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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

8th Purple Mountain Forum on Smart Grid Protection and Control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Neural Networks and Deep Learning: Innovations and Applications in Soft Computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems Informatics Biological neural networks Robots

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Oil Spill Identification using Deep Convolutional Neural Networks 14

Oil Spill Identification using Deep Convolutional Neural Net...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Feinauer, David M. Latif, Ghazanfar Alenazy, Abeer M. Tayem, Nizar Alghazo, Jaafar Alzubaidi, Loay Virginia Military Institute Department of Electrical and Computer Engineering LexingtonVA United States Prince Mohammad Bin Fahd University Computer Science Department Khobar Saudi Arabia Texas A&m University Department of Engineering and Technology Commerce United States Higher Colleges of Technology Faculty of Computer Information Systems United Arab Emirates

ISBN: (纸本)9781665487719

Oil spill detection is an extremely important topic in which Machine Learning (ML) can be utilized because oil spills that go undetected can cause huge environmental negative impacts. The science of how an oil spill can cause devastation to wildlife has been widely viewed with sorrow and the early detection of oil spills can greatly reduce the negative impact oil spills have on the environment. If an optimal solution is found for oil spill detection, continuous monitoring can be achieved either through satellite images or images obtained from unmanned aerial vehicles such as drones. In this paper, we develop a dataset for oil spill detection collected from images from the Internet and other online resources. The dataset consists of 783 images of Oil Spills and 783 normal images. Since these are real-world images, research done on this dataset will produce a more realistic and practical solution. In this paper, we also propose an enhanced CNN model based on GoogleNet and VGG16 combined with transfer learning for the detection and classification of oil spills. The GoogleNet Transfer Learning model achieved better results of training accuracy of 97.5%, training loss of 0.0894, and validation accuracy of 95.6%. Since this is a new dataset, the results cannot be compared to anything in the extant literature. © 2022 IEEE.

关键词： Oil spills

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A Comprehensive Analysis of Deep Learning Frameworks to Mitigate the Impact of Varied Lighting and Weather Conditions

A Comprehensive Analysis of Deep Learning Frameworks to Miti...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Anshuman Mohanty Balasundaram A Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Center for Cyber Physical Systems Vellore Institute of Technology Chennai India

Object detection is an extensively researched field in computer vision and deep learning-related use cases. Popular frameworks such as YOLO, RCNN, SSD, FPN, RetinaNet, etc., and other cutting-edge detection systems are extensively used in various applications, such as autonomous vehicle driving scenarios and surveillance systems. However, in practical applications, the performance of the generic object detection systems suffers when subject to images with insufficient or poor lighting due to adverse weather conditions and various external factors. Several frameworks utilizing different deep learning and image processing-based approaches have been presented to enhance object detection considering recent advancements in this field. This paper provides a comprehensive overview of several contemporary techniques under various challenging lighting and weather situations. This study also talks about several public benchmark datasets and applications where varying illumination, or adverse weather conditions play a crucial role in estimating the efficacy of detection. Finally, the study summarizes the future scope and research direction.

关键词： Deep learning Solid modeling computer vision Surveillance Lighting Object detection Detectors

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Automating the Annotation of Medical Images in Capsule Endoscopy Through Convolutional Neural Networks and CBIR 12th

Automating the Annotation of Medical Images in Capsule Endos...

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Fernandes, Rodrigo Salgado, Marta Paçal, Ishak Cunha, António UTAD—University of Trás-os-Montes and Alto Douro Vila Real5001-801 Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Centro Hospitalar Universitário de Santo António Porto4099-001 Portugal Igdir University Iğdır Turkey

ISBN: (纸本)9783031606649

This research addresses the significant challenge of automating the annotation of medical images, with a focus on capsule endoscopy videos. The study introduces a novel approach that synergistically combines Deep Learning and Content-Based Image Retrieval (CBIR) techniques to streamline the annotation process. Two pre-trained Convolutional Neural Networks (CNNs), MobileNet and VGG16, were employed to extract and compare visual features from medical images. The methodology underwent rigorous validation using various performance metrics such as accuracy, AUC, precision, and recall. The MobileNet model demonstrated exceptional performance with a test accuracy of 98.4%, an AUC of 99.9%, a precision of 98.2%, and a recall of 98.6%. On the other hand, the VGG16 model achieved a test accuracy of 95.4%, an AUC of 99.2%, a precision of 97.3%, and a recall of 93.5%. These results indicate the high efficacy of the proposed method in the automated annotation of medical images, establishing it as a promising tool for medical applications. The study also highlights potential avenues for future research, including expanding the image retrieval scope to encompass entire endoscopy video databases. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Endoscopy

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Effect of Electron Thermal Conductivity on Resonant Plasmonic Detection in Terahertz Hot-Electron Bolometers Based on Metal/Black-AsP/Graphene FETs

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Physical Review Applied 2023年第6期19卷 064033-064033页

作者： V. Ryzhii C. Tang T. Otsuji M. Ryzhii V. Mitin M.S. Shur Research Institute of Electrical Communication Tohoku University Sendai 980-8577 Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu 965-8580 Japan Department of Electrical Engineering University at Buffalo SUNY Buffalo New York 14260 USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA

We analyze the operation of terahertz (THz) bolometric detectors based on field-effect transistor (FET) structures with graphene channels (GCs) and black-phosphorus and black-arsenic gate barrier layers (BLs). Such GC FETs use two-dimensional electron gas (2DEG) heating by the incident THz radiation leading to the thermionic emission of hot electrons from the GC via the BL into the gate. Due to the excitation of plasmonic oscillations in the GC by THz signals, the GC FET detector response can be pronouncedly resonant, leading to elevated values of the detector responsivity. The lateral thermal conductivity of the 2DEG and its Peltier cooling can markedly affect the GC FET responsivity, in particular its spectral characteristics. These effects should be considered for the optimization of GC FET detectors.

关键词： Optoelectronics Plasmonics Field-effect transistors Graphene Phosphorene Bolometers Terahertz techniques

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Design of a Lower Limb Exoskeleton Rehabilitation Robot System Based on Forward Dynamics Simulation

Design of a Lower Limb Exoskeleton Rehabilitation Robot Syst...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yudong Fang Yuefei Wang Liucun Zhu Zhen Liu Lei Shi Muxin Du Jianchu Lin School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Guangzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower limb exoskeleton rehabilitation robots can effectively improve the rehabilitation of stroke patients and can substantially reduce the workload of rehabilitation therapists and improve work efficiency. In this paper, we propose a method to investigate the influence of muscle groups on the hip joint during the gait cycle through kinetic modeling simulation. The method is validated using electromyographic signals (EMG). The patients are treated according to their gait characteristics. Simulation with OpenSim; optimisation by Scone controller; joint motion change curves were analysed by Matlab simulation to verify the correctness of the mathematical model, and the simulation showed excellent following effect. The feasibility of the theory is verified, and a new scheme is proposed for the treatment of stroke patients.

关键词： Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Mathematical models Kinetic theory MATLAB Robots Optimization

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Linking network- and neuron-level correlations by renormalized field theory

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Physical Review Research 2024年第3期6卷 033264页

作者： Michael Dick Alexander van Meegen Moritz Helias Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10) Department of Computer Science 3 - Software Engineering Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1) Institute of Zoology Department of Physics Faculty 1

It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.

关键词： Neuronal network activity Statistical field theory Cortical networks Critical phenomena Dynamical mean field theory

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