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Decentralized control in active distribution grids via supervised and reinforcement learning

Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

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Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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A sentiment analysis of the Ukraine-Russia War tweets using knowledge graph convolutional networks

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International Journal of Information Technology (Singapore) 2025年 1-18页

作者： Menaouer, Brahami Fairouz, Safa Meriem, Mohammed Boulekbachi Mohammed, Sabri Nada, Matta LABAB Laboratory Computer Systems Engineering Department National Polytechnic School of Oran Oran Algeria Computer Systems Engineering Department National Polytechnic School of Oran Oran Algeria TechCICO Laboratory University of Technology of Troyes Troyes France

Nowadays, social networks play a critical role in online social discourse, particularly during major events such as elections, health crises, and wars. Furthermore, individuals have spent significant time on social networks that consume news and express their opinions and viewpoints on various topics, namely, the Russia-Ukraine War. The Ukraine-Russia War is one of the most complicated and unfortunate events of this decade, with many aspects to be considered to have an informed opinion. For this reason, sentiment analysis research can be useful in analyzing sentiments or opinions about the Ukraine-Russia war, such as Twitter (X) posts. In this study, we propose a new deep learning-based sentiment classification model called "Knowledge Graph Convolutional Networks" that predicts and analyzes sentiments concerning the Russia-Ukraine war. There were 500,000 tweets collected in total, however only 410,428 were left for sentiment analysis after cleaning, lemmatization stemming, and deleting duplicate tweets. Results show that "war", "people", "world", "putin", "energy", "gas", "weapon", and "peace" were some of the most frequently occurring words in the tweets. The main aim of this work is to develop a robust system that provides an understanding of public sentiment towards the Russia-Ukraine war on the X platform. Experimental results demonstrate that it is possible to obtain more accurate sentiment classification results by the proposed method. They also have managerial, economic, and research implications. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Classification Decision support systems Graph Convolutional Networks (GCN) Knowledge graph Knowledge management Opinion mining Twitter sentiment analysis Ukraine-Russia War

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TeleRehaB DSS Project: Advancing Balance Rehabilitation Through Digital Health Technologies 30

TeleRehaB DSS Project: Advancing Balance Rehabilitation Thro...

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30th ICE IEEE/ITMC Conference on engineering, Technology, and Innovation, ICE/ITMC 2024

作者： Manta, Ourania Vasileiou, Nikolaos Giannakopoulou, Olympia Bromis, Konstantinos Georgas, Konstantinos Vagenas, Theodoros P. Kouris, Ioannis Haritou, Maria Matsopoulos, George Koutsouris, Dimitris Institute of Communication and Computer Systems Biomedical Engineering Laboratory Athens Greece

ISBN: (纸本)9798350362435

Balance disorders pose a significant health concern, especially among the elderly, often leading to falls and a reduced quality of life. The TeleRehabilitation of Balance clinical and economic Decision Support System (TeleRehaB DSS) project aims to tackle these challenges by integrating personalised interventions with advanced digital health technologies. This multidisciplinary effort, funded under the Horizon 2020 framework, leverages cutting-edge technologies such as artificial intelligence and augmented reality to deliver tailored rehabilitation programs. TeleRehaB DSS aims to optimise treatment efficacy, improve patient engagement, and enhance the overall quality of life for individuals with balance disorders. Additionally, the project emphasises personalisation based on multiple criteria, including socio-economic factors and digital skills, to provide the most effective intervention for each participant. The overall concept will be assessed during a clinical validation phase to be conducted in 5 countries and hospitals. © 2024 IEEE.

关键词： Electronic health record

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Angle robust transmitted plasmonic colors with different surroundings utilizing localized surface plasmon resonance

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Chinese Physics B 2023年第7期32卷 226-233页

作者：高旭峰王琦张世杰洪瑞金张大伟 Shanghai Key Laboratory of Modern Optic Systems Engineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optical SystemsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Color filters in different surroundings inherently suffer from angular sensitivity,which hinders their practical ***,we present an angle-insensitive plasmonic filter that can produce different color responses to different surrounding *** color filters are based on a two-dimensional periodically and randomly distributed silver nanodisk array on a silica *** proposed plasmonic color filters not only produce bright colors by altering the diameter of the Ag nanodisk,but also achieve continuous color palettes by changing the surrounding *** to the weak coupling between the metallic nanodisks,the plasmonic color filters can enable good incident angle-insensitive properties(up to 30°).The strategy presented here could exhibit robust and promising applicability in anti-counterfeiting and imaging technologies.

