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Unsupervised anomaly detection and localization via bidirectional knowledge distillation

Neural Computing and Applications 2024年第29期36卷 18499-18514页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model provides evidence for anomaly detection and localization. However, the teacher model is pretrained for classification, while the anomaly scores in the distillation-based anomaly detection method are indirectly derived from the classification scores. The mismatch between the two tasks can hinder the optimization of the model. To tackle this issue, we propose an innovative bidirectional knowledge distillation model. In this approach, forward knowledge distillation is pivotal in bolstering the model’s capacity for generalization. Simultaneously, backward knowledge distillation promotes diversity in representing anomalies. This reciprocal knowledge exchange effectively wards off potential performance declines due to target inconsistency. Through bidirectional knowledge distillation, we establish a more encompassing and resilient framework for knowledge transfer. Additionally, we introduce a novel data augmentation strategy to simulate anomalies and effectively eliminate unnecessary noise. In experiments on the MVTec AD, the proposed model achieves competitive results compared to state-of-the-art methods, 97.47% on image-level AUC, 98.23% on pixel-level AUC, and 94.77% on instance-level PRO. These results demonstrate the practicality of our approach in anomaly detection and localization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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Developing A Universal Framework for Modeling DC-Connected Hybrid Real and Virtual Energy Storage Systems

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IEEE Transactions on Energy Conversion 2024年第2期40卷 1022-1035页

作者： Jackson, Derek Cao, Yue Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States

A universal modeling framework for hybrid energy storage systems is proposed. The framework can model various energy storage types, both real and virtual. Hybrid systems employ power electronics connected in various topologies to coordinate the operation of multiple storages, which this framework generalizes into a single, switching-averaged model. Multiple common dc/dc converter types are shown to be compatible with this framework. The universal model aims to simplify system design and analysis by recognizing the commonalities of hybrid topologies, converter operation, and storage dynamics to focus on the overall system behavior and energy bandwidth requirements. The generalized parameters can be transformed into real values to continue the system design process. This paper focuses on defining the theory of the universal model. A demonstration of the model shows the framework's validity by comparing simulation results to the proven physics switching models. © 1986-2012 IEEE.

关键词： Hybrid power

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Anonymous TollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

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computers, Materials & Continua 2024年第6期79卷 3495-3518页

作者： Jane Kim Soojin Lee Chan Yeob Yeun Seung-Hyun Seo Department of Electronic&Electrical Engineering Graduate SchoolHanyang UniversitySeoul04763Korea Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates School of Electrical Engineering Hanyang UniversityERICAAnsan15588Korea

As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation *** Toll Collection(ETC),which is one of the services included in ITS systems,is an automated system that allows vehicles to pass through toll plazas without stopping for manual *** ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system *** ensure secure and efficient toll payments,designing a distributed model for sharing toll payment information among untrusted toll service providers is ***,the current ETC system operates under a centralized ***,both toll service providers and toll plazas know the toll usage history of *** raises concerns about revealing the entire driving routes and patterns of *** address these issues,blockchain technology,suitable for secure data management and data sharing in distributed systems,is being applied to the ETC *** enables efficient and transparent management of ETC ***,the public nature of blockchain poses a challenge where users’usage records are exposed to all *** tackle this,we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of *** proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ***,malicious vehicles’identities can be traced when they attempt to reuse *** conducting simulations,we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10%(when the ring size is 50)of the fees required in previous studies.

关键词： Blockchain electronic toll collection smart contract traceable ring signature

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Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

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Energy Informatics 2025年第1期8卷 1-43页

作者： Rajaperumal, T.A. Columbus, C. Christopher School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure

关键词： Risk management

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Predefined-time distributed multiobjective optimization for network resource allocation

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science China(Information sciences) 2023年第7期66卷 58-72页

作者： Kunpeng ZHANG Lei XU Xinlei YI Zhengtao DING Karl H.JOHANSSON Tianyou CHAI Tao YANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering and Computer Science KTH Royal Institute of Technology School of Electrical and Electronic Engineering The University of Manchester

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted Lppreference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.

