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检索条件"主题词=Detection Algorithms"

共 7428 条记录，以下是891-900 订阅

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GCSAR: Small-Object-Enhanced detection Algorithm Based on Gradient Aggregation and Channel Enhancement for SAR Ship detection

GCSAR: Small-Object-Enhanced Detection Algorithm Based on Gr...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Kefu Lin Yujuan Han School of Computer Science Shanghai Maritime University Shanghai China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

High-resolution Synthetic Aperture Radar (SAR) utilizes pulse compression technology to accurately resolve the distance between targets and enable imaging,the application of detection algorithm in ship identification task is promoted. However, the current CNN architecture does not perform well for tasks such as low-resolution images and small objects. The features of small targets may be lost during the training process, is one of the factors that makes ship detection missions perform poorly in SAR *** study presents GCSAR, an enhanced detection algorithm tailored for detecting small objects in SAR imagery, focusing specifically on maritime ship detection. The model improves upon the YOLOv8 architecture by incorporating SPDconv in the backbone and Gradient-Aggregation-and-Channel-Enhancement-Module (GACEM) in the neck, allowing for better detection performance in SAR images dataset containing sea vessels images.

关键词： Training Image resolution Object detection Network architecture Feature extraction Radar polarimetry Neck Marine vehicles detection algorithms Synthetic aperture radar

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Bag of Tricks Toward Enhanced Shuttlecock detection from Badminton Videos

Bag of Tricks Toward Enhanced Shuttlecock Detection from Bad...

引用

International Electrical Engineering Congress (iEECON)

作者： Muhammad Abdul Haq Shuhei Tarashima Norio Tagawa Faculty of Systems Design Tokyo Metropolitan University Tokyo Japan Innovation Center NTT Communications Corporation Tokyo Japan

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

Shuttlecock detection is crucial for video analysis in badminton matches, but identifying tiny and fast-moving shuttlecock from videos is still challenging. In this work, we propose several tricks that can be attached to a state-of-the-art shuttlecock detection algorithm, TrackNetV2. Our proposed tricks include introducing residual network, Leaky ReLU, and post-addition batch normalization, all based on the deep learning theory. The contribution of this work is to apply the well-known techniques to shuttlecock detection successfully, and this is the first attempt in shuttlecock detection literature. Our experiments on a public badminton dataset demonstrate that our proposed tricks can substantially improve TrackNetV2 regarding precision, recall, F1, and accuracy from 84.8%, 81.8 % ,83.3 % , and 75.8% to 88.2%, 83.1%,85.3 % , and 78.1%, respectively.

关键词： Training Electrical engineering Transformers detection algorithms Residual neural networks

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A semi-supervised GCN-based community detection algorithm

A semi-supervised GCN-based community detection algorithm

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shu-Han Shi Lei-Lei Shi Lu Liu Zi-Xuan Han Liang Jiang Fu-Xiang Chen School of Computer Science and Communication Engineering Jiangsu University China Department of Computer Science University of Exeter UK School of Ocean Jiangsu University of Science and Technology China School of Computing and Mathematical Sciences University of Leicester UK

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms.

关键词： Training Representation learning Measurement Location awareness Accuracy Graph convolutional networks Network analyzers Prediction algorithms Reliability detection algorithms

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Backdoor detection in Digital Twin-Driven Smart Grid via Contribution Analysis

Backdoor Detection in Digital Twin-Driven Smart Grid via Con...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Peiming Xu Peilin Luo Yixin Jiang Tian Dong Zhihong Liang Fazhong Liu Yan Meng Haojin Zhu Electric Power Research Institute CSG Guangzhou Guangdong China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

With the integration of smart grid and digital twin technologies, federated learning becomes pivotal in ensuring privacy-preserving AI model training. In such scenarios, each user within the smart grid is geographically dispersed, prohibiting the sharing of raw data to safeguard privacy. However, this setup is susceptible to backdoor attacks. When an attacker initiates such an attack, the smart grid model is manipulated to produce the attacker's desired output, while maintaining normal functionality under standard conditions. The inherent distributed nature of federated learning complicates the detection of backdoor attacks. To address this, we model the federated learning process within a digital twin as a cooperative game. By employing the Shapley value to measure the contribution of nodes in each training round, we propose an outlier detection algorithm to identify malicious users. The effectiveness of the proposed algorithm has been evaluated via extensive experiments.

