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arXiv 2025年

作者： Rahal, Manal Ahmed, Bestoun S. Szabados, Gergely Fornstedt, Torgny Samuelsson, Jörgen Department of Mathematics and Computer Science Karlstad University Universitetsgatan 2 Karlstad65188 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague16627 Czech Republic Department of Engineering and Chemical Sciences Universitetsgatan 2 Karlstad65188 Sweden

Poor data quality limits the advantageous power of Machine Learning (ML) and weakens high-performing ML software systems. Nowadays, data are more prone to the risk of poor quality due to their increasing volume and complexity. Therefore, tedious and time-consuming work goes into data preparation and improvement before moving further in the ML pipeline. To address this challenge, we propose an intelligent data-centric evaluation framework that can identify high-quality data and improve the performance of an ML system. The proposed framework combines the curation of quality measurements and unsupervised learning to distinguish high- and low-quality data. The framework is designed to integrate flexible and general-purpose methods so that it is deployed in various domains and applications. To validate the outcomes of the designed framework, we implemented it in a real-world use case from the field of analytical chemistry, where it is tested on three datasets of anti-sense oligonucleotides. A domain expert is consulted to identify the relevant quality measurements and evaluate the outcomes of the framework. The results show that the quality-centric data evaluation framework identifies the characteristics of high-quality data that guide the conduct of efficient laboratory experiments and consequently improve the performance of the ML system. Copyright © 2025, The Authors. All rights reserved.

关键词： Data quality

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Unsafe at any Bandwidth: Towards Understanding Risk Factors for Ransomware in Higher Education

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Procedia computer Science 2024年 238卷 815-820页

作者： Logan Suarez Dakhilallah Alshubrumi Tj O’Connor Sneha Sudhakaran Department of Electrical Engineering and Computer Science Florida Institute of Technology Melbourne FL USA Faculty L3Harris Institute for Assured Information Florida Institute of Technology Melbourne FL USA

United States higher education institutions host an assortment of services that are accessible via public IP addresses. The wide variety of network services and the important personal and institutional data stored on such services make higher education institutions particularly desirable targets for attackers. This study analyses the vulnerabilities found through Shodan scans on these networks, in conjunction with institutional characteristics data taken from the National Center for Education Statistics (NCES), to examine correlations between an institution’s characteristics and the vulnerabilities found on its networks. By exploring this data, the study aims to bring awareness to the current state of higher education institution network security and determine vulnerability trends between certain institutions. Our analysis reveals that most institutions have many medium impacts but highly exploitable vulnerabilities, with most being on Apache HTTP servers. We also present that the most significant indicators of an institution’s vulnerability are its enrolment and yearly total expenses. We investigate how smaller institutions have lower numbers of vulnerabilities, but their vulnerabilities have the potential for higher impact. We conclude that there is a significant chance of ransomware risk in US higher educational institutions.

关键词： Shodan Vulnerability Assessment Higher Education

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controlling the Movement Path of Leg Exoskeletons Through Fractional Order Sliding Mode control

Controlling the Movement Path of Leg Exoskeletons Through Fr...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Aicha Znidi Fatma Abdelhedi Ahmed Said Nouri Nabil Derbel Lab: Numerical Control of Industrial Processes National Engineering School of Gabes University of Gabes Gabes Tunisia Department of Electrical and Computer Engineering college of Engineering King Abdulaziz: University Jeddah Saudi Arabia National Engineering School of of Sfax University of Sfax

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

This research introduces a new method, Fractional Order Sliding Mode control (FOSMC), to manage leg exoskele-tons during gait rehabilitation. This innovative algorithm utilizes fractional calculus principles to precisely regulate the joints of the exoskeleton. By integrating fractional order dynamics, the controller effectively handles the complexities and uncertainties inherent in human walking patterns. This leads to more precise tracking, lower energy usage, and increased comfort for patients during rehabilitation. Additionally, the application of Lyapunov theory ensures the stability of the system. The effectiveness of this control method is assessed through simulation examples in the study.

关键词： Legged locomotion Uncertainty Tracking Heuristic algorithms Exoskeletons Medical services Stability analysis

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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A Study of Different Machine Learning Approaches for Relation Discovery Task

A Study of Different Machine Learning Approaches for Relatio...

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2025 International Conference on Energy, Power, Environment, control and Computing, ICEPECC 2025

作者： AL-Aswadi, Fatima N. Za’al Alma’aitah, Wafa Chan, Huah Yong Gan, Keng Hoon Iqbal, Zahid Al-Dhamari, Ahlam Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur 56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University P.O. Box 3114 Hodeidah Yemen Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt 19117 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor 11800 Malaysia Department of Computer Science Air University Kharian Pakistan Department of Computer Science University of Gujrat Gujrat Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Sarawak Miri 98009 Malaysia

ISBN: (纸本)9781837243235

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models— Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections. © The Institution of engineering & Technology 2025.

