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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 90597 条记录，以下是351-360 订阅

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Unsupervised Vehicle Re-Identification with Clustering and Appearance Style Exploration 24

Unsupervised Vehicle Re-Identification with Clustering and A...

引用

3rd international conference on algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Taleb, Hamdan Zhu, Xiaobo Wang, Chao College of Electronic and Information Engineering Tongji University Shanghai China School of Informatics and Engineering Suzhou University Jiangsu Suzhou China

ISBN: (纸本)9798400718120

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system’s ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

引用

2024 international conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Parella, Teja Ponnam, Balakrishna Santhiya, P. Praveen, A. Sujihelen, L. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers. © 2024 IEEE.

关键词： Anomaly detection

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A ROS-2 based Path Planning and Maze Solving of Autonomous Robots

A ROS-2 based Path Planning and Maze Solving of Autonomous R...

引用

2024 international conference on Advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Nuhel, Ahsan Kabir Hossain, Sazzad Hassan, Md.Tonim Haider, Iftekhar Saha, Ankur Taha, Adnan Sabith Ony, Asma-UI Husna Electrical and Electronics Engineering Bangladesh University of Hertfordshire Computer Networks and Systems Security United Kingdom Electrical and Electronics Engineering Bangladesh Computer Science and Engineering Bangladesh University of Information Technology and Sciences Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350350289

The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launch files are used to initialize the sensor and control nodes. Attaining optimal path planning and accurate localization are the main objectives. The combination of novel motion planning based on the orientation of the robot with graph search algorithms like DFS, A∗, and Dijkstra's leads to effective maze discovery and traversal. Thorough testing shows that the system can successfully navigate unfamiliar maze configurations, demonstrating the dependability of ROS2 and modern robotics techniques. By completing this study, a foundation for future advancements in perception and planning to manage more complex settings is laid. © 2024 IEEE.

关键词： Computer vision

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Iterative computing algorithms implemented in FPGA using ALTERA_CORDIC library as an example 28th

Iterative computing algorithms implemented in FPGA using ALT...

引用

28th international conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Poczekajlo, Pawel Koszalin University of Technology ul Sniadeckich 2 Koszalin75-453 Poland

The article discusses the general issues of implementing pipelined and iterative algorithms. These are typical solutions for engineering algorithms implemented in hardware like reprogrammable systems. The authors focused on the implementation of such algorithms in field-programmable gate array (FPGA) circuits. Key aspects and advantages of pipelined processing were discussed. The focus was on an example CORDIC algorithm. Analyses of the iterative implementation using the ALTERA_CORDIC library were performed. Key parameters such as frequency and circuit occupancy were measured. Also, the obtained results were compared with the exact values - the errors were determined. Realizations based on 8-, 12- and 16-bits of fractional in fixed-position notation were analyzed. © 2024 The Authors.

关键词： Precision engineering

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The Optimal Preventive Maintenance Strategy for Balanced systems Considering Energy-Saving 7

The Optimal Preventive Maintenance Strategy for Balanced Sys...

引用

7th international conference on Advanced algorithms and Control engineering, ICAACE 2024

作者： Lin, Mengmeng Meng, Yinghao Xu, Zihao Yin, Zhen Wenzhou University Department Of Industrial Engineering Zhejiang Wenzhou China

ISBN: (纸本)9798350361445

Reliability research of the balanced system has become a key factor in the safe completion of important tasks for complex systems. In recent years, due to the proposal of carbon peak and carbon neutrality targets, the problems of energy saving, emission reduction, and green development have received extensive attention. In existing studies, energy-related reliability modeling and preventive maintenance strategies are rarely considered, and we focus on balancing the energy-saving potential of the system, by considering the power degradation of system components and optimizing preventive maintenance strategy to achieve the goal. Different from other systems, the failure criterion of balanced systems is given according to the power degradation state in each pair of components, which can be divided into failure state and unbalanced state, the system fails when each pair of units in the system is in an unbalanced state or in a failure state. To avoid system imbalance or failure, when the power state difference between the two symmetric components exceeds the maintenance threshold, the unit is unbalanced, and preventive maintenance is performed with energy conservation in mind. Finally, a numerical example is given to verify the feasibility of the proposed maintenance policy. © 2024 IEEE.

关键词： Energy conservation

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Deep Learning: An In-Depth Study of algorithms and Application Areas

Deep Learning: An In-Depth Study of Algorithms and Applicati...

