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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211-220 订阅

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Clustering Algorithm for Vessel Trajectory Anomaly Detection Based on Binary Hashing Method 13

Clustering Algorithm for Vessel Trajectory Anomaly Detection...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Liang, Yuhan Ng, Wing W.Y. Li, Qihua Tian, Xing Su, Xiong Qiu, Qin Hou, Zhengzhi School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China Anti-Drug Tethnology Center of Guangdong Province Guangzhou China

ISBN: (纸本)9798331516147

Timely identification of abnormal ships through trajectories make daily commercial trade transportation safer. However, current methods for identifying abnormal ships through vessel trajectories have issues such as high time cost and high sensitivity to parameters. To address such issues efficiently, a clustering method based on hashing algorithm applying Long Short-Term Memory neural networks is proposed to capture abnormal local and temporal information in trajectories. The trajectories are converted into hash codes that present thier characteristics succinctly. Then, the dominant set clustering method that does not require setting hyperparameters is used to cluster and find abnomal trajectories according to similarity relationship computed by corresponding hash codes. The proposed method is validated on a dataset of real vessel trajectories, which basically achieves the identification of abnormal vessel trajectories with lower time cost compared to other methods. © 2025 IEEE.

关键词： Clustering algorithms

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Evaluation of UAV Path Planning Approaches for Urban Air Mobility 3

Evaluation of UAV Path Planning Approaches for Urban Air Mob...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Pasha, Tanzeem Shri Hari, M. Shreeram, K.R. Bhuvana Suganthi, D. Munavalli, Jyoti Raghavendra Bnm Institute of Technology Dept. of Electronics & Communication Engineering Bengaluru India

ISBN: (纸本)9798331515911

In contemporary society, it is pertinent to address the feature of the Urban Air Mobility (UAM), a new mode of urban transport, whereby unmanned aerial vehicles transport people and goods with the view to rendering relief to ground traffic congestion. However, challenging as it may be, the deployment of the UAVs within the confines of the urban built environment is complex. In this systematic review, we provide state-of-the-art developments found in multi-UAV path planning for UAM. We highlight the different aspects of navigation strategies, cooperative algorithms, optimization methods, as well as collision avoidance techniques over the years. In total, we conducted analyses on thirty recent studies, such as those that deal with the combination of machine learning, bio-inspired algorithms, and other environmental-induced factors involved in the path planning process. The review results exhibit significant advances in navigation-aware planning, cooperative systems, advanced optimization techniques, and real-alive applications. However, while all these have been achieved, adaptations in real-time, dynamic environment management, and the integration of learning-based methods with conventional control methods are challenges that have continued to be faced. Future research will propose directions to fill in these gaps to further the progress towards developing efficacious, safe, and sustainable urban air transportation systems. © 2025 IEEE.

关键词： Traffic congestion

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Structure design and analysis of a rigid-soft robotic hand with large load capacity 3

Structure design and analysis of a rigid-soft robotic hand w...

引用

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

作者： Wu, Peichen Lan, Fuming Hong, Wen Chen, Xiaoping Shuai, Wei Cui, Guowei Gao, Yang Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Shanghai Institute of Aerospace System Engineering Shanghai201100 China Intelligent Manufacturing Institute of HFUT HeFei230041 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510688179

In recent years, soft grippers have gained significant attention due to their adaptability and safety. However, when performing horizontal object grasping, their limited structural stiffness prevents them from withstanding the torque generated by friction between the gripper and the object, leading to a loss of contact and limiting their horizontal load capacity. To address this limitation while preserving the adaptability of soft grippers, this paper proposes a gripper structure called the rigid-soft robotic hand, which incorporates lateral rigid connecting rods outside the soft joints. The paper analyzes the bending capacity of the rigid-soft joint using kinematic methods and investigates the influence of link design parameters on maximum bending angle. Subsequently, a finite element analysis is conducted to study the finger structure with the added connecting rods, aiming to optimize the link design parameters. Experimental results demonstrate the grasping capabilities of the rigid-soft gripper in both vertical and horizontal orientations. The findings show that the rigid-soft gripper significantly improves the load-carrying capacity during horizontal object grasping while maintaining the adaptability of the soft gripper. © The Authors.

