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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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3rd international conference on Industrial Applications of Big Data and Artificial Intelligence, BDAI 2021

3rd International Conference on Industrial Applications of B...

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3rd international conference on Industrial Applications of Big Data and Artificial Intelligence, BDAI 2021

The proceedings contain 18 papers. The topics discussed include: latent topic analysis of the post property for sales to predict a selling price of second-hand condominiums;self-amplificated network: learning fine-grained learner with few samples;recognition method of pipeline damage degree based on one-dimensional convolutional neural network;rainfallnet: a dual-source of spatial-channel attention fusion network for precipitation nowcasting;an intra-pulse modulation type recognition algorithm for radar signals based on the improved residual network;design of intelligent security management and control system for a large nuclear facility;design of fault diagnosis algorithm for electromechanical system based on artificial intelligence;deep reinforcement learning for stock recommendation;the reform scheme of fundamentals of computer culture based on multi-level blended teaching;and workspace analysis of spray painting robot with two working modes for large ship blocks in ship manufacturing.

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Increasing Efficiency and Capacity Expansion and computer Monitoring Reformation of Aibu III Hydropower Station 3

Increasing Efficiency and Capacity Expansion and Computer Mo...

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2022 3rd international conference on Mechanical Engineering and Materials, ICMEM 2022

作者： Hu, Huali Yang, Hongfeng Chen, Bingsen Tang, Zhengquan Liu, Qibo Guangxi Vocational College of Water Resources and Electric Power Guangxi Nanning530023 China Nanning Jingneng Power Generation Equipment Co. Ltd. Guangxi Nanning530031 China

The main problems and potential safety hazards of Aibu III Hydropower Station after extended service are analyzed. In view of the serious waste of hydraulic resources and low equipment efficiency of the power station, the expansion and reconstruction scheme of hydraulic turbine generator units is proposed, and the supporting design and implementation are carried out from all aspects of power station buildings, electrical engineering, calculation monitoring system, etc., which makes full use of hydraulic resources and completely solves the potential safety hazards of the power station, It has received good social and economic benefits. For the hydropower station after technical transformation, the operator on duty can monitor and control the operation status of main electromechanical equipment such as water turbine generator units, main transformers, transmission lines, high-voltage switches, station power, intake gates, unit main valves, and auxiliary equipment of the power station on the operator workstation in the central control room of the power station. © Published under licence by IOP Publishing Ltd.

关键词： Hydroelectric power plants

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Policy Iteration Based Microgrid Frequency control

Policy Iteration Based Microgrid Frequency Control

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Power Systems and Electrical Technology (PSET), IEEE international conference on

作者： Byungchul Kim Eyad H. Abed Department of Electrical and Computer Engineering University of Maryland College Park USA

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Microgrid inverter control shall be more efficient if the systems are controlled without system parameters owing to influenced by disturbances from other systems. We concentrate on solving optimal nonlinear input affine system via reinforcement learning. We express input-to-state stability (ISS) and fixed admissibility (FA) of the input affine system with the policies created by integral policy iteration (IPI). However, previously mentioned approaches need the system parameters to control the systems. Due to these constraints, on account of three online explorized IPI and integral Q-learning A, B are suggested, all of them create the same convergent succession within the vital excitation status on the exploration. All the suggested approaches are fully or partially model free and can concurrently explore the state-space in a stable way during the online learning sequences. Finally, we simulate the proposed approaches within microgrid inverter fault ride through on transient stability to verify performance.

关键词： Q-learning Voltage source inverters Simulation Optimal control Microgrids Power system stability Stability analysis Space exploration Transient analysis Frequency control

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Query-Driven Video Summarization for Long Video Footage Analysis Using Faster-RCNN and Determinantal Point Processes

Query-Driven Video Summarization for Long Video Footage Anal...

