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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61-70 订阅

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Use neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols

Use neural Networks to Recognize Students' Handwritten Lette...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options. © 2025 SPIE.

关键词： Education and training Machine learning Data modeling Neural networks Image classification Visual process modeling computer vision technology Deep learning Performance modeling Transformers

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intelligent control of Air Combat Maneuvers Using Fuzzy Logic 11

Intelligent Control of Air Combat Maneuvers Using Fuzzy Logi...

引用

11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Deif, Mohanad A. Hafez, Mohamed A. Attar, Hani Rezeka, S.F. Awad, T. Attia, E.M. Badawi, M.B. Research Institute of Sciences and Engineering University of Sharjah United Arab Emirates INTI-IU Universi Faculty of Engineering Feqs Nilai Malaysia Zarqua University Department of Energy Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Alexandria University Faculty of Engineering Mechanical Engineering Department Alexandria Egypt Alexandria University Faculty of Engineering Mechanical Engineering Department El-Chatby Alexandria21544 Egypt Faculty of Computer Science and Engineering Department of Computer Science South Sinai Governorate El Tor Egypt

ISBN: (纸本)9798331509293

With the expanding capabilities of fighter aircraft in gauging their targets with great accuracy, comes the necessity that fighter pilots possess higher talents in making the correct decision. Because human error is frequently a barrier to making precise and timely decisions, a control mechanism is required to regulate the trajectory of aircraft during combat maneuvers. In this paper, a combat-maneuver fuzzy program is developed and applied to the inverse kinematics of the F-16 aircraft. The program produces a new path for the plane to achieve the maneuver such that the minimum distance between the resulting path and the object aircraft path is larger than a specified distance (critical distance). Also, spatial defensive maneuvers against spatial offensive maneuvers of bandits were realized, accordingly, this research emphasizes the importance of process innovation in the design of adaptive air combat control systems, enabling more effective response strategies. © 2025 IEEE.

关键词： Adaptive control systems

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Machine Learning Models for Advancing Heart Disease Prediction and Diagnosis 3

Machine Learning Models for Advancing Heart Disease Predicti...

引用

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

作者： Reddy, Santosh Shetty, Kruthi K. Apeksha, G.S. Department of Computer Science and Engineering B N M Institute of Technology Bangalore India

ISBN: (纸本)9798331527549

Cardiovascular diseases are still among the leading causes of deaths worldwide, thus calling for early detection and precise risk assessment. Traditional methods of diagnosing cardiovascular disease often rely on standardized risk factors, which might not always catch the condition in its early stages. Machine learning and AI are revolutionizing CVD prediction by examining complex data in healthcare to find previously unknown patterns. The paper discusses advances in ML techniques such as supervised and unsupervised learning, deep learning, and ensemble methods. It emphasizes hyperparameter tuning, feature engineering, and model interpretability. AI models like Random Forest, SVM, and Neural Networks have shown better accuracy in predicting cardiovascular disease risk, enabling earlier and more personalized interventions. AI’s potential reaches from wearable devices to predictive cardiac event models, promising huge advances in cardiovascular care. Still, there are data privacy issues, model interpretability, and regulatory problems that will continue to pose significant challenges. Future advancements in multimodal imaging, precision medicine, and collaboration between clinicians and data scientists will be pivotal to the realization of AI’s full potential in transforming the prediction and prevention of CVD. © 2025 IEEE.

关键词： Artificial Intelligence Cardiovascular Diseases (CVDs) Early Detection Heart Disease Prediction Machine Learning Neural Networks Personalized Healthcare Random Forest Support Vector Machines (SVMs)

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Optimizing Vehicle Coordination at Multi-lane Intersections Using Traffic control Algorithms 25th

Optimizing Vehicle Coordination at Multi-lane Intersections ...

引用

25th international conference on intelligent Data engineering and Automated Learning

作者： Gonzalez, Cesar L. Delgado, Santiago L. Alberola, Juan Miguel Nino, Luis F. Julian, Vicente Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Nacl Colombia Lab Invest Sistemas Inteligentes LISI Ingn Sistemas Ind Carrera 45 26-85 Bogota 111321 Colombia Univ ECCI Grp Invest SIGCIENTY Carrera 10 19-64 Bogota 110311 Colombia

ISBN: (纸本)9783031777370;9783031777387

intelligent Transportation Systems (ITS) encompass several strategies aimed at ensuring safe and efficient vehicle-to-vehicle interactions on roads. These strategies involve the implementation of advanced technologies, including intelligent traffic lights, wireless (V2X) communication systems, visual recognition systems, and algorithms. In this research, we introduce the implementation of a distributed algorithm tailored for managing highly complex road infrastructure, particularly intersections in urban areas with multiple lanes. Our evaluation of the system's performance involves applying a type of density distribution called "Biased" and comparing it against a traditional traffic control system (traffic lights - "Green Wave"). The inclusion of this type of density distribution aims to align our approach with real-world traffic conditions, specifically the variation in densities across different lanes. This comprehensive assessment allows for a more thorough evaluation of system performance.