关键词： Robotic arms

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Deep Learning and IoT-Enabled Framework for Accurate Classification and Monitoring of Alzheimer's Disease Based on EEG Signal Analysis 5

Deep Learning and IoT-Enabled Framework for Accurate Classif...

引用

5th IEEE international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Sathish, R. Muthukumar, R. Dhivya, K. Karthikkumar, S. Builders Engineering College Department of Electrical and Electronics Engineering Tamilnadu Tirupur India Erode Sengunthar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Perundurai India Jai Shriram Engineering College Department of Electrical and Electronics Engineering Tamilnadu Tirupur India

ISBN: (纸本)9798350356236

Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. The efficacy of Brain- computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. The biomedical sector is crucial for precise and efficient patient diagnostics using digital signal processing and wireless sensor network technologies. Alzheimer's disease (AD) is an irreversible brain disorder. Inadequate Alzheimer's disease diagnosis in the early stages, together with exact identification, may aid in halting the progression of the illness. Precise and effective detection and categorization of Alzheimer's disease enhance quality of life and increase life expectancy for patients. The advanced deep learning technology surpasses traditional machine learning methods in classifying complex configurations in high-dimensional composite data, particularly in digital signal processing. The deep learning application for the early detection and accurate classification of Alzheimer's disease might be suggested for a neuron-signal methodology in extensive data analysis. This research paper proposes an intelligent decision-making system (IDMS) for the diagnosis and categorization of Alzheimer's disease (AD) and the continuous monitoring of patients utilizing connected wearable devices. Methods for measuring neuronal synchrony are proposed to diagnose and characterize Alzheimer's disease. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Research on dynamic network security detection technology based on SDN 4

Research on dynamic network security detection technology ba...

引用

4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Zhao, Kai Beijing Polytechnic Beijing100176 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Traditional network architecture can no longer meet the development needs of technologies such as cloud computing and big data. SDN network architecture has the characteristics of high openness and programmability, which can quickly respond to changes in business requirements. SDN decouples the control management function of the network from the data forwarding function, simplifies network configuration work based on logic centralized controllers and open programming interfaces, thereby achieving a flat management mode and flexible data forwarding function. The centralized control and programmability of SDN networks also pose significant security threats to SDN. Based on the CSE-CIC-IDS2018 dataset, this article tests the performance of various intelligent learning algorithms, analyzes data flows, distinguishes normal data from abnormal data, improves detection accuracy, and has practical significance for the security defense of SDN networks. © 2025 SPIE.

关键词： Network security computer security Network architectures control systems Machine learning Data transmission Defense and security Detection and tracking algorithms Information security Security technologies

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Challenges and Prospects of Power Network Security Protection in the Context of a New Power System: A Case Study of Jiangxi 4th

Challenges and Prospects of Power Network Security Protectio...

引用

4th international conference on Data and Information in Online Environments

作者： Xiao, Yongcai Xu, Jian Liu, Kuangye Ge, Jingjing Hu, Lei Wang, Chenxi State Grid Jiangxi Elect Power Co Ltd Elect Power Res Inst Jiangxi Prov Key Lab Operat Control New Energy Po Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Nanchang 330013 Jiangxi Peoples R China Jiangxi Normal Univ Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

In recent years, the power grid in Jiangxi Province has entered a new phase of construction in the context of a new power system. However, the traditional network security protection system of the company is facing increasing security risks [1]. This paper first elucidates the network structure characteristics of the new power system and identifies the security risks it faces. Subsequently, combining the characteristics of boundary protection and static protection in the existing network security protection system, this paper analyzes the challenges faced by the protection system from aspects such as power source structure, grid configuration, business models, and technical foundations. Based on the content of security challenges, the paper proposes the requirements for network security protection in terms of trusted access, intelligent perception, and precise protection. Lastly, in conjunction with the development trends of security systems and new technologies, the paper summarizes the technological development directions in areas such as perception technology, blocking technology, security detection and evaluation technology, and validation technology.

