YOLOv5 and Faster RCNN Personnel detection

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Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation

Multi-Task Driven Multi-Level Dynamical Fusion for Single-Ce...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Li Shizhan Chen Zongbo Han Wei Li Jijun Tang Fei Guo College of Intelligence and Computing Tianjin University Tianjin China College of Artificial Intelligence Nankai University Tianjin China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes and heterogeneity. However, single-cell multi-omics data are inherently high-dimensional and heterogeneous. Due to technical limitations and scarce starting materials, the data are often affected by noise and dropout effects. To address these challenges, we propose a novel multitask driven multi-level dynamical fusion algorithm for single-cell multi-omics cell type annotation, named scMMDyn. Our approach incorporates reconstruction and classification auxiliary tasks to guide the training of trustworthy modules at both the feature and modality levels. It executes dynamical fusion during these stages and finally achieves cross-modality fusion via an attention mechanism. This method effectively mitigates data quality issues through reconstruction tasks and feature-level dynamical fusion while providing interpretability at both feature and modality levels. Experimental results across diverse single-cell multi-omics datasets show that our method surpasses existing approaches in cell type annotation.

关键词： Training Sequential analysis Phenotypes Annotations Heuristic algorithms Data integrity Noise Multitasking Classification algorithms Reliability

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