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arXiv 2024年

作者： Malialis, Kleanthis Mavri, Nefeli Vrachimis, Stelios G. Kyriakou, Marios S. Eliades, Demetrios G. Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficiently manage resources. This work focuses on short-term forecasting of DMA consumption using deep learning and aims to address two key challenging issues. First, forecasting based solely on a DMA's historical data may lack broader context and provide limited insights. Second, DMAs may experience sensor malfunctions providing incorrect data, or some DMAs may not be monitored at all due to computational costs, complicating accurate forecasting. We propose a novel method that first identifies DMAs with correlated consumption patterns and then uses these patterns, along with the DMA's local data, as input to a deep learning model for forecasting. In a real-world study with data from five DMAs, we show that: i) the deep learning model outperforms a classical statistical model;ii) accurate forecasting can be carried out using only correlated DMAs' consumption patterns;and iii) even when a DMA's local data is available, including correlated DMAs' data improves accuracy. © 2024, CC BY-NC-SA.

关键词： Water distribution systems

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Maintaining Fuse in the Presence of Distributed Generation Sources in the Distribution Network to Improve Protection System 17

Maintaining Fuse in the Presence of Distributed Generation S...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Taleb, Mahdi Fani, Bahador Shahgholian, Ghazanfar Mosavi, Amir Fathollahi, Arman Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

The need to reduce greenhouse gas emissions and the high price of fossil fuels have made renewable resources attractive in energy-based economies around the world. Renewable energy sources will make up a significant part of the modern energy system in the future because they have promising potential. Many countries are already working to increase their capacity for renewable energy. The placement of renewable resources in power systems has gained significant attention due to their potential to provide power to distribution feeders or near consumers. However, the integration of these resources can have adverse effects on the distribution network, which necessitates their placement to be carefully considered. In this study, a novel method of coordinating protection devices based on the current control of distributed production sources using their current-voltage diagram during fault conditions is suggested. The proposed method aims to address the coordination and regulation problems encountered when integrating scattered resources into the network. To evaluate the effectiveness of our approach, we compare the impact of the presence of renewable sources at various points on the network during flooding. We conduct simulations using the ETAP software and present the obtained results. The proposed protection coordination method effectively mitigates the challenges of integrating renewable resources into the distribution network, providing a promising solution to support the transition towards a more sustainable energy future. © 2023 IEEE.

关键词： Renewable energy resources

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Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

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China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education Beijing 100044China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading *** weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data *** to the non-analytic expression of the WSA *** optimization variables and the unknowability of future network information,the problem cannot be solved with known solution ***,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems *** analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of *** results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline *** is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

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Power Budget Calculation and Sensitivity Enhancement for N×25 Gbps TDM-PON Configuration 3

Power Budget Calculation and Sensitivity Enhancement for N×...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Feituri, Ahmed B Abdullah, M.F.L. Swedan, Abdulhameed Almabrok Suliman, Hager A Universiti Tun Hussen Onn Malaysia Faculty of Electrical and Electronic Engineering Batu Pahat Malaysia The Electronic and Communication Research Center Applied Research and Development Authority Tripoli Libya University of Misurata Department of Computer Science Misurata Libya

ISBN: (纸本)9798350319897

Passive Optical Network (PON) standards relied on Time Division Multiplexing (TDM) and Wavelength Division Multiplexing (WDM) methods to maximize data rate. The latest 25GS-PON specifications were declared by Multi-Source Agreement (MSA) group in 2022. The current work stacks two and four 25-Gb/s TDM-PON downstream (DS) channels. The simulation, evaluation, and outcomes are completed using various versions of *** software. O-band wavelength plan as well as Continuous-Wave (CW) laser with Non-Return-to-Zero/On-Off-Keying (NRZ-OOK) format support the system to mitigate Chromatic Dispersion (CD) and upgrade the system capacity. The power budget of 31.1 dB is suitable for Optical Distribution Network (ODN) standards, and the standard value of receiver sensitivity (-24.1 dBm) is achieved. The transmitted power of 1, 4, and 7 dBm are needed for 32, 64, and 128 users/λ, respectively. The transmission is successful for 512 subscribers and 20 km optical reach without the use of amplifiers, Forward Error Correction (FEC), and compensation techniques. The effect of four-wave mixing (FWM) is solved by the wavelength space of 0.8 nm as well as the slight increase in transmitted power from 7 to 7.8 dBm. The open and clear eye diagrams prove the quality of received signals. The simplicity of structure is high because of utilizing basic components and Intensity Modulation/Direct Detection (IM/DD) strategy without amplification and equalization. FEC and compensation techniques are recommended for future work. © 2023 IEEE.

