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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Zhifeng Gao Yabin Liu Donghao Liu Shipeng Hu College of Automation Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory for Internet of Things and Intelligent Robotics Nanjing China

ISBN: (纸本)9781665401166

In this study, an adaptive tracking control scheme is developed for interconnected nonlinear time delay systems with asymmetric full state dynamic constraints and unmodeled dynamics by using dynamic surface control(DSC) technique. Firstly, the unstructured uncertainty functions are approximated by using neural network functions(NNS), the unknown output time delay functions are addressed by Lyapunov-Krasovskii functions, the unmodeled dynamic problem is solved by a dynamic signal. Secondly, a state dependent nonlinear functions are constructed to address the asymmetric full state dynamic constraints. To simplify the calculation, an adaptive tracking control strategy is developed base on DSC technique, it could guarantee that the systems states are constrained in the bounds imposed dynamically and the output signal has good tracking effect. Finally, a simulation example is given to show the effectiveness of the developed scheme.

关键词： Adaptation models Adaptive systems Uncertainty Delay effects Fault detection Neural networks Process control

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Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio

Contactless Human Activity Recognition using Deep Learning w...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Muhammad Zakir Khan Jawad Ahmad Wadii Boulila Matthew Broadbent Syed Aziz Shah Anis Koubaa Qammer H. Abbasi James Watt School of Engineering University of Glasgow Glasgow UK School of Computing Edinburgh Napier University UK Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia Research Centre for Intelligent Healthcare Coventry University UK

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

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Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio

arXiv

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arXiv 2023年

作者： Khan, Muhammad Zakir Ahmad, Jawad Boulila, Wadii Broadbent, Matthew Shah, Syed Aziz Koubaa, Anis Abbasi, Qammer H. James Watt School of Engineering University of Glasgow Glasgow United Kingdom School of Computing Edinburgh Napier University United Kingdom Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia Research Centre for Intelligent Healthcare Coventry University United Kingdom

关键词： Long short-term memory

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Chance Constrained Routing and Charging Problem of Electric Vehicles

Chance Constrained Routing and Charging Problem of Electric ...

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Chinese Automation Congress (CAC)

作者： Shibo Chen Canqi Yao Haoyu Miao Zhicheng Li Zaiyue Yang Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Shenzhen Polytechnic Internet of Things Research Institute Shenzhen China

Different from traditional vehicles, the routing problem of electric vehicles (EVs) is closely coupled with their charging. Hence, operating EV requires a joint optimization of both routing and charging. Moreover, the travel time and energy consumption uncertainties also bring challenges to the EV operation in practice. In this paper, we study the routing and charging problem of EVs under the chance constrained optimization framework, where uncertainties are tackled with probabilistic constraints. An iterative algorithm is proposed, which transforms the original challenging mixed-integer nonlinear program into the iteration of two linear programs. The finite convergence of this algorithm is also proved. Numerical results show that our proposed algorithm can achieve near-optimal solutions in rather short computation times.

关键词： Energy consumption Uncertainty Automation Transforms Routing Electric vehicles Probabilistic logic

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LiDAR-SGMOS: Semantics-Guided Moving Object Segmentation with 3D LiDAR

LiDAR-SGMOS: Semantics-Guided Moving Object Segmentation wit...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Shuo Gu Suling Yao Jian Yang Chengzhong Xu Hui Kong PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau Macau China

Most of the existing moving object segmentation (MOS) methods regard MOS as an independent task, in this paper, we associate the MOS task with semantic segmentation, and propose a semantics-guided network for moving object segmentation (LiDAR-SGMOS). We first transform the range image and semantic features of the past scan into the range view of current scan based on the relative pose between scans. The residual image is obtained by calculating the normalized absolute difference between the current and transformed range images. Then, we apply a Meta-Kernel based cross scan fusion (CSF) module to adaptively fuse the range images and semantic features of current scan, the residual image and transformed features. Finally, the fused features with rich motion and semantic information are processed to obtain reliable MOS results. We also introduce a residual image augmentation method to further improve the MOS performance. Our method outperforms most LiDAR-MOS methods with only two sequential LiDAR scans as inputs on the SemanticKITTI MOS dataset.

