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QoS Prediction Model of Cloud Services Based on Deep Learning

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA 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

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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An Improved Convolutional Neural Network for Medical Image Segmentation

An Improved Convolutional Neural Network for Medical Image S...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Obaidi Al-Khafaji, Safaa Hakeem Palle, Kowstubha Thingbaijam, Lenin Prasad, P. Venkata Mallala, Balasubbareddy Pund, Sachin College of Dentistry University of Babylon University of Babylon Computer Science Babylon Iraq Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India Mlcu Faculty It India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350329773

This paper provides a progressed convolutional neural community (CNN) version for medical picture segmentation. It specializes in improving the accuracy and speed of today's segmenting of scientific photos and improving modern semantic segmentation of first-rate present-day clinical photos. Modern accuracy is improved by introducing pre-processing layer, residual connection, attention module, and up sampling layer to the CNN. The stepped-forward overall performance state-of-the-art pace is achieved through dual-project cutting-edge, wherein the segmentation task is divided into two sequential sub-obligations, modern-day semantic segmentation and shape regression. The improved version is examined on publicly available scientific datasets, achieving 49a2d564f1275e1c4e633abc331547db segmentation performance. The results show that the proposed method is robust, green, and generalizable.. © 2023 IEEE.

关键词： Semantic Segmentation

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Data Completeness-aware Transmission Control for Large Spatio-Temporal Data Retention

Data Completeness-aware Transmission Control for Large Spati...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Kaneyasu, Hotaka Nobayashi, Daiki Tsukamoto, Kazuya Ikenaga, Takeshi Lee, Myung Graduate School of Engineering Kyushu Institute of Technology Fukuoka Japan Kyushu Institute of Technology Faculty of Engineering Department of Electrical and Electronics Engineering Fukuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Computer Science and Networks Fukuoka Japan City College of New York Department of Electrical and Computer Engineering New York United States

ISBN: (纸本)9781665441544

With the development of IoT technology, various kinds of data are generated by IoT devices. Some of this data contains information on geographical location and time. We refer to such data as spatio-temporal data (STD). Since the 'local production and consumption' of STD is effective for location-dependent applications, we have proposed an STD retention system that utilizes vehicles as data sources. STD sources ranges from simple sensory data to large video data. However, since previous methods assumed to retain an STD consisting of only one packet, retention of large STD sources may suffer from frequent packet collisions due to the increase in the number of packet transmissions. In this paper, we propose a data completeness-aware transmission control mechanism for large STD retention. Our simulation results showed that the proposed scheme reduces channel collision by suppressing the forwarding of incomplete data, and achieved a nearly 100% coverage rate. © 2022 IEEE.

关键词： Internet of things

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A Mathematical Optimization Approach for Preference Learning in Movie Recommender systems with Shared Accounts

A Mathematical Optimization Approach for Preference Learning...

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Conference on Information and Knowledge Technology

作者： Milad Khademali Fazlollah Aghamohammadi Marjan Kaedi Alireza Nasiri Industrial Engineering Department Faculty of Engineering University of Hormozgan Bandar Abbas Iran Faculty of Computer Engineering University of Isfahan Isfahan Iran Electrical Engineering Department Faculty of Engineering University of Hormozgan Bandar Abbas Iran

ISBN: (数字)9798331522254

ISBN: (纸本)9798331522261

A recommender system typically assumes that each row of the user-item rating matrix reflects the preferences of a single user. However, in many cases, an account is shared among multiple household members, resulting in mixed ratings data that do not accurately represent individual preferences. As a consequence, the recommendations will fail to align with the specific interests of each user. To address this issue, we introduce the concept of a “user character,” which represents a common latent factor in both movie and account features. By establishing a movie feature matrix based on these character representations, we can identify the presence of different characters in shared accounts. This is achieved by factoring the account feature binary matrix from the rating matrix, a process that can be modeled as a binary quadratic optimization problem. For scalability, we relax the binary constraint using a penalty function and approximate the solutions through the gradient descent method. Additionally, we apply a thresholding function to obtain binary solutions that reveal the user characters within each account. Once we identify the characters associated with each account, we can learn users' distinct preferences through a demixing procedure. This allows us to reconstruct the rating matrix so that each row accurately represents a single user's preferences. To evaluate our method, we generated a shared account dataset from MovieLens ratings based on the CAMRa2011 dataset. Experiments conducted on this dataset demonstrate the effectiveness of our proposed approach.

关键词： Scalability Training data Media Motion pictures Vectors User preference Reliability Joints Recommender systems Optimization

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DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework

arXiv

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arXiv 2024年

作者： Lin, Yu-Zheng Shi, Qinxuan Yang, Zhanglong Latibari, Banafsheh Saber Shao, Sicong Salehi, Soheil Satam, Pratik Department of Electrical and Computer Engineering University of Arizona United States Department of Systems and Industrial Engineering University of Arizona United States School of Electrical Engineering and Computer Science University of North Dakota United States

Digital twin (DT) technology has emerged as a transformative approach to simulate, predict, and optimize the behavior of physical systems, with applications that span manufacturing, healthcare, climate science, and more. However, the development of DT models often faces challenges such as high data requirements, integration complexity, and limited adaptability to dynamic changes in physical systems. This paper presents a new method inspired by dynamic data-driven applications systems (DDDAS), called the dynamic data-driven generative of digital twins framework (DDD-GenDT), which combines the physical system with LLM, allowing LLM to act as DT to interact with the physical system operating status and generate the corresponding physical behaviors. We apply DDD-GenDT to the computer numerical control (CNC) machining process, and we use the spindle current measurement data in the NASA milling wear data set as an example to enable LLMs to forecast the physical behavior from historical data and interact with current observations. Experimental results show that in the zero-shot prediction setting, the LLM-based DT can adapt to the change in the system, and the average RMSE of the GPT-4 prediction is 0.479A, which is 4.79% of the maximum spindle motor current measurement of 10A, with little training data and instructions required. Furthermore, we analyze the performance of DDD-GenDT in this specific application and their potential to construct digital twins. We also discuss the limitations and challenges that may arise in practical implementations. © 2024, CC BY-NC-ND.

关键词： Machining centers

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Combining unsupervised and supervised deep learning approaches for surface anomaly detection 16

Combining unsupervised and supervised deep learning approach...

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16th International Conference on Quality Control by Artificial Vision

作者： Racki, Domen Tomazevic, Dejan Skocaj, Danijel Sensum Computer Vision Systems Tehnoloski park 21 Ljubljana Slovenia University of Ljubljana Faculty of Electrical Engineering Trzaska cesta 25 Ljubljana Slovenia University of Ljubljana Faculty of Computer and Information Science Vecna pot 113 Ljubljana Slovenia

ISBN: (数字)9781510667471

ISBN: (纸本)9781510667464

Anomaly detection in an unsupervised manner has become the go-To approach in applications where data labeling proves problematic. However, these approaches aren t completely unsupervised, since they rely on the weak knowledge of the dataset distribution into anomalous and anomaly-free subsets and typically require post-Training threshold calibration in order to perform anomaly detection. Yet, they do not take advantage of available positive samples during training. In contrast, fully supervised approaches have proven to be more accurate and more efficient, however, they require a sufficient number of anomalous images to be labeled on a per-pixel level, which represents a labour-intensive task. In this paper, we propose a new hybrid approach that utilizes the best of both worlds. We use an unsupervised approach to build a model for generating pseudo labels, followed by a supervised approach in order to robustify anomaly detection. Moreover, we extend this approach with an active learning schema, that results in learning with mixed supervision. We achieve several improvements, i.e., the utilization of available positive image samples, improved anomaly detection performance, and the retention of real-Time performance. The proposed approach yields results that are comparable to the fully supervised approach, and at the very least, reduces the number of required labeled anomalous samples. © 2023 SPIE. All rights reserved.

关键词： Anomaly detection

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer systems Science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Implementation of the Internet of Things Technology in the Energy Sector

Implementation of the Internet of Things Technology in the E...

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2023 Seminar on electrical engineering, Automation and Control systems: Theory and Practical Applications, EEACS 2023

作者： Kozlova, Liudmila P. Kozlova, Olga A. Saint Petersburg Electrotechnical University "LETI" Faculty of Industrial Automation and Electrical Engineering Saint Petersburg Russia The Bonch-Bruevich St. Petersburg State University of Communication Faculty of Information Systems and Technologies Saint Petersburg Russia

ISBN: (纸本)9798350358537

Ensuring the reliability and quality of energy supply becomes impossible without digital technologies. The introduction of the Internet of things (IoT) will improve the control of substations, power lines and other network elements, improve the accuracy of energy metering and distribution. When using IoT, a number of problems arise, including network addressing of devices, low bandwidth of two-way data exchange, privacy, and others. The article proposes approaches to their solution. © 2023 IEEE.

关键词： Internet of things

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