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检索条件"主题词=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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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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Reputation-Based Classification for Malicious User detection in Cognitive Radio Networks

Reputation-Based Classification for Malicious User Detection...

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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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Optimization of Financial Fraud detection Algorithm by Combining Variational Autoencoder with Generative Adversarial Networks

Optimization of Financial Fraud Detection Algorithm by Combi...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yu Liu Audit & Assurance Deloitte & Touche LLP Detroit MI United States

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This article delves into the field of financial fraud detection and proposes innovative solutions based on big data, machine learning, and deep learning technologies to address the challenges posed by the complexity and technology driven new features of modern financial fraud that traditional credit evaluation methods face. The latest progress in financial fraud risk, detection technology, and machine learning applications was systematically reviewed through literature research, and the shortcomings of existing research were identified. To solve the problem of sample imbalance, this paper innovatively designs AE-G and VAE-G models, which use normal transaction samples to train and generate fraudulent samples, achieving efficient binary classification learning and effectively alleviating the problem of data imbalance. In empirical analysis, the excellent performance of AE-G and VAE-G models in fraud detection was verified using the credit card transaction dataset jointly developed by the Free University of Brussels and Worldline. In particular, the VAE-G model performed well in AUC, recall rate, and F1 score, and significantly improved the recognition ability of minority fraud transactions. This article not only provides new ideas for the field of financial fraud detection, but also provides technical support for practical applications. At the same time, it points out the limitations of dataset diversity and the authenticity of generated fraud samples, which provides direction for future research and is of great significance for promoting the development of financial technology in the field of anti fraud.

关键词： Performance evaluation Fintech Face recognition Diversity reception Finance Generative adversarial networks Feature extraction Fraud detection algorithms Optimization

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Improved YOLOv8 Factory Safety Wearable detection Algorithm

Improved YOLOv8 Factory Safety Wearable Detection Algorithm

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Wenhao Wang Buying Fu Yanlun Wu Zhenyang Yu Chenjin Sun Xin Chen Faculty of Computer Engineering Huaiyin Institue of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In industrial scenarios, the YOLOv8 algorithm is difficult to extract key features due to the complexity of background information in images such as helmets, safety vests, and gloves, and is prone to problems such as missed detection and false detection. The C2f_MSCA structure is constructed to replace the C2f module in the Backbone network of YOLOv8 to improve the key feature extraction ability of the Backbone network. The visual information contained in the small target objects in the dataset is very limited, it is difficult to capture its features, and it is easy to be covered by complex backgrounds. A GS_Head detection branch is added to the Head part to enhance the learning ability of small targets. The receptive field attention convolution is used in the neck, which reduces background interference by adjusting the response of the feature map in the dataset and ignoring the region unrelated to the target. The experimental results show that the proposed algorithm improves the mean average precision (mAP) by 1.2 compared with the original YOLOv8 model, which effectively improves the performance of the model.

关键词： Visualization Convolution Object detection Feature extraction Production facilities Robustness Safety Neck detection algorithms Kernel

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Non-coherent Signal detection Algorithm Based on Molecular Communication and Its Experimental Verification

Non-coherent Signal Detection Algorithm Based on Molecular C...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Leifu Ren Yitao Hu Xiaoyi Bian Chuwen Lan Jiaxing Wang Guoan Zhao Zhimin Zheng Chunfeng Cui Zehua Gao China Mobile Information Technology Co. Ltd Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China North China Institute of Aerospace Engineering Hebei China China Mobile Research Institute Beijing China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

Demodulation at the receiver is a pivotal process in molecular communication (MC), involving the identification and transformation of transmitted signals to retrieve the original information from the sender. Due to the unique properties of MC, traditional demodulation techniques from electromagnetic communication cannot be directly applied. The demodulation process in MC must account for complex factors such as molecular diffusion, reaction kinetics, and environmental influences, which can affect the receiver's ability to recognize and process signals. Most existing signal detection methods rely on an unbounded model, which does not align with real-world communication scenarios, and are unsuitable for bounded environments. Addressing these issues, this paper proposes a non-coherent signal detection method suitable for bounded models, using pre-smoothing of data to set thresholds for current time slots. Through experimental simulations of the communication process using the molecular communication simulator developed by China Mobile, we analyze the channel capacity and bit error rate (BER). The results demonstrate that our approach significantly reduces the BER and enhances the reliability of MC in bounded environments.

