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36th International Conference on Electrical Drives and Power Electronics, EDPE 2023

作者： Korkosz, Mariusz Kutsyk, Andriy Krzywdzinska-Kornak, Krystyna Suliga, Mateusz Semeniuk, Mykola Rzeszow University of Technology Faculty of Electrical and Computer Engineering Rzeszow Poland Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350322750

The paper analyzes the characteristics of a dual-channel three-phase induction motor. Motors designed for multi-channel power supply are dedicated to applications requiring greater reliability. Each channel allows a certain degree of increase in the ability to continue operation after the failure states. The use of a dual channel power supply is the most common way to increase drive train redundancy. This paper presents the tests of the behaviour of a dual-channel three-phase induction motor in the event of loss of power to one of the phases (in one or two channels) and single-channel operation. A correctly designed motor allows for continuous operation in single-channel power mode. Numerical and laboratory tests confirmed this assumption. It has been demonstrated that the loss of power to one phase of a channel should result in the channel being turned off. © 2023 IEEE.

关键词： Fault tolerance

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Comparator Design Resistance Mitigation by using Cadence Virtuoso Tools in 45 Nanometer Process technology

Comparator Design Resistance Mitigation by using Cadence Vir...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Syn Yi, Michelle Ang Hussin, Razaidi Rokhani, Fakhrul Zaman University Malaysia Perlis Faculty of Electronic Engineering Technology Arau Malaysia University Putra Malaysia UPM Department of Computer and Communications System Engineering Serdang Malaysia

ISBN: (纸本)9798350333312

This paper presents the resistance mitigation techniques for comparator design. Resistance plays an important role to semiconductor field. If resistance value higher than the design specification value, this might cause the electronic device burning and destroy. This project implements the techniques to mitigate resistance for the comparator design layout which are using multiple metal layers for routing, routing track improvement, increase metal width, increase number of vias and using higher metal layer for routing by using Cadence Virtuoso tools in 45 nanometer process technology. Based on the result, the total percentage for resistance mitigation for using multiple metal layers for routing is 73.66%, routing track improvement is 67.67%, increase metal width is 51.74%, increase number of vias is 50% and using higher metal layer for routing is 18.79%. © 2023 IEEE.

关键词： Comparators (optical)

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Analysis of the Characteristics of a Six-phase Induction Motor 36

Analysis of the Characteristics of a Six-phase Induction Mot...

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36th International Conference on Electrical Drives and Power Electronics, EDPE 2023

作者： Korkosz, Mariusz Kutsyk, Andriy Krzywdzinska-Kornak, Krystyna Nowak, Marek Semeniuk, Mykola Rzeszow University of Technology Faculty of Electrical and Computer Engineering Rzeszow Poland Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350322750

The paper includes a report on the preliminary results of six-phase induction motor tests. Using a power supply with more than three phases requires a slightly different approach in the motor design process. In theory, a six-phase induction motor design should be more resistant to the loss of power to one, two, or three phases. A numerical analyzis of selected operating states of an asymmetrical power supply to an induction motor was carried out. Verification of the numerical results was carried out under laboratory conditions. © 2023 IEEE.

关键词： Induction motors

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

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computer systems Science & Engineering 2022年第12期43卷 1175-1188页

作者： Norwini Zaidi Iskandar Ishak Fatimah Sidi Lilly Suriani Affendey System Development and Engineering Center Universiti Sains Islam Malaysia71800Negeri SembilanMalaysia Faculty of Computer Science and Information Technology Universiti Putra MalaysiaUPM Serdang43400SelangorMalaysia

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

关键词： Data migration data transformation column-oriented database relational database big data

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Hand-Foot-Mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data 4

Hand-Foot-Mouth Disease Classification using Features from F...

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4th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2022

作者： Mahmood, Atif Azzuhri, Saaidal Razalli Qureshi, Adnan N. Jaan, Palwasha Sadia, Iqra University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University of Malaya Department of Computer System & Technology Kuala Lumpur Malaysia Virtual University of Pakistan Faculty of Science & Technology Lahore Pakistan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Hand, Foot and Mouth disease (HFMD) is a common viral childhood disease affected by the family of enterovirus and Coxsackie. Current laboratory identification is based on the RT-PCR test, which is expensive, time-consuming, and unsuitable for the pandemic. The SPR- TFBG was biofunctionalized with monoclonal antibody (Mab). Mab is a bioreceptor with an affinity for the virus for detecting EV-A71. A dataset of reflectance spectra of 660 samples of different virus impurities measured with SPR-TFBG biosensor to detect EV-71 virus was developed. The extracted signal has around 4000 different features based on wavelength information. The first subset was selected based on the region of interest analysis, and the dimension has reduced from 4000 to 1496 features. The dimensionality of the large feature set is reduced based on the statistical feature engineering procedure using 10 features including mean, variance, skewness, RMS, kurtosis, standard deviation, range, crest factor, impulse factor and shape factor. Subsequently, classification of the virus (signal) data is achieved through SVM and it is evaluated with different types of kernels. For the evaluation of classifiers, we used accuracy, sensitivity, precision and F1 score performance metrics. The obtained results of accuracy are 87.88 for linear SVM, 86.06 for radial basis, 75.76 for sigmoid SVM, and 75.15 for polynomial SVM, respectively. The results show that for our experiments, Linear SVM performs better than radial, polynomial and sigmoid kernels. This is because projecting the data onto higher dimensions is not required as data exhibits linear properties confirmed by White Neural Network (WNN) test for nonlinearity. © 2022 IEEE.

