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检索条件"任意字段=International Conference on Electrical Engineering Computing Science and Automatic Control"

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Resource Provisioning in Cloud computing using Fuzzy Logic control System: An adaptive approach 2

Resource Provisioning in Cloud Computing using Fuzzy Logic C...

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2nd international conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif. E. A. Muhammad, Muhammad Hassan Ghulam Hassan, Zahid Fatima, Mayraj Aslam, Muhammad Shoukat Ibrahim, Amer Ateeq, Karamath Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan PMAS-Arid Agriculture University Rawalpindi Pakistan American University in Th Emirates College of Computer and Information Technology Dubai United Arab Emirates Skyline University BSIT Faculty Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

The need for cloud computing has been increasing widely in this era since last two decades due to its continual benefits include security, disaster recovery, increased collaboration, cost saving and virtualization etc. This innovation introduces an enormous transformation of conventional applications in business industry. However, cloud users indulge themselves not acquiring a sufficient amount of computing resources caused by resource confliction. A challenging and significant problem in cloud computing is Resource Provisioning in virtualization environment that relates with selecting, deploying and run time management of software and hardware resources. On the contrary, such situations also exist where resources are found in virtual servers either under or over-utilization. In order to deal with these problems, proposed framework in this paper is demonstrated using Fuzzy Logic control System towards characterization the processes' priorities by means of employing input parameters (workload, processing time, and process age) keeping in view of the fact that expert judgment is required for taking into account these parameters. © 2023 IEEE.

关键词： Virtualization

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Training with a Neurofeedback System for the control of a Drone Using Electroencephalographic Signals 19

Training with a Neurofeedback System for the Control of a Dr...

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19th international conference on electrical engineering, computing science and automatic control, CCE 2022

作者： Gonzalez-Munoz, Leasly Atzuri Orozco-Magdaleno, Luis Ortiz-Yescas, Vania Alice Octavio Ramirez-Morales, Adrian Tovar-Corona, Blanca Zacatelco-Barrios, Luis Brayan Instituto Politecnico Nacional Upiita Mexico City Mexico

ISBN: (数字)9781665455084

ISBN: (纸本)9781665455084

In the present work, a portable electroencephalography system was developed in order to monitor the attention coefficient during a neurofeedback training using a drone as a visual stimulus. A four channels EEG signal acquisition system was developed using the 10-20 international standard, positioned at FP1, FP2, F3 and F4. The Power Spectral Density was calculated using the Modified Covariance method, and the attention coefficient was calculated using the ratio of the area under the curve between the high Beta and Theta bands. With the help of the Neurofeedback system and the drone, a series of eight tests of ten minutes each was carried out with a group of eleven volunteers. Only one session per day was carried out. In each test, the attention coefficient was calculated while the subject watched the drone performing one of the 16 determined trajectories. If the attention coefficient exceeded a threshold, the drone could continue moving, otherwise the drone remained static. As a result of using this neurofeedback system, it was found an increase in sustained attention time from 7.7 to 9.4 s, as well as a reduction in inattention time from 6.0 to 4.8 s. © 2022 IEEE.

关键词： Electroencephalography

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State Derivative Optimal control Law for Submersible Autonomous Robotic Vehicle in Steering Plane 1

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international conference on Advances in Data science and computing Technologies, ASDC 2022

作者： Vadapalli, Siddhartha Mahapatra, Subhasish School of Electronics Engineering VIT-AP University Andhra Pradesh Vijayawada Amaravati522237 India

ISBN: (数字)9789819936564

ISBN: (纸本)9789819936557

In this paper, an algorithm for submersible autonomous robotic vehicle (SARV) is proposed using a derivative feedback-based optimal control technique to track the yaw orientation. The optimal control law is obtained by using a linear quadratic regulator (LQR). A linear matrix inequalities (LMI) approach is used to develop a control algorithm and the realization is carried out using semi-definite programming (SDP). An optimal control action is subjected to a derivative controller for smooth tracking of desired yaw. It is found that energy consumption exerted due to the control input is reduced because of the derivative controller. YALMIP toolbox is used to solve the LMI using MATLAB/Simulink in realizing the control law. The simulation results exhibit that the control algorithm is capable of tracking the desired yaw. A difference in tracking of yaw in the presence of derivative feedback and the absence of derivative feedback is clearly shown and discussed. It is also shown that the tracking of desired yaw has a better performance in terms of derivative feedback. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear matrix inequalities

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Intelligent information processing of Fiber Bragg Grating biosensor for stress monitoring in microfluidics system 3

Intelligent information processing of Fiber Bragg Grating bi...

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3rd international computing Imaging conference, CITA 2023

作者： Zhang, Zijun Lu, Zhenghao Wang, Jing Pan, Han Li, Peiyun Li, Xiaoyao Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China Departmen of Physics and Astronomy University College London London United Kingdom Key Laboratory of More Electric Aircraft Technology of Zhejiang Province University of Nottingham Ningbo China Ningbo China Departmen of Applied Computational Science and Engineering Imperial College London London United Kingdom

ISBN: (纸本)9781510671409

A Fiber Bragg Grating (FBG) biosensor with good biocompatibility promises label-free, anti-electromagnetic interference, and non-destructive detection of stress monitoring, it holds the potential to rapidly identify the stress changes in microfluidics systems. Based on signal processing, the correlation between wavelength shift in FBG and flowrate is discussed, an alternative to manual processing of wavelength offset calculation is analyzed, and the automatic selection of FBG wavelength change under high noise conditions is realized. In addition, we propose some technologies of intelligent processing algorithms such as machine learning (BP—back propagation neural network) and moving average. Finally, the feasibility and stability of the signal processing method are verified by experimental validation and multiple data sets testing. This study provides a promising intelligent information processing strategy for rapid and accurate stress sensing of microfluidics systems. © 2023 SPIE.

关键词： Fiber Bragg gratings

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Forward-Forward Algorithm on Morphological-Linear Neural Networks

Forward-Forward Algorithm on Morphological-Linear Neural Net...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Gerardo Hernández-Hernández Alejandro Rodríguez-Molina Máximo E. Sánchez-Gutiérrez Mario Villafuerte-Bante College of Science and Technology Autonomous University of Mexico City Mexico City Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This paper proposes using the Forward-Forward (FF) algorithm to train hybrid neural networks. We implement the FF algorithm in Morphological-Linear Neural Networks (MLNNs), which consist of two layers. The first layer is made up of morphological neurons and the second consists of perceptron-type neurons. For this we adapt the FF training algorithm in the calculation of the goodness and loss functions derived from the morphological neuron layer. The calculation of the goodness and the loss functions was not modified for the perceptron-type neuron layer. The experimental results show that it is possible to train morphological-linear neural networks with the FF algorithm, obtaining results similar to those obtained with the original FF algorithm. The comparison between the original training algorithm and the proposed one is carried out with low-dimensionality and binary classification datasets from the IMDB natural language processing dataset. Likewise, the ability of the FF algorithm is analyzed and compared in terms of its ability to disentangle patterns for classification tasks.

关键词： Training Neurons electrical engineering computing Natural language processing Classification algorithms Biological neural networks

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Research on lightweight detection algorithm of Infrared Image based on YOLOv5 4

Research on lightweight detection algorithm of Infrared Imag...

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4th international conference on Advanced Algorithms and Signal Image Processing, AASIP 2024

作者： Zhang, Wenyi Mo, Hongwei Wu, Songling Li, Xiaomin Harbin Engineering University School of Intelligent Science and Engineering Harbin150000 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

At present, infrared image can show good detection effect in complex environment because of its unique imaging characteristics, which brings a lot of convenience to people's production and life. However, because the infrared image contains a great lot of unnecessary information, it has great requirements for computing power and memory capacity, so it is challenging to satisfy the demands of real-time detection on resource-constrained portable mobile devices. To solve this problem, this paper proposes a refined lightweight algorithm built upon the YOLOv5, which changes the backbone network of the model to GhostNet network, introduces CA attention mechanism into the model to improve the detection effect of small targets, and changes the loss function of the model to Focal-EIoU loss function to improve the location effect of the boundary box and quicken the model's rate of convergence. Then replace the conventional convolution used in the sampling process of the model with the space-to-depth convolution module, in order to enhance the model's detection performance on low-resolution images and enable the extraction of more feature information. According to the experimental results, the enhanced detection algorithm is just 1% less than the initial YOLOv5 algorithm, and the model volume and floating point calculation are reduced by 31.9% and 36% respectively. This is better suited for deployments that are lightweight. © 2024 SPIE.

关键词： Convolution

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An Artificial Intelligence-based Integrated Framework for Lean and Smart Manufacturing: A Case Study of the Rail Industry

An Artificial Intelligence-based Integrated Framework for Le...

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2024 international conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ale, Felix Daniyan, Ilesanmi Aderoba, Olugbenga Adediran, Adeolu A. National Space Research & Development Agency Engineering & Space Systems Abuja Nigeria Achievers University Department of Mechanical & Mechatronics Engineering Owo Nigeria Landmark University Department of Mechanical Engineering Omu-Aran Nigeria Tshwane University of Technology Department of Industrial Engineering Pretoria South Africa University of Johannesburg Department of Mechanical Engineering Science South Africa