关键词： Autonomous driving Distributed intersection management Multiple lanes intelligent transportation system Density distribution

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

引用

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

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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intelligent Charge controllers for EVs Using Whale Optimization Algorithm for Demand Management 1

Intelligent Charge Controllers for EVs Using Whale Optimizat...

引用

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

作者： Jose, A. Sujin Ram, S Sakthiya Bharathi, C.R. Devarajan, M. Krishna, A.P. Jaya Madhan, A. New Horizon College of Engineering Department of Automobile Engineering Bangalore India Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Tamil Nadu Sathyamangalam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sethu Institute of Technology Department of Mathematics Tamil Nadu Virdhunagr India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India Velammal College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518578

This paper proposes the development of a smart charge controller for electric vehicles that may take advantage of the Whale Optimization Algorithm (WOA) to improve on energy efficiency and demand management. The technology optimized charging parameters through dynamic adjustment of the charging process in relation to the energy demand from the grid, SOC, and power pricing. This ability of WOA to balance between exploration and exploitation allows the controller to minimize thermal losses in systems and prevent battery degradation. Moreover, energy wastage is avoided so that the controller can ensure efficient energy utilization. The experiment results show that compared to the current charge controllers, a WOA-based controller would improve grid load management by 10%, reduce battery degradation by 15%, and increase efficiency in the utilization of energy by 20%. This system has the scalability as well as the real-time adaptability to changing requirements of batteries and grid conditions that would make it suitable for areas with high penetration of electric vehicles. This system outperforms the others by optimizing the load distribution necessary for optimal charging. © 2025 IEEE.

关键词： Battery management systems

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Design and implementation of a Raspberry Pi-driven intelligent cart driving system 3

Design and implementation of a Raspberry Pi-driven intellige...

引用

3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Wu, Lin Cheng, Jinfeng Huang, Haoran Chang, Ho-Hsuan Zeng, Miaowang DGUT-CNAM Institute Dongguan University of Technology Dongguan523106 China

ISBN: (纸本)9781510688179

The rapid advancement of science and technology has underscored the significant potential of intelligent driving systems in the fields of robotics and automotive industries. We focus on the design and implementation of intelligent trolley driving system based on Raspberry Pi driver in this paper, in which a system that utilizes Raspberry Pi as its central control platform, leveraging its robust computational capabilities and diverse interface resources. By integrating the OpenCV computer vision library, the proposed system performs real-time environmental image acquisition and processing, accurately identifying and locating obstacles. In this study the hardware configuration is meticulously detailed, encompassing the selection and integration of Raspberry Pi components and sensor arrangements. The software design process is thoroughly explored, covering image acquisition algorithms, obstacle recognition and localization techniques, and the development of driving strategies. The rigorous testing demonstrated the system's ability to make swift and precise decisions across various environmental conditions, implementing effective problem-solving measures with high stability and reliability. Finally, we expect that this approach offers a viable technical solution for applications in related fields, contributing to the development of autonomous systems and intelligent transportation technologies. © The Authors.

关键词： Automotive industry

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ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Motor Imagery Recognition

ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Mo...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zhao, Zheng Xue, Haixiao Huang, Yuwen Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%. © 2025 SPIE.

关键词： Electroencephalography

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Disturbance Observer-based H∞ control of TCP/AQM System 24

Disturbance Observer-based H∞ Control of TCP/AQM System

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2nd international conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Liu, Huahao Ma, Pengfei Hou, Linlin Sun, Haibin School of Computer Science Qufu Normal University Shandong China College of Engineering Qufu Normal University Shandong China

ISBN: (纸本)9798400710681

In this issue, the disturbance observer-based H∞ control is studied for TCP/AQM system with multiple disturbances. The T-S fuzzy system containing two different kinds of disturbances is adopted to describe the TCP/AQM system. The disturbance observer is designed to obtain the estimation of the external disturbances. Then the state feedback controller is constructed using disturbance estimation. By virtue of Lyapunov-Krasovskii functional theory, the exponential stability with H∞ performance is analyzed. The sufficient conditions for the solvable gains of the disturbance observer and controller are obtained by using linear matrix inequalities. Finally, simulation experiments demonstrate the effectiveness of the proposed control scheme. © 2024 Copyright held by the owner/author(s).

关键词： State feedback

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Analyzing Deep Learning in IoT Platform 5th

Analyzing Deep Learning in IoT Platform

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5th international conference on advances in Information Communication Technology and Computing, AICTC 2024

作者： Venugopal, Anita Goar, Vishal Kumar Deptartment of IT Unit Dhofar University Salalah Oman Engineering College Bikaner Bikaner India

ISBN: (纸本)9789819761050

Internet of Things (IoT), a modern technology, is a network of devices with sensors and embedded processors which can work even without the use of internet. It can digitally control the real world wired or wirelessly. It receives attention as IoT-enabled products make human more intelligent and can be involved in different aspects of our lives. Continuous development of IoT domain and large number of devices connected to internet is one of the challenges faced by this technology. Many modern approaches such as computer vision and machine learning have been devised to cope up to meet the challenges. In this work, basic IoT elements are discussed and provide a review of modern addressing method: deep learning (DL) in IoT platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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