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

共 47494 条记录，以下是1-10 订阅

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Conventional and intelligent control Schemes for Handling A Helicopter's Pitch Axis 1

Conventional and Intelligent Control Schemes for Handling A ...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chaudhary, Abhishek Delhi Technological University DTU Electrical Engineering Department New Delhi India

ISBN: (纸本)9798331518578

In this paper, a comparative study of conventional LQR controller, optimized PID controller and intelligent Fuzzy logic controller is demonstrated while achieving the desired pitch axis trajectories of a 2-DOF helicopter system. During this process, TLBO optimized PID controller is used to track down the trajectory angle of pitch axis of helicopter system and this performance is compared with inference rules of fuzzy system and a conventional LQR controller. The membership function parameters, exploration - exploitation scheme of optimization algorithm are discussed while addressing the problem of trajectory tracking. The parameter tuning and stable response of pitch axis of helicopter model is achieved on MATLAB/Simulink platform, on which the comparison is accomplished through time response analysis. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Artificial Intelligence, intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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Cloud Based Automated control System Workshops and Rooms for controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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The Application of Artificial Intelligence in computer Network Technology in The Era of Big Data

The Application of Artificial Intelligence in Computer Netwo...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zhou, Yan Li, Zhi Intelligent Finance and Economics Industry School Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

With the continuous development of computer network technology, artificial intelligence stands out. Although its technology is not mature enough, artificial intelligence is also a kind of human intelligence technology presented through ordinary computer programs. Artificial intelligence technology can not only help people process computer data, but also gradually turn services into humanized and intelligent ones, bringing convenience to people's life and work. Therefore, this paper analyzes the definition and characteristics of AI, and discusses the application of AI in computer network technology in the era of big data. © 2025 SPIE.

关键词： application artificial intelligence Big data computer network technology

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intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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Design Process and Development of Automation for Smart Buildings 1

Design Process and Development of Automation for Smart Build...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sethi, Gunjan Khurana, Deepika Verma, Monika Pant, Surmadhur Chauhan, Priyanka Bhatt, Bhaskar Department of Computer Science and Engineering Delhi Technical Campus Uttar Pradesh Greater Noida India IIMT College of Engineering Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Shivalik College of Engineering Department of ECE Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Mechanical Engineering Dwarahat Almora India

ISBN: (纸本)9798331518578

In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial concerns, and market demands increase, traditional design methodologies are becoming less effective. Current framework's plan and activity have been totally changed by the presentation of automated smart building, which ensures expanded security, better inhabitant solace, and energy effectiveness. With an emphasis on MATLAB-based reproductions, this study offers a careful assessment of the plan interaction and improvement procedures for computerizing savvy structures. The study delineates how MATLAB makes it simpler to depict, reproduce, and investigate savvy building frameworks with certainty. Through the mix of numerous advancements, like sensors, regulators, and correspondence conventions, the research shows the turn of events and check of a mechanized smart building framework. The intelligent building's building automation system prioritizes a humanized interface and uses a combination of mechanical, electrical, and fire control systems, access control systems, and parking management systems to give building occupants a convenient and comfortable place to live and work. The discoveries show prominent additions in functional viability, natural adaptability, and energy utilization. The conventional heuristic and ad hoc design paradigms are becoming ineffective and inadequate for the future due to cost, the requirement to handle growing functional complexity, adaptability, supply chain fragmentation, and time-to-market pressure. © 2025 IEEE.

关键词： Smart homes

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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Motion Planning in Automated Vehicles Based on Hierarchical MPC 1

Motion Planning in Automated Vehicles Based on Hierarchical ...

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1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kapila, Pooja Kumar, Pankaj Negi, Rahul Bharti, Bhupender Kumar Punhani, Akash Bisht, Suraj Singh IIMT College of Engineering Department of Artifical Intelligence & Data Science India Amity University Department of Statistics Kolkata India Tula's Institute Department of ECE Dehradun248197 India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India KIET Group of Institutions Department of Computer Science Noida India Bipin Tripathi Kumaon Institute of Technology Department of ECE Dwarahat Almora India

ISBN: (纸本)9798331518578

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors. © 2025 IEEE.

关键词： Motion planning

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Research on the Applicability of Data Processing Based on Real-Time intelligent Seismic Processing System in Inner Mongolia 6th

Research on the Applicability of Data Processing Based on Re...

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6th international conference on advances in Civil and Ecological engineering Research

作者： Zhai, Hao Liu, Tiantian Xu, Yan Wang, Yong Ma, Liang Li, Zhenyu Shu, Lei Inner Mongolia Autonomous Reg Earthquake Adm Hohhot Peoples R China

ISBN: (纸本)9789819616299;9789819616275;9789819616268

The research objects for evaluating the reliability of the real-time intelligent seismic processing system (RISP) in the Inner Mongolia seismic network were 31 earthquake events that occurred in the region within 30 days before and after the Helingeer M-L 4.5 earthquake on March 30, 2020. The manual cataloging was compared to the output results of the real-time intelligent seismic processing system (automatic catalog). It was observed that the number of events identified by the RISP system was approximately 2.5 times that of the manual ones, with 30 automatic catalogs matching the manual catalogs (31). The event recall rate was as high as 96.8%. The automatic catalog had a small deviation from the manual catalog in terms of earthquake occurrence time, epicenter location, magnitude, and P-wave and S-wave phase arrival time. When the results of the automatic and manual catalogs are compared, it is evident that the cataloging of the same events in both catalogs is consistent, with the earthquake occurrence time and epicenter deviation usually settling within +/- 2 s and +/- 10 km, respectively. The automatic catalog meets the error range requirements of the manual catalog. The Real-time intelligent Seismic Processing System (RISP) produces data that meets expected goals and supports scientific research, such as rapid aftershock sequence production and earthquake swarm trend judgment post-earthquake.

关键词： Seismic phase identification Earthquake detection Deep learning Automatic cataloging

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