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Sustainable Power and Energy Conference (iSPEC)

作者： Yunlong Hu Qi Wang Ming Ni Yi Yang Yongkang Zhu Xiecheng Yao College of Electrical and Automation Engineering Nanjing Normal University Nanjing China State Key Lab of Smart Grid Protection and Control NARI Group Corporation Nanjing China State Grid Jiangsu Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665414401

Combined heat and power state estimation is the basis for safety assessment, optimal dispatching and other functions in multi-flow energy management centers, and has important theoretical value and practical significance. The measurement system inevitably has gross errors or bad data. The traditional weighted least squares method can’t restrain its influence, so the robust estimation method of combined heat and power network based on Huber estimation is proposed. The results of calculation examples show that the proposed method has good robustness and can restrain the influence of gross errors on the estimation accuracy.

关键词： Couplings Cogeneration Measurement uncertainty Robustness Safety Power systems State estimation

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A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges

arXiv

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arXiv 2022年

作者： Xu, Minrui Ng, Wei Chong Lim, Wei Yang Bryan Kang, Jiawen Xiong, Zehui Niyato, Dusit Yang, Qiang Shen, Xuemin Sherman Miao, Chunyan The School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba Group Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Webank China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Dubbed "the successor to the mobile Internet", the concept of the Metaverse has grown in popularity. While there exist lite versions of the Metaverse today, they are still far from realizing the full vision of an immersive, embodied, and interoperable Metaverse. Without addressing the issues of implementation from the communication and networking, as well as computation perspectives, the Metaverse is difficult to succeed the Internet, especially in terms of its accessibility to billions of users today. In this survey, we focus on the edge-enabled Metaverse to realize its ultimate vision. We first provide readers with a succinct tutorial of the Metaverse, an introduction to the architecture, as well as current developments. To enable ubiquitous, seamless, and embodied access to the Metaverse, we discuss the communication and networking challenges and survey cutting-edge solutions and concepts that leverage next-generation communication systems for users to immerse as and interact with embodied avatars in the Metaverse. Moreover, given the high computation costs required, e.g., to render 3D virtual worlds and run data-hungry artificial intelligence-driven avatars, we discuss the computation challenges and cloud-edge-end computation framework-driven solutions to realize the Metaverse on resource-constrained edge devices. Next, we explore how blockchain technologies can aid in the interoperable development of the Metaverse, not just in terms of empowering the economic circulation of virtual user-generated content but also to manage physical edge resources in a decentralized, transparent, and immutable manner. Finally, we discuss the future research directions towards realizing the true vision of the edge-enabled Metaverse. © 2022, CC BY.

关键词： Blockchain

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Development of machine learning edge iot based non-destructive food quality monitoring system using raspberry pi

Development of machine learning edge iot based non-destructi...

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2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

作者： Sahu, Bhumika Tiwari, Abhai Raheja, J L Kumar, Santosh Guru Ghasidas Central University Bilaspur Dept. of Pure and Applied Physics Chhattisgarh India AIGROEDGE Technologies Pvt. Ltd. New Delhi India Automation Control System Group CSIR- Central Electronics Engineering Research Institute Raj. India

ISBN: (纸本)9781728150703

Food wastage dew to spoilage is one of the key global problems, the quantity of food loses and spoilage per year is 40-50% for root crops, fruits, and vegetables. This paper proposes an edge IoT machine learning based approach for food quality monitoring. The need for this type of system is to correctly classify the vegetables and fruits in three categories fresh, semi-fresh spoiled, further to alert about environmental conditions, either it's favorable or not using the expert algorithm developed under particular standard environmental data. The proposed system employed with Raspberry Pi as the main processing unit, temperature humidity sensors along with three VoC gas sensors. To classify the vegetable and fruit, the sensed data (Gas Sensors MQ2, MQ3 MQ9 along with humidity temperature sensor) were collected over a week machine learning models Logistic Regression, Linear SVC, rbf SVM Classifier, Random Forest with the accuracy of 99.1%, 99.9%, 99.96% 99.96% respectively have been used to classify the data. Further, the classified and processed result was uploaded on the firebase cloud to make it possible to be monitored from anywhere in the world using a particular developed android application. The uniqueness of the system is, it uses edge IoT and its software is programmable so that it can be used to preserve any type of food items by just filling up some credentials in the settings section of GUI. © 2020 IEEE.

关键词： Spoilage

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Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

Nothing About Us Without Us: a participatory design for an I...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Emanuele Antonioni Cristiana Sanalitro Olga Capirci Alessio Di Renzo Maria Beatrice D'Aversa Domenico Bloisi Lun Wang Ermanno Bartoli Lorenzo Diaco Valentina Presutti Daniele Nardi Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Telematic University UNINETTUNO Rome Italy Institute for Cognitive Sciences and Technologies - National Research Council (ISTC-CNR) Rome Italy School of LIS - SILIS Group Rome Italy Dept. of Mathematics Computer Science and Economics University of Basilicata Potenza Italy LILEC University of Bologna Bologna Italy

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

The success of the interaction between the robotics community and the users of these services is an aspect of considerable importance in the drafting of the development plan of any technology. This aspect becomes even more relevant when dealing with sensitive services and issues such as those related to interaction with specific subgroups of any population. Over the years, there have been few successes in integrating and proposing technologies related to deafness and sign language. Instead, in this paper, we propose an account of successful interaction between a signatory robot and the Italian deaf community, which occurred during the Smart City Robotics Challenge (SciRoc) 2021 competition 1 . Thanks to the use of a participatory design and the involvement of experts belonging to the deaf community from the early stages of the project, it was possible to create a technology that has achieved significant results in terms of acceptance by the community itself and could lead to significant results in the technology development as well.

关键词： Deafness Social networking (online) Smart cities Sociology Collaboration Gesture recognition Organizations

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Multi-block SSD based on small object detection for UAV railway scene surveillance

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Chinese Journal of Aeronautics 2020年第6期33卷 1747-1755页

作者： Yundong LI Han DONG Hongguang LI Xueyan ZHANG Baochang ZHANG Zhifeng XIAO North China University of Technology Beijing 100144China Key Laboratory of Large Structure Health Monitoring and Control Shijiazhuang 050043China Unmanned System Research Institute Beihang UniversityBeijing 100083China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Information Engineering in Surveying Wuhan UniversityWuhan 430079China

A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle *** address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is ***,the original input images are segmented into several overlapped ***,each patch is separately fed into an SSD to detect the ***,the patches are merged together through two *** the first stage,the truncated object of the sub-layer detection result is *** the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of *** boxes that are not detected in the main-layer are *** addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this *** deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case *** results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.

关键词： Deep learning Multi-block Single Shot MultiBox Detector(SSD) Objection detection Railway scene Unmanned aerial vehicle remote sensing

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Application of convolutional neural networks in optical text recognition to junk data filtering 16

Application of convolutional neural networks in optical text...

引用

16th International Conference Optical Methods of Flow Investigation, OMFI 2021

作者： Razumov, T.E. Churikov, D.V. Kravchenko, O.V. Mail.ru Group Leningradsky pr. 39–79 Moscow125167 Russia Scientific and Technological Centre of Unique Instrumentation Butlerova str. 15 Moscow117342 Russia Kotel’nikov Institute of Radio Engineering and Electronics of RAS Mokhovaya st. 11–7 Moscow125009 Russia Federal Research Center "Computer Science and Control" of RAS Vavilova st. 40 Moscow119333 Russia

In this paper, the problem of constructing a model for detecting and filtering unwanted spam messages is solved. A fully connected convolutional neural network (FCNN) was chosen as the model of the classifier of unwanted emails in email. It allows you to divide emails into two categories: spam and not spam. The main result of the research is a software application in the C++ language, which has a micro–service architecture and solves the problem of image classification. The app can handle more than 106 requests per minute in real–time. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Application programs

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Digital Twin-based Automatic Train Regulation for Integration of Dispatching and control

Digital Twin-based Automatic Train Regulation for Integratio...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Min Zhou Zhuopu Hou Jun Liu Clive Reborts Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences CRSC Research & Design Institute Group Co. Ltd. School of Electronic Electrical and Systems Engineering University of Birmingham United Kingdom

This paper proposes a framework of digital twin-based integration of train regulation and control for metro systems. We study the integration of train regulation and train control by using deep learning and hybrid search methods. Specifically, a well-trained convolutional neural network is adopted to fit the nonlinear function mapping relationship between the input set (the line conditions and running time of the train in each section) and the output set (the energy consumption of the optimal speed profile and the switching points), which greatly reduces the calculation time of the optimal recommended speed profile in the process of train regulation. An integrated optimization model is proposed with the aim to minimize the total train delay and energy consumption, a hybrid search algorithm based on particle swarm optimization and a genetic algorithm was developed to solve the problem. The effectiveness of the proposed method is verified based on the actual operating data of a Beijing subway line.

关键词： Energy consumption Neural networks Switches Search problems Regulation Delays Particle swarm optimization

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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