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检索条件"主题词=Detection algorithms"

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Spatial Feature and Mish Loss Function Enhanced YOLOv8n for Efficient Volleyball detection

Spatial Feature and Mish Loss Function Enhanced YOLOv8n for ...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Jintao Pei Guangmin Liu Yanchen Gao Diancai Yang Yang Wan Yanfan Duan School of Railway Transportation Shandong Jiaotong University Jinan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China School of Science and Technology Qingdao University Qingdao China Qingdao Academy of Intelligent Industries Engineer Qingdao China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

To address the challenge of inaccurate volleyball height detection and localization due to complex environments and low image resolution during volleyball bobble assessments, a refined detection algorithm, YOLOv8n-SPD, is proposed. This algorithm enhances the YOLOv8n model by incorporating several key improvements. Firstly, a meticulously labeled dataset of volleyball bobble images is created. The algorithm integrates a Spatial Depth Convolution module (SPD-Conv), which preprocesses images prior to their entry into the neural network. By eliminating the max-pooling layer, redundant pixel information is reduced. Secondly, the activation function Mish is employed instead of SiLU to diminish the interdependence of parameters and enhance the adaptability of the network. Additionally, the DIoU replaces the original CIoU loss function, aiming to better align the predicted and actual bounding boxes. Experimental results show that YOLOv8n-SPD improves the mAP@0.5 metric from 99.3% to 99.4% and the mAP@0.5-0.95 metric from 86.5% to 87.6%, with an increase in GFLOPS from 8.1 to 11.5. This algorithm not only enhances accuracy but also improves real-time performance and stability in complex scenarios.

关键词： Measurement Location awareness Training Accuracy Image resolution Computational modeling Semantics Real-time systems Stability analysis detection algorithms

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On Data-Centric Intrusion detection in Wireless Sensor Networks

On Data-Centric Intrusion Detection in Wireless Sensor Netwo...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Michael Riecker Ana Barroso Matthias Hollick Sebastian Biedermann Secure Mobile Networking Laboratory Technische Universität Darmstadt Germany Security Engineering Group Technische Universität Darmstