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检索条件"主题词=Detection Algorithms"

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Classroom Automation System Using Human detection Based on YOLO Algorithm and IoT Technology

Classroom Automation System Using Human Detection Based on Y...

引用

Electrical and Information Technology (IEIT), International Conference on

作者： Mazaya Muhammad T. S. El Syaff Vincentius Jason Samhadi Brahmantya Aji Pramudita School of Electrical Engineering Telkom University Bandung Indonesia The University Center of Excellence for Intelligent Sensing-IoT Telkom University Bandung Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

Classroom is an important facility in the school or university to be utilized as a learning space and lessons happen. Students and teachers meet in the classroom on a daily, with different schedules. Hence, every class needs to be monitored and controlled for finding out the class conditions. However, the staffs have to monitor the classroom by checking the classroom one by one. Thus, it is not efficient and increase the workload if there are many classes to be checked. Therefore, automated technology can be implemented to reduces the workload and increases staff efficiency when controlling and monitoring the classroom, especially in the context of time. In this study, an IoT-based system was proposed as a solution to control the classroom condition. This system used class schedules and human presence as a parameter to control the classroom. A camera utilized to detect the object and performed YOLOv8 algorithm to analyze the condition. in this system, firebase was leveraged to store every data from detecting process. Then, the data was used for controlling the actuators, such as door locking, lamp, and fan, which were controlled by ESP32. The result showed that the proposed system could perform with accuracy of 57%, sensitivity of 67%, and positive predictive value of 79%. The proposed system can achieve a success rate of 90% with less than 20 second of time delay which makes this system time effective. But it is not yet known whether this system will work effectively in low light conditions.

关键词： YOLO Schedules Fans Accuracy Sensitivity Prediction algorithms Data transfer Information technology detection algorithms Monitoring

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A Real-time detection Algorithm Based On Improved Nanodet For Shaft Parts

A Real-time Detection Algorithm Based On Improved Nanodet Fo...

引用

International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yuhan Wang Yifei Wu School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331540241

ISBN: (纸本)9798331540258

Aiming at the problems of excessive parameter amount, slow speed and low accuracy of traditional mechanical parts target detection model, a real-time shaft parts detection algorithm based on improved Nanodet is proposed. Firstly, we introduce two attention mechanism modules with no or little parameter into the original backbone network, which improve the detection effect of small targets while hardly increasing the parameters of the model. Secondly, we adopt GELU instead of the original activation function to improve the convergence speed and shorten the detection time. Finally, we adopt Soft-NMS to reduce the probability of missed and false detection in the case of target aggregation. Through experiments on the simulated shaft part dataset, mean average precision of the improved Nanodet reaches 84%, which is 12% higher than the original model, and the detection speed reaches 91 frames per second, which is 21% higher. Compared with other mainstream lightweight detection models, it also behaves better in various aspects such as speed, accuracy and model volume. The experiment results show that the improved model blances high efficiency, high accuracy and lightweight, which can accurately and quickly identify shaft part categories. Therefore, it is easy to be used on small mobile devices and has good deployment prospects.

关键词： Shafts Deep learning Solid modeling Accuracy Attention mechanisms Human-machine systems Object detection Real-time systems Mobile handsets detection algorithms

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E-DDoS: An Evaluation System for DDoS Attack detection

E-DDoS: An Evaluation System for DDoS Attack Detection

引用

International Conference on Network Protocols

作者： Kaiwen Chi Xiaohui Xie Yannan Hu Dongyang Zhao Yuming Xie Liang Zhang Yong Cui Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China AI4NET Lab Huawei Technologies Company Ltd Beijing China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

Research in the area of Distributed Denial of Service (DDoS) attack detection is of paramount importance in the field of network security. Many existing studies employ static evaluation methods that fail to account for the reduction in accuracy due to the impact of inference latency on the timeliness of classification results. Furthermore, these studies frequently rely on simulated datasets for experimentation, which often lack the complexity and challenge of real-world attacks. These limitations significantly hinder the applicability of such research in practical scenarios. To overcome these challenges, we propose an evaluation methodology for real-time DDoS attack detection incorporating inference latency considerations. Additionally, we have developed a challenging DDoS dataset named THU-DDoS2024 and conducted experiments across four classification algorithms. This novel evaluation method and the newly generated dataset are integrated into an evaluation framework named E-DDoS. Leveraging E-DDoS, the “Intelligent Classification of High-Speed Network Traffic (ICNT)”, Grand Challenge was initiated. This event aims to motivate both academic and industrial sectors to delve into high-speed traffic classification tasks, thereby enhancing the applicability of research outputs to real-world applications.

