Pub Date : 2023-09-13DOI: 10.1109/JETCAS.2023.3301348
Xi Zhang;Jiajing Wu;Abraham O. Fapojuwo;Zbigniew Galias;Chi K. Tse
This Special Issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to disseminating the latest research results and practical applications on the analysis and decision-making of complex cyber-multitudinal-physical systems (CMPSs).
{"title":"Guest Editorial Complex Cyber-Multitudinal-Physical Systems: Analysis, Decision-Making, and AI Applications","authors":"Xi Zhang;Jiajing Wu;Abraham O. Fapojuwo;Zbigniew Galias;Chi K. Tse","doi":"10.1109/JETCAS.2023.3301348","DOIUrl":"https://doi.org/10.1109/JETCAS.2023.3301348","url":null,"abstract":"This Special Issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to disseminating the latest research results and practical applications on the analysis and decision-making of complex cyber-multitudinal-physical systems (CMPSs).","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5503868/10251074/10251076.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50347859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1109/JETCAS.2023.3312163
Hritom Das;Rocco D. Febbo;Charles P. Rizzo;Nishith N. Chakraborty;James S. Plank;Garrett S. Rose
The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. Our proposed synapse demonstrates reliability in the face of process variation and the inherent stochastic behavior of memristors. Up to an 82% energy optimization can be seen during the SET operation over prior work. In addition, the READ process shows up to 54% energy savings. Our current-controlled approach also provides more reliable programming over traditional programming methods. This design is demonstrated with a 4-bit memory precision configuration. Using a spiking neural network (SNN), a neuromorphic application analysis was performed with this precision configuration. Our optimized design showed up to a 82% improvement in control applications and a 2.7x improvement in classification applications compared with other design cases.
{"title":"Optimizations for a Current-Controlled Memristor- Based Neuromorphic Synapse Design","authors":"Hritom Das;Rocco D. Febbo;Charles P. Rizzo;Nishith N. Chakraborty;James S. Plank;Garrett S. Rose","doi":"10.1109/JETCAS.2023.3312163","DOIUrl":"10.1109/JETCAS.2023.3312163","url":null,"abstract":"The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. Our proposed synapse demonstrates reliability in the face of process variation and the inherent stochastic behavior of memristors. Up to an 82% energy optimization can be seen during the SET operation over prior work. In addition, the READ process shows up to 54% energy savings. Our current-controlled approach also provides more reliable programming over traditional programming methods. This design is demonstrated with a 4-bit memory precision configuration. Using a spiking neural network (SNN), a neuromorphic application analysis was performed with this precision configuration. Our optimized design showed up to a 82% improvement in control applications and a 2.7x improvement in classification applications compared with other design cases.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134892469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies a heterogeneous multiplex network model that allows different dynamics in different layers. We explore intralayer synchronization of the multiplex network under distinct types of interlayer connections. From the perspective of spectral graph theory, we propose a set of edge weight requirements to synchronize the multiplex network. Focusing on the effect of interlayer connections to intralayer synchronization, it is found that a multiplex network can achieve intralayer synchronization with a large enough interlayer coupling strength even if a single network of one layer cannot synchronize by itself. In fact, the synchronizability of the multiplex network is found to be stronger than that of the single-layer network. These results provide insights into the practical application of multiplex network theory in engineering networks.
{"title":"Intralayer Synchronization in Heterogeneous Multiplex Dynamical Networks Based on Spectral Graph Theory","authors":"Hui Liu;Shiman Zhang;Chai Wah Wu;Xiaoqun Wu;Zengyang Li;Jiangqiao Xu","doi":"10.1109/JETCAS.2023.3297012","DOIUrl":"10.1109/JETCAS.2023.3297012","url":null,"abstract":"This paper studies a heterogeneous multiplex network model that allows different dynamics in different layers. We explore intralayer synchronization of the multiplex network under distinct types of interlayer connections. From the perspective of spectral graph theory, we propose a set of edge weight requirements to synchronize the multiplex network. Focusing on the effect of interlayer connections to intralayer synchronization, it is found that a multiplex network can achieve intralayer synchronization with a large enough interlayer coupling strength even if a single network of one layer cannot synchronize by itself. In fact, the synchronizability of the multiplex network is found to be stronger than that of the single-layer network. These results provide insights into the practical application of multiplex network theory in engineering networks.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42965096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-14DOI: 10.1109/JETCAS.2023.3295501
Han Yang;Junyuan Fang;Jiajing Wu;Zibin Zheng
In recent years, the cryptocurrency market has been booming with an ever-increasing market capitalization. However, due to the anonymity of blockchain technology, this market has become a hotbed of financial crimes. As the largest blockchain platform supporting smart contracts, financial crimes including scams and hacking frequently happen on Ethereum and have caused serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this paper, we construct an Ethereum transaction network based on transaction records and find that this network is with heterophily. However, most of the current work on account classification ignores the role of this heterophily information. We first figure out that the heterophily information of the neighborhood may also be beneficial for the final predictions. Based on this, we propose a new graph neural network (GNN) model, named BPA-GNN, which incorporates both homophilic and heterophilic information into the neighborhood aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance of BPA-GNN, showing that the model can effectively extract and utilize neighborhood information to improve the distinguishability of node representations. As an effective solution for Ethereum account de-anonymization, BPA-GNN can help identify illegal activities and promote the healthy development of the Ethereum ecosystem.
{"title":"Both Homophily and Heterophily Matter: Bi-Path Aware Graph Neural Network for Ethereum Account Classification","authors":"Han Yang;Junyuan Fang;Jiajing Wu;Zibin Zheng","doi":"10.1109/JETCAS.2023.3295501","DOIUrl":"10.1109/JETCAS.2023.3295501","url":null,"abstract":"In recent years, the cryptocurrency market has been booming with an ever-increasing market capitalization. However, due to the anonymity of blockchain technology, this market has become a hotbed of financial crimes. As the largest blockchain platform supporting smart contracts, financial crimes including scams and hacking frequently happen on Ethereum and have caused serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this paper, we construct an Ethereum transaction network based on transaction records and find that this network is with heterophily. However, most of the current work on account classification ignores the role of this heterophily information. We first figure out that the heterophily information of the neighborhood may also be beneficial for the final predictions. Based on this, we propose a new graph neural network (GNN) model, named BPA-GNN, which incorporates both homophilic and heterophilic information into the neighborhood aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance of BPA-GNN, showing that the model can effectively extract and utilize neighborhood information to improve the distinguishability of node representations. As an effective solution for Ethereum account de-anonymization, BPA-GNN can help identify illegal activities and promote the healthy development of the Ethereum ecosystem.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43917256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/JETCAS.2023.3293253
Chao Ren;Tianjing Wang;Han Yu;Yan Xu;Zhao Yang Dong
Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel effective and secure distributed DSA method based on horizontal federated learning (HFL) and differential privacy (DP), namely EFedDSA. It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed DSA operating data, EFedDSA incorporates Gaussian mechanism into DP. To reduce the computational burden from multiple transmission communication rounds, a discounting method for the total communication round is proposed to reduce the total transmission rounds. Theoretical analysis on the Gaussian mechanism of EFedDSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed EFedDSA method can achieve advantageous DSA performance with fewer communication rounds, while protecting the privacy of the local model information compared to the state of the art.
{"title":"EFedDSA: An Efficient Differential Privacy-Based Horizontal Federated Learning Approach for Smart Grid Dynamic Security Assessment","authors":"Chao Ren;Tianjing Wang;Han Yu;Yan Xu;Zhao Yang Dong","doi":"10.1109/JETCAS.2023.3293253","DOIUrl":"10.1109/JETCAS.2023.3293253","url":null,"abstract":"Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel effective and secure distributed DSA method based on horizontal federated learning (HFL) and differential privacy (DP), namely EFedDSA. It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed DSA operating data, EFedDSA incorporates Gaussian mechanism into DP. To reduce the computational burden from multiple transmission communication rounds, a discounting method for the total communication round is proposed to reduce the total transmission rounds. Theoretical analysis on the Gaussian mechanism of EFedDSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed EFedDSA method can achieve advantageous DSA performance with fewer communication rounds, while protecting the privacy of the local model information compared to the state of the art.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47655088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing adoption of renewable energy and HVDC transmission systems, the power system may experience large power fluctuations due to HVDC faults, potentially causing the rate of change of frequency (RoCoF) or frequency deviation limit to be exceeded during the inertia response phase. The system’s ability to withstand these disturbances primarily depends on the amount of system inertia, making it crucial to accurately estimate the effective inertia. The traditional power system frequency analysis commonly employs the system frequency response (SFR) model based on the center of inertia (COI), which does not account for the spatial differences in frequency, and consequently results in reduced accuracy. To address this issue, this paper proposes a modal analysis-based analytical method (MAAM) for analyzing the system frequency characteristics during the inertia response phase. The proposed method retains the frequency dynamics of all generator rotors in the system and more accurately reflects the spatial variation characteristics of frequency compared to the COI model. This paper also introduces the concept of the effective inertia of the system, along with its calculation method. The proposed method is validated using the IEEE 2-region 4-generator system and New England 68 bus system.
{"title":"Modal Analysis-Based Analytical Method for Frequency Estimation During Inertia Response Stage of Power Systems","authors":"Tiezhu Wang;Shicong Ma;Shanshan Wang;Weilin Hou;Juncheng Gao;Jianbo Guo;Xiaoxin Zhou","doi":"10.1109/JETCAS.2023.3291455","DOIUrl":"10.1109/JETCAS.2023.3291455","url":null,"abstract":"With the increasing adoption of renewable energy and HVDC transmission systems, the power system may experience large power fluctuations due to HVDC faults, potentially causing the rate of change of frequency (RoCoF) or frequency deviation limit to be exceeded during the inertia response phase. The system’s ability to withstand these disturbances primarily depends on the amount of system inertia, making it crucial to accurately estimate the effective inertia. The traditional power system frequency analysis commonly employs the system frequency response (SFR) model based on the center of inertia (COI), which does not account for the spatial differences in frequency, and consequently results in reduced accuracy. To address this issue, this paper proposes a modal analysis-based analytical method (MAAM) for analyzing the system frequency characteristics during the inertia response phase. The proposed method retains the frequency dynamics of all generator rotors in the system and more accurately reflects the spatial variation characteristics of frequency compared to the COI model. This paper also introduces the concept of the effective inertia of the system, along with its calculation method. The proposed method is validated using the IEEE 2-region 4-generator system and New England 68 bus system.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43095101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.1109/JETCAS.2023.3291145
Ziyuan Song;Yuehui Huang;Hongbin Xie;Xiaofei Li
The uncertainty of photovoltaic (PV) output power has an increasing impact on power balance with the increase of installed capacity. The construction of day-ahead PV output scenarios-set is an important basis for the stochastic optimal scheduling of the power system. For the uncertainty modeling of multi-regional day-ahead PV output, a scenarios-set generation method based on improved conditional generation adversarial network (CGAN) is proposed. This method learns the potential spatio-temporal characteristics of the output power of PV clusters distributed in different regions by convolutional neural networks. Moreover, a mapping relationship between the input PV prediction results and the output scenarios-set is established. Thereafter, the scenarios-set with correlation characteristics for day-ahead multi-regional PV clusters is generated simultaneously. By comparing with the traditional Latin hypercube sampling (LHS) method, the results of the proposed method show the comprehensive advantages in terms of the uncertainty range and the spatial correlation coefficient.
{"title":"Generation Method of Multi-Regional Photovoltaic Output Scenarios-Set Using Conditional Generative Adversarial Networks","authors":"Ziyuan Song;Yuehui Huang;Hongbin Xie;Xiaofei Li","doi":"10.1109/JETCAS.2023.3291145","DOIUrl":"10.1109/JETCAS.2023.3291145","url":null,"abstract":"The uncertainty of photovoltaic (PV) output power has an increasing impact on power balance with the increase of installed capacity. The construction of day-ahead PV output scenarios-set is an important basis for the stochastic optimal scheduling of the power system. For the uncertainty modeling of multi-regional day-ahead PV output, a scenarios-set generation method based on improved conditional generation adversarial network (CGAN) is proposed. This method learns the potential spatio-temporal characteristics of the output power of PV clusters distributed in different regions by convolutional neural networks. Moreover, a mapping relationship between the input PV prediction results and the output scenarios-set is established. Thereafter, the scenarios-set with correlation characteristics for day-ahead multi-regional PV clusters is generated simultaneously. By comparing with the traditional Latin hypercube sampling (LHS) method, the results of the proposed method show the comprehensive advantages in terms of the uncertainty range and the spatial correlation coefficient.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41828947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-28DOI: 10.1109/JETCAS.2023.3290418
Zigui Jiang;Qihao Yuan;Rongheng Lin;Fangchun Yang
Electricity consumption behaviors are influenced by various external and internal factors such as climate, location, building type, consumer characteristics and even other energy consumption. In order to investigate the electricity consumption behaviors of diverse consumers, we propose a methodology based on canonical correlation analysis to explore the correlation among electricity consumption, gas consumption and climate change under different circumstances. We first preprocess three multivariable datasets that contain 24-value daily data in a one-year period, and conduct consumer segmentation based on climate zones, locations and building types. Then an optimized canonical correlation analysis model with an optimal result selection mechanism is adopted to calculate the canonical correlations and weights of every set of daily data. Finally, we propose a post-processing analysis for further comparison on the calculated results. We investigate three research questions to present and discuss the analysis results, including canonical correlation and weights overview, typical patterns analysis, and comparison on climate zones and locations.
{"title":"Canonical Correlation Analysis and Visualization for Big Data in Smart Grid","authors":"Zigui Jiang;Qihao Yuan;Rongheng Lin;Fangchun Yang","doi":"10.1109/JETCAS.2023.3290418","DOIUrl":"10.1109/JETCAS.2023.3290418","url":null,"abstract":"Electricity consumption behaviors are influenced by various external and internal factors such as climate, location, building type, consumer characteristics and even other energy consumption. In order to investigate the electricity consumption behaviors of diverse consumers, we propose a methodology based on canonical correlation analysis to explore the correlation among electricity consumption, gas consumption and climate change under different circumstances. We first preprocess three multivariable datasets that contain 24-value daily data in a one-year period, and conduct consumer segmentation based on climate zones, locations and building types. Then an optimized canonical correlation analysis model with an optimal result selection mechanism is adopted to calculate the canonical correlations and weights of every set of daily data. Finally, we propose a post-processing analysis for further comparison on the calculated results. We investigate three research questions to present and discuss the analysis results, including canonical correlation and weights overview, typical patterns analysis, and comparison on climate zones and locations.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42551969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-28DOI: 10.1109/JETCAS.2023.3290319
Wanchang Zhang;Zhongyuan Jiang;Qingsong Yao
Network disintegration is a fundamental problem in network science, the core of which is how to determine the smallest set of nodes whose removal can weaken the function of the network and quickly paralyze it. It is computationally NP-hard and usually cannot be solved in polynomial time complexity. Many network disintegration methods have been proposed, but they mainly focus on undirected networks. Due to the complex structure of directed networks and the fact that it is necessary to consider the direction of edges to aggregate neighbor node information, solving the disintegration problem of directed networks is a challenge. Inspired by machine learning technology to solve the network disintegration problem, this paper studies feasible disintegration methods in directed networks and proposes a deep learning-based framework, DND (directed network disintegrator), for directed network disintegration, which has a small time complexity when dismantling large directed networks. DND can be trained in small, artificially generated synthetic directed networks and then applied to real-world, complex application scenarios. To test the disintegration effect of DND, we conducted extensive experiments on different types of synthetic directed networks and compared them with other methods. The experimental results show that the disintegration effect of DND is weaker than the CoreHD method, and better than the disintegration method based on local structural features, but the disintegration speed is the fastest with the increase in network size. We also disintegrate directed networks in the real world, and DND achieves a better disintegration effect, providing new insights into solving complex network-related problems and enabling us to design more robust networks to withstand attacks and failures.