ISBN: (纸本)9798350358155

This study deals with the development of an Artificial Intelligence-based integrated digital technology framework for lean and smart manufacturing implementation. Some technologies and techniques driving Industry 4.0 that are considered in this study include Additive Manufacturing (AM), automatic Train control (ATC), smart sensors, Internet of Things (IoT), big data, cloud computing, Total Productive Maintenance, Predictive Maintenance (PM), and Artificial Intelligence (AI). Using the rail industry as a case study, an overview of the enabling technologies was presented to acquaint the manufacturing industries with the potentials and applications of these technologies to manufacturing. The study also develops a conceptual framework which incorporates the main driving technologies of lean and smart manufacturing for adoption by the rail industry. The validation of the proposed integrated framework involves the use of AI technology for total productive and predictive maintenance using the rail industry as a case study was conducted in the MATLAB 2020 b environment. The outcome of the demonstration shows the Remaining Useful Life (RUL) of a bearing component of a railcar can be predicted using AI. The predicted RUL of the wheel bearing indicates 500 hours over the next 40 days before failure occurs. Thus, this study adds to the understanding of AI in the lean and smart manufacturing domain. The implementation of the integrated framework can promote efficiency and effectively service delivery in industries that employs the concept of lean and smart manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

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Multi-GPU processing of unstructured data for machine learning 39

Multi-GPU processing of unstructured data for machine learni...

引用

39th international conference on High Performance computing, ISC High Performance 2024

作者： Ratsaby, Joel Timashkov, Alexander Electrical and Electronics Engineering Department Ariel University Ariel Israel

ISBN: (纸本)9783982633602

We introduce a method for processing unstructured data for machine learning based on an LZ-complexity string distance. computing the LZ-complexity is inherently a serial data compression process;hence, we introduce a string distance computed by a parallel algorithm that utilizes multiple GPU devices to process unstructured data, which typically exists in large quantities. We use this algorithm to compute a distance matrix representation of the unstructured data that standard learning algorithms can use to learn. Our approach eliminates the need for human-based feature definition or extraction. Except for some simple data reformatting done manually, our proposed approach operates on the original raw data and is fully automatic. The parallel computation of the distance matrix is efficient. It obtains a speed-up factor of 528 in computing the distance matrix between every possible pair of 16 strings of length 1M bytes. We show that for learning time-series classification, relative to the ubiquitous TFIDF data representation, the distance-matrix representation yields a higher learning accuracy for most of a broad set of learning algorithms. Thus, the parallel algorithm can be helpful in efficiently and accurately learning from unstructured data. © 2024 Research Paper Proceedings of the ISC High Performance 2024. All rights reserved.

关键词： Parallel algorithms

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A fine-tuned MobileNet Model for Coral Classification using Images 9

A fine-tuned MobileNet Model for Coral Classification using ...

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9th IEEE international Women in engineering (WIE) conference on electrical and Computer engineering, WIECON-ECE 2023

作者： Kusuma, K. Prabhakar T V, Nidhin Padam Singh, Rimjhim Kumar, Priyanka Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India University of Texas at San Antonio Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350319651

Identification of the coral species present underwater is becoming rigorous due to several reasons like: (a) the available datasets contain information only about their texture but not the overall global structure of the species, (b) several coral species have similar characteristics and (c) difficulty in defining the space between two different coral species as they appear together in groups, etc. The aim of the proposed work is to automate the coral species classification by reducing the need of human intervention. For this purpose, the paper proposes a fine-tuned MobileNet architecture for automatic coral classification in different scenarios. As most of the datasets available are imbalanced, the work utilizes four standard datasets to comprehensively evaluate the proposed model. Also due to the varying nature of the datasets, each one of them pose different challenges to the model, hence, different activation functions and optimizers have been considered to get the optimal results on these datasets. The proposed model has been compared against several state-of-art convolutional models namely, Inceptionv3, ResNet50, DenseNet121, VGG-16, and vanilla MobileNet to support the strength of the proposed model for coral classification under different challenging conditions presented by various datasets. © 2023 IEEE.

关键词： Classification (of information)

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Quizzing the Quizzes: Improving Cancer Risk Factor Analysis Quizzes 24

Quizzing the Quizzes: Improving Cancer Risk Factor Analysis ...

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24th international conference on control Systems and Computer science, CSCS 2023

作者： Vlach, Jared Dascalu, Sergiu Shen, Yantao Park, Jeongwon University of Nevada Reno Department of Biomedical Engineering Reno United States University of Nevada Reno Department of Computer Science and Engineering Reno United States University of Nevada Reno Department of Electrical and Biomedical Engineering Reno United States

ISBN: (纸本)9798350313390

This user study addresses the characteristics of breast cancer risk factor analysis quizzes in order to understand the correlation between completion time, question clarity, and user satisfaction. The user study was conducted with 10 participants to understand what users find preferable in their overall experience with cancer risk factor analysis tools. Three risk factor analysis tools (one of them created by us) were compared in terms of completion time, completion time per question, questions asked to the coordinator, and preferred tool and/or features preferred. Results showed that shorter completion time is not necessarily preferred by users but understanding and clarity of questions enabled users to complete the questionnaires with less confusion. However, results were inconclusive regarding completion time per question as the tools had similar results and, perhaps surprisingly, the most preferred tool was the one with the longest average completion time per question. © 2023 IEEE.

关键词： Factor analysis

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