关键词： Viruses

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N-gram and Word2Vec Feature Engineering Approaches for Spam Recognition on Some Influential Twitter Topics in Saudi Arabia 22

N-gram and Word2Vec Feature Engineering Approaches for Spam ...

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6th International Conference on Information system and Data Mining, ICISDM 2022

作者： Balfagih, Ahmed Keselj, Vlado Taylor, Stacey Faculty of Computer Science Dalhousie University Canada Information System Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

ISBN: (纸本)9781450396257

Social media platforms, such as Twitter, have become powerful sources of information on people's perception of major events. Many people use Twitter to express their views on various issues and events and use it to develop their opinion on the diverse economic, political, technical, and social occurrences related to their daily lives. Spam and non-relevant tweets are a major challenge for Twitter trend detection. Saudi Arabia is a top ranked country in Twitter usage worldwide, and in recent years has experienced difficulties due to the use and rise of hashtags based on misleading tweets and spam. The goal of this paper is to apply machine learning techniques to identify spam on the Saudi tweets collected to the end of 2020. To date, spam detection on Twitter data has been mostly done in English, leaving other major languages, such as Arabic, insufficiently covered. Additionally, publicly accessible Arabic Twitter datasets are hard to find. For our research, we use eight Twitter datasets on some significant topics in politics, health, national affairs, economy, and sport, to train and evaluate different machine learning algorithms, with a focus on two feature generation techniques based on N-grams and Word2Vec embeddings. One contribution of this paper is providing these new labelled datasets with embeddings. The experimental results show improvement from using embeddings over N-grams in more balanced datasets vs. more unbalanced ones. We also find a superior performance of the Random Forest algorithm over other algorithms in most experiments. © 2022 ACM.

关键词： Embeddings

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A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features

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Chinese Journal of Electronics 2021年第2期30卷 289-295页

作者： LI Youjiao ZHUO Li LI Jiafeng ZHANG Jing Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology College of Micro-electronics Beijing University of Technology College of Computer Science and Technology Shandong University of Technology

A two-level hierarchical scheme for video-based person re-identification(re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically,given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network(CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover,local features of Local maximal occurrence(LOMO) of the walking cycle are extracted to represent the pedestrian's appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and i LIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose,lighting, and camera viewpoint.

关键词： video surveillance image sequences two-level hierarchical scheme video content local features image representation video-based re-id methods pedestrian walking cycle video signal processing convolutional neural nets local maximal occurrence noisy frames lightweight PCANet complete walking cycle extraction pedestrian appearance information walking-cycle-based person re-id approaches LOMO features lightweight convolutional neural network learning (artificial intelligence) pedestrian appearance model consecutive frames video noises video-based person re-identification iLIDS-VID benchmark dataset LOMO PRID 2011 benchmark dataset video sequence image matching detected key frame object detection

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Deep Learning-Based Real-Time Face Mask Detection for Human Using Novel YOLOv2 with Higher Accuracy 5th

Deep Learning-Based Real-Time Face Mask Detection for Human ...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Tun, Nay Kyi Myat, Aye Min Oo, Htet Ne Thu, Amar Myo Win, Kyaw Thu Faculty of Computer System and Technology Myanmar Information Institute of Technology Mandalay Myanmar Pyin Oo Lwin Myanmar Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Electronic Department Defence Services Academy Pyin Oo Lwin Myanmar

ISBN: (纸本)9789819997060

A novel deep learning-based face mask detection system is designed to enhance public safety in various environments. With the ongoing global health concerns, the need for efficient and accurate methods to identify individuals wearing or not wearing face masks has become crucial. By utilizing convolutional neural networks (CNNs) and transfer learning techniques, proposed model achieves impressive accuracy while maintaining high-speed processing capabilities. This paper outlines the architecture, training process, and performance evaluation of the proposed deep learning-based face mask detection system, highlighting its promising role in contributing to a safer and healthier society. The development of a vision-based safety system, the transfer of a small YOLO object detection model, and the creation of a CNN-based classification model are the key objectives of this study. According to experimental findings, proposed system is capable of real-time face mask detection and classification with an accuracy of over 98%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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