关键词： plasmonic color filter color sensing high angular tolerance

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Parsing Objects at a Finer Granularity: A Survey

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Machine Intelligence Research 2024年第3期21卷 431-451页

作者： Yifan Zhao Jia Li Yonghong Tian School of Computer Science Peking UniversityBeijing100871China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing100191China

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

关键词： Finer granularity visual parsing part segmentation fine-grained object recognition part relationship

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Multimodal Price Prediction

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Annals of Data Science 2023年第3期10卷 619-635页

作者： Zehtab-Salmasi, Aidin Feizi-Derakhshi, Ali-Reza Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Nabipour, Saeideh Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department Computer and Electrical Engineering University of Mohaghegh Ardabili Ardabil Iran

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Price prediction Multimodal learning Convolutional neural network Inception Classification Prediction Multimodal Price Deep learning Data science Mobile phone

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Handwritten text recognition and information extraction from ancient manuscripts using deep convolutional and recurrent neural network

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Soft Computing 2024年第20期28卷 12249-12268页

作者： El Bahi, Hassan L2IS Laboratory of Computer and Systems Engineering Cadi Ayyad University B.P. 511 Marrakech40000 Morocco

Digitizing ancient manuscripts and making them accessible to a broader audience is a crucial step in unlocking the wealth of information they hold. However, automatic recognition of handwritten text and the extraction of relevant information such as named entities from these manuscripts are among the most difficult research topics, due to several factors such as poor quality of manuscripts, complex background, presence of ink stains, cursive handwriting, etc. To meet these challenges, we propose two systems, the first system performs the task of handwritten text recognition (HTR) in ancient manuscripts;it starts with a preprocessing operation. Then, a convolutional neural network (CNN) is used to extract the features of each input image. Finally, a recurrent neural network (RNN) which has Long Short-Term Memory (LSTM) blocks with the Connectionist Temporal Classification (CTC) layer will predict the text contained in the image. The second system focuses on recognizing named entities and deciphering the relationships among words directly from images of old manuscripts, bypassing the need for an intermediate text transcription step. Like the previous system, this second system starts with a preprocessing step. Then the data augmentation technique is used to increase the training dataset. After that, the extraction of the most relevant features is done automatically using a CNN model. Finally, the recognition of names entities and the relationship between word images is performed using a bidirectional LSTM. Extensive experiments on the ESPOSALLES dataset demonstrate that the proposed systems achieve the state-of-the-art performance exceeding existing systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Supervised learning techniques for blood product prediction in patients with hematologic diseases: a multi-centre study in Western Algeria

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Ykhlef, Amel Labri, Nedjla Selma Brahami, Menaouer LABAB Laboratory Computer Systems Engineering Department National Polytechnic School of Oran Oran Algeria

Blood transfusion is a medical procedure that involves transfusing blood or one of its components from one or more donors into a patient. Digital technology and machine learning have played a crucial role in the blood field and have provided real prospects for the production and distribution of blood products. In this study, we propose supervised machine learning techniques for the multi-label classification of blood products in patients with hematologic diseases. We used three multi-label approaches from the problem transformation category to create a decision support system for blood products label power set (LP), binary relevance (BR), and classifier chain (CC). Multi-label classification using the problem transformation approach is a flexible approach. In this study, we used data from different hospitals in hematology departments and blood transfusion centers to explore the application of contemporary supervised learning algorithms to blood product prediction. The experiment was performed by calculating the Hamming loss and accuracy to facilitate the classification of blood products. As a result, the prediction model achieved an area under the ROC curve of 99.80%, a Hamming loss of 0.30, and an accuracy of 98.8%. The proposed model has been developed to provide accurate and fast results that can save patients’ lives. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blood product Blood transfusion Hematological diseases Knowledge management Machine learning Multi-label classification Problem transformation methods

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Generic model of metadata management system for data lakes

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International Journal of Metadata, Semantics and Ontologies 2023年第4期16卷 315-328页

作者： Elkina, Hamza Sahib, Mohamed Rida Zaki, Taher Department of Computer Engineering and Intelligent Systems Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibnou Zohr University Agadir Morocco

Data lake metadata management is crucial for clearly describing stored data and ensuring efficient search query results, especially for semi-structured and unstructured data. Moreover, high-quality metadata provides the necessary information for decision-making. Therefore, we propose a new approach called Metadata Management for Data Lakes (MMDL) based on metadata quality and classification. Additionally, we introduce a generic model adapted to the context of a data lake by including data lake zones to identify data location during its lifecycle. The model aligns with the proposed approach and enables the handling of metrics used to evaluate metadata quality and metadata sources for provenance classification. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Data quality

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