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检索条件"任意字段=International Conference on Advanced Computer Control"

共 19837 条记录，以下是81-90 订阅

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Modeling of Disturbance Rejection in Combined Feedwater control Systems for Steam Generators in Nuclear Power Plants 13

Modeling of Disturbance Rejection in Combined Feedwater Cont...

引用

13th international conference on advanced computer Information Technologies, ACIT 2023

作者： Lyubchyk, Leonid Dorofieiev, Yuri Malko, Maxim National Technical University Department of Computer Mathematics and Data Analysis Kharkiv Polytechnic Institute Kharkiv Ukraine National Technical University Kharkiv Polytechnic Institute Department of Systems Analysis and Information-Analytical Technologies Kharkiv Ukraine

The problem of advanced control systems modeling for feedwater-level control in steam generators of nuclear power plants is considered. To suppress disturbances caused by changes in reactor thermal power and steam flow to the turbine, the control system is built according to the combined principle and includes a dynamic disturbance compensator. Compensator design is carried out on the basis of inverse model control theory. Specialized software for modeling a feedwater control system has been developed, which allows feedback controllers and disturbance compensators to tune and study disturbances' influence on control process performance. The results of the simulation of the effect of technological disturbances on the deviations of the water level in the steam generator and the pressure at the control feed valve and assessing the degree of their suppression are presented. © 2023 IEEE.

关键词： Nuclear power plants

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Artificial Neural Network to Estimate Deterministic Indices in control Loop Performance Monitoring 15th

Artificial Neural Network to Estimate Deterministic Indices ...

引用

15th international conference on Intelligent Human-computer Interaction (IHCI)

作者： Gomez-Munera, John A. Diaz-Charris, Luis Jimenez-Cabas, Javier Corp Estudiantes & Profes Marinilla CORUM Marinilla Antioquia Colombia Univ Costa Barranquilla Atlantico Colombia

ISBN: (纸本)9783031538292;9783031538308

In many industrial processes, the control systems are the most critical components. Evaluate performance and robustness of a control loops is an important task to maintain the health of a control system and an efficiency in the process. In the area of control-Loop Performance Monitoring (CPM), there are two groups of indices to evaluate the performance of the control loops: stochastic and deterministic. Using stochastic indices, a control engineer can calculate the performance indices of a control loop with the data in normal operation and a minimum knowledge of the process;but the problem is that to do a performance analysis is so hard, due it is necessary an advanced knowledge about the interpretation. Instead, an interpretation or analysis of deterministic indices is simpler;however, the problem with this approach is that an invasive monitoring of the plant is required to calculate the indices. In this paper, it is proposed to use an Artificial Neural Network to estimate deterministic indices, considering as input the stochastic indices and some process information, taking advantage of the fact that data collection for stochastic indices is simpler.

关键词： Artificial Neural Network control Loop Performance Monitoring Deterministic Indices Stochastic Indices

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Modeling the Interaction of Unmanned Aerial Vehicles in a Swarm as an Object with Distributed Parameters 14

Modeling the Interaction of Unmanned Aerial Vehicles in a Sw...

引用

14th international conference on advanced computer Information Technologies, ACIT 2024

作者： Dyvak, Mykola Spivak, Iryna Dyvak, Taras Kindzerskyi, Oleksandr West Ukrainian National University Department of Computer Science Ternopil Ukraine

ISBN: (纸本)9798350350036

Many scientific studies using unmanned aerial vehicle swarms were considered and the principles of control and interaction between swarm elements were analyzed (the models of swarm behavior, the motion coordination model, the decision-making models). An approach to the management of unmanned aerial vehicle swarms based on an algorithm is proposed, which will allow, by changing only part of the parameters of the elements, to adjust all other elements of the swarm. The feasibility of developing a mathematical model of characteristics of unmanned aerial vehicle swarm as objects with dispersion parameters is demonstrated. The article describes the proposed approach for the synthesis of mathematical models of the characteristics of swarm elements as objects with dispersion parameters using structural-parametric analysis. The validation of the model is carried out on the basis of obtained data in the specified observation area, and the adequate prognostic features of the model are confirmed. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Advancing Wildfire Detection: Using YOLOv8 on Multifarious Images 9

Advancing Wildfire Detection: Using YOLOv8 on Multifarious I...