引用

2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Agnihotri, Nishant Hole, Komal Lovely Professional University Department Of Computer Science And Engineering Punjab India

ISBN: (纸本)9798350366846

Deep learning, an innovative paradigm within artificial intelligence, brings about significant transformations across diverse sectors, ranging from healthcare to autonomous systems. This work explores the latest developments and uses of Deep Learning algorithms. Highlighted domains include cancer diagnosis, precision medicine, autonomous vehicles, and speech recognition. The review thoroughly explores optimization techniques, aiming to improve training accuracy and reduce time across numerous architectural designs, such as recurrent neural networks, residual networks, and convolutional networks. © 2024 IEEE.

关键词： Recurrent neural networks

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Improving Alerting in the Monitoring System Using Machine Learning algorithms 17

Improving Alerting in the Monitoring System Using Machine Le...

引用

17th IEEE international conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Klymash, Mykhailo Zablotskyi, Serhii Pohranychnyi, Vasyl Institute of Telecommunications Radioelectronics and Electronic Engineering Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798331520564

The paper discusses the methods for building monitoring systems. It analyses standard methods for detecting and responding with alert systems based on given values of time series metrics. A method for improving the alert system by integrating artificial intelligence is also proposed. It will help filter out false and system alerts. Additionally, using AI analysis, the system administrator will receive suggested solutions or improvements in specific network segments. © 2024 IEEE.

关键词： AI alertmanager anomaly exporter metrics ML prometheus word2vec

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Optimization of Data Encryption algorithms in Information Construction

Optimization of Data Encryption Algorithms in Information Co...

引用

2024 international conference on Distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Zhu, Shuai You, Yi Zhou, Bo Chen, Chuanting Yi, Xueqin Hankou University Network Information Center Wuhan China Hankou University School of Computer Science and Technology Wuhan China Wuhan University of Engineering Science Library and Information Center Wuhan China Hankou University School of Music Wuhan China Hankou University Board Office Wuhan China

ISBN: (纸本)9798350375442

Data encryption algorithms play a crucial role in information construction, effectively protecting data security. The problems in traditional research include a lack of targeting, a balance between efficiency and safety, and a lack of comprehensive consideration. Therefore, this article proposed targeted optimization strategies and methods by in-depth analyzing the needs and scenarios of information construction, combined with the characteristics and problems of data encryption algorithms. Meanwhile, this article also comprehensively considered the integration, deployment, management, and maintenance of algorithms to achieve optimal matching and collaborative work between algorithms and information systems. Through research, this article aimed to promote the application and development of data encryption algorithms in information construction, and improve the efficiency, security, and availability of data encryption algorithms in protecting data security. The results showed that the efficiency of the data encryption algorithm based on RSA (Rivest-Shamir-Adleman) reached the highest of 89.6%. This article provides strong support for the application and development of data encryption algorithms, promoting the sustainable development of information construction and the protection of data security. © 2024 IEEE.

关键词： advanced encryption standard data encryption algorithm information technology construction RSA algorithm

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Optimizing Fraud Detection with XGBoost and CatBoost for Social Media Profiles and Payment systems 3

Optimizing Fraud Detection with XGBoost and CatBoost for Soc...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Asha, V. Nithya, B. Prasad, Arpana Kumari, Monika Hujaifa, Mirza Sharma, Akash New Horizon College of Engineering Department of Mca Bengaluru India

ISBN: (纸本)9798331509675

This study assesses advanced machine learning algorithms, namely XGboost and Gradient Boosting Machine (GBM), for fraud detection in social media profiles and payment systems. It focuses on two very different types of datasets: one dealing with fake social media accounts and another with fraudulent payment records. Consequently, the study aims toward establishing a solid methodology for the detection of outliers. The main tasks comprise deep cleaning and preprocessing, feature engineering, and fine-tuning of the algorithm to present optimal detection accuracy and performance. Results exhibit the usability of ensemble learning techniques in handling various fraud detection conditions for providing better security and reliability across the respective platforms. © 2025 IEEE.

关键词： Sentiment analysis

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Deep Learning Based Optimization of UAV Orbital Paths 6

Deep Learning Based Optimization of UAV Orbital Paths

引用

6th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Li, Zhanghan Chen, Yuandian School of Automation Guangdong University of Technology Guangzhou510006 China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350374315

UAV path planning is an important part of UAV technology, and genetic algorithms and ant colony algorithms are used for it in UAV path planning tasks. When facing some unknown environments, the performance of classical algorithms is less satisfactory. For the path planning problem of a single UAV, this paper proposes the APF-DDQN model using the artificial potential field method combined with the multi-intelligence body algorithm DDQN. The model utilizes the dominance function as a metric for selecting action values and measuring the goodness of the tie-breaking values of all actions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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