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On Data Windows for Fault Detection with Neural Networks

IFAC-PapersOnLine 2021年第4期54卷 38-43页

作者： Matej Šprogar Matjaž Colnarič Domen Verber Faculty of Electrical Engineering and Computer Science University of Maribor Maribor SI 2000 Slovenia

Fault-tolerant industrial systems need effective fault detection, which we can treat as a classification problem based on the observable system properties. Artificial neural networks (ANN) are provenly strong contenders in this field, capable of supervised learning from experience. They operate with large amounts of data, which we need to prepare and use carefully. This paper explores how different temporal data quantities provided to a long short-term memory network affect its performance and fault detection delay. The case study for this research was the Tennessee Eastman Process data. Test results indicate that networks detect faults best when given the full-length time series data, regardless of the data shape during training. On the downside, the longer the time series, the more delayed the recognition of a possible fault. The systematic approach in feeding the ANN with specific amounts of data revealed a potential overfitting problem of an otherwise perfect classifier.

关键词： Fault detection time series machine learning classification neural networks LSTM data window time delay

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Constrained Intelligent Frequency Control in an AC Microgrid: An Online Reinforcement Learning Based PID Tuning Approach

Constrained Intelligent Frequency Control in an AC Microgrid...

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IEEE General Meeting Power& Energy Society

作者： K. Nosrati A. Tepljakov E. Petlenkov V. Skiparev J. Belikov Y. Levron Department of Computer Systems Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Variable output power in isolated microgrids (MGs) threatens frequency stability and may even degrade power quality. In response, intelligent control methods have been developed and applied to frequency deviation control systems with excellent results. Nevertheless, a potential problem is that the application of such advanced techniques with a large search space is not enough to deal with highly dynamic environment and real-time operations of MGs. In this light, the present study introduces a flexible artificial neural network (ANN)-based frequency deviation control solution in a constrained structure that operates as follows. First, the stable controller parameter space of the PID-based AC microgrid is derived by using the stability boundary locus method. Then, the controller parameters are tuned and updated online by searching for an optimal combination of the coefficients with consideration of output variations sensed by a constrained ANN in the derived reduced parameter space. To accomplish this step, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed technique has been verified under a given scenario to demonstrate how the reduced parameter space should facilitate the optimization procedure.

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

Transformer-based Text Classification on Unified Bangla Mult...

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Information Technology (ACIT)

作者： Md Sakib Ullah Sourav Mohammad Sultan Mahmud Hua Zheng Mohammad Aljaidi Md. Simul Hasan Talukder Rejwan Bin Sulaiman Abdullah Hafez Nur Ahmad Al-Qerem School of Management Science and Engineering Shandong University of Finance and Economics Jinan China College of Computer Science and Software Engineering Shenzhen University Senzhen China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Dhaka School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom Northumbria UK Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world’s most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.

关键词： Measurement Human computer interaction Sentiment analysis Analytical models Codes Accuracy Web 2.0 Text categorization Transformers Information technology

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Proposing System to Recognize Emotions in Public Network Using Phobert Deep Learning Model 9

Proposing System to Recognize Emotions in Public Network Usi...

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9th NAFOSTED Conference on Information and computer science, NICS 2022

作者： Huu, Phat Nguyen Anh, Tuan Nguyen Phi, Long Hoang Dang, Dinh Dang Le Bao, Chau Nguyen Minh, Quang Tran School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Hanoi-Amsterdam Highschool for the Gifted Hanoi Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Department of Information Systems Hochiminh Viet Nam Vietnam National University Hochiminh HoChiMinh City Viet Nam

ISBN: (纸本)9781665454223

People can receive information faster especially in the 4.0 revolution with the continuous development of revolutions. The information can affect our emotional, psychological and spiritual well-being, especially in the recent high school graduation exam across the country. Therefore, we propose to build a user emotion analysis system in a public network with the PhoBert training model in the paper. Besides, we built our dataset aggregated from social networks, articles, blogs, etc. We next use the PhoBert model to solve the processing data problems. The simulation results have shown an accuracy of 86.5% on the training and 81.32% on the validation dataset with a training time of 3 hours (about 180 minutes). The results also show that we can build a warning system to avoid health and psychological effects with great emotion. © 2022 IEEE.

