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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Christoph Sachs Fabian Stamer Jan Allgeier Duleepa Thrimawithana Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Institute for System Dynamics University of Stuttgart Germany Robert Bosch GmbH Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains.

关键词： MOSFET Costs Silicon carbide Modulation Mechanical power transmission Motors Electric vehicles Inverters Topology Optimization

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Study on Potability Water Quality Classification Based on Integrated Learning 16

Study on Potability Water Quality Classification Based on In...

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16th IEEE International Conference on Intelligent systems and Knowledge engineering, ISKE 2021

作者： Fen, Li Fen Lei, Zhou Ting, Chen Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. The effect of set classifier is higher than that of simple classifier. © 2021 IEEE.

关键词： Water quality

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Fast Division Algorithm for Finite Real Number 16

Fast Division Algorithm for Finite Real Number

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16th IEEE International Conference on Intelligent systems and Knowledge engineering, ISKE 2021

作者： Chen, Ting Li, Fenfen Lin, Jinbao National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Huai'an China

ISBN: (纸本)9781665405539

The data representation and division for finite real number in computers has always been a hot topic in the fields of scientific research and engineering technology. With the research of existing real number division algorithm, design, improvement and adjustment for data structure, algorithm and programming language, and realize the fast division for finite real number. The algorithm can apply to the engineering requirement of finite real number of 506 bits. It replaces the commonly used trial division method with a comparison method, reducing the time complexity of the algorithm. The experimental results demonstrate the correctness and efficiency of the algorithm. The division of the algorithm uses comparison instead of trial division, which reduces the time complexity of the algorithm from O(n2) to O(n). The experimental results show that the algorithm is correct and efficient. © 2021 IEEE.

关键词： Computational complexity

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Application of AI to formal methods — an analysis of current trends

arXiv

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arXiv 2024年

作者： Stock, Sebastian Dunkelau, Jannik Mashkoor, Atif Johannes Kepler University Linz Institute of Software System Engineering Linz4040 Austria Heinrich Heine University Düsseldorf Faculty of Mathematics and Natural Sciences Institute of Computer Science Düsseldorf40225 Germany

With artificial intelligence (AI) being well established within the daily lives of research communities, we turn our gaze toward an application area that appears intuitively unsuited for probabilistic decision-making: the area of formal methods (FM). FM aim to provide sound and understandable reasoning about problems in computer science, which seemingly collides with the black-box nature that inhibits many AI approaches. However, many researchers have crossed this gap and applied AI techniques to enhance FM approaches. As this dichotomy of FM and AI sparked our interest, we conducted a systematic mapping study to map the current landscape of research publications. In this study, we investigate the previous five years of applied AI to FM (2019–2023), as these correspond to periods of high activity. This investigation results in 189 entries, which we explore in more detail to find current trends, highlight research gaps, and give suggestions for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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Computing (ICOCO), IEEE International Conference on

作者： Muntadher Saadoon Siti Hafizah Ab. Hamid Hazrina Sofian Hamza Altarturi Nur Nasuha Zati Hakim Azizul Haqi Khalid Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer and Communication Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism.

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Joko Siswanto Hendry Untung Rahardja Irwan Sembiring Erick Alfons Lisangan Muhammad Iman Nur Hakim Feri Wibowo Road Transportation System Engineering Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Information Technology Satya Wacana Christian University Indonesia Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Computer Science Satya Wacana Christian University Indonesia Computer Science and Software Engineering The University of Western Australia Perth Australia Automotive Engineering Technology Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Engineering and Science Muhammadiyah University Purwokerto Banyumas Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

A crucial role in the BRT transportation system’s planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers.

关键词： Deep learning Training Correlation Accuracy system performance Predictive models Data models Planning Stakeholders Long short term memory

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach

Hadiyyissa Automatic Speech Recognition Using Deep Learning ...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Selamu Shamore Amin Tuni Gure Mohammed Abebe Yimer Department of Computer Science Wachemo University Hossana Ethiopia Department of Information System Arsi University Arsi Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language.

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Web service composition in cloud: A fuzzy rule model

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Recent Advances in computer Science and Communications 2020年第3期13卷 446-453页

作者： Alhadithy, Hussien Al-Shargabi, Bassam Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Information System Faculty of Information Technology Middle East University Amman Jordan

Background: Cloud Computing has drawn much attention in the industry due to its cost-efficient schema along with more prospects, such as elasticity and scalability nature of Cloud Com-puting. One of the main service models of a Cloud is software as a service, where many web services are published and hosted in the Cloud environment. Many web services offered in a Cloud have similar functionality, with different of characteristics non-functional requirements such as Quality of Service (QoS). In addition, as individual web services are limited in their capability. Therefore, there is a need for composing existing services to create new functionality in the form of composite service to fulfill the requirements of Cloud user for certain processes. Methods: This paper introduces a fuzzy rule approach to compose web service based on QoS from different Cloud Computing providers. The fuzzy rule is generated based on QoS of discovered web service from Cloud in order to compose web services that only match user requirements. The proposed model is based on an agent that is responsible for discovering and composing web service that only stratified user requirements Result: the experimental result shows that the proposed model is efficient in terms of time and the use of fuzzy rules to compose web services from different Cloud providers under different specifica-tions and configurations of Cloud Computing environment. Conclusion: In this paper, an agent-based model was proposed to compose web services based on fuzzy rule in Cloud environment. The agent is responsible for discovering web services and generat-ing composition plans based on offered QoS for each web service. The agent employs a set of fuzzy rules to carry out an intelligent selection to select the best composition plan that fulfills the requirements of the end user. The model was implemented on CloudSim to ensure the validity of the proposed model and performance time analysis was performed that showed good result i

关键词： Web services

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