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New technologies for optimal scheduling of electric vehicles in renewable energy-oriented power systems: A review of deep learning, deep reinforcement learning and blockchain technology

Energy Conversion and Economics

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Energy Conversion and Economics 2022年第6期3卷 345-359页

作者： Ma, Wenshuai Hu, Junjie Yao, Li Fu, Zhuoming Morais, Hugo Marinelli, Mattia Changping District Beijing China INESC-ID Department of electrical and computer engineering Instituto Superior Técnico-IST Universidade de Lisboa Lisbon Portugal Department of Wind and Energy Systems Technical University of Denmark Roskilde Denmark

With global concerns about carbon emissions, the proportion of renewable energy generation worldwide is increasing, and the demand for flexible resources in power systems is growing. In recent years, as a clean means of transportation, the number of electric vehicles has increased, and the optimal scheduling of electric vehicles has become a research hotspot. The rise of artificial intelligence, blockchain, and other innovative technologies has enriched research on optimal scheduling of electric vehicles. To reveal the latest developments in electric vehicle optimal scheduling studies, this paper summarises the application of state-of-the-art technologies, including deep learning, deep reinforcement learning, and blockchain technology in the optimal scheduling of electric vehicles. Moreover, the advantages and disadvantages of various technical applications are highlighted. Finally, considering the shortcomings and developmental status of applications of the above three technologies, some suggestions for future research directions are proposed. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Blockchain

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An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

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Plant Phenomics 2023年第2期5卷 268-284页

作者： Yubao Deng Haoran Xi Guoxiong Zhou Aibin Chen Yanfeng Wang Liujun Li Yahui Hu College of Computer&Information Engineering Central South University of Forestry and TechnologyChangsha410004HunanChina College of Mechanical&Electrical Engineering Central South University of Forestry and TechnologyChangsha410004HunanChina National University of Defense Technology 410015ChangshaHunanChina Department of Soil and Water Systems University of IdahoMoscowID83844USA Plant Protection Research Institute Academy of Agricultural Sciences410125ChangshaHunanChina

Tomato disease control is an urgent requirement in the field of intellectual agriculture,and one of the keys to it is quantitative identification and precise segmentation of tomato leaf *** diseased areas on tomato leaves are tiny and may go unnoticed during *** edge also makes the segmentation accuracy *** on UNet,we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module(MC-UNet).First,a Multi-scale Convolution Module is *** module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes,and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation ***,a Cross-layer Attention Fusion Mechanism is *** mechanism highlights tomato leaf disease locations via gating structure and fusion ***,we employ SoftPool rather than MaxPool to retain valid information on tomato ***,we use the SeLU function appropriately to avoid network neuron *** compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32%accuracy and 6.67M *** method achieves good results for tomato leaf disease segmentation,which demonstrates the effectiveness of the proposed methods.

关键词： network precise sizes

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Polygon Detection from a Set of Lines

arXiv

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arXiv 2023年

作者： Ferreira, Alfredo Fonseca, Manuel J. Jorge, Joaquim A. Department of Information Systems and Computer Science INESC-ID IST University of Lisboa R. Alves Redol 9 Lisboa1000-029 Portugal

Detecting polygons defined by a set of line segments in a plane is an important step in analyzing vector drawings. This paper presents an approach combining several algorithms to detect basic polygons from arbitrary line segments. The resulting algorithm runs in polynomial time and space, with complexities of O((N + M)4) and O((N + M)2) respectively, where N is the number of line segments and M is the number of intersections between line segments. Our choice of algorithms was made to strike a good compromise between efficiency and ease of implementation. The result is a simple and efficient solution to detect polygons from lines. © 2023, CC BY.

关键词： Polynomial approximation

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Receding horizon optimization for distributed control of electric vehicle charging stations

Receding horizon optimization for distributed control of ele...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Anna Malkova Jan Martin Zepter Mattia Marinelli Herbert Amezquita Hugo Morais Department of Wind and Energy Systems Technical University of Denmark Roskilde Denmark Department of Electrical and Computer Engineering INESC-ID Instituto Superior Técnico (IST) Universidade de Lisboa Lisbon Portugal

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

With the continuous increase of electric vehicle (EV) adoption, deploying smart charging techniques offer a practical solution to mitigate the impact of grid overloading caused by simultaneous EV charging. At the same time, smart charging can help to stabilize the fluctuations in the production from local renewable energy sources (RES). This article introduces a receding horizon optimization model for the distributed control of EV chargers at charging stations, focusing on maximizing the profit of the charging station, while enhancing the utilization of local PV generation. The proposed model operates in 5-minute intervals, determining the power reference for the EV cluster at the charging station. Results demonstrate that the proposed model effectively lowers electricity cost for charging stations, while ensuring more than 90% energy delivery for charging EVs. Future research will be focused on integrating wind energy and refining the model in controlled lab tests for practical implementation and validation.

关键词： Electric potential Costs Wind energy Electricity Decentralized control Europe Charging stations Wind power generation Optimization Smart charging

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Toward VR in VR: Assessing Engagement and Social Interaction in a Virtual Conference

Toward VR in VR: Assessing Engagement and Social Interaction...

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作者： Moreira, Catarina Simoes, Francisco P. M. Lee, Mark J. W. Zorzal, Ezequiel R. Lindeman, Robert W. Pereira, Joao Madeiras Johnsen, Kyle Jorge, Joaquim Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Universidade Federal Rural de Pernambuco Departamento de Computação Recife52171-900 Brazil La Trobe University Computer Science and Information Technology BundooraVIC3086 Australia University of Canterbury HIT Lab NZ Christchurch8041 New Zealand Instituto Superior Técnico Universidade de Lisboa INESC-ID Department of Computer Science and Engineering Lisboa1000-029 Portugal University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

The pandemic brought about an unprecedented number of virtual conferences, given the heavy restrictions on travel to in-person meetings. Despite all the advances in technology, people still complain about virtual events. There is Zoom fatigue, confinement malaise, and a longing for personal social interactions. This paper discusses our experience organizing the IEEE Virtual Reality Conference (IEEE VR) as a virtual event. IEEE VR was a success with 1200+ registered paying participants, dozens of workshops and tutorials, and hundreds of technical papers. We used (1) a virtual environment platform, together with (2) discussion tools and (3) videoconferencing/broadcast/online tools to further provide effective social interaction and increase engagement. In this paper, we explore the synergies between virtual environments and other online tools and assess user engagement by analyzing the messages exchanged between participants across different genders and geographical regions. To this end, we apply diverse engagement metrics for online conferences. Our analysis shows that these metrics have the potential to highlight engagement, diversity, and inclusion by combining textual messages, participant geographic and gender information, communities of participants, and visitation patterns in a virtual environment. Drawing on our results and experiences, we propose guidelines for organizing technical virtual events to increase diversity and social interaction. © 2013 IEEE.

关键词： Virtual reality

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks

Secured Cluster-Based Electricity Theft Detectors Against Bl...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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Exploring the Boundaries of Lateral Movement Detection Through Unsupervised Learning

SSRN

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SSRN 2024年

作者： Smiliotopoulos, Christos Kambourakis, Georgios Kolias, Constantinos Gritzalis, Stefanos Department of Information and Communication Systems Engineering University of the Aegean Samos Karlovasi83200 Greece Department of Computer Science University of Idaho Idaho FallsID83402 United States Department of Digital Systems University of Piraeus Piraeus18532 Greece

