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Performance prediction of a single-phase capacitor-run axial flux induction motor under static eccentricity

Australian Journal of Electrical and Electronics engineering 2014年第1期11卷 93-104页

作者： Nasiri-Gheidari, Zahra Lesani, H. Tootoonchian, F. Department of Electrical Engineering Sharif University of Technology Tehran Iran Center of Excellence on Control and Intelligent Processing School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran High Speed & High Precision Electrical Machinery Research Lab Iran University of Science and Technology Tehran Iran

Nowadays single-phase axial flux induction motors are widely used in home applications. This increasing usage is because of their distinct advantages over conventional induction motors. Some of these advantages are their planar and adjustable air gap, better power to weight and diameter to length ratio, compact construction, better efficiency, high utilisation of the active materials and thus a favourable power density. This paper deals with analysis and experiment of a single-phase capacitor-run axial flux induction motor (AFIM). The mathematical model of studied motor in transient and steady-state is investigated based on d-q axis theory. In particular, the proposed model takes into account the effect of core saturation, supply voltage and eccentricity. An experimental test setup is built to calculate core B-H curve. Also, a new definition for static eccentricity in direct drive systems is presented and its definition in shaft-coupled axial flux machines is improved. Finally, the studied AFIM is fabricated and tested. The simulation results are in good agreement with experimental data obtained from the prototype motor. © Institution of Engineers Australia, 2014.

关键词： Induction motors

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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An online algorithm optimally self-tuning to congestion for power management problems

An online algorithm optimally self-tuning to congestion for ...

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9th International Workshop on Approximation and Online algorithms, WAOA 2011

作者： Bein, Wolfgang Hatta, Naoki Hernandez-Cons, Nelson Ito, Hiro Kasahara, Shoji Kawahara, Jun Center for Information Technology and Algorithms School of Computer Science University of Nevada Las Vegas NV United States Department of Communications and Computer Engineering Graduate School of Informatics Kyoto University Japan Department of Systems Science Graduate School of Informatics Kyoto University Japan JST ERATO MINATO Project Japan

ISBN: (纸本)9783642291159

We consider the classical power management problem: There is a device which has two states ON and OFF and one has to develop a control algorithm for changing between these states as to minimize (energy) cost when given a sequence of service requests. Although an optimal 2-competitive algorithm exists, that algorithm does not have good performance in many practical situations, especially in case the device is not used frequently. To take the frequency of device usage into account, we construct an algorithm based on the concept of "slackness degree." Then by relaxing the worst case competitive ratio of our online algorithm to 2 + Ε, where Ε is an arbitrary small constant, we make the algorithm flexible to slackness. The algorithm thus automatically tunes itself to slackness degree and gives better performance than the optimal 2-competitive algorithm for real world inputs. In addition to worst case competitive ratio analysis, a queueing model analysis is given and computer simulations are reported, confirming that the performance of the algorithm is high. © 2012 Springer-Verlag.

关键词： Queueing theory

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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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Combined heat and power economic dispatch solution using iterative cultural algorithm

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2017 International Conference on Artificial Intelligence, ICAI 2017 at 2017 World Congress in computer science, computer engineering and Applied Computing, CSCE 2017

作者： Shayanfar, Heidarali Bonab, Reza Dadashzadeh Tabrizi, Navid Mehdinejad, Mehdi Center of Excellence for Power Systems Automation and Operation School of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9781601324603

With increasing the environmental concerns and fuel prices of the power industry, the optimal utilization of Combined Heat and Power (CHP) systems has become one of the essential subjects in power systems. Different artificial intelligence methods are used for optimization of CHP operation. Cultural Algorithm (CA) is based on some theories originated in archaeology and sociology which try to model cultural evolution. The basic disadvantage of the original culture algorithm is the fact that, it can’t give better optimal solution rather than most of other artificial intelligent methods. In this paper an iterative cultural algorithm is proposed to solve Combined Heat And Power Economic Dispatch (CHPED) problem. The effect of valve-point that make the CHPED problem as a non-convex issue is considered in the cost function and formulated in the conventional polynomial cost function as an absolute sinusoidal term. The proposed method is applied to three small (with three different test cases), medium and large test systems in order to evaluate its efficiency and feasibility. From obtained results, it is seen that proposed method converged to a better and feasible solution in all cases in comparison with some recently published papers. © 2017 CSREA Press. All rights reserved.