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

作者： Bhute, Maitrey M. Tare, Sanskar S. Sridhar Raj, S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

With ever growing volume of video content, video summarization becomes crucial for efficiently condensing lengthy videos into informative representations. This paper introduces a novel approach for video summarization by combining object identification with Determinantal Point Processes (DPP). Object identification, powered by advanced computer vision techniques, tracks and recognizes objects across video frames (Convolutional Neural Network/computer vision), allowing for identification and prioritization of frames with key objects and respective interactions. The proposed algorithm leverages DPP and R-CNN concurrently to select frames while avoiding redundancy and preserving diversity Extensive experiments using diverse video datasets demonstrates that this method performs competitively with existing summarization techniques while tested on TVSum dataset [12], a popular baseline. The results highlight the efficacy of the proposed algorithm in striking a balance between information retention and summary length. It provides accuracy of 84.34% and recall of 13.09%. This research contributes to the field of multimedia content analysis, with potential applications in video indexing, retrieval, and content recommendation systems. Through integration of object identification and DPP, this work introduces an innovative dimension to video summarization. The proposed approach holds promise for generating more informative and visually coherent video summaries. This work holds the potential to advance the state-of-the-art in video summarization techniques, benefiting various multimedia applications by aiding content analysis and user experience. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Object detection

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Intelligent Safe Driving Assistance System Based On Improved YOLOv5

Intelligent Safe Driving Assistance System Based On Improved...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Peiyang Ni Chaoyun Qiu Xiaojie Li Ruixuan Liu Jingwen Wu Junlin Guan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

The car accident caused by driver's violation behaviors has always been one of the biggest dangers that threaten the safety of human life and property. In order to reduce the occurrence of car accidents, vehicle-mounted assistance driving systems have emerged as the times require. However, the cost of assistance driving systems in the current market is relatively high, and they only appear in high-end car brands, leaving gaps in the mid-to-low-end market. Starting from the low cost and reliability, this paper designs a set of assistance driving system to intelligently detect and warn the driver's violation behavior. The vision algorithm of the system is YOLOv5 optimized through attention mechanism and edge detection, and it's deployed on Jetson Xavier NX to ensure data security and model robustness. The algorithm combines hardware equipment, client, back-end server and Hadoop big data technology to realize the functions of drunk driving detection, intelligent monitoring of driver behavior and early warning. The solution proposed in this paper uses self-optimized open-source vision algorithms, and the main expenses come from the procurement, development of hardware equipment and cloud service. The cost is low, and it provides the possibility for the introduction of assisted driving systems in the low-end automobile market. After actual testing, the system is low in cost, easy to maintain, and at the same time meets the requirements of accuracy and real-time performance, ensuring the safety of drivers and passengers during driving.

关键词： Costs Hardware Robustness Behavioral sciences Safety Automobiles Servers

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Internet of Things: Security Mechanisms 3rd

Internet of Things: Security Mechanisms

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3rd international conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Sultana, Syeda Sabah Sangeetha, J. Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bengaluru560054 India

ISBN: (纸本)9789811688614

The Internet of Things (IoT) is changing cities industries and homes. Each of these benefits, however, comes with an enormous risk of security issues and privacy loss. One of the problems is to operate the IoT devices safely and securely, because the complexity increases with the increased number of IoT devices. This expanded complexity makes new security challenges such as safety, privacy, and usability far beyond the difficulties that individual face to provide security to an individual device. This paper highlights security aspects, challenges, importance of security at different layers in IoT, and the existing solutions. Also, different approaches proposed to address security and privacy issues concerning security requirements: authentication, confidentiality, and access control are discussed and analyzed explicitly in the realm of the Internet of Things. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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An IoT and Information Mining Algorithm-Based Self-Monitoring and Analysis System for Solar Power Plants

An IoT and Information Mining Algorithm-Based Self-Monitorin...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ali Hadi Abdulwahid Kruti Jani Navdeep Dhaliwal Rajesh Singh A. Bhasha Jayapal Lande Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq Shri Chimanbhai Patel Post Graduate Institute of Computer Application Ahmedabad Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University India Dept of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

Because of their affordability and sustainability, renewable sources of energy are receiving a lot of study interest. Particularly, solar power plants are regarded as a type of renewable sources of energy that may be employed in many areas since, while requiring less space than traditional systems, they demand less money for installation and maintenance. The majority of small generating units control space occupancy by putting the machinery on an exposed terrace. However, acres of land is needed for the building of huge power producing stations. It is difficult for human workers to maintain such a huge region of a power plant. The suggested algorithm would assist human workers in identifying the consistency of energy production and failure or faulty areas in solar energy systems using IoT and database mining (DM) techniques. This enables immediate fault repair action to be taken, which increases producing station effectiveness.

