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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

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Implementation of Fast Switching of Multiple Examination Systems Based on the Same System 3

Implementation of Fast Switching of Multiple Examination Sys...

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3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Wang, Dong Xiao, Jianhua Tang, Meixiu Tang, Pengju School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China Huaihua University Library Huaihua University Hunan Huaihua China

ISBN: (纸本)9798400716775

The National Unified Legal Profession Qualification Examination and the National computer Classification Examination respectively use their own systems to achieve the examination, and the examination machines have certain limitations, how to quickly switch the system to ensure that the two exams within the same day can be held in separate time slots, is worth exploring. In this paper, for different types of machine rooms, put forward different solutions to solve the problem, enhance the ability of system administrators to quickly switch to the examination system, for the preparation of the future examination environment has accumulated experience, but also for peers to provide a little reference. © 2023 ACM.

关键词： Exam platforms Paperless exams Time slots

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A Hybrid Strategy Integrating Ant Colony Optimization and Locust-Inspired Algorithm (HACO-LA) to Boost Efficiency and Performance in cloud Resource Management

A Hybrid Strategy Integrating Ant Colony Optimization and Lo...

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Power Electronics & IoT Applications in Renewable Energy and its control (PARC), 2020 international conference on

作者： S Saraswathy J. Malathi N. Subiksha S. LakshmiNarayanan K.Sathish Kumar L. Livinesh Department of Computer Science and Business Systems Sri Sai Ram Engineering College Chennai Tamilnadu India Department of Computer science and Engineering(5 Years Integrated) Sri Sai Ram Engineering college Chennai India

Efficient cloud resource management is vital, as it ensures the accurate selection and allocation of resources to diverse workloads or applications. This entails real-time balancing of workload performance, compliance, and cost to achieve optimization. Since there are many cloud users involved in scheduling tasks, or cloudlets, the scheduling process becomes complex. It involves selecting appropriate data centers, servers (hosts), and virtual machines (VMs). There are several bioinspired algorithms that are effective and popular for solving the NP-complete problem of cloudlet scheduling. By using these algorithms, cloudlets can be efficiently allocated to achieve faster execution times, better resource utilization, and a shorter waiting time. This study presents a hybrid technique that combines the strengths of both ant colony optimization and locust-inspired algorithm (HACO-LA), which have been shown to outperform other bio-inspired algorithms in cloud computing environments. Implementing this technique will lead to a decrease in the average response time, while also increasing the utilization of VMs and servers. The method was evaluated using the Cloud Sim toolbox with authentic data. It was compared to the preexisting Artificial Bee Colony Optimization (ABC) algorithm and Particle Swarm Optimization (PSO) algorithm. The findings demonstrated that HACO-LA surpassed both methods, leading to notable enhancements in server utilization, increased reliability, and decreased average response time.

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Traction and Charging Systems for an Electric Motorcycle 3rd

Traction and Charging Systems for an Electric Motorcycle

引用

3rd EAI international conference on Sustainable Energy for Smart Cities (SESC)

作者： Carvalho, Jorge Sousa, Tiago J. C. Pedrosa, Delfim Univ Minho Ctr ALGORITMI Campus Azurem Guimaraes Portugal

ISBN: (纸本)9783030970277;9783030970260

Low energy consumption vehicles such as Electric Motorcycles (EMs) are a very viable solution to reduce energy consumption in the transportation sector. Due to their low power and weight, EMs have high energy efficiency and are optimized for urban transit. In this context, it becomes necessary to develop systems prototypes for any type of Electric Vehicles (EVs). Therefore, the focus of this paper is the implementation of traction and charging systems for an EM. The traction system is composed by a DC motor and a power converter that operates the motor. The power converter control allows the motor to operate in different modes. Besides, the traction system's input is a hand accelerator/brake that can control the motor speed/torque. The charging system acts as an interface between the power grid and the motorcycle system. With this, the first stage of the charger is AC-DC rectification that, besides regulating the DC-link voltage, should also act as a Power Factor Corrector (PFC) and consume a sinusoidal current from the power grid. The charger should also ensure the battery's safety and offer the possibility of regulating the charging rate. This paper details the development of traction and charging systems from the presentation of topologies to the computational simulations, and respective experimental tests and validation.

关键词： Battery charging Electric motorcycles Brushed DC motor Regenerative braking Torque control

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RW-STT: random walk embedded spatial-temporal transformer networks for traffic flow forecasting

RW-STT: random walk embedded spatial-temporal transformer ne...