关键词： Deep learning

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The Significance of the Torso Electrodes for Selected Cardiac Regions

The Significance of the Torso Electrodes for Selected Cardia...

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International Conference on Measurement

作者： Beata Ondrusova Peter Tino Jana Svehlikova Institute of Measurement Science Slovak Academy of Sciences Bratislava Slovakia Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia School of Computer Science University of Birmingham Birmingham UK

The significance of the torso electrodes for the solution of the inverse problem of electrocardiography using a single dipole depends on the position of the true origin of the cardiac activity, i.e., ground truth. However, the ground truth is not known in clinical practice. Therefore, we studied whether similar electrode significances would be computed for the dipole corresponding to the ground truth, the computed inverse solution, and dipoles within the 30 mm radius around the inverse solution. The significance of torso electrodes was computed for 8 datasets of patients with a known position of the ground truth. The significance assessment of torso electrodes was performed using algebraic properties of the transfer matrix computed for the given dipole position. We studied three variants of the significance assessment of torso electrodes and for two of them similar electrode significances were obtained for all positions of dipoles within 30 mm of the inverse solution. For those two variants, the most significant electrodes created a subset of 47 ± 6% and 40 ± 4% from the full set of electrodes. The results suggest that the electrode significances for a given patient can be established from the inverse solution if the inverse solution lies within 30 mm of the ground truth.

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Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

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computer Systems science & engineering 2023年第12期47卷 3127-3144页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life *** CI is useful in the UAV domain as it produces efficient,precise,and rapid ***,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city *** the benefits of UAVs,security remains a major challenging *** addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,*** paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city *** purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class *** proposedMDLS-UAVIC model follows a two-stage process:encryption and image *** encryption technique for image encryption effectively encrypts the UAV ***,the image classification process involves anXception-based deep convolutional neural network for the feature extraction ***,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the *** experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various *** achieved a high accuracy of 98%.

关键词： Computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification

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Ensemble Modeling for the Classification of Birth Data

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Intelligent Automation & Soft Computing 2024年第4期39卷 765-781页

作者： Fiaz Majeed Abdul Razzaq Ahmad Shakir Maqbool Ahmad Shahzada Khurram Muhammad Qaiser Saleem Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Osama E.Sheta Department of Information Technology University of GujratGujrat50700Pakistan School of Digital Convergence Business University of Central PunjabRawalpindi46000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia School of Computer Science and Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaLibreville1989Gabon Department of Electrical and Electronic Engineering Science School of Electrical EngineeringUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia College of Computer Science and Engineering University of Ha’ilHa’il55476Saudi Arabia College of Science Zagazig UniversityZagazig44511Egypt

Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in *** in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the *** ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better *** is observed that there is a huge difference between women depending on the region and their social *** to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected *** this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base *** is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual *** algorithms are more accurate due to ensemble models,which reduce variance and classification *** is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the *** such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.

关键词： Birth data classification ensemble model machine learning

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Markov Chain-Based Model of Blockchain Radio Access Networks

Markov Chain-Based Model of Blockchain Radio Access Networks

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Vasileios Kouvakis Stylianos E. Trevlakis Alexandros-Apostolos A. Boulogeorgos Hongwu Liu Theodoros A. Tsiftsis Octavia A. Dobre Department of Research and Development InnoCube P.C. Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China Department of Informatics & Telecommunications University of Thessaly Lamia Greece Faculty of Engineering and Applied Science Memorial University St. John's NL Canada

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Security has always been a priority, for re- searchers, service providers and network operators when it comes to radio access networks (RAN). One wireless access approach that has captured attention is blockchain enabled RAN (B-RAN) due to its secure nature. This research introduces a framework that integrates blockchain technology into RAN while also addressing the limitations of state-of-the-art models. The proposed framework utilizes queuing and Markov chain theory to model the aspects of B-RAN. An extensive evaluation of the models performance is provided, including an analysis of timing factors and a focused assessment of its security as- pects. The results demonstrate reduced latency and comparable security making the presented framework suitable for diverse application scenarios.

