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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

Science China(Information Sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph $\mathcal{CONGEST}$ model communication complexity round complexity

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices 3

Random Number Generator for Securing Data Exchange in Smart ...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Sama, Andi Meyliana Heryadi, Yaya Sahroni, Taufik Roni Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information System Information System Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Jakarta Computer Science Department Jakarta11530 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350395419

IT/OT convergence in the form of a Cyber-Physical system (CPS) presents an opportunity to apply recent Information Technology (IT) advancements to Operational Technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption. © 2024 IEEE.

关键词： Data transfer

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Enhancing Power Grid Stability: Design and Integration of a Fast Bus Tripping system in Protection Relays

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 561-570页

作者： Ghiasi, Mohammad Wang, Zhanle Mehrandezh, Mehran Alhelou, Hassan Haes Ghadimi, Noradin University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada School of Engineering CambridgeMA United States Monash University Department of Electrical and Computer System Engineering Australia Islamic Azad University Young Researchers and Elite Club Ardabil Branch Ardabil Iran

This article introduces a novel method for efficiently and promptly operating protection relays within a power system, with a specific emphasis on adaptive overcurrent (OC) protection in a power grid. The approach utilizes SEL751 and SEL751A relays that communicate through the IEC-61850 GOOSE protocol, establishing connections using intelligent electronic devices (IED) at several locations in the system. The power system, consisting of transformers, bus bars, and feeders, employs protection relays to identify OC faults based on time and current parameters. Relay configurations are tested in a case study at the Omicron Lab involving a 138/25kV substation. The test encompasses various fault scenarios on the low-voltage (LV) and high-voltage (HV) sides, incorporating different current values and fault times, utilizing the chosen time-overcurrent U3 (very inverse) curve. The proposed approach exhibits the ability to promptly remove faults in transformers and substations at both the HV and the LV levels. Furthermore, the experimental results closely match the theoretical calculations, validating the effectiveness of the proposed approach in the protection of AC grids. Additionally, built-in cybersecurity measures in SEL relays ensure secure and reliable operation, mitigating the risks of cyber-related relay failures. © 1975-2011 IEEE.

关键词： Relay protection

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Dynamic event-triggered formation control of second-order nonholonomic systems

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Journal of systems engineering and Electronics 2023年第2期34卷 501-514页

作者： WANG Xiaoyu SUN Sijia XIAO Feng YU Mei School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditions are *** event-detecting processes,an inactive time is introduced after each sampling instant,which can ensure a positive minimum sampling *** increase the flexibility of the event-triggered scheme,internal dynamic variables are included in event-triggering ***,the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive *** addition,event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation(low-pass)*** asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.

关键词： nonholonomic system dynamic event-triggered control consensus-based formation

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Jinpeng Zhao, Chunna Yang, Jing Huang, Yaqun Yang, Yaoyuan Yee, Por Lip School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data;2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model. © 2025 IEEE.

关键词： dynamic graph learning Fractional Residual decomposition spatial-temporal graph Traffic prediction

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Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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Control and Stability of Large-scale Power system with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

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European Physical Journal Plus 2024年第5期139卷 1-13页

作者： Hameed, Madiha Zameer, Aneela Khan, Saddam Hussain Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer System Engineering The University of Engineering and Applied Science (UEAS) Swat Pakistan Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images, which add an additional layer of difficulty. The overlapping image intensities in tumor and non-tumor areas pose a significant hurdle in extracting meaningful insights from raw data using predictive models. In response to these challenges, we introduce ARiViT, a novel framework based on residual learning. ARiViT merges vision transformers, convolutions, and adversarial learning to tackle the complexities of brain tumor detection. ARiViT’s core generator integrates RiT blocks, merging residual convolutions and transformers, with residual connections, channel compression, and strategic weight sharing for enhanced capabilities and computational efficiency. ARiViT demonstrates exceptional robustness in handling noisy images and adapts well to various modality setups. Our systematic approach involves dataset splitting into training, testing, and validation sets, maintaining distributions of 80:20 and subsequently 70:30, ensuring rigorous evaluation. Additionally, our data preprocessing strategies are tailored to handle noisy and low-quality images, as well as those with unusual tumor shapes. The GAN architecture embedded in ARiViT effectively manages the complexities inherent in such image variations, ensuring reliable tumor detection even in challenging scenarios. Through exhaustive comparative evaluations, ARiViT proves its superiority over existing CNN and transformer techniques, as evidenced by both qualitative and quantitative analyses. Our efforts culminate in remarkable achievements, including an F1-score of 98.09%, establishing a pioneering solution in the demanding field of brain tumor detection. © The Author(s), under

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Cluster-preserving sampling algorithm for large-scale graphs

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Science China(Information Sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

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CSEE Journal of Power and Energy systems 2023年第1期9卷 315-325页

作者： Ancheng Xue He Kong Feiyang Xu Junbo Zhao Naichao Chang Joe H.Chow Haiyan Hong State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of ConnecticutStorrs06268USA National Electric Power Dispatching and Control Center Beijing 100031China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart ***,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on *** address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are *** general formulations for PAD estimation are then *** simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of *** latter is used by second-order Taylor series expansion to estimate PADs *** reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is *** using both simulated and field data verify the effectiveness and robustness of the proposed methods.

关键词： Correction line parameters parameter identification phasor measurement time-varying phase angle difference deviation

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