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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

共 46884 条记录，以下是181-190 订阅

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Campus Navigator: Your Go-To Chatbot For Academic Advice And Resources

Campus Navigator: Your Go-To Chatbot For Academic Advice And...

引用

2024 IEEE international conference on Recent advances in Science and engineering Technology, ICRASET 2024

作者： Kavana, M.D. Tasdeeq, Areebah Poshitha, D. Hemith Kumar, R. Dhavan, K.S. Vidyavardhaka College of Engg. Dept. of Cse Mysore570002 India Vidyavardhaka College of Engg. Dept. of Computer Science and Engineering Mysore570002 India

ISBN: (纸本)9798350388602

The creation of an intelligent chatbot for the Educational Institute's website is the subject of this study. Driven by conversational AI, the chatbot aims to enhance student engagement by providing accurate and timely responses. Objectives include admission guidance, course information, troubleshooting, and continuous improvement through user feedback. The systematic methodology covers project planning, data collection, chatbot development, personalization, security, and deployment. Positioned as a strategic move in response to modern challenges, the chatbot represents a 24/7 accessible virtual assistant, aligning with the broader trend of digital transformation in higher education. The article provides a comprehensive approach to creating an intelligent and user-friendly chatbot that seamlessly aligns with our college's digital presence. © 2024 IEEE.

关键词： Chatbot Conversational AI Educational technology Natural Language Processing

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Development of Autonomous Mobile Service Robot as A Reception in Department Stores 21

Development of Autonomous Mobile Service Robot as A Receptio...

引用

21st international Joint conference on computer Science and Software engineering (JCSSE)

作者： Vongbunyong, Supachai Teerabundit, Tanakorn Khantee, Natkamon Thamrongaphichartkul, Kitti King Mongkuts Univ Technol Thonburi Inst Field Robot Bangkok Thailand

ISBN: (纸本)9798350381771;9798350381764

Autonomous mobile robots (AMRs) are widely used in various applications, including industry and other service sectors. Mobile service robots (MSRs) are usually AMRs designed to be service robots to serve non-industrial tasks. MSRs are found in many public places, including department stores, hospitals, and etc. Therefore, the challenging issue arises when the robot needs to effectively and safely interact with human, in this research, the MSR is developed to operate as a reception robot in a department store. The robot is able to interact with the customers by offering basic services of the reception, e.g. providing the retail stores related information such as locations and details, leading the way to specific locations by free-navigation, and etc. The MSR in this research is designed to comply with the safety standard of mobile service robot, ISO13482:2014. At the high-level control, an intelligent agent with a state machine is used for task planning and chatbot. As a result, the robot was tested in the actual environment in a department store.

关键词： Service Robot Autonomous Mobile Robot State Machine Chatbot intelligent Agent

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Design of Electromagnetic Tracing intelligent Vehicle 4

Design of Electromagnetic Tracing Intelligent Vehicle

引用

4th international conference on computer Science, Electronic Information engineering and intelligent control Technology, CEI 2024

作者： Chen, Zekai Li, Jincan Wei, Dexian Sun, Guokai School of Artificial Intelligence Zhujiang College South China Agricultural University Guangzhou China

ISBN: (纸本)9798350378542

With the development of the times and the progress of technology, people's travel methods are becoming more and more convenient, and intelligent driverless cars have also entered our field of vision. However, during the driving process of a car, many unstable phenomena such as deviation are inevitable. To enable the car to maintain a stable trajectory on the track while in motion, electromagnetic signals were collected for guidance. Design an intelligent car controlled by a microcontroller that automatically drives by collecting electromagnetic signals. The car uses a gyroscope and TOF infrared ranging module to recognize track elements and make responsive reactions. By testing its speed and stability on the track, the vehicle exhibited significant performance in high-speed trajectory tracking on the electromagnetic track. If it could be applied in future autonomous vehicles' operation, it would greatly enhance the safety and reliability of smart car operations. © 2024 IEEE.