关键词： new power system network security trusted access intelligent perception

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Comparative Analysis of Machine Learning Methods in Climate Data Extraction and Forecasting

Comparative Analysis of Machine Learning Methods in Climate ...

引用

2025 international conference on Emerging Technologies for intelligent Systems, ETIS 2025

作者： Khajgiwale, Parth Ajit Naveen, Atul Jethani, Nishtha Shitole, Ajitkumar International Institute of Information Technology Dept. of Computer Engineering Hinjewadi Pune India

ISBN: (纸本)9798331507541

Climate forecasting plays a critical role in understanding and mitigating the impacts of climate change. advances in machine learning (ML) have significantly enhanced the accuracy of climate projections, particularly when dealing with large and complex datasets derived from regional climate models (RCMs) and global climate models (GCMs). This study investigates the application of various ML techniques for predicting precipitation based on temperature data extracted from climate datasets. We evaluate a range of regression models, including linear models and advanced ensemble methods such as Gradient Boosting and XGBoost, along with deep learning approaches like Convolutional Neural Networks (CNNs). Through a rigorous evaluation of these models, we focus on their performance as measured by Mean Squared Error (MSE) to identify the most effective techniques for capturing the intricate relationships within climate data. By analyzing existing studies and performance metrics, we aim to provide insights into the capabilities and limitations of these ML models in improving climate predictions. Furthermore, this research highlights the need for incorporating additional climate variables and optimizing model configurations to enhance predictive accuracy in climate forecasting. © 2025 IEEE.

关键词： Convolutional neural networks

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CropSmart-A Crop Prediction Application Using Machine Learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

引用

12th international conference on Frontiers of intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks 4

Deep Learning-Based Spectrum Sharing for Dynamic Resource Al...

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4th international conference on Power, control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Venu, Nookala Kukreti, Kamlesh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331530402

We conduct an extensive study on deep learning-based spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for 6G networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow's communications. © 2025 IEEE.

关键词： Cognitive radio

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AI-Enabled Robotic Surveillance System for Critical Infrastructure

AI-Enabled Robotic Surveillance System for Critical Infrastr...

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2025 international conference on Emerging Technologies for intelligent Systems, ETIS 2025

作者： Thankappan, Manesh Nataraj, Neetha K Divya, K.S. Jayaraj, Akshaya Joby, Alen Asokan, Abhilash Adi Shankara Institute of Engineering and Technology Department of Computer Science and Engineering Kerala Kalady India

ISBN: (纸本)9798331507541

Traditional monitoring approaches often experience blind spots and high resource utilization at such a crucial time of security. In our study, we design a small-scaled autonomous vehicle model with smart sensors that is to be equipped with an AI model in a Raspberry Pi for effective and reliable self-sustained monitoring of critical infrastructure to overcome the problems. The core AI model was specifically designed to detect in real-time anomalies such as a fire, a fight, vandalism, explosion, etc. The AI model is a modified version of an open source repository available on github named 'Real-world Anomaly Detection in Surveillance Videos (pytorch) [11]'. This technology ensures speedy response time by stopping the vehicle, starting video recording, and forwarding the real-time footage to security personnel upon detecting an anomaly. This innovative solution allows for mobilization and cost-effectiveness, improving surveillance coverage, and diminished dependence on constant human oversight. Exploiting advances in artificial intelligence and robotics, the project delivers a scalable real-time monitoring system deployable into a wide range of critical infrastructure environments. This initiative represents practical application of AI towards improving public safety and building a stronger security posture in facilities that house critical infrastructure. © 2025 IEEE.

关键词： Critical infrastructures

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