关键词： Training Data privacy Federated learning Games Smart grids Digital twins Game theory detection algorithms Anomaly detection Standards

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Fast detection Algorithm of Food Nutrients Based on Multi-Feature Information

Fast Detection Algorithm of Food Nutrients Based on Multi-Fe...

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Telecommunications and Power Electronics (TELEPE), International Conference on

作者： Xinglin Zeng Yanchun Ruan Ruichen Bu Mei Lin Xudong Peng College of Cuisine Science and Technology Jiangsu Vocational College of Tourism Yangzhou Jiangsu China College of Food Science and Engineering Yangzhou University Yangzhou Jiangsu China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

This study is devoted to solving the problems of low efficiency and high cost in the detection of food nutrients, and proposes a fast detection algorithm of food nutrients based on multi-feature information. The algorithm uses the color, shape, texture and other characteristic information of food images, and combines with machine learning technology to realize the rapid prediction of food nutritional components. In the research process, this paper first collects and preprocesses the food images to extract rich feature information. Then, through feature extraction and selection, the mapping relationship model between nutritional components and feature information is established. Through experimental design and result analysis, this paper verifies the effectiveness and accuracy of the algorithm, and compares it with the traditional chemical analysis method, further proving the superiority and feasibility of the algorithm. Finally, the performance and applicability of the algorithm are comprehensively evaluated, and the future research direction and improvement space are pointed out. This study provides a new idea and method for rapid detection of food nutrients, which has certain theoretical and application value.

关键词： Accuracy Shape Image color analysis Chemical analysis Prediction algorithms Feature extraction Nutrients Telecommunications detection algorithms Optimization

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Spectrogram and Mel-Spectrogram Based Dysphonic Voice detection Using Convolutional Neural Network

Spectrogram and Mel-Spectrogram Based Dysphonic Voice Detect...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Rumana Islam Mohammed Tarique Dept. of Elect. Engg. Univ. of Sci. and Tech. of Fujairah (USTF) Fujairah UAE

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

A non-invasive pathological voice detection system has been presented in this paper. This work considers two spectral images of voice signals namely spectrogram and Mel-spectrogram to detect dysphonic voices. The spectrogram is a convenient representation of voice signals on a time-frequency scale and has been popularly investigated in pathological voice detection algorithms. However, the spectrogram uses a linear frequency scaling and hence, does not consider the resolution of the human auditory model. The Mel-spectrogram overcomes this limitation by using a quasi-logarithmic frequency spacing. This work uses vowel samples available in the Saarbrucken voice database. The simulation results show that the Melspectrogram outperforms the spectrogram in terms of classification accuracy.

关键词： Deep learning Pathology Time-frequency analysis Accuracy Image resolution Simulation Feature extraction detection algorithms Spectrogram Signal resolution

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YOLOv7-SC:A Traffic Sign detection Algorithm Based On SimAM and improved SPPCSPC-C

YOLOv7-SC:A Traffic Sign Detection Algorithm Based On SimAM ...

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

作者： Qian Huang Haiyuan Nong Wenquan Peng Chao Deng School of Computer Science and Engineering Guangdong Ocean University Yangjiang Guangdong China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

With the rapid development of autonomous vehicles, traffic sign detection has become a prominent problem. Traffic sign detection is the basic premise of automatic driving environment perception. Aiming at the low accuracy of traditional traffic sign detection, an enhanced YOLOv7 algorithm incorporating the Simple Attention Mechanism and SPPCSPC-C module has been proposed. SimAM is introduced to improve the recognition ability of the original algorithm in complex dynamic scenes, and SPPCSPC-C module is introduced to enhance the detection ability of the model for small targets. The model is trained based on Speed Limit detection data. The experimental results show that the mAP, Precision and Recall of the improved YOLOv7 reach 97.09%, 97.85% and 93.84%. Compared with YOLOv7, the mAP is increased by 1.19%, the Precision is increased by 2.90%, and the Recall is increased by 2.83%. It is an algorithm worthy of consideration in traffic sign detection.