{"title":"DND: Deep Learning-Based Directed Network Disintegrator","authors":"Wanchang Zhang;Zhongyuan Jiang;Qingsong Yao","doi":"10.1109/JETCAS.2023.3290319","DOIUrl":"10.1109/JETCAS.2023.3290319","url":null,"abstract":"Network disintegration is a fundamental problem in network science, the core of which is how to determine the smallest set of nodes whose removal can weaken the function of the network and quickly paralyze it. It is computationally NP-hard and usually cannot be solved in polynomial time complexity. Many network disintegration methods have been proposed, but they mainly focus on undirected networks. Due to the complex structure of directed networks and the fact that it is necessary to consider the direction of edges to aggregate neighbor node information, solving the disintegration problem of directed networks is a challenge. Inspired by machine learning technology to solve the network disintegration problem, this paper studies feasible disintegration methods in directed networks and proposes a deep learning-based framework, DND (directed network disintegrator), for directed network disintegration, which has a small time complexity when dismantling large directed networks. DND can be trained in small, artificially generated synthetic directed networks and then applied to real-world, complex application scenarios. To test the disintegration effect of DND, we conducted extensive experiments on different types of synthetic directed networks and compared them with other methods. The experimental results show that the disintegration effect of DND is weaker than the CoreHD method, and better than the disintegration method based on local structural features, but the disintegration speed is the fastest with the increase in network size. We also disintegrate directed networks in the real world, and DND achieves a better disintegration effect, providing new insights into solving complex network-related problems and enabling us to design more robust networks to withstand attacks and failures.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44496058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-28DOI: 10.1109/JETCAS.2023.3290161
Zhicong Huang;Canjun Yuan;Hanchen Ge;Ting Hou
To guide the construction of large-scale offshore wind farms, optimization for substation siting and connection topology are both necessary, which is a multiobjective optimization problem. Non-iterative methods are based on greedy strategies and they are only suitable to optimize the connection topology. Iterative methods can update the solutions iteratively to approach the optimum using common optimizers such as particle swarm and firefly algorithm (FA), which are more adaptive in multiobjective optimization. Thus, it is feasible to explore iterative methods to synchronously optimize substation siting and connection topology. This paper proposes a modified FA for the optimization of substation siting and connection topology in a large-scale offshore wind farm. The objective function comprehensively considers critical factors including substation siting, partition of wind turbines, connection topology, cable types, and power loss. The optimization ability of the proposed FA is enhanced by adopting reproduction and resetting mechanisms with dynamic hyperparameters. An implementation that bridges the topological space and Euclidean space is detailed to help with improving the convexity and continuity of search spaces. To validate the efficacy, the proposed FA is first tested in an offshore wind farm with a single substation and then it is applied in a large-scale offshore wind farm with multiple substations to demonstrate the synchronous optimization of substation siting and connection topology.
{"title":"Optimization of Substation Siting and Connection Topology in Offshore Wind Farm Based on Modified Firefly Algorithm","authors":"Zhicong Huang;Canjun Yuan;Hanchen Ge;Ting Hou","doi":"10.1109/JETCAS.2023.3290161","DOIUrl":"10.1109/JETCAS.2023.3290161","url":null,"abstract":"To guide the construction of large-scale offshore wind farms, optimization for substation siting and connection topology are both necessary, which is a multiobjective optimization problem. Non-iterative methods are based on greedy strategies and they are only suitable to optimize the connection topology. Iterative methods can update the solutions iteratively to approach the optimum using common optimizers such as particle swarm and firefly algorithm (FA), which are more adaptive in multiobjective optimization. Thus, it is feasible to explore iterative methods to synchronously optimize substation siting and connection topology. This paper proposes a modified FA for the optimization of substation siting and connection topology in a large-scale offshore wind farm. The objective function comprehensively considers critical factors including substation siting, partition of wind turbines, connection topology, cable types, and power loss. The optimization ability of the proposed FA is enhanced by adopting reproduction and resetting mechanisms with dynamic hyperparameters. An implementation that bridges the topological space and Euclidean space is detailed to help with improving the convexity and continuity of search spaces. To validate the efficacy, the proposed FA is first tested in an offshore wind farm with a single substation and then it is applied in a large-scale offshore wind farm with multiple substations to demonstrate the synchronous optimization of substation siting and connection topology.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49236731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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