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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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State Monitoring Method of Optical Cable Combined with computer Multi-Information Sensing Fusion Technology 23

State Monitoring Method of Optical Cable Combined with Compu...

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proceedings of the 3rd international conference on Electronic Information Technology and Smart Agriculture

作者： Yilong Chen Jingkai Zhu Zhong Gan Chen Feng State Grid Shanghai Municipal Electric Power Company China

ISBN: (纸本)9798400716775

In order to improve the real-time monitoring effect of optical cable state, this paper combines computer multi-information sensing fusion technology to monitor optical cable state in real time, and develops a technology based on computing network convergence, which integrates communication network facilities with heterogeneous computing facilities, and uniformly arranges and controls various resources such as data, calculation and network, so as to improve the speed and efficiency of information dissemination. Under the control of computer program, the optical cable monitoring system can automatically realize the roll call test, periodic test and alarm test of optical cable. Moreover, it can compare the results with standard data in the optical cable characteristic library to find the occurrence of faults and the location of fault points. In order to verify the effectiveness of emerging technologies based on computing network convergence in solving information technology problems, simulation methods and experimental design are used to verify and evaluate. Through the analysis of the experimental results, we can see that the method proposed in this paper has some advantages compared with the traditional optical cable state monitoring methods.

关键词： Multi-information sensing fusion optical fiber cable signal monitoring state monitoring

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A Comparative Study of Power Electronics and control Techniques for Renewable Energy Integration in Smart Grids

A Comparative Study of Power Electronics and Control Techniq...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Shashank Pareek Yogendra Kumar Harpreet Kaur Raman Kumar Jasgurpreet Singh Chohan Department of Computer Science and Engineering Chandigarh University Mohali India Department of Electrical Engineering GLA University Mathura India Computer Science and Engineering Department Gulzar Group of Institutes Khanna India Department of Mechanical Engineering Chandigarh University Mohali India University Centre for Research and Development Chandigarh University Mohali India

The development of smart grid technology for more effective and sustainable energy management has been prompted by the growing use of renewable energy sources like solar and wind power. In order to integrate renewable energy sources into the grid, power electronics and control approaches are essential. The purpose of this study is to examine the pros and cons of the various power electronics and control approaches now in use for integrating renewable energy sources into smart grids. The development of smart grid technology for more effective and sustainable energy management has been facilitated by the use of renewable energy sources like solar and wind power. When it comes to incorporating renewable energy sources into the grid, power electronics and control approaches play a crucial role. This article provides a comprehensive analysis of the literature on the topic of renewable energy integration in smart grids, with a particular focus on power electronics and control strategies. The study is based on a review of relevant literature and an analysis of the benefits and drawbacks of each method. Efficiency, dependability, cost-effectiveness, scalability, and compatibility with various renewable energy sources are some of the characteristics included in the comparison study. The research shows that each method has advantages and disadvantages, and that the choice of method is context-specific. The work has significant significance for the field of electrical and computer engineering and aids in the development of more efficient and sustainable energy management systems for the future smart grid.

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A Frequency control Strategy of Large Grid with Energy Storage Based on Multi-Agent Algorithm

A Frequency Control Strategy of Large Grid with Energy Stora...

引用

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

作者： Mujie Zhang Dejun Shao Xiaojie Pan Junbo Fu Yukun Wang Jun Yang Centralchina Branch of State Grid Wuhan China NR Electric Co. LTD. Nanjing China School of Electrical Engineering and Automation Wuhan University Wuhan China

Under the "dual carbon" strategic goal, the development of the dual-high power system is accelerating, and the power grid regulation capacity is constantly declining, and more flexible control resources are urgently needed. Electrochemical energy storage can be used as a good control resource and can adapt to different time dimensions. Its widespread use and coordinated, optimal operation with "source, network, load" and other regulating resources can significantly increase the system's capacity for stable operation. Therefore, a multi-agent algorithm-based frequency control technique for large power grids with energy storage is suggested in this study. Firstly, a frequency control architecture for a large power grid that includes thermal power, renewable energy, and large-scale energy storage power stations is created based on the frequency control model of large-scale energy storage units that takes the randomness of consumers into account. Second, a grid frequency controller structure with energy storage participation is suggested based on the MA-DDPG algorithm, and the state space, action space, and reward function of the agent are constructed in accordance with the actual control requirements of the power grid. Finally, the simulation results demonstrate the high efficiency of the multi-agent coordinated control technology, which is well suited to the erratic properties of energy storage clusters and new energy units. This technology also significantly enhances the stability and robustness of system frequency control.