Lateral movement (LM) is an umbrella term used for techniques that threat actors adopt to infiltrate and subsequently manipulate remote systems on a network. Typically, LM involves both pivoting through multiple systems and privilege escalation. As LM techniques proliferate and evolve, there is a need for advanced security controls able to detect and possibly nip such attacks in the bud. Based on the published literature, we argue that although LM-focused intrusion detection systems (idS) have received considerable attention, a prominent issue remains largely unaddressed. This concerns the detection of LM through unsupervised machine learning (ML) techniques. This work contributes to this field by capitalizing on the LMD-2023 dataset containing traces of 15 diverse LM attack techniques as they were logged by the system monitor (Sysmon) service of the MS Windows platform. We offer a panorama of this sub-field and associated methodologies in an attempt to explore the boundaries in terms of standard ML-based detection. In further detail, in addition to analyzing feature selection and preprocessing, we detail and evaluate a plethora of unsupervised ML techniques, both shallow and deep. The derived scores for the best performer in terms of the AUC and F1 metrics are quite promising, around 94.6%/93% and 95.1%/93.8%, for the best shallow and deep neural network (DNN) model, respectively. On top of that, in an effort to further improve on those metrics, we devise and evaluate a two-stage ML model, surpassing the previous best score by approximately 3%. Overall, to our knowledge, this work provides the first full-blown study on LM detection via unsupervised learning techniques, therefore it is anticipated to serve as a groundwork for anyone working in this timely field. © 2024, The Authors. All rights reserved.

关键词： Network security

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Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification

arXiv

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arXiv 2024年

作者： Alharbi, Fadi Vakanski, Aleksandar Zhang, Boyu Elbashir, Murtada K. Mohammed, Mohanad College of Engineering Department of Computer Science University of Idaho MoscowID83844 United States College of Computer and Information Sciences Department of Information Systems Jouf University Sakaka Aljouf72441 Saudi Arabia School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Scottsville3209 South Africa

Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study addresses these challenges and evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN) for classifying 31 cancer types and normal tissues. To address the high-dimensionality of multi-omics data, we employed LASSO (Least Absolute Shrinkage and Selection Operator) regression for feature selection, leading to the creation of LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOTGN models. Graph structures for the networks were constructed using gene correlation matrices and protein-protein interaction networks for multiomics integration of messenger-RNA, micro-RNA, and DNA methylation data. Such data integration enables the networks to dynamically focus on important relationships between biological entities, improving both model performance and interpretability. Among the models, LASSO-MOGAT with a correlation-based graph structure achieved state-of-the-art accuracy (95.9%) and outperformed the LASSO-MOGCN and LASSO-MOTGN models in terms of precision, recall, and F1-score. Our findings demonstrate that integrating multi-omics data in graph-based architectures enhances cancer classification performance by uncovering distinct molecular patterns that contribute to a better understanding of cancer biology and potential biomarkers for disease progression. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification

arXiv

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arXiv 2024年

作者： Alharbi, Fadi Vakanski, Aleksandar Elbashir, Murtada K. Mohammed, Mohanad College of Engineering Department of Computer Science University of Idaho MoscowID83844 United States College of Computer and Information Sciences Department of Information Systems Jouf University Sakaka Aljouf72441 Saudi Arabia School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Scottsville Pietermaritzburg3209 South Africa

The application of machine learning methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinning cancer development and progression. Combining gene expression data with other types of omics data has been reported by numerous works to improve cancer classification outcomes. Despite these advances, effectively integrating high-dimensional multi-omics data and capturing the complex relationships across different biological layers remains challenging. This paper introduces LASSO-MOGAT (LASSO-Multi-Omics Gated ATtention), a novel graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types. Utilizing differential expression analysis with LIMMA and LASSO regression for feature selection, and leveraging Graph Attention Networks (GATs) to incorporate protein-protein interaction (PPI) networks, LASSO-MOGAT effectively captures intricate relationships within multi-omics data. Experimental validation using five-fold cross-validation demonstrates the method's precision, reliability, and capacity for providing comprehensive insights into cancer molecular mechanisms. The computation of attention coefficients for the edges in the graph by the proposed graph-attention architecture based on protein-protein interactions proved beneficial for identifying synergies in multi-omics data for cancer *** Codes 68 Copyright © 2024, The Authors. All rights reserved.

关键词： Gene expression

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