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China International electrical and Energy Conference (CIEEC)

作者： Yuanqing Liu Qihao Sun Xiaoqiang Fang Yingyi Liu Menghao Lin Yang Ding High Voltage Department China Electric Power Research Institute Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China State Grid Xinjiang Company Limited Electric Power Research Institute Xinjiang China

Audible noise is an important factor that must be considered in the design of high volt direct current lines, but the outdoor audible noise test is vulnerable to environmental noise interference. This paper starts with the relationship between the wire power supply current and the audible noise to study the audible noise. Through the experimental study of corona current and audible noise of 6 * 1250mm 2 true split conductor in the corona cage, this paper obtains that the corona current and audible noise have similar characteristics of the CPB spectrum. Through the comparison of the results of converting the CPB spectrum to A sound level, it is found that the conversion law of audible noise can be obtained from the measured data of corona current. The research results of this paper can lay a foundation for further searching for the intrinsic correlation between corona current and audible noise.

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Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction

arXiv

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arXiv 2022年

作者： Tavassoli, Mehrdad Katyara, Sunny Pozzi, Maria Deshpande, Nikhil Caldwell, Darwin G. Prattichizzo, Domenico Department of Advanced Robotics Italian Institute of Technology Genoa16163 Italy Robotics and Automation team Irish Manufacturing Research DublinD24 Ireland Department of Information Engineering and Mathematics University of Siena 53100 Italy

The uses of robots are changing from static environments in factories to encompass novel concepts such as Human-Robot Collaboration in unstructured settings. Pre-programming all the functionalities for robots becomes impractical, and hence, robots need to learn how to react to new events autonomously, just like humans. However, humans, unlike machines, are naturally skilled in responding to unexpected circumstances based on either experiences or observations. Hence, embedding such anthropoid behaviours into robots entails the development of neuro-cognitive models that emulate motor skills under a robot learning paradigm. Effective encoding of these skills is bound to the proper choice of tools and techniques. This paper studies different motion and behaviour learning methods ranging from Movement Primitives (MP) to Experience Abstraction (EA), applied to different robotic tasks. These methods are scrutinized and then experimentally benchmarked by reconstructing a standard pick-n-place task. Apart from providing a standard guideline for the selection of strategies and algorithms, this paper aims to draw a perspectives on their possible extensions and improvements. © 2022, CC BY.

关键词： Reinforcement learning

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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Vibrational Stabilization of Complex Network Systems

Vibrational Stabilization of Complex Network Systems

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American Control Conference (ACC)

作者： Alberto Maria Nobili Yuzhen Qin Carlo Alberto Avizzano Danielle S. Bassett Fabio Pasqualetti Department of Mechanical Engineering University of California Riverside Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Perceptual Robotics Laboratory at the IIM Institute Pisa Italy Department of Bioengineering the Department of Electrical & Systems Engineering the Department of Physics & Astronomy the Department of Psychiatry and the Department of Neurology University of Pennsylvania and the Santa Fe Institute Collaborative Research Network Aligning Science Across Parkinson’s (ASAP) Chevy Chase MD

Many natural and man-made network systems need to maintain certain patterns, such as working at equilibria or limit cycles, to function properly. Thus, the ability to stabilize such patterns is crucial. Most of the existing studies on stabilization assume that network systems’ states can be measured online so that feedback control strategies can be used. However, in many real-world scenarios, systems’ states, e.g., neuronal activity in the brain, are often difficult to measure. In this paper, we take this situation into account and study the stabilization problem of linear network systems with an open-loop control strategy—vibrational control. We derive a graph-theoretic sufficient condition for structural vibrational stabilizability, under which network systems can always be stabilized. We further provide an approach to select the locations in the network for control placement and design corresponding vibrational inputs to stabilize systems that satisfy this condition. Finally, we provide some numerical results that demonstrate the validity of our theoretical findings.

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Design and Application of Automatic Recovery System for Outer Material of Cigarette parcel 3

Design and Application of Automatic Recovery System for Oute...

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2020 3rd International Symposium on Power Electronics and Control engineering, ISPECE 2020

作者： Huang, Linqi Liu, Wenfeng Luo, Jiazheng College of Electrical and Information Engineering Guangxi University of Science and Technology Liuzhou Guangxi545006 China System Engineering Department Liuzhou Cite Automation Science Research Institute Liuzhou Guangxi545001 China

In order to solve the problems of high labor-intensity and low-efficiency in recycling the outer materials of cigarette parcel manually, an automatic recycling system is designed. The system consists of visual recognition system and material grabbing system. After recognizing the type of outer material through the VGG16 network, the visual recognition system will output the corresponding label to make the material grabbing system execute the according process. The material grabbing system adopts rectangular coordinate robot with flexible structure. With transmitting data and signals by PROFINET, each axis of the robot can perform combined actions. In consequence, the system is able to separate and remove materials that are highly overlapped with the cigarette parcel, and send materials to outside for recycling and sorting by conveyor. The results of on-site test showed that the accuracy of the system to identify the material was 95%, the success rate of grabbing reached 96% and the system took an average of 18s to recover materials, the system could fulfil the requirements of real-time and automatic recycling of materials, and provide technical support for improving the automation level of cigarette factory unpacking line. © Published under licence by IOP Publishing Ltd.