关键词： distributed optimization multiobjective optimization predefined-time algorithms time-based generators weighted ${L_p}$ preference index

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Constrained temperature and relative humidity predictive control:Agricultural greenhouse case of study

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Information Processing in Agriculture 2024年第3期11卷 409-420页

作者： Hafsa Hamidane Samira EL Faiz Iliass Rkik Mohamed El Khayat Mohammed Guerbaoui Abdelali Ed-Dahhak Abdeslam Lachhab Computer Engineering and Intelligent Electrical Systems Laboratory ModelingMaterials and Control of Systems TeamHigher School of TechnologyMoulay Ismail University of MeknesMorocco

The importance of Model Predictive control(MPC)has significant applications in the agricultural industry,more specifically for greenhouse’s control ***,the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the *** methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed *** this paper,we introduce an application of Constrained Model Predictive control(CMPC)for a greenhouse temperature and relative *** this purpose,two Multi Input Single Output(MISO)systems,using Numerical Subspace State Space System Identification(N4SID)algorithm,are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation *** this sense,linear state space models were adopted in order to evaluate the robustness of the control *** the system is identified,the MPC technique is applied for the temperature and the humidity *** results show that the regulation of the temperature and the relative humidity under constraints was guaranteed,both parameters respect the ranges 15℃≤T_(int)≤30℃and 50%≤H_(int)≤70%*** the other hand,the control signals uf and uh applied to the fan and the heater,respect the hard constraints notion,the control signals for the fan and the heater did not exceed 0≤uf≤4.3 Volts and 0≤uh≤5 Volts,respectively,which proves the effectiveness of the MPC and the tracking ***,we show that with the proposed technique,using a new optimization toolbox,the computational complexity has been significantly *** greenhouse in question is devoted to Schefflera Arboricola cultivation.

关键词： Constrained model predictive control Greenhouse MISO systems Optimization Relative humidity Temperature

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Time-delay Correction control Strategy for HVDC Frequency Regulation Services

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2027-2037页

作者： Yuqing Dong Kaiqi Sun Jinning Wang Shunliang Wang He Huang Tianqi Liu Yilu Liu School of Electrical Engineering Shandong UniversityJinan 250061China School of Electrical Engineering Shandong UniversityJinan 250061China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA.

With the advancements in voltage source converter(VSC)technology,VSC based high voltage direct current(VSCHVDC)systems provide system operators with a prospective approach to enhance system operating stability and *** addition to long-distance transmission,the VSC-HVDC system can also provide multiple ancillary services,such as frequency regulation,due to its power ***,if a time delay exists in the control signal,the VSC-HVDC system may bring destabilizing influences to the system,which will decrease the system resilience under the *** order to reduce control deviation caused by time delay,in this paper,a small signal model is first conducted to analyze the impact of time delay on system *** a time-delay correction control strategy for HVDC frequency regulation control is developed to reduce the influence of the time *** control performance of the proposed time-delay correction control is verified both in the established small signal model and the runtime simulation in a modified IEEE 39 bus *** results indicate that the proposed time-delay correction control strategy shows significant improvement in system stability.

关键词： Frequency regulation control HVDC small signal analysis time delay time-delay correction control strategy

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PS-CoT-Adapter: adapting plan-and-solve chain-of-thought for scienceQA

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science China(Information sciences) 2025年第1期68卷 392-393页

作者： Qun LI Haixin SUN Fu XIAO Yiming WANG Xinping GAO Bir BHANU School of Computer Science Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Department of Electrical and Computer Engineering University of California at Riverside

Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.

关键词：

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RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay

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Journal of Systems science & Complexity 2022年第4期35卷 1262-1277页

作者： XU Juanjuan ZHANG Zhaorong School of Control Science and Engineering Shandong UniversityJinan250061China School of Electrical Engineering and Computer Science University of NewcastleNewcastleCallaghanNSW2308Australia

This paper is concerned with the multi-agent systems with both packet dropout and input delay.A novel receding horizon control(RHC)based consensus protocol is proposed by solving a distributed RHC based optimization *** novelty of the optimization problem lines in the involvement of the neighbours’predictor information in the cost *** on the derived RHC based consensus protocol,the necessary and sufficient condition for the mean-square consensus is *** addition,the authors give a specific sufficient condition to guarantee the mean-square consensus.

关键词： Consensus delay packet dropout predictor-like protocol receding horizon control

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