关键词： Wireless communication Technological innovation Privacy Computational modeling Blockchains Timing History Usability Queueing analysis Radio access networks

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Inverse Solution Accuracy Using 12-Lead ECG vs 9 Significant Electrodes Derived by Greedy Algorithm

Inverse Solution Accuracy Using 12-Lead ECG vs 9 Significant...

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computers in Cardiology (CinC)

作者： Beata Ondrusova Peter Tino Jana Svehlikova Slovak Academy of Sciences Institute of Measurement Science Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Bratislava Slovakia School of Computer Science The University of Birmingham Birmingham United Kingdom

The inverse problem of electrocardiography is solved using potential recordings from numerous torso electrodes. However, there is a growing interest in minimizing the number of used electrodes to enhance clinical applicability. A total of 8 datasets of patients with pacemakers obtained from the EDGAR database were used. The inverse problem assuming a single dipole cardiac source was solved for 3 different electrode configurations. The configurations included all $196\ \pm\ 28$ electrodes, 9 corresponding to the 12-lead ECG, and the 9 most significant electrodes. The significance of the electrodes was derived by the greedy algorithm from the singular value decomposition of the transfer matrix. The accuracy of the inverse solution was expressed as the localization error (LE) computed as the Euclidean distance between the pacemaker electrode and the inverse solution. The average LE for all 8 datasets using all torso electrodes was $26.1\pm 6.1\ mm$ , whereas using 12-lead ECG electrodes yielded a LE of $40.5\pm 23.1\ mm$ , and the greedy algorithm's selection resulted in a LE of $29.6\pm 14.0\ mm$ . The results suggest that the use of the most significant electrodes outperformed the use of the 12-lead ECG. The study emphasizes the need for back electrodes for an accurate inverse solution.

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Examining the optoelectronic and photovoltaic characteristics of Mg₃SbM₃ (M = F, Cl, Br) perovskites with diverse charge transport layers through numerical optimization and machine learning techniques

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Inorganic Chemistry Communications 2025年 179卷

作者： Shimul, Asadul Islam Sarker, Swarna Rani Ghosh, Avijit uz Zaman, Md Towfiq Alrafai, H.A. Hassan, Abeer A. Department of Electrical and Electronic Engineering Gopalganj Science and Technology University Gopalganj 8100 Bangladesh Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur 5400 Bangladesh Department of Electrical and Computer Engineering Lamar University Beaumont 77710 TX United States Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study employs first-principles density functional theory (DFT) to investigate the structural, optical, electrical, and mechanical properties of magnesium-based halide perovskites, specifically Mg₃SbM₃ (M = F, Cl, Br). The computations indicate that the direct band gaps are 3.374 eV for Mg₃SbF₃, 1.589 eV for Mg₃SbCl₃, and 0.879 eV for Mg₃SbBr₃. The compounds exhibit mechanical stability. The optical study identifies Mg₃SbCl₃ as a viable candidate for photovoltaic (PV) applications owing to its elevated dielectric constant, conductivity, reflectivity, and absorption coefficients, essential for photon absorption in solar devices. The SCAPS-1D simulation software was employed to assess the PV performance of solar cells utilizing Mg₃SbCl₃ as the absorber. Various hole transport layers (HTLs), specifically Cu₂O and CuI, were evaluated in conjunction with CdS and WS₂ as electron transport layers (ETLs). Considerations included layer thickness, doping, defects, temperature, carrier concentration, quantum efficiency (QE), and current density–voltage (J-V) properties. The optimization of HTL and ETL layers resulted in a maximum PCE of 27.41 % using Cu₂O as HTL and WS₂ as the ETL, achieving a J_SC of 24.81 mA/cm², an FF of 89.42 %, and a V_OC of 1.23 V. These findings illustrate the capability of Mg₃SbCl₃ to function as an absorber layer, with WS₂ and Cu₂O serving as the ETL and HTL, respectively. Additionally, we developed a machine learning algorithm that predicts solar cell performance with an accuracy of 86.5 %, underscoring the potential of high-performance perovskite heterostructure solar cells and future innovations in solar energy technology. © 2025 Elsevier B.V.

关键词： DFT Machine Learning Mg3SbCl3 Mg3SbM3 (M = F, Cl, Br) SCAPS-1D

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