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Control design under attacks of multi-agent switched systems via arbitrary switching

Control design under attacks of multi-agent switched systems...

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Chinese Automation Congress (CAC)

作者： Zhanjie Li Yajing Ma Institute of Advanced Technology for Carbon Neutrality Nanjing University of Posts and Telecommunications Nanjing China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology Sichuan Xihua University Chengdu China School of Internet of Things Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

In this paper, the consensus control against attacks for a type of nonlinear multi-agent switched systems under arbitrary switching is studied. The attacks corrupt the information data, which cannot be used for the feedback design. Meanwhile, the switching logic is not allowed to be designed. To tackle the complicated effects, some intermediate variables and adaptive laws are introduced to compensate the uncertainties. By constructing a common Lyapunov function, an adaptive protocol is proposed such that the outputs of the agents achieve consensus against attacks under arbitrary switching.

关键词： Switched systems Uncertainty Automation Adaptive systems Control design Switches Consensus control

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Lightweight helmet detection algorithm based on improved YOLOv5 2

Lightweight helmet detection algorithm based on improved YOL...

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2nd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： Signal detection

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Driving-Torque Self-Adjusted Triboelectric Nanogenerator for Effective Harvesting of Random Wind Energy

SSRN

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SSRN 2022年

作者： Wang, Yuqi Li, Xiang Yu, Xin Zhu, Jianyang Shen, Ping Wang, Zhong Lin Cheng, Tinghai Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing101400 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau Macao999078 China School of Materials Science and Engineering Georgia Institute of Technology AtlantaGA30332-0245 United States

Triboelectric nanogenerators (TENGs), as a new energy technology for distributed power, are used widely in the field of the natural environment energy harvesting. Because the natural energy is random and unstable, dynamic matching between TENGs driving-torque and natural environment energy is fundamental for improving the applications of TENGs. Therefore, a driving-torque self-adjusted triboelectric nanogenerator (SA-TENG) for effective harvesting of random wind energy is developed in this paper. When the external wind speed is unstable, the SA-TENG automatically self-adjusted its driving-torque to dynamically match the wind speed and obtain higher output power. Experiments showed that the SA-TENG can adjust its driving-torque in accordance to the wind speed ranging in 5.0–13.2 m/s, and that, the output peak power can reach 7.69 mW. Under the same conditions, in comparison with a normal TENG, the power growth rate and the highest energy conversion efficiency of the SA-TENG were boosted by more than 4.3 and 12.2 times, respectively;values that are also 3.2 and 6.5 times higher, respectively, than those of an electromagnetic generator. Additionally, the SA-TENG can supply power to sensors for monitoring environment, proving its feasibility as a distributed energy source. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

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Improvement of pose recognition by sparse regularized convolutional neural network

Improvement of pose recognition by sparse regularized convol...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Zhang, Yuan Yao, Xiao Wang, Zhongli Su, Hao Yu, Zihan Huo, Guanying Xu, Ning Liu, Xiaofeng Changzhou Key Laboratory of Robotics and Intelligent Technology College of Internet of Things Engineering Chang Zhou China Beijing Jiaotong University Electronic and Information Engineering Beijing China College of Internet of Things Engineering Chang Zhou China Changzhou Key College of Internet of Things Engineering Changzhou Campus of Hohai University Chang Zhou China

ISBN: (纸本)9781728137261

The paper proposes a method of using deep model (CNN) with sparse regularization term to improve the performance of pose recognition. Convolutional neural network shows its limitations for pose recognition because of its bad convergence. It is believed that the activation function could be simplified by adding the sparseness term. Therefore, we present an algorithm applying the sparse regularization for the deep model with ReLU. Experimental results confirm that the proposed method can accelerate the convergence speed while a high recognition rate maintained. © 2019 IEEE.

关键词： Convolution

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