关键词： Flexible structures

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Generating Realistic VRU-AV Scenarios via Social Force-Based Gradient Optimization

Generating Realistic VRU-AV Scenarios via Social Force-Based...

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International Conference on Intelligent Transportation

作者： Tingting Long Mengru Zong Chi Zhang Yuehu Liu Shuangxun Ma Li Li National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China School of Electrical Engineering Xi’ an Jiaotong University Xi'an Shaanxi China School of Future Transportation Changan University Xi'an China Department of Automation BNRist Tsinghua University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Validating safe and reliable interaction with vulnerable road users (VRUs) is imperative for autonomous vehicles, especially in simulation testing procedures before on-road applications. However, existing VRU agents are either with predefined routes or with collision-oriented behaviors, which limits the fidelity and diversity of scenarios. Bridging the gap between the simulated world and reality requires realistic VRU behaviors. In this paper, we apply social force to capture natural human behavior, considering how individuals are influenced by their surroundings and adhere to real-world rules. To this end, we propose HybridSFM, an improved social force model designed for characterizing human-vehicle interacting behavior. Specifically, the steering and acceleration of the VRUs are parameterized and integrated with social norms to forecast trajectories. Based on this, we further implement a gradient optimization framework for generating VRU-AV safety-critical scenarios. Firstly, VRUs are positioned randomly around the ego-agent. Differentiable simulation updates states and unrolls trajectories while backpropagation optimizes the actions of VRUs. The modified scenario is further optimized in the subsequent time step until a collision happens. The experiments demonstrate that our approach can enhance realistic safety-critical scenarios at intersection scenarios, facilitating the testing of interaction capabilities for autonomous vehicles.

关键词： Pedestrians Roads Force Trajectory Reliability Scenario generation Optimization Autonomous vehicles Intelligent transportation systems Testing

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Design and Implementation of IoT Enabled Smart Assistive Systems for Healthcare Applications

Design and Implementation of IoT Enabled Smart Assistive Sys...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Dankan Gowda V Srinivas D Rachakonda Srinivas Balaji Shesharao Ingole Priya Dongare Jadhav KDV Prasad Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India School of Business SR University Warangal Telangana India Department of Computer Science & Engineering Aditya Institute of Technology and Management Srikakulam Andhra Pradesh India Department of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India (Research) Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integrated smart assistive health care system to enhance the precision and speed of patient health assessment. In the proposed system, wearable sensors are used to capture respiratory rate, pulse rate, blood pressure, oxygen saturation, and body temperature with data to be sent to the cloud server as and when it is received. Some of the features are, however, small data transmission delays, high accuracy in the physiological signals and long durations of monitoring due to the low power consumption. The simulations showed that the response time falls below 500ms, and physicians retain up to a 2 % variance to clinical grade devices, which actualized robust and effective patient care. The incorporation of predicting capacities, thus providing ways of early identification of health complications, makes the system applicable in chronic disease treatments and telemedicine.

关键词： Temperature sensors Temperature measurement Patient monitoring Accuracy Power demand Medical services Blood pressure Time factors Biomedical monitoring Wearable sensors

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Vibrational Stabilization of Complex Network Systems

arXiv

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arXiv 2023年

作者： Nobili, Alberto Maria Qin, Yuzhen Avizzano, Carlo Alberto Bassett, Danielle S. Pasqualetti, Fabio The Department of Mechanical Engineering University of California Riverside United States The Perceptual Robotics Laboratory The IIM Institute Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Pisa Italy The Department of Bioengineering The Department of Electrical & Systems Engineering The Department of Physics & Astronomy The Department of Psychiatry The Department of Neurology University of Pennsylvania The Santa Fe Institute United States Collaborative Research Network Chevy ChaseMD United States

关键词： Linear networks

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A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-Based Feature Fusion

A Wavelet Guided Attention Module for Skin Cancer Classifica...

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IEEE International Symposium on Biomedical Imaging

作者： Ayush Roy Sujan Sarkar Sohom Ghosal Dmitrii Kaplun Asya Lyanova Ram Sarkar Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in features across the spatial dimensions and symmetry of the lesion, thereby focusing on the dissimilarities of various classes based on symmetry, uniformity in texture and color, etc. Additionally, to take into account the variations in the boundaries of the lesions for different classes, we employ a gradient-based fusion of wavelet and soft attention-aided features to extract boundary information of skin lesions. We have tested our model on the multi-class and highly class-imbalanced dataset, called HAM10000, and achieved promising results, with a 91.17% F1-score and 90.75% accuracy. The code is made available at: https://***/AyushRoy2001/WAGF-Fusion.

关键词： Solid modeling Image color analysis Biological system modeling Melanoma Medical services Feature extraction Skin

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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IEEE International Conference on robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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