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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Zulfiqar Ali Tahir Hussain Chun-Lien Su Anca Delia Jurcut Shazia Baloch Muhammad Sadiq Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung city Taiwan Department of Informatics Graduate School of Informatics and Engineering The University of Electro-Communication Tokyo Japan School of Computer Science University College Dublin Belfield Dublin 4 Ireland Department of Electrical Engineering Balochistan University of Engineering and Technology Khuzdar Pakistan

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity.

关键词： Training Accuracy Prevention and mitigation Artificial neural networks Microgrids Detectors Power system stability Stability analysis Cyberattack Resilience

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High isolation and gain metamaterial based MIMO antenna for 5G millimiter wave applications

High isolation and gain metamaterial based MIMO antenna for ...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Elalaouy, Ouafae Aghoutane, Bilal El Ghzaoui, Mohammed Das, Sudipta Foshi, Jaouad El Faylali, Hanan University of Moulay Ismail Faculty of Sciences and Techniques Errachidia Errachidia Morocco Faculty of Sciences IbnTofail University Informatics Systems and Optimization Laboratory Department of Computer Science Kenitra Morocco Sidi Mohamed Ben Abdellah University Faculty of Sciences Dhar El Mahraz Fes Morocco Imps College of Engineering and Technology Department of Electronics and Communication Engineering W.B Malda India

ISBN: (纸本)9798350365306

In the contemporary era, harnessing millimeter-wave frequencies emerges as a compelling strategy to fulfill the swift data transfer requirements for 5G communications. Consequently, this study involves designing a metamaterial-based multiple-input multiple-output (MIMO) antenna with a focus on achieving high isolation and gain within the millimeter-wave spectrum. The suggested MIMO is printed on a Rogers RT5800 measuring 18 × 38 × 0.8 mm3. The single antenna element demonstrates a broad frequency coverage from 27.2 to 28.72GHz. To elevate the antenna's effectiveness, a two-port MIMO configuration is implemented by introducing a second antenna element parallel to benchmark antenna. However, the isolation between the two elements is deemed satisfactory. To address this, a 4 × 1 metamaterial unit cell is introduced, significantly enhancing isolation from 31 dB to 39 dB at 28 GHz. Furthermore, it improves the gain by 0.5 dBi at 28 GHz, elevating it from 8.5 to 9 dBi. The collective features of the suggested two ports MIMO antenna, encompassing its compactness and simplicity, position it as an excellent option for upcoming portable and shrink devices in Internet of Things uses. © 2024 IEEE.

关键词： Microwave antennas

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An Improved Dictionary Cracking Scheme Based on Multiple GPUs for Wi-Fi Network

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computers, Materials & Continua 2021年第3期66卷 2957-2972页

作者： Majdi K.Qabalin Zaid A.Arida Omar A.Saraereh Falin Wu Imran Khan Peerapong Uthansakul Moath Alsafasfeh Department of Computer Science Princess Sumaya University for TechnologyAmman13133Jordan School of Computing and Informatics Al Hussein Technical UniversityAmman11831Jordan Department of Electrical Engineering Hashemite UniversityZarqa13133Jordan School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing100191China Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering Al-Hussein Bin Talal UniversityMa’anJordan

The Internet has penetrated all aspects of human society and has promoted social ***-crimes in many forms are commonplace and are dangerous to society and national *** has become a major concern for citizens and *** Internet functions and software applications play a vital role in cybersecurity research and *** of the cyber-attacks are based on exploits in system or application *** is of utmost urgency to investigate software security *** demand for Wi-Fi applications is proliferating but the security problem is growing,requiring an optimal solution from *** overcome the shortcomings of the wired equivalent privacy(WEP)algorithm,the existing literature proposed security schemes forWi-Fi protected access(WPA)/***,in practical applications,the WPA/WPA2 scheme still has some weaknesses that attackers *** destroy a WPA/WPA2 security,it is necessary to get a PSK pre-shared key in pre-shared key mode,or an MSK master session key in the authentication ***-force cracking attacks can get a phase-shift keying(PSK)or a minimum shift keying(MSK).In real-world applications,many wireless local area networks(LANs)use the pre-shared key ***,brute-force cracking of WPA/WPA2-PSK is important in that *** article proposes a new mechanism to crack theWi-Fi password using a graphical processing unit(GPU)and enhances the efficiency through parallel computing of multiple GPU *** results show that the proposed algorithm is effective and provides a procedure to enhance the security of Wi-Fi networks.

关键词： Networks password cybersecurity password cracking mechanism

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging informatics

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Yearbook of Medical informatics 2023年第1期32卷 282-285页

作者： Baumgartner, Christian Rittner, Leticia Deserno, Thomas M. Institute of Health Care Engineering with European Testing Center of Medical Devices Graz University of Technology Austria Medical Imaging Computing Lab (MICLab) School of Electrical and Computer Engineering University of Campinas Brazil Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Braunschweig Germany

OBJECTIVES: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022. METHOD: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII. RESULTS: Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field. CONCLUSIONS: Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized bas

关键词： Biomedical informatics deep learning machine learning personalized medicine

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Analysing and Modelling the Accuracy and Latency Trade-offs in Rate Limiting on API-Gateway

Analysing and Modelling the Accuracy and Latency Trade-offs ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： Krishnamoorthy Caucidheesan Guhanathan Poravi Dept. of Computer Science and Engineering University of Westminster London UK Dept. of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

The rate limiting service in API gateways controls request entry by throttling requests with a boundary. The rate limiting accuracy determines how efficiently it works and whether it allows requests within the throttle count or exceeds the throttle count. Latency, on the other hand, is the round-trip time of a particular API call. The accuracy of rate limiting service is defined using spillover error percentage. It is the error calculation for the requests that rate limiting service allows more than the throttle count. Requests must be sent to the rate limiting service in order to rate limit requests in the API gateway. The rate limiting service decides whether the incoming requests must be throttled. The time taken for the requests to be decided by the rate limiting service adds additional latency to the round-trip time of requests. This additional latency can be controlled by introducing a timeout. However, this could result in a degradation in the accuracy of the rate limiting. This paper investigates the particular problem and models the relationship between accuracy and round-trip latency. The findings of this research address the analysis of the accuracy and latency trade-off with respect to the parameters influencing them, and also address the prediction outcome using random forest regressor and present key findings.

关键词： Hands Degradation Analytical models Limiting Accuracy Logic gates Predictive models Low latency communication

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding 41

What Improves the Generalization of Graph Transformers? A Th...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony BrookNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 638-655页

作者： Panayiotis M.Papadopoulos Vasso Reppa Marios M.Polycarpou Christos G.Panayiotou IEEE KIOS Research and Innovation Center of Excellence Department of Electrical and Computer EngineeringUniversity of CyprusNicosia 1678Cyprus Department of Maritime and Transport Technology Delft University of TechnologyDelft2628 CDThe Netherlands

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants' productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning(HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building's energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC *** the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

关键词： Building automation fault diagnosis fault location smart homes

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Liu, Haoxuan Qi, Nan Li, Xiaojie Boulogeorgos, Alexandros A. A. Tsiftsis, Theodoros A. Xiao, Ming Röning, Juha Nanjing University of Aeronautics and Astronautics Nanjing China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden University of Oulu Biomimetics and Intelligent Systems Group Finland

ISBN: (纸本)9798350368369

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy. © 2025 IEEE.

关键词： Deep reinforcement learning

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A Framework for Tele-rehabilitation Gaming System 6

A Framework for Tele-rehabilitation Gaming System

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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