关键词： Space power stations Clustering algorithms Power system stability Aerospace electronics Power grids Stability analysis Robustness

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COVID-19 and Pneumonia Detection using Hybrid VGG-16 model using Chest X-rays 3

COVID-19 and Pneumonia Detection using Hybrid VGG-16 model u...

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3rd international conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022

作者： Mathew, Tobin P. Surya, S. Perumon Kerala Technical University Computer and Information Science College of Engineering Kerala India

ISBN: (纸本)9781665499361

The time frame of 2020 to present day 2022 primarily highlights the COVID-19 pandemic. The humanity is being largely affected by SARS-CoV-2(The Severe Acute Respiratory Syndrome CoronaVirus 2) because of its highly infectious characteristic which can be even fatal in severe cases. The World Health Organization (WHO), have reported over 544.3 million verified cases of COVID-19 globally till date, including over 6.3 million deaths. The reason why SARS-CoV-2 is considered to be a dangerous illness is due to this relatively high mortality and contagious rates, in addition to asymptomatic individuals also being carriers of the virus. The only way to identify susceptible populations and to attempt to control the spread would be via RT-PCR COVID testing of all individuals, which is time consuming and expensive. The challenges of this testing mechanism and the prolonging end of the pandemic are the primary motivation to bring up an effective system over a large test cases with a reduced time constraints. This paper proposes a combination of the pretrained convolutional neural network, VGG-16(Visual Geometry Group-16) and GRU(Gated Recurrent Unit) to differentiate the Pneumonia and COVID-19 attack from chest X-rays(CXRs). The proposed model employs VGG-16 to extract features from the CXR inputs, and the GRU classifies it. We experimented this model over 6939 CXR images with 3 classes (COVID-19, Pneumonia, and Normal) and the training produced encouraging macro average precision, recall, and f1-score of 0.9525, 0.9524, and 0.9524 respectively. These results indicate hybrid deep learning systems can greatly aid in the early detection of COVID-19 using CXRs and thereby reduce the widespread of the pandemic. We believe that early diagnosis can be easily and effectively done using this model. © 2022 IEEE.

关键词： COVID-19

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Blockchain Based File Storage and File Sharing System with Time-Restricted Access Using InterPlanetary File System

Blockchain Based File Storage and File Sharing System with T...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： M. Venkat Prashanth K. Hemesh Verma Jaykishor Prasad Chauhan Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This paper presents a file storage and sharing system implemented using IPFS Pinata, *** and Hardhat which offer decentralized, effective, and secure access to digital assets. This system also enables the ability to upload files to the IPFS network for distributed storage and the ability to share them with peers. It also implements a time based access control system using smart contracts to easily control access to the files uploaded. This innovation improves security and facilitates timely exchange of information while also eradicating the necessity of having to manage access. This is due to the versatility of blockchain's unalterable nature together with IPFS distributed file-sharing system which provides a secure file-sharing method while keeping costs low and helps maintain adaptability.

关键词： Access control Technological innovation Costs Smart contracts Blockchains InterPlanetary File System Internet of Things Data communication

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Study on inertia based MPPT control using reinforcement learning algorithm for extra power production

Study on inertia based MPPT control using reinforcement lear...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Nyam Jargalsaikhan Sergelen Byambaa Furukakoi Masahiro Senjyu Tomonobu Grad. School of Engineering and Science University of the Ryukyus Okinawa Japan Department of Electrical Engineering Mongolian University of Science and Technology Ulaanbaatar Mongolia National Institute of Technology Sasebo College Nagasaki Japan Faculty of Engineering University of the Ryukyus Okinawa Japan

Permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) also known as Type 4-WECS has many advantages over other types of WECS. One of the advantages is full variable speed operation for low wind speed. With the MPPT control, most wind turbines try to follow optimal rotational speed for maximum possible power capture. However, due to rotor inertia, tracking loss is inevitable, which causes decreased power production. This study investigates the use of inertia in the rotational masses of the WT for increased power production. The idea is to use the rotor inertia of the large-scale wind turbine (VSWT) to temporarily retain the rotational speed at a higher value for a short time rather than following optimal rotational speed in a variable speed region. As a result, the wind turbine can capture more power. The optimal tip speed ratio (TSR) method is used as the main MPPT control method, and additional Reinforcement Learning (RL) algorithm-based reference rotational readjustment was used for the purpose mentioned above. The simulation studies have been conducted in Matlab/Simulink ® software and it showed that power production of wind turbines has increased slightly than the standard method.

