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Non-Cyclic Scheduling of Single-Armed Cluster Tools for Processing Two Wafer Types

IEEE Transactions on Automation Science and engineering 2025年 22卷 14133-14146页

作者： Yang, Fajun Xu, Xiang Yang, Zhiyuan Li, Chao Zhen, Lu Wu, Naiqi Shanghai University School of Management Shanghai200444 China Shanghai International Studies University School of Business Xianda College of Economics and Humanities Shanghai200083 China Macau University of Science and Technology Institute of Systems Engineering Taipa China

Cluster tools are extensively used for processing wafers in semiconductor manufacturing. To enhance the throughput of cluster tools, concurrent processing of multiple types of wafers has become a popular approach. In practice, due to mass customization and the shrinking of chips’ feature sizes, the lot size of wafers tends to become smaller, leading to more frequent transient and non-cyclic periods. Therefore, the scheduling problem of single-armed cluster tools that concurrently process two types of wafers from start-up to close-down period should be urgently addressed. To do so, a mixed integer programming (MIP) model is established to optimize the release sequence of wafer types and robot task sequence so that the total completion time of the two wafer types can be minimized. Since solving the MIP model for large-sized instances is hard, a two-combined backward sequence (2-CBS) is further proposed. Experimental results show that the 2-CBS can save a significant amount of computing time and increase the total completion time by only 1.09% on average. However, by quickly determining the completion time of each wafer lot, the throughput of cluster tools could be enhanced to some extent, as the idle time caused by the arrival delay of overhead hoist transports can be reduced. © 2004-2012 IEEE.

关键词： Electric circuit breakers

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Mining Quantitative Association Rules 17th

Mining Quantitative Association Rules

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Yen, Show-Jane Lee, Yue-Shi Lee, Wen-Hao Department of Computer Science and Information Engineering Ming Chuan University Taipei Taiwan Department of Management Information Systems National Chiayi University Chiayi City Taiwan

ISBN: (纸本)9789819606948

Mining quantitative association rules involves identifying the relationships between items based on their purchased quantities from a transaction database. In general, items with varying quantities are treated as distinct new items, making it challenging to meet the criteria for these new items. Previous methods have often relaxed these criteria to uncover quantitative association rules;however, excessive relaxation can lead to the discovery of many irrelevant rules, while insufficient relaxation may result in few or no rules being found. Additionally, the quantity ranges associated with items can be divided into intervals, and some of these intervals need to be combined, potentially leading to information loss during the process. In this article, we present new definitions and an approach for discovering quantitative association rules that address the issues of user-defined criteria, range segmentation, and interval integration, allowing us to identify the quantitative association rules that genuinely interest users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data mining

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A comparison study between Modbus and IEC 61850 MMS Protocols 33

A comparison study between Modbus and IEC 61850 MMS Protocol...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Johnson, Rayner Krishnamurthy, Senthil Mataifa, Haltor Esmail, Mohammed Cape Peninsula University of Technology Center for Substation Automation and Energy Management Systems Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

The growing adoption of renewable energy primary plants has been seen globally. South Africa has seen a significant development in utility-scale photovoltaic (PV) farms in recent years due to the alleviated regulations stipulated in the latest Electricity Regulation Amendment Act. Connecting the PV plants to the power grid has also undergone a technical evolution by incorporating modern embedded computers known as Intelligent Electronic Devices (IEDs). The International Electrotechnical Committee (IEC) 61850 standard defines a suite of protocols used in modern power systems. This study compares the IEC 61850 Manufacturing Messaging Specification (MMS) with the well-known MODBUS protocol. The comparison is achieved by simulating a solar farm's Power Plant Controller (PPC). © 2025 IEEE.

关键词： Solar power plants

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Research on De-identification Applications of LLMs in Medical Records

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International Journal of Network Security 2025年第1期27卷 213-222页

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yang-Shu Wu, Nan-I Lin, Iuon-Chang Department of Computer Science & Information Engineering Asia University 500 Lioufeng Rd. Wufeng Taichung41354 Taiwan Department of Management Information Systems National Chung Hsing University Taiwan Department of Information Management Lunghwa University of Science and Technology Taoyuan33306 Taiwan

The primary objective of this research is to develop and promote efficient and secure de-identification technology to address the application of sensitive personal information in healthcare big data. Considering the outstanding performance of large language models (such as GPT) in understanding text, we aim to utilize these advanced models to process medical records. Compared to traditional methods such as manual processing, rule-based, or machine learning methods, large language models can more effectively de-identify unstructured medical record data. We can enhance the recognition and de-identification accuracy of personal identification information in medical records through deep learning techniques. Additionally, the contextual understanding capabilities of large language models can comprehensively process medical record data, ensuring that the de-identification process does not affect the integrity and availability of medical information. Therefore, the research methodology will include model training, optimization, and validation to ensure that the developed de-identification technology achieves efficient and reliable results in practical applications. We explore different large language models, considering the diversity of personal medical data to address various de-identification needs in different medical record data. In this article, we propose three research issues for de-identification: 1) Research on the application of deep language models and de-identification technology in medical cases;2) optimization of prompt engineering to enhance de-identification of medical cases by large language models;3) Optimization and promotion of de-identification technology for practical applications in the medical. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Electronic health record