关键词： Analytical models Computational modeling Bit error rate Molecular communication Receivers Reliability Channel models detection algorithms Signal detection Demodulation

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Novel Abnormal Data Mining and detection Algorithm for Intelligent Systems

Novel Abnormal Data Mining and Detection Algorithm for Intel...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Shaomei Xu Yanling Fan Bing Lin Continuing Education Service Center Shandong Institute Of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In intelligent systems, as the amount of data increases, how to analyse data and extract abnormal information is an important task. Based on this, in order to improve the detection efficiency and accuracy of abnormal data in intelligent systems, this study proposes a new and efficient abnormal data mining and detection algorithm. In the initial modelling process, this study introduced the minimum modification cost principle based on cross entropy for modelling. This algorithm optimizes the time series data cleaning process, thereby removing noise data while maintaining the integrity of the data sequence. Then, the extreme gradient boosting algorithm is combined with the Gaussian kernel function to achieve data dimension reduction. This operation can reduce data dimensions and feature redundancy. Finally, the overall anomaly detection operation by integrating the improved Ada boost algorithm with the sampling balance technology can improve the accuracy of the classifier while solving the problem of unbalanced data. The experimental results show that the proposed method is superior to the traditional algorithms in comprehensive indicators.

关键词： Sentiment analysis Accuracy Time series analysis Redundancy Noise Classification algorithms Intelligent systems detection algorithms Kernel Optimization

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UAVLite-YOLOv8: UAV Small Target Lightweight detection Algorithm

UAVLite-YOLOv8: UAV Small Target Lightweight Detection Algor...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Huan Liu Yusen Nie Xiaodong Cheng School of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Aiming at the problems of UAVs in target detection, such as small target size, dense target, difficulty in feature extraction, and the excessive amount of parameters and high computation when the YOLO algorithm is applied to actual deployment, a UAV small target lightweight detection algorithm, UAVLite-YOLOv8, which is based on the improvement of YOLOv8 algorithm, is proposed. Dilation-wise Reparam Residual (DWRR) is firstly used to improve the C2f layer in the backbone, which improves the accuracy of the model while reducing the resource consumption. Global and local feature fusion (GLFF) is then used in the neck to improve the accuracy of the algorithm without significantly affecting the number of network parameters and computational effort. Finally, the detail fusion shared convolution lightweight detection (DFSCLD) head is proposed to replace the original algorithm detection head, which reduces the computational burden while maintaining the detection performance and makes the algorithm more suitable for real-time application scenarios. By conducting comparative experiments of different algorithms on the VisDrone2019 dataset, the results show that the UAVLite- YOLOv8 algorithm significantly outperforms other target detection algorithms. Compared with YOLOv8, the mAP@0.5 of UAVLite-YOLOv8 grows from 31.4% to 32.2%, the amount of parameters is reduced by 25.6%, and the amount of computation is reduced by 17.3%. In this way, it proves that the algorithm can better meet the needs of UAV small target lightweight detection.

关键词： YOLO Technological innovation Accuracy Head Quantization (signal) Object detection Autonomous aerial vehicles Real-time systems Reliability detection algorithms

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A Step-type Obstacle detection Method based on 3D Point Cloud Map

A Step-type Obstacle Detection Method based on 3D Point Clou...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Fanghong You Pengcheng Wang Rui Zhou Yihan Zhang Jian Zhang Tao Zhao College of Electrical Engineering Sichuan University Chengdu China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Addressing the challenge of precise detection of traversable obstacles resembling steps in 3D point cloud map generated by LiDAR, this paper proposes a detection algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and geometric constraints. The proposed method achieves accurate detection of step-type traversable obstacles in 3D point cloud map. Firstly, the 3D point cloud map is filtered to remove outliers and extract regions of interest. Then, the Random Sample Consensus (RANSAC) plane fitting algorithm is employed to detect and remove ground points for eliminating clustering interference. Subsequently, the DBSCAN algorithm is applied for density clustering of the point cloud map without ground points. Finally, geometric constraints resembling step-type traversable obstacles are applied to the clustering results to select the optimal detection targets. Experimental results demonstrate that the proposed detection algorithm can accurately detect various step-type traversable obstacles in indoor 3D point cloud maps, achieving desirable outcomes.

关键词： Point cloud compression Three-dimensional displays Accuracy Laser radar Noise Neural networks Clustering algorithms Robustness Labeling detection algorithms

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