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A Comprehensive Analysis of Object Tracking in UAVs using computer vision

A Comprehensive Analysis of Object Tracking in UAVs using Co...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Ilavendhan A Bhenedix Paul Joshnavi Pokala School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Object tracking has proven to be an essential in a wide range of applications and unmanned aerial vehicles (UAVs) provide advantages to this field in accurately observing and tracking moving targets in challenging environments. This paper presents an overview of various object tracking methodologies which can be utilized on UAVs. It discusses different approaches to attain an improved method of UAVs to help track moving objects accurately, even under challenging conditions such as occlusions or changes in object size and speed. This research study highlights the combinations of traditional tracking techniques infused with modern machine learning techniques to help explore advancements and opportunities of performance improvement. Key topics discussed are the use of algorithms that enable prediction of location of objects, techniques for tracking even small objects in dynamic environments and possibilities of UAVs cooperating with ground vehicles to improve tracking accuracy by sharing data. Overall, the paper discusses the importance of developing more efficient and reliable tracking systems which have applications in various fields such as surveillance, search and rescue operations.

关键词： Renewable energy sources Target tracking Machine learning algorithms Heuristic algorithms Surveillance Machine learning Prediction algorithms Object tracking Reliability Vehicle dynamics

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3rd international conference on Adaptive Instructional Systems, AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

3rd International Conference on Adaptive Instructional Syste...

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3rd international conference on Adaptive Instructional Systems, AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

ISBN: (纸本)9783030778729

The proceedings contain 62 papers. The special focus in this conference is on Adaptive Instructional Systems. The topics include: Dynamic Analytics for the Detection of Quality and Alignment Issues in an Online Hybrid Adaptive Instructional and Assessment System;enhancing Personalization by Integrating Top-Down and Bottom-Up Approaches to Learner Modeling;measuring and Integrating Facial Expressions and Head Pose as Indicators of Engagement and Affect in Tutoring Systems;dynamic Cognitive Modeling for Adaptive Serious Games;using Principled Assessment Design and Item Difficulty Modeling to Connect Hybrid Adaptive Instructional and Assessment Systems: Proof of Concept;early Prediction of At-Risk Students in a Virtual Learning Environment Using Deep Learning Techniques;predicting Literacy Skills via Stealth Assessment in a Simple Vocabulary Game;measuring Cognitive Load for Adaptive Instructional Systems by Using a Pressure Sensitive computer Mouse;designing a Learner Model for Use in Training Analysts in a Social Media Practice Environment;helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations;helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations;individual Differences in the Relationship Between Emotion and Performance in Command-and-control Environments;staying Ahead of the Curve: Selecting Students for Newly Arising Tasks;an Experiential Competency Application Framework;competency-Based Experiential-Expertise and Future Adaptive Learning Systems.

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3rd international conference on Adaptive Instructional Systems, AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

3rd International Conference on Adaptive Instructional Syste...

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3rd international conference on Adaptive Instructional Systems, AIS 2021, Held as Part of the 23rd HCI international conference, HCII 2021

ISBN: (纸本)9783030778569

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Motion Segmentation Based on Pixel Distribution Learning on Unseen Videos 3rd

Motion Segmentation Based on Pixel Distribution Learning on...

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3rd international conference on Smart Multimedia, ICSM 2022

作者： Lu, Zhiyuan Chen, Youwei Zhao, Chenqiu Department of Computing Science University of Alberta Edmonton ABT6G2R3 Canada

ISBN: (纸本)9783031220609

Motion segmentation plays an important role in many applications including autonomous driving, computer vision and robotics. Previous works mainly focus on segmenting objects from seen videos. In this paper, we present a novel approach based on pixel distribution learning for motion segmentation in unseen videos. In particular, optical flow is extracted from consecutive frames to describe motion information. We then randomly permute these modified motion features, which are used as input of a convolution neural network. The random permutation process forces the network to learn the pixels’ distributions rather than local pattern information. Consequently, the proposed approach has a favorable generalization capacity and can be applied for unseen videos. In contrast to previous approaches based on deep learning, the training videos and testing videos of our proposed approach are completely different. Experiments based on videos from the KITTI-MOD dataset demonstrates that the proposed approach achieves promising results and shows potential for better motion segmentation on unseen videos. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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