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Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1008-1052页

作者： Awadallah, Abeer Eledlebi, Khouloud Zemerly, Mohamed Jamal Puthal, Deepak Damiani, Ernesto Taha, Kamal Kim, Tae-Yeon Yoo, Paul D. Raymond Choo, Kim-Kwang Yim, Man-Sung Yeun, Chan Yeob Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Indian Institute of Management Bodh Gaya IT Systems and Analytics Bodh Gaya824234 India Khalifa University Civil and Environmental Engineering Department Abu Dhabi United Arab Emirates University of London Department of Computer Science and Information Systems LondonWC1E 7HX United Kingdom The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States Korea Advanced Institute of Science and Technology Department of Nuclear and Quantum Engineering Daejeon34141 Korea Republic of

The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions. © 1998-2012 IEEE.

关键词： Cybersecurity

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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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Computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport systems(ITS)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LSTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model

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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

arXiv

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arXiv 2025年

作者： Jiang, Xia Zhu, Linglingzhi Zheng, Taoli So, Anthony Man-Cho Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of O(∊−4), surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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Designing a cellular manufacturing system as a step toward creating sustainable cells under uncertain demand condition

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Journal of Remanufacturing 2025年第1期15卷 151-177页

作者： Almasarwah, Najat Abdelall, Esraa Bhutta, Mohammed Khurrum S. Saraireh, Mohammad Department of Industrial Systems Engineering Mutah University Alkarak 61710 Jordan Industrial Engineering Department Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Department of Management College of Business Ohio University Athens 45701 OH United States Department of Computer Engineering Mutah University Alkarak 61710 Jordan

This paper focuses on designing a cellular manufacturing system as a step toward creating sustainable cells. The proposed design aims to manufacture final products and remanufacture the returned products to diminish waste, where the return rate for each product is known. Firstly, the product families are formed using the uncapacitated mathematical model. Secondly, a nonlinear stochastic mathematical model is utilized for the propounded design as a step toward creating sustainable cells, where machine duplications are allowed. Besides, a simulation model is developed to handle the uncertainty in demand. Further, the performance of the proposed design with flexible cells is compared to regular cells. The results explain the ability of the proposed design to minimize the total number of machines. Furthermore, allowing machine duplication in the proposed design enhances the system flexibility;thus, machine breakdown will not impact system performance. The total number of machines required to cover the demand increases as the risk level of demand cover decreases. Therefore, the average waiting time and average work in process increase. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Designing a cellular manufacturing system as a step toward creating sustainable cells Flexible cells Nonlinear mathematical model Simulation Stochastic demand

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation

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ACM Transactions on Privacy and Security 2025年第2期28卷 1-33页

作者： Hore, Soumyadeep Ghadermazi, Jalal Paudel, Diwas Shah, Ankit Das, Tapas Bastian, Nathaniel Department of Industrial and Management Systems Engineering University of South Florida Tampa United States Department of Operations and Decision Technologies Indiana University Bloomington United States Army Cyber Institute United States Military Academy West Point United States

Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the security posture of cybersecurity operations centers (defenders) through the development of ML-aided network intrusion detection systems (NIDS). Concurrently, the abilities of adversaries to evade security have also increased with the support of AI/ML models. Therefore, defenders need to proactively prepare for evasion attacks that exploit the detection mechanisms of NIDS. Recent studies have found that the perturbation of flow-based and packet-based features can deceive ML models, but these approaches have limitations. Perturbations made to the flow-based features are difficult to reverse-engineer, while samples generated with perturbations to the packet-based features are not *** methodological framework, Deep PackGen, employs deep reinforcement learning to generate adversarial packets and aims to overcome the limitations of approaches in the literature. By taking raw malicious network packets as inputs and systematically making perturbations on them, Deep PackGen camouflages them as benign packets while still maintaining their functionality. In our experiments, using publicly available data, Deep PackGen achieved an average adversarial success rate of 66.4% against various ML models and across different attack types. Our investigation also revealed that more than 45% of the successful adversarial samples were out-of-distribution packets that evaded the decision boundaries of the classifiers. The knowledge gained from our study on the adversary's ability to make specific evasive perturbations to different types of malicious packets can help defenders enhance the robustness of their NIDS against evolving adversarial attacks. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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