关键词： Deep Learning Machine Learning Ontology Learning Relation Extraction Semantic Relation

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Efficient voltage control strategy: observability design for multistage DC–DC buck converter

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Frontiers in Energy Research 2024年 12卷

作者： Albariqi, Khalid A. Alhussainy, Abdullah Ali Bawayan, Haneen Alturki, Yusuf A. Alghamdi, Sultan Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical Engineering College of Engineering University of Prince Mugrin Madinah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

This paper emphasizes the significance of implementing an effective control system to enhance the performance of a three-stage DC–DC buck converter (TDDC). Herein, we present the development of an observer controller (OC) for TDDC, where average modeling of the DC–DC converter was employed alongside an observer algorithm for the OC. The primary focus is on the adoption of an observation topology aimed at enhancing system responsiveness under various conditions, including variable input voltages, reference voltage changes, load fluctuations, integration with photovoltaics, and battery charging. This approach ensures improved stability and performance, addressing the dynamic challenges inherent in such systems. A comparative analysis was conducted on two distinct control topologies for the proposed TDDC system, namely proportional–integral (PI) and advanced OC. Simulink software was used to model and simulate the proposed system. The results demonstrate lower rising and settling times of the TDDC for the OC and PI implementations, respectively. Additionally, the system with the OC eliminates 20% of the overshoot, while the system with OC minimizes the output voltage oscillations from 13% to 2% compared to the PI system. The OC was integrated into the TDDC to enhance system stability and improve the control loops, particularly the third loop. These improvements make the TDDC with OC suitable for application to renewable energy systems, microgrids, telecommunication networks, and electric vehicles, where precise control and stability are essential. Copyright © 2024 Albariqi, Alhussainy, Bawayan, Alturki and Alghamdi.

关键词： Buck converter

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5G Streaming: IP-based vs. High-Power High-Tower broadcast

5G Streaming: IP-based vs. High-Power High-Tower broadcast

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Proceedings of the International Convention MIPRO

作者： Jeta Dobruna Enjesa Spahiu Matevž Pogač nik Mojca Volk ICT Department Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Telecommunication Department Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Republic of Kosova

The new generation of the cellular communication (5G) presents a highly flexible and scalable network that is designed to connect virtually everyone and everything including machines, objects, and *** new generation is expected to handle ultra-high-definition video streaming and provide multimedia services in an efficient way to meet the users’ expectation. In particular, 5G Broadcast has a wide application in facilitating the distribution of audiovisual media content when covering popular live events with large audience. Broadcasting in 5G can be accomplished using either a high-power high-tower (HPHT) transmitter or IP-based *** this paper, we evaluated the Quality of Experience (QoE) for different broadcasting configurations. QoE is calculated from packet loss and delay parameters. A range of scenarios are simulated in MATLAB. The results demonstrate that the deployment of HPHT base stations reduces delays and packet loss rate, and the QoE is improved.

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Virtual Reality Solutions for Troubleshooting Autonomous Industrial Mobile Robots

Virtual Reality Solutions for Troubleshooting Autonomous Ind...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Cihat Talat Akpinar Özcan Koşar Akif Durdu Computer Engineering Research and Development Elfatek Elektronik Ltd. Co. Konya Turkey Department of Electrical and Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Turkey

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study aims to develop a virtual reality (VR) application to improve the fault detection and troubleshooting processes of autonomous industrial mobile robots. The complex systems and operating environments of autonomous robots make fault detection challenging for users, requiring effective and user-friendly solutions to overcome these difficulties. In this context, the Robot Operating System (ROS) was utilized to detect errors in robots and record these errors in a database. Thanks to the modular structure and robust communication infrastructure offered by ROS, faults in the robots were monitored and analyzed through ROS *** study demonstrates the effective use of VR technology in the maintenance and repair processes of autonomous industrial mobile robots. By integrating ROS-based data collection and fault detection into a VR environment for user training, robot operators are enabled to troubleshoot faults more quickly and effectively. In this way, it is aimed to achieve time and cost savings in industrial processes.

关键词： Training Service robots Databases Fault detection Operating systems Virtual reality Maintenance engineering Maintenance Mobile robots Monitoring

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Enhancing the Performance of Machine Learning Classification Models

Enhancing the Performance of Machine Learning Classification...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study aims to compare and improve the performance of four different machine learning algorithms (Naive Bayes, Multi-Layer Perceptron (MLP), Decision Trees, and Support Vector Machines (SVM)) in classification problems. The analyses emphasize the impact of data preprocessing steps and hyperparameter optimization on model performance. As part of the data preprocessing, missing values were imputed, categorical data were transformed into numerical data, and normalization procedures were applied. It was observed that normalization significantly enhanced the performance of the MLP and SVM algorithms in particular. Furthermore, additional improvements in accuracy rates were achieved through hyperparameter optimization. Naive Bayes and Decision Trees were found to exhibit stable performance regardless of data scaling. This study demonstrates that proper data preprocessing and model selection can significantly enhance algorithm performance in classification problems.

关键词： Support vector machines Accuracy Machine learning algorithms Data preprocessing Hyperparameter optimization Data models Bayes methods Classification algorithms Numerical models Decision trees

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Numerical solution of the problem of turbulent currents that occur in the case of edge hardening

Numerical solution of the problem of turbulent currents that...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Marius Codrean Mihaela Cornelia Novac Mihaela Codrean Cornelia Emilia Gordan Ovidiu Constantin Novac Gyongyi Bujdoso Francisc Slovac Department of Electrical Engineering Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Computer Science and Library and Information Science Faculty of Informatics University of Debrecen Debrecen Hungary

The aim of this work is to develop numerical procedures for analyzing edge hardening using induction heating by turbulent currents. The section through the steel bar, whose lower edge needs to be hardened, is presented. For the inductor adapted to the piece configuration, the 2D model is assumed. The surface treatment of the piece must be differentiated: in the tip area, the heating must be greater than in the rest of the surface. Only the tip area needs to be subjected to hardening, while the rest of the piece must remain elastic to ensure the desired mechanical strength. The main purpose of this work is to propose inductor models for achieving differentiated heating of the blade.

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