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3rd international conference on control Theory and Applications (ICoCTA 2023)

作者： W. Kong Y. Ju J. Luo J. Wang L. Huang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China Department of Road Traffic Management Sichuan Police College Luzhou People's Republic of China School of Public Administration University of Electronic Science and Technology of China Chengdu People's Republic of China

Predicting traffic patterns is crucial for urban traffic management and planning, which predicts future traffic data based on current traffic conditions. To acquire better prediction accuracy, traffic flow forecasting requires complex dependencies in both temporal and spatial information. The spatial information is highly related to the traffic network, and how to effectively capture the relationships between graph nodes in the traffic network is crucial. To deal with this problem, we propose to utilize random walk embedding to capture the information transmission and correlations between nodes. Then, we combine random walk with transformer and propose random walk embedded spatial-temporal transformer (RW-STT) model to deal with the traffic flow forecasting problem. Experimental results on three traffic datasets demonstrate that RW-STT can capture more comprehensive correlations between spatial nodes and outperforms other baselines significantly.

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A Weighted Regular Heterogeneous-Center Triplet Loss for Visible-Infrared Person Re-identification

A Weighted Regular Heterogeneous-Center Triplet Loss for Vis...

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computer, control and Robotics (ICCCR), international conference on

作者： Ke Qi PoKun Yang Yicong Zhou Yutao Qi School of Computer Science and Cyber Engineer Guangzhou University Guangzhou China School of Computer and Information Science University of Macau Macau China School of Computer and Information Engineering Guangzhou Huali College Guangzhou China

Cross-modal person re-identification (Re-ID) compensates for the supervision of suspicious persons under dark conditions and has important significance in real life. Existing related studies based on triplet loss adopt either too strong or too weak constraints to aggregate features, so in order to further improve accurate person Re-ID, this paper proposes a weighted regular heterogeneous-center loss, which adopts a weighted approach to adaptively keep the centers of person features of different identities away from each other while pulling the centers of same identities of different modalities closer. Besides, this paper also proposes a network for adaptive extraction of local features, which adaptively extracts local features through an attention mechanism, and then employs a combination of global features and local features to effectively enhance the robustness of person features and improve the detection accuracy of person Re-ID. Experiments on two publicly available cross-modal person re-identification datasets SYSU-MM01 and RegDB in this paper demonstrate the effectiveness of the proposed method, which achieves comparable or leading detection results compared with current mainstream algorithms.

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Examining the Role of Error control in ATM Networks

Examining the Role of Error Control in ATM Networks

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Advancement in Technology (ICONAT), international conference for

作者： Sachin Goswami Shrinivas Patil Dhananjay Kumar Yadav Department of Management Sanskriti University Mathura Uttar Pradesh India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

This abstract examines the role of mistake manipulation in asynchronous transfer mode (ATM) networks. Errors management is an error correction that ensures dependable fact transfers in computer networks, and that is especially essential in ATM networks that require the transfer of large quantities of records at high speeds. To better understand the role of error control in ATM networks, this abstract will pay attention to two primary areas: blunder correction protocols and error control mechanisms. Mistake correction protocols assist in sure the accuracy of facts by detecting and correcting errors. Blunder control mechanisms are used to reduce or cast off error propagation, and this is done via the usage of float management, retransmission, and buffer management strategies. Via a theoretical and empirical examination of error control in ATM networks, this abstract will offer a better understanding of the role that blunder manipulation plays in ensuring dependable and efficient fact transfers.

关键词： Accuracy computer network reliability Reliability engineering computer networks Error correction Complexity theory Asynchronous transfer mode

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A Comparative Analysis of Convolutional Neural Network Architectures for Coffee Leaf Rust Detection

A Comparative Analysis of Convolutional Neural Network Archi...

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2023 IEEE international conference on Automatic control and Intelligent Systems, I2CACIS 2023

作者： Lelis, Andrea Kristine Ferriols, Emanuel Gethresito I. A.vallesteros, Kim Marcial Delmo, Jen Aldwayne B. Batangas State University Computer Engineering Program Calamba City Philippines

ISBN: (纸本)9798350321302

Coffee plays a cultural part in the lives of people. Eight out of ten adults in the Philippines drink an average of 2.5 cups of coffee per day, and nine out of ten households have coffee in their pantries. In 2021, it is reported that coffee will be an approximately PHP 3.0 billion (USD 5 million) industry in the country. As such, much effort is placed into sustaining its economic production. Botanical studies reveal that some factors severely affect the quality of coffee plants. One of them is coffee leaf rust which is caused by the fungus known asHemileia vastatrix and affects many coffee-growing *** not detected early enough it can greatly reduce coffee yield or worse wipe out an entire plantation affecting the farmers. This study proposes a methodology for detecting coffee leaf rust disease using computer vision and employs multiple pre-trained architectures of Convolutional Neural Networks (CNNs). Specifically, this study used twenty-five convolutional neural networks, and the best-performing models are ResNet101V2, InceptionV3, ResNet50V2, Xception, and DenseNet169. They have a training accuracy of 92.62%, 96.90%, 93.45%, 98.45%, and 96.79% respectively. Meanwhile, their validation accuracy is 91.67%, 90%, 94.44%, 93.89%, and 95.56% respectively. Out of the top five CNNs, ResNet101V2 achieved the highest test accuracy with 95.56% and it also excels in other evaluation metrics such as Precision, Recall, and F1 - score. Although this study used a variety of CNNs, it is also recommended to use more types of algorithms as well as increase the number of epochs. Future studies can also consider the detection of other coffee diseases and a larger dataset for a wider scope. Overall, the result of this study is a step closer to achieving improved coffee production and the livelihoods of farmers. © 2023 IEEE.