关键词： Passive optical networks

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Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification

arXiv

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arXiv 2024年

作者： Malialis, Kleanthis Li, Jin Panayiotou, Christos G. Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected. © 2024, CC BY-SA.

关键词： Decision making

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Study and Simulation of Wind Farms Based on Squirrel Cage Induction Generator in electrical Distribution System 17

Study and Simulation of Wind Farms Based on Squirrel Cage In...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Zanjani, Sayed Mohammad Ali Moazzami, Majid Honarvar, Mohammad Amin Mosavi, Amir Fathollahi, Arman Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

Wind power sources are evolving and becoming more prevalent in energy systems, which has implications for a variety of operational metrics, including stability and frequency regulation. To guarantee the stability and reliability of the modern power system, it is crucial that grid-connected wind turbines operate consistently and reliably. It is still challenging to accurately and reliably characterize these generators and their components to analyze sequestered operational possibilities like short-circuit faults, soft-starting, and generator switching. To comprehensively understand the stability interactions within distribution grids or localized networks, an accurate simulation is essential. The current study investigates the impact of a static synchronous compensator (STATCOM) on a distribution system that incorporates an induction generator wind farm. Specifically, we analyze and simulate the effect of STATCOM on a 25 kV distribution system, which delivers the power generated by a 9 MW wind farm to the grid via a 25 kV line. The outcomes demonstrate that STATCOM plays a pivotal role in regulating the voltage changes at the common connection point between the distribution system and the wind farm during both normal and faulty conditions. In addition to improving the stability and efficiency of the distribution network, STATCOM can also provide active power during short-circuit fault conditions. Based on the outcomes, the effectiveness of the proposed approach is highly dependent on the wind energy system's parameters. Due to the inherent parameter uncertainty, non-stationarity, and nonlinearity in the wind power system, the use of a parameter prediction approach based on artificial intelligence (AI) is recommended for future research. © 2023 IEEE.

关键词： Static synchronous compensators

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PriRoAgg: Achieving Robust Model Aggregation With Minimum Privacy Leakage for Federated Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5690-5704页

作者： Hou, Sizai Li, Songze Jahani-Nezhad, Tayyebeh Caire, Giuseppe Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Southeast University School of Cyber Science and Engineering Nanjing 211189 China Supervision and Management (Southeast University) Ministry of Education Engineering Research Center of Blockchain Application Nanjing 211189 China Technische Universität Berlin Department of Electrical Engineering and Computer Science Berlin 10587 Germany

Federated learning (FL), as a promising machine learning paradigm for large-scale distributed data, faces two security challenges of privacy and robustness: the transmitted model updates potentially leak sensitive user information, and the lack of central control over local model updates leaves the global model susceptible to malicious attacks. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) design for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update);and 2) have partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of aggregated statistics of users’ updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines. © 2005-2012 IEEE.

关键词： Byzantine resilience Federated learning privacy preservation secure and robust aggregation

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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Science China Earth Sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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A Protection Methodology for Supporting Distributed Generations with Respect to Transient Instability 17

A Protection Methodology for Supporting Distributed Generati...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Taheri, Milad Shahgholian, Ghazanfar Fani, Bahador Mosavi, Amir Fathollahi, Arman Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

The reliable and stable operation of electrical power systems is crucial for maintaining power delivery to end-users. Transient stability analysis is a fundamental aspect of power system protection. In addition, the increased penetration of distributed generations (DGs) into the power system creates new challenges for protection devices. Thus, understanding the impact of DGs on power system protection is critical. This study investigates the transient stability of a power distribution grid with DGs to determine the optimal protection settings for overcurrent relays (OCRs). Specifically, the study aims to propose a novel technique for calculating the modified value of the overcurrent relay time dial setting (TDS) to ensure the coordination of OCRs and prevent the instability of DG sources. The transient stability of DGs is evaluated under various fault locations and DG penetration rates. The constraints imposed by DGs are then considered in the protection coordination of OCRs. The proposed protection strategy maintains coordination between the overcurrent relays and prevents the instability of DG sources. To validate the effectiveness of the proposed protection strategy, numerical simulations are conducted using ETAP Software on an IEEE 33-bus distribution system. The simulation results show that the proposed method can achieve reliable protection coordination of OCRs and maintain the stability of DG sources. Finally, to enhance the proposed method's practicality and efficiency, future research directions are identified, including the integration of artificial intelligence (AI) techniques into the proposed protection strategy. These efforts would consider the practical constraints and enable the proposed method's scalability and applicability to larger and more complex power systems. © 2023 IEEE.

关键词： Artificial intelligence

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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