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9th international conference on control and Robotics Engineering (ICCRE)

作者： Haroun, Asma Tagmouni, Abdelkader Chergui, Sofia Ladlani, Randa Bechar, Amine Melchane, Selestine Kherbachi, Sonia Ecole Super Sci & Technol Informat & Numer Lab LITAN RN 75 Bejaia 06300 Algeria Univ Bejaia Dept Management LRMTQ FSECSG Bejaia 06000 Algeria

ISBN: (纸本)9798350372694;9798350372700

The escalating threat of forest fires, attributed to climate change and rising temperatures, underscores the urgency for advanced detection methods. This paper explores the effectiveness of employing the You Only Look Once object detection algorithm, specifically utilizing Bounding boxes to improve the model's performance by capturing texture variations between the fire and the background. The study aims to refine wildfire detection through the meticulous curation and annotation of a diverse image dataset. A comparative analysis of various YOLO models evaluates their performance. The dataset comprises both fire and non-fire images, showcasing diverse backgrounds to expose the model to real-world scenarios for comprehensive training. Despite the inherent challenges posed by variations in image textures and backgrounds, the models demonstrated consistency. The best-performing model achieved an mAP50 (mean Average Precision at 50% intersection over union) of 91%, while an unprecedented mAP50:95 of 68.5% further attested to the model's efficacy. These results present a significant advancement in wildfire detection capabilities.

关键词： Wildfire Detection computer Vision YOLOv8 Object Detection Bounding Box

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The Activities and Procedures on Tactical-Level Command and control with Targeting Mission and Dependence of Criteria of Relevant Information 17

The Activities and Procedures on Tactical-Level Command and ...

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17th IEEE international conference on advanced Trends in Radioelectronics, Telecommunications and computer Engineering, TCSET 2024

作者： Tymchuk, Volodymyr Lytvyn, Vasyl Mezhevikin, Yevhen Popov, Oleksandr Sashchuk, Igor Trysnyuk, Vasyl National Army Academy Scientific and organizational dept. Lviv Ukraine Lviv Polytechnic National University Information systems and networks Lviv Ukraine National Security and Defense Council of Ukraine Kyiv Ukraine Armed Forces of Ukraine Targeting dept. Kyiv Ukraine Ministry of Defense of Ukraine Educational and information dept. Kyiv Ukraine Institute of Telecommunications and Global Information Space of Ukraine Kyiv Ukraine

The paper is dedicated to data and information processing in tactical-level Command and control systems with targeting mission and dependence of criteria of relevant information to ensure the potential possibilities d... 详细信息

ISBN: (纸本)9798331520564

关键词： C4ISR Command and control data processing information consolidation intelligence reconnaissance

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Design and Research of Intelligent control System Based on New Artificial Intelligence Algorithm

Design and Research of Intelligent Control System Based on N...

引用

2024 international conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Zhang, Shunru Qilu Institute of Technology Shandong Jinan China

ISBN: (纸本)9798350383348

With the rapid development of science and technology, artificial intelligence (AI) has penetrated into every aspect of our lives. Especially in the field of intelligent control system, the application of AI algorithm is increasingly becoming the key force to promote technological innovation and industrial upgrading. Intelligent control system relies on advanced AI algorithm, which can realize highly automated and intelligent decision-making and operation, and is widely used in industrial manufacturing, smart city, medical and health and other fields. © 2024 IEEE.

关键词： computer technology Intelligent control system Internet of Things control technology new artificial intelligence algorithms PLC industrial methods The technological level of intelligent machines

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Interactive Navigation with Human in the Loop Using Reinforcement Learning 10

Interactive Navigation with Human in the Loop Using Reinforc...