关键词： Convolutional neural networks

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Images super-resolution using improved generative adversarial networks 3

Images super-resolution using improved generative adversaria...

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3rd international conference on Intelligent control, Measurement and Signal Processing and Intelligent Oil Field, ICMSP 2021

作者： Hu, Yuliang Jing, Mingli Jiao, Yao Sun, Kun Xi'An Shiyou University School of Electrical Engineering Xi'an China Shaanxi Provincial Key Lab of Oil and Gas Well Measurement and Control Technology Xi’an China

ISBN: (纸本)9781665437158

Image super-resolution (ISR) is an important image processing technology to improve image resolution in computer vision tasks. The purpose of this paper is to study the super-resolution reconstruction of single image based on the depth learning method. Aiming at the problem that the existing pixel loss-based super-resolution image reconstruction algorithms have poor reconstruction effect on high-frequency details, such as textures, a lighter algorithm is proposed on the basis of the existing deep learning method (SRGAN). Firstly, the feedback structure is applied in the generator to process the feedback information and enhance the high frequency information of the image. Secondly, a general residual feature aggregation framework (RFA), is applied to make full use of the residual information of each layer to improve the quality of the SR image. Finally, the solution space of the function is further reduced and the image quality is improved by using a new loss function. The algorithms are implemented on pytorch framework. The experimental results on VOC2012 data sets show that, compared with the original SRGAN algorithm, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed algorithm on the benchmark data set Set5 are improved by 0.83dB and 0.028, respectively, on Set14, the PSNR and SSIM of the proposed algorithm are improved by 0.56dB and 0.009, on Urban100, the PSNR and SSIM of the proposed algorithm are improved by 0.51dB and 0.031, on BSD100, the PSNR and SSIM of the proposed algorithm are improved by 0.33dB and 0.014, and compared with other improved algorithms, the effect of this algorithm is also better than other algorithms. © 2021 IEEE.

关键词： Generative adversarial networks

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Application Analysis of FlexE Technology in the New Type of Power System

Application Analysis of FlexE Technology in the New Type of ...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Shengqi Qian Zhiyu Chen Junhua Huang Jinping Cao Yongyuan He Wenjing Liu Hairui Xing Xin Jin State Grid Information & Telecommunication Branch Beijing China

With the explosive growth of various devices, terminals and services, communication networks are in urgent need of security, reliability, flexible access and real-time interaction. Meanwhile, the power communication network extends to the low-voltage side, which needs to analyze a lot of interactive information to realize the perception, prediction and control of the system operation state. The traditional network model has certain deficiencies in taking into account the economy, efficiency and performance. This paper studies and analyzes the application of FlexE (Flexible Ethernet) technology in new type power systems to adapt to services with different granularities and performance requirements, realize unified access of various services, and promote the deep integration and adaptation of FlexE technology to the new type of power system.

关键词： computer network reliability Systems operation Ethernet Real-time systems Power system reliability Safety Communication networks

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Action Recognition from Videos using Multi-Feature Trained Improved LinkNet Model

Action Recognition from Videos using Multi-Feature Trained I...

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3rd international conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Faridi, Arman Rasool Wani, Mohd Hanief Masood, Faraz Zaid, Mohd. Aligarh Muslim University Uttar Pradesh Aligarh202002 India

Video action recognition is a key application of computer vision (CV). Using data from prior observations, its primary objective is to accurately describe human behavior and interactions. The ability to recognize, understand, and predict complex human behaviors advances a number of important applications. In recent years, the CV community has given deep learning (DL) particular attention. Thus, utilizing DL technology, this work suggests a novel action recognition model in videos. First, the median filter model is used to pre-process the input frames following the video-to-frame conversion. Features such as the Median Binary Pattern (MBP) and Motion Boundary Scale-Invariant Feature Transform (MoBSIFT) are then extracted. Improved Link Net, which has better activation and loss functions, is presented for action detection after feature extraction. For TD=90%, the enhanced link net achieves a higher value of 0.9590. However, the accuracy results for LinkNet, DenseNet, LeNet, ResNet, SqueezeNet, and LSTM were lower, at 0.87329, 0.8795, 0.8708, 0.8944, 0.8795, and 0.884, respectively. © 2025 The Author(s).

关键词： computer vision

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