关键词： Attention mechanisms Accuracy Target recognition Heuristic algorithms Computational modeling Object detection Robustness Data models Vehicle dynamics detection algorithms

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An FTN Sequence detection Algorithm Based on Soft Information Iteration and ADMM Optimization

An FTN Sequence Detection Algorithm Based on Soft Informatio...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Zhiwen Li Qiang Chen Hui Xie School of Electronic and Information Engineering Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

To address the severe inter-symbol interference (ISI) problem in faster-than-Nyquist (FTN) communication systems, this paper proposes an Iterative Soft ADMM detection (ISAD) sequence detection algorithm. The ISAD algorithm ingeniously integrates soft information iterative updating with ADMM optimization by alternately estimating residual ISI and updating symbol soft values while utilizing constellation prior knowledge and observation information to guide symbol estimation, ultimately achieving efficient detection in FTN systems. Compared with traditional methods, the ISAD algorithm effectively suppresses ISI while significantly reducing computational complexity, particularly in medium to high-order modulation schemes, where it exhibits outstanding performance. Simulation results demonstrate that the proposed algorithm can effectively suppress ISI in medium to high-order modulation and achieve bit error rate performance close to that of Nyquist orthogonal transmission. The proposed ADMM soft detection algorithm provides an effective solution for FTN communication systems.

关键词： Communication systems Simulation Bit error rate Modulation Symbols Interference Iterative algorithms detection algorithms Optimization Intelligent control

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A Novel detection Scheme for Generalized Spatial Modulation System Based on Sparse Reconstruction

A Novel Detection Scheme for Generalized Spatial Modulation ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Xiteng Liu Yang Zhang Chunsheng Qin Aviation Maintenance NCO Academy of Air Force Engineering University Xinyang China 94778 unit of Chinese People's Liberation Army Shanghai China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Aiming at the problem of high complexity of traditional detection algorithms for generalized spatial modulation *** theory of compressed sensing was used to construct a signal detection model. According to the sparse property of generalized spatial modulation signals, a stagewise orthogonal matching pursuit detection algorithm based on sparse reconstruction was proposed. The active antenna index sequence was selected according to the correlation threshold judgment and the foreseen sparsity was not needed in the iterative process of the algorithm, the index set and signal residuals were *** the sent data was restored based on optimized termination condition and signal quantization. Analysis and experiments show the algorithm has smaller root mean square error and the detection performance is improved compared with reconstruction algorithms of the same category,and has substantially lower complexity compared with the maximum likelihood algorithm.

关键词： Sparse approximation Matching pursuit algorithms Modulation Reconstruction algorithms Approximation algorithms Prediction algorithms Iterative algorithms Complexity theory Indexes detection algorithms

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Regularization and dimensionality reduction techniques for pattern detection in GitHub repositories.

Regularization and dimensionality reduction techniques for p...

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IEEE Colombian Communications Conference (COLCOM)

作者： Wilson Chango Geovanny Silva Pamela Buñay-Guisñan Santiago Logroño Computer Science and Electronics Information Technology ESPOCH Riobamba Ecuador Universidad Nacional de Chimborazo UNACH Riobamba Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

In the constant pursuit of improving the efficiency of machine learning models, various strategies have been employed to tackle the challenges related to the complexity and simplicity of datasets. Identifying the features that impact machine learning models or algorithms is essential. Avoiding overfitting is crucial, as it can result in irrelevant variables, increased computational cost, and generation of missing values that introduce significant biases, affecting the predictive capacity. A thorough evaluation of various machine learning algorithms was conducted to build an efficient predictive model, focusing on error reduction. algorithms such as PCA, IPCA, and KPCA were compared, along with regularization methods such as Linear, Lasso, Ridge, and Elastic Net to improve model accuracy. The analysis was carried out using a large dataset that encompasses all repositories on GitHub, providing a comprehensive representation of the information needed for the comparison and evaluation of the aforementioned algorithms. The results obtained offer crucial insights for the continuous optimization of the model, with an exclusive focus on the context of GitHub. The metrics used to analyze software projects in repositories such as GitHub provide an overview of the state and dynamics of a project in terms of activity, participation, and code quality. These metrics are fundamental for making informed decisions about software management and development. Identifying the features that impact machine learning models and evaluating various algorithms and regularization methods allow for the construction of efficient predictive models that can be applied in various areas of the software industry.

关键词： Software development management Machine learning algorithms Predictive models Prediction algorithms Software algorithms Measurement Heuristic algorithms Computational modeling Principal component analysis detection algorithms Pattern recognition Data models Codes Access control Continuous integration

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