关键词： Accuracy Protocols High-speed networks Organizations Network security Denial-of-service attack Real-time systems Classification algorithms Computer crime detection algorithms

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Single-Frame Infrared Point Target detection Based on YOLO-v5

Single-Frame Infrared Point Target Detection Based on YOLO-v...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Bendong Zhao Fengshun Lu Xingzhi Hu Mingjie Li Jianqi Lai China Aerodynamics Research and Development Center Mianyang Sichuan China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In order to solve the problem of target detection in infrared images, a single-frame infrared point target detection method based on YOLO-v5 is proposed. Firstly, the problems and difficulties of single-frame infrared target detection are analyzed, and the principles of three commonly used single-frame detection algorithms based on background prediction are introduced. Then the algorithm principle and network structure of YOLO-v5 are elaborated in detail. Finally, 12 groups of infrared images with different backgrounds are constructed for target detection experiments, and the experimental results prove that YOLO-v5 has great advantages over traditional single-frame detection algorithms.

关键词： Analytical models Automation Object detection Network analyzers Prediction algorithms detection algorithms Complex systems Information technology Periodic structures

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Reducing Unfairness in Distributed Community detection

Reducing Unfairness in Distributed Community Detection

引用

IEEE International Conference on Data Mining (ICDM)

作者： Hao Zhang Malith Jayaweera Bin Ren Yanzhi Wang Sucheta Soundarajan Syracuse University Northeastern University William & Mary

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Big graph data mining and processing have emerged as a crucial area of study. Distributed graph frameworks are commonly employed to process such big graph data in various applications. These frameworks have proven to be highly effective in improving both the accuracy and efficiency of processing large-scale graph data, but little attention has been paid to the algorithmic fairness of such methods. In this paper, we propose a novel graph reweighting algorithm, Homophily-Based Graph Reweighting (HBGR), which can be used with different distributed community detection frameworks. The findings of our study demonstrate that HBGR can significantly enhance the fairness of detected community results, without altering the overall distributed community detection algorithm workflow. Our analysis demonstrates that HBGR outperforms traditional performance-based distributed graph data processing frameworks in terms of fairness across 13 real social network datasets. This enhancement enables us to achieve fairness levels that are comparable, or even superior, to those achieved by linear community detection algorithms while maintaining good efficiency performance. Additionally, we examine the causes of unfairness in distributed community detection algorithms and conduct an interpretability analysis of HBGR's improved fairness performance. Finally, we provide a comprehensive evaluation of the trade-offs between efficiency, accuracy, and fairness in distributed community detection algorithms.

关键词： Accuracy Social networking (online) Distributed databases Data processing Data mining detection algorithms

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Research on Policy Conflict detection Technology Based on Interdomain Interconnection

Research on Policy Conflict Detection Technology Based on In...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Daqiu Wang Liangyu Dong Ziyi Wang Peijun Chen Weiruo Pu Yixiang Li Nuclear Power Institute of China Chengdu China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Security policies can effectively maintain the order of interconnection between domains and secure access. However, with the expansion of network scale and the increasing number of policies, the problem of policy conflict occurs from time to time. In order to achieve efficient detection of conflicts between security policies, this paper proposes a fusion index conflict detection algorithm based on B+-tree and bitmap, which further reduces the scope of policy comparison by dividing the dimensions of policy attributes and constructing a fusion index, so as to effectively improve the detection efficiency. Experiments show that this conflict detection algorithm can shorten the detection time consumption by up to 23% compared with the conflict detection algorithm with a single B+ tree index without losing the detection accuracy.

关键词： Production Libraries Indexes Security detection algorithms Computational intelligence

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Research on an Intelligent Obstacle detection Algorithm for High Voltage Lines Based on Improved YoLo Algorithm

Research on an Intelligent Obstacle Detection Algorithm for ...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Yanghong Qin School of Artificial Intelligence Chongqing Three Gorges Vocational College Chongqing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In this paper, an intelligent obstacle detection method based on improved YOLO (You Only Look Once) algorithm is proposed to improve the efficiency and safety of automated inspection of high-voltage transmission line corridors in power system. The operating environment of high-voltage lines is complex and changeable, and the existence of obstacles such as kites, nesting birds and hanging foreign objects poses a major threat to the stability and safety of the power system. Traditional manual inspection is not only inefficient, but also faces many security risks. Therefore, it is particularly important to develop an intelligent detection algorithm that can identify and locate obstacles in high voltage lines in real time and accurately. This study first analyzes the limitations of the original YOLO algorithm in the high-voltage line obstacle detection scenario, including the lack of precision for small target detection and the poor robustness against complex background. In order to solve these problems, we propose the following improvement measures: First, the Feature Pyramid Network (FPN) structure is introduced to enhance the detection ability of multi-scale obstacles; The second is to use the deep residual learning framework to solve the problem of gradient disappearance in the deep network training process, and further improve the detection accuracy of the model. The experimental results show that the improved YOLO algorithm has achieved significant improvement in the obstacle detection task of high-voltage lines, and has obvious advantages in Average Precision (AP) index compared with other classical target detection algorithms.

关键词： YOLO Accuracy High-voltage techniques Power system stability Inspection Feature extraction Robustness Real-time systems Safety detection algorithms

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Reputation-Based Classification for Malicious User detection in Cognitive Radio Networks

Reputation-Based Classification for Malicious User Detection...

引用

International Communication Systems and Networks and Workshops, COMSNETS

作者： Ashutosh Nath Tiwari Ankit Chouhan Ashok Parmar Kamal Captain Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cognitive radio technology has advanced beyond traditional radio systems to efficiently utilize underutilized spectrum. In cognitive radio, cooperative secondary users (CSUs) engage in cooperative spectrum sensing (CSS) to access available frequency bands, ensuring no interference with primary users (PUs). However, the performance of cognitive radio networks (CRNs) is significantly impacted by security threats such as malicious users (MUs), who mimic genuine CSUs and transmit false information to the fusion center (FC), degrading the system’s effectiveness. To address this challenge, we propose a reputation score based algorithm to distinguish between genuine CSUs and MUs. The reputation score serves as a classifier, effectively separating trustworthy CSUs from malicious ones. Extensive simulations are conducted to demonstrate the effectiveness of this approach.

关键词： Interference Robustness Classification algorithms Sensors Cognitive radio Security Fake news detection algorithms Radiofrequency interference Radio spectrum management

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Traffic Sign detection Algorithm Based on Improved YOLOv7

Traffic Sign Detection Algorithm Based on Improved YOLOv7

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Computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Haodong Wang Xindong Zhang College of Electronic Science and Engineering Jilin University Changchun China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

With the development of autonomous driving, people have more requirements for traffic sign detection technology, and it is very important to accurately identify the traffic signs roadside for people's safety. In view of the problem of low traffic sign detection accuracy, a traffic sign detection algorithm based on YOLOv7 is proposed. In this paper, Efficient Channel Attention (ECA) is introduced, making the YOLOv7 pay more attention to effective features. The experimental results show that compared with the original YOLOv7 algorithm, the improved YOLOv7 mAP@0.5 was 77.46%, which increased by 3.86%.

关键词： Accuracy Safety detection algorithms Autonomous vehicles

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Anomaly detection Algorithm for Multivariate Complex Data Based on Deep Neural Network

Anomaly Detection Algorithm for Multivariate Complex Data Ba...

引用

Artificial Intelligence, Internet and Digital Economy (ICAID), International Conference on

作者： Guangzhi Deng Haikun Wu Wen He Sheng Ding Qingqing Pan Zekun Ma State Grid Yinchuan Electric Power Supply Company Yinchuan Ningxia

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

The conventional data anomaly detection structure is mostly single target execution, and the detection efficiency is relatively low, resulting in an increase in the final false detection rate. Therefore, the design and practical verification of the anomaly detection algorithm for multivariate complex data based on deep neural network are proposed. At present, the data anomaly detection feature extraction is carried out first, and the multi-target method is used to improve the detection efficiency and build a multi-target anomaly monitoring and calculation structure. Based on this, the deep neural network data anomaly detection and calculation model is designed, and the adaptive cascade scheduling method is used to achieve anomaly detection. The test results show that compared with YOLOv7 target detection algorithm and multiple data enhancement strategy MODIS data segmentation optimization and typical related forest algorithm. The final false detection rate of the multi complex data anomaly detection algorithm of the deep neural network designed this time is relatively low, which indicates that the detection algorithm designed for abnormal data is more efficient, the detection range is identifiable, targeted and has practical application value with the help and support of the deep neural network technology.

关键词： Representation learning Noise Artificial neural networks Object detection Feature extraction Robustness detection algorithms Anomaly detection Optimization Periodic structures

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