引用

10th international conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Srija, N. Sindhuja, U. Bobby, M.P. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

In this document, we present a groundbreaking concept that has the potential to revolutionize human-computer interaction: 'Hands-Free Mouse control with Facial Recognition.' Our focus is on seamlessly integrating facial recognition technology with traditional mouse control systems, introducing a hands-free approach that transcends conventional input devices. The primary objective is to enhance accessibility, particularly for individuals facing mobility challenges or disabilities, by enabling users to navigate and interact with their computers through intuitive facial movements. This transformative concept prioritizes user convenience by eliminating the need for external hardware like mice or touchpads. Users can control their computers using natural facial gestures, fostering a more intuitive and user-friendly computing experience. Beyond its practical applications, this approach signifies a paradigm shift in the field of human-computer interaction, offering new possibilities for a more inclusive and accessible digital environment. To assist authors in effectively communicating their research, a concise guide and template are provided. The template covers essential sections such as the abstract, introduction, methodology, results, discussion, and conclusion. By employing this template, authors can contribute to the advancement of this transformative field, exploring the implications, challenges, and future developments of the hands-free mouse control with facial recognition concept. © 2024 IEEE.

关键词： Reinforcement learning

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Farmer's Crop Welfare System 15

Farmer's Crop Welfare System

引用

15th international conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2024

作者： Dhamodhar, G. Krishna, B. Vamsi Reddy, D. Sampath Reddy, A. Sameer Amutha, S. Ganesan, Kothai Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Virudhnagar India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Dist. Tamil Nadu India

ISBN: (纸本)9798331300579

The "Farmer's Crop Welfare System" is a technology that uses advanced data analytics and artificial intelligence to improve crop management and yield. It combines crop prediction, disease detection, and fertilizer recommendation. Crop prediction uses historical and real- time data, while disease detection uses image recognition and deep learning to identify early signs of diseases, promoting sustainable farming practices. © Grenze Scientific Society, 2024.

关键词： Livestock

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Lane and Obstacle Detection System for Autonomous Vehicles using computer Vision 5

Lane and Obstacle Detection System for Autonomous Vehicles u...

引用

5th international conference on IoT Based control Networks and Intelligent Systems, ICICNIS 2024

作者： Kamate, Shruti Sharma, Suyash Pandey, Harshvardhan Iyer, Sridhar KLE Technological University Dr. MSSCET Belagavi Department of CSE Karnataka India KLE Technological University Dr. MSSCET Belagavi Department of ECE Karnataka India

ISBN: (纸本)9798331518097

This research study proposes a multi-modal sensor fusion system combined with a hybrid Artificial Intelligence (AI) model. The data is integrated from cameras, LiDAR, and radar sensors, using an advanced deep learning architecture that efficiently processes and fuses this information. The proposed model incorporates both, traditional computer vision techniques and advanced neural networks, trained on a diverse dataset of real-world scenarios. The aim of the model is to implement an attention mechanism for focusing on critical areas of the environment, and reducing computational load. Further, the model also aims to provide a robust error-handling system to enable continuous online learning capabilities with adaptivity to new scenarios situations. The results of this study show that by combining strengths of various state-of-the-art techniques, the proposed approach creates a more comprehensive and reliable system for autonomous vehicle navigation. © 2024 IEEE.

关键词： Obstacle detectors

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Depth Estimation Algorithm Based on Transformer-Encoder and Feature Fusion 7

Depth Estimation Algorithm Based on Transformer-Encoder and ...

引用

7th international conference on advanced Algorithms and control Engineering, ICAACE 2024

作者： Xia, Linhan Liu, Junbang Wu, Tong United International College Computer Science And Technology Zhuhai China

ISBN: (纸本)9798350361445

This research presents a novel depth estimation algorithm based on a Transformer-encoder architecture, tailored for the NYU and KITTI Depth Dataset. This research adopts a transformer model, initially renowned for its success in natural language processing, to capture intricate spatial relationships in visual data for depth estimation tasks. A significant innovation of the research is the integration of a composite loss function that combines Structural Similarity Index Measure (SSIM) with Mean Squared Error (MSE). This combined loss function is designed to ensure the structural integrity of the predicted depth maps relative to the original images (via SSIM) while minimizing pixel-wise estimation errors (via MSE). This research approach addresses the challenges of over-smoothing often seen in MSE-based losses and enhances the model's ability to predict depth maps that are not only accurate but also maintain structural coherence with the input images. Through rigorous training and evaluation using the NYU Depth Dataset, the model demonstrates superior performance, marking a significant advancement in single-image depth estimation, particularly in complex indoor and traffic environments. © 2024 IEEE.

关键词： computer vision

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