关键词： Cost functions

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A Hybrid CNN-GRU model for Session-based Recommender Systems 9

A Hybrid CNN-GRU model for Session-based Recommender Systems

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9th International Conference on computer and Communications, ICCC 2023

作者： Tegene, Abebe Tamrat Liu, Qiao Gan, Yanglei Jimale, Elias Ayenew, Melak Leka, Habte University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Addis Ababa Science and Technology University Center of Excellence of Ai and Robotics Addis Ababa Ethiopia University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350317251

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users' past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used. © 2023 IEEE.

关键词： Recommender systems

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Biological Classification System Knowledge Graph and Semi-automatic Construction of Its Invertebrate Fossil Branches

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Journal of Earth science 2024年第6期35卷 2119-2128页

作者： Shaochun Dong Yukun Shi Yizao Ran Haijun Wu Yiying Deng Junxuan Fan Xinyu Dai School of Earth Sciences and Engineering Frontiers Science Center for Critical Earth Material CyclingNanjing UniversityNanjing210046China Jiangsu Deep-Time Digital Earth Research Center for Excellence Suzhou215004China School of Computer Science Nanjing UniversityNanjing210046China School of Resources and Environmental Engineering Hefei University of TechnologyHefei230009China State Key Laboratory for Mineral Deposits Research Nanjing210046China National Key Laboratory for Novel Software Technology Nanjing210046China

Biological classification is the foundation of biology and paleontology,as it arranges all the organisms in a hierarchy that humans can easily follow and *** is further used to reconstruct the evolution of life.A biological classification system(BCS)that includes all the established fossil taxa would be both useful and challenging for uncovering the life *** fossil taxa were originally recorded in various published books and articles written by natural languages,the primary step is to organize all those taxa information in a manner that can be deciphered by a computer system.A Knowledge Graph(KG)is a formalized description framework of semantic knowledge,which represents and retrieves knowledge in a machine-understandable way,and therefore provides an eligible method to represent the *** this paper,a model of the BCS KG including the ontology and fact layers is *** put it into practice,the ontology layer of the invertebrate fossil branches was manually developed,while the fact layer was automatically constructed by extracting information from 46 volumes of the Treatise of Invertebrate Paleontology series with the help of natural language processing *** a result,27348 taxa nodes spanning fourteen taxonomic ranks were extracted with high accuracy and high efficiency,and the invertebrate fossil branches of the BCS KG was thus *** study demonstrates that a properly designed KG model and its automatic construction with the help of natural language processing are reliable and efficient.

关键词： biological classification system knowledge graph ontology invertebrate fossil big data

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Localization for V2X communication with noisy distance measurement

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International Journal of Intelligent Networks 2023年第1期4卷 355-360页

作者： Javed, Iram Tang, Xianlun Saleem, Muhammad Asim Javed, Ashir Zia, Muhammad Azam Shoukat, Ijaz Ali School of Computer Science and Technology Chongqing University of Posts and Telecommunications China Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Aging Research Center Karolinska Institutet Stockholm171 65 Sweden Department of Computer Science University of Agriculture Faisalabad Pakistan Riphah College of Computing Riphah International University Faisalabad Campus Pakistan

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. © 2023 The Authors

关键词： Extended Kalman filters

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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Ethnographic examination for studying information sharing practices in rural South Africa

Ethnographic examination for studying information sharing pr...

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5th International Conference on Advances in computer-Human Interactions, ACHI 2012

作者： Dhir, Amandeep Moukadem, Imad Jere, Nobert Kaur, Puneet Kujala, Sari Ylä-Jääski, Antti Department of Computer Science and Engineering School of Science Aalto University 02150 Finland Kuwait Computer Science Department Telkom Center of Excellence in ICTD University of Fort Hare Alice-5700 South Africa Department of Design School of Art and Design Aalto University 00076 Finland

ISBN: (纸本)9781612081779

This progress paper describes a study plan and research theme with the focus on information sharing practices, social habits and behavioral access patterns in the context of the mobile phone and ICT usage of rural users in the emerging economies such as South Africa. The aim is to design new solutions to support rural users and their economic existence. An ethnographic field study was performed in order to understand the living conditions of rural communities and the related problems and opportunities in the field of mobile phone and ICT usage. A variety of qualitative measures such as contextual inquiry, participant observation, design exercises, focus and individual interviews were used during the field. However, only preliminary results from field studies are described in this paper. In our study, we found that needs and expectations of the users dwelling in rural context of different emerging economies are very much different from that of developed world. Product designers must consider these needs in order to deliver successful ICT and mobile-based services. Copyright © IARIA, 2012.

关键词： Cellular telephones

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