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Apple Leaf Disease Detection and Classification Using L1 Regularized Multi Head vision Transformer

Apple Leaf Disease Detection and Classification Using L1 Reg...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Gayatri Math Vishwanath P Zayd Ajsan Balsem Supriya Suvendu Kumar Jena Department of Electronics and Communication Engineering PDA College of Engineering Kalaburagi India Affiliated to VTU Belagavi India Department of Electronics and Communication Engineering HKE Society's Sir M Visvesvaraya college of Engineering Raichur India Affiliated to VTU Belagavi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Information Science and Engineering Nitte Meenakshi Institute of technology Bengaluru India Department of computer science engineering and applications Indira Gandhi Institute of technology Sarang Dhenkanal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Timely identification of apple leaf diseases is important to avoid disease spread and ensure healthy growth of apple industry. Recent developments in digital cameras and electronic devices integrated with Machine Learning (ML) and Deep Learning (DL) based apple leaf disease detection has emerged as efficient to traditional visual inspection models. Therefore, this research proposes a L1 regularized Multi Head vision Transformer (L1-MHViT) for apple leaf disease detection and classification. The L1-MHViT utilized multi-head attention to capture complex patterns and improve classification performance. Furthermore, it incorporates advantages of convolution-based and transformer-based models to enhance the detection and classification accuracy. Furthermore, the Median Filter (MF) is applied for preprocessing which removes noise and enhance the image quality thereby helps to enhance the accuracy of the consequent steps. Then, EfficientNet is applied for feature extraction which is known for higher accuracy and effective scaling of model size. The effectiveness assessment of proposed L1-MHViT uses standard metrics which include precision (Pre), recall (Rec), F1 score (F1) and accuracy (Acc). The L1-MHViT achieved 99.46% pre, 99.54% rec, 99.50% F1 and 99.82% are for Apple dataset which is better than baseline models.

关键词： Image quality computer vision Accuracy Head Noise Transformers Feature extraction Magnetic heads Integrated circuit modeling Diseases

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Leveraging towards Multi-objective IoT Application Placement in Fog Computing Environment

Leveraging towards Multi-objective IoT Application Placement...

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Smart Technologies for Power, Energy and control (STPEC), IEEE international conference on

作者： Hemant Kumar Apat Pranati Sattarapu Rudra Narayan Dash Veena Goswami Sagarika Mohanty Rabindra K. Barik School of Computer Application KIIT University Bhubaneswar India School of Technology Woxsen University Hyderabad India School of Electrical Engineering KIIT University Bhubaneswar India Department of CSE National Institute of Technology Rourkela India

Internet of Things (IoT) is one of the most talked-about innovations of recent years because it paves the way for novel interactions between devices and people that can improve daily life. Interconnected devices are generating an unprecedented amount of data due to the growth of IoT devices. When it comes to processing the data of IoT applications, a new paradigm known as fog computing is developing as an intriguing alternative. To leverage the maximum benefit optimal deployment of services into the fog computing devices has paramount importance. To find the optimal placement of services into the heterogeneous fog computing is a NP complete problem. In the present work, we implement genetic algorithm with Multi-objective IoT applications placement to minimise the three important parameters i.e. makespan, energy and cost. It also presents a comparison with the placement of single objective IoT applications. The findings indicate that employing Multi-objective placement approaches yields greater benefits in comparison to utilising a single objective placement strategy.

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Deep Learning Framework Based on Audio–Visual Features for Video Summarization 1

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3rd international conference on Machine Intelligence and Signal Processing, MISP 2021

作者： Rhevanth, M. Ahmed, Rashad Shah, Vithik Mohan, Biju R. Department of Information Technology National Institute of Technology Karnataka Surathkal Mangalore575025 India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

The techniques of video summarization (VS) has garnered immense interests in current generation leading to enormous applications in different computer vision domains, such as video extraction, image captioning, indexing, and browsing. By the addition of high-quality features and clusters to pick representative visual elements, conventional VS studies often aim at the success of the VS algorithms. Many of the existing VS mechanisms only take into consideration the visual aspect of the video input, thereby ignoring the influence of audio features in the generated summary. To cope with such issues, we propose an efficient video summarization technique that processes both visual and audio content while extracting key frames from the raw video input. Structural similarity index is used to check similarity between the frames, while mel-frequency cepstral coefficient (MFCC) helps in extracting features from the corresponding audio signals. By combining the previous two features, the redundant frames of the video are removed. The resultant key frames are refined using a deep convolution neural network (CNN) model to retrieve a list of candidate key frames which finally constitute the summarization of the data. The proposed system is experimented on video datasets from YouTube that contain events within them which helps in better understanding the video summary. Experimental observations indicate that with the inclusion of audio features and an efficient refinement technique, followed by an optimization function, provides better summary results as compared to standard VS techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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