关键词： Microcontrollers

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Exploration on intelligent Virtual Reality Technology and Algorithms for VR Platforms under intelligent control and computer Vision 2

Exploration on Intelligent Virtual Reality Technology and Al...

引用

2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Wei, Yuhua Chen, Yunxing Guangzhou Huali Science and Technology Vocation College Guangzhou China

ISBN: (纸本)9798350317558

intelligent control and computer vision algorithms can achieve clearer, more delicate, and realistic image effects through extremely high image quality and target recognition technology, as well as complete image processing tasks faster, thereby improving user experience and interaction effects. This study is based on intelligent control and computer vision technology and algorithms, aiming to design a highly integrated VR (virtual reality) platform. This article collected and established evaluation data and related parameters for the intelligent VR platform. intelligent control and computer vision algorithms can evaluate and analyze intelligent VR platforms, and improve their user experience and interaction effects by optimizing structural parameters and performance indicators. After experimental testing, the subjective image quality rating of the algorithm platform in this article was between 8.41 and 9.32, and the target recognition rate was between 6.72 and 9.87. The design based on intelligent control and computer vision algorithms can improve the image quality and target recognition rate of the VR platform, achieving precise detection and recognition of target objects. This study provided an effective method and technology for the design of intelligent VR platforms, and provided strong support for improving the image quality and target recognition rate of VR platforms. © 2024 IEEE.

关键词： computer vision image quality intelligent control intelligent VR platform target recognition rate

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A1 Voice Assistant Using Python and API

A1 Voice Assistant Using Python and API

引用

2024 international conference on advances in Data engineering and intelligent Computing Systems, ADICS 2024

作者： Sai Surya Vigneshwar, M. Kiran Kumar, L. Sathyabama University Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

This paper proposes the development of a voice assistant integrated into an augmented reality (AI) environment. Voice assistants have become increasingly popular in recent years, with the widespread adoption of smart speakers and mobile devices. Meanwhile, AI technology has also advanced rapidly, offering new ways to interact with digital information and objects in the real world. The proposed system aims to combine the convenience and ease of use of a voice assistant with the immersive and interactive nature of AI. The user will be able to access the voice assistant through a wearable device, such assmart glasses or a headset. The voice assistant will respond to the user's voice commands and provide relevant information and assistance, displayed as virtual overlays in the AI environment. The implementation of the system will require overcoming technical challenges related to the integration of voice recognitionand AI technology, as well as ensuring user privacy and security. However, the potential benefits of a voice assistant in any environment make it a promising area for future research and development © 2024 IEEE.

关键词： Augmented reality

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Research on intelligent control technology for enhancing precision airdrop system autonomy 4

Research on intelligent control technology for enhancing pre...

引用

4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Xu, Ronghua Yu, Gang Aerospace Life-Support Industries Ltd. Xiangyang China Aviation Key Laboratory of Science and Technology on Life-Support Technology Xiangyang China Hubei University of Arts and Science Xiangyang China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

The Precision Airdrop System (PADS) is widely used in the accurate recovery of spacecrafts and the precision delivery of supplies. The accuracy of control is the core of the PADS achieving the precision airdrop. To enhance control accuracy, an intelligent control technology for enhancing PADS autonomy based on simulation environment is developed. The dynamic model employed to calculate the PADS state is a 6-degree-of-freedom (6-DOF) model, and the reinforcement learning control training method is based on the A-C architecture. Through analysis of simulation results in both windless and windy environments, it is evident that the reinforcement learning control strategy based on the AC architecture outperforms the traditional PID strategy. The analysis highlights the challenges posed by wind in achieving precise control and presents an effective approach for enhancing PADS autonomy through intelligent control. © 2024 SPIE.

关键词： intelligent control

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intelligent control of Power Converters using Reinforcement Learning 9

Intelligent Control of Power Converters using Reinforcement ...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Krishna, R. Sathya Barna Sudharani, R. Devi, S. Rukmani Kulkarni, Pramodkumar H. Rohini, G. Sindhu, M. Sathyabama Institute of Science And Technology Department of Computer Science Engineering Tamil Nadu Chennai600119 India Panimalar Engineering College Department of Mathematics Tamil Nadu Poonamallee600123 India Saveetha College of Liberal Arts And Sciences Deemed To Be University SIMATS Department of Computer Science Tamil Nadu Chennai600124 India Dr. D. Y. Patil Institute of Technology Department of Electronics And Telecommunications Maharashtra Pimpri411018 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Thiruverkadu600077 India R.M.K. Engineering College Department of Computer Science And Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798350365092

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. This research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. This research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. The explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. This research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. The report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems. © 2024 IEEE.

关键词： Power converters

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Numerical Simulation of the Number of Joints on the Stability of Rock Mass 5

Numerical Simulation of the Number of Joints on the Stabilit...

引用

5th international conference on computer engineering and intelligent control, ICCEIC 2024

作者： Shen, Lizhen Xin, Yanchen Liu, Xuetong College of Resources Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798331507992

Rock mass in nature often has joints, and many geotechnical projects need to be carried out in cracked rock masses. The presence of joints in rock mass has an significant consequence on the stability of geotechnical engineering. Based on the finite element analysis software ABAQUS, this paper uses Python language for secondary development to establish a parametric finite element model with different number of joints. The influence of joint number on rock stability is studied. The resultant indicates that in the course of rock failure, the fissure first starts from the joint tip, and the propagation route of crack is usually along the stress and the maximum principal plastic strain concentration zone. With the upsurge of the magnitude of joints, the rock trauma is more severe. The research outcome supply a conferment for the safety construction of geotechnical engineering. © 2024 IEEE.

关键词： Geotechnical engineering

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Research on intelligent monitoring and optimization control system model of greenhouse environment based on deep learning 4

Research on intelligent monitoring and optimization control ...

引用

4th international conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Shao, Xue Chen, Yangfeiyi Chen, Xiwei Zhu, Fengzhe JiLin Agricultural Science and Technology University Electrical and Information Engineering College Jilin Jilin China

ISBN: (纸本)9798331534622

This paper presents an improved intelligent control model for greenhouse environments, integrating environmental monitoring data with a deep learning approach combining multilayer CNN and LSTM networks with model predictive control (MPC). By optimizing model parameters and utilizing data fusion techniques, the system's prediction accuracy and response speed are enhanced. Experimental results from a real greenhouse monitoring system show that the improved model significantly outperforms traditional models and other machine learning algorithms, with a mean absolute error (MAE) of 2.342, mean square error (MSE) of 6.749, and a coefficient of determination (R2) of 0.931, demonstrating its efficiency and reliability in controlling key environmental indicators like temperature, humidity, and CO2 concentration. © 2024 IEEE.

关键词： Environmental monitoring

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Analysis of Techniques for Cotton Crop Disease Detection & Monitoring

Analysis of Techniques for Cotton Crop Disease Detection & M...

引用

2024 IEEE international conference on intelligent Systems and Advanced Applications, ICISAA 2024

作者： Gupta, Prajakta S. Zade, A.V. G. H. Raisoni University Computer Science & Engineering Amravati India

ISBN: (纸本)9798331539948

Cotton is one of the most important crops in the world, playing an important role in the textile industry and contributing to the economy of many countries. However, cotton plants are susceptible to many diseases that reduce yield and quality. Early and accurate detection of these diseases is important for effective management and control, thus minimizing financial losses. Traditional diagnostic methods require expert judgment, are time-consuming and complex, and are generally impractical for large-scale applications. Therefore, there is interest in advances in machine learning and computer vision to develop automated systems for cotton disease prediction and detection. This project focuses on the development and evaluation of machine learning models for cotton leaf disease detection using Transfer Learning (TL) techniques and hybrid models. Cotton, being a vital crop, is susceptible to numerous diseases that can significantly affect yield and quality. Early detection and accurate classification of these diseases are crucial for timely intervention and minimizing economic losses. In this study, we applied several TL models, including VGG16 and VGG19, and assessed their performance using a comprehensive dataset of cotton leaf digital images. © 2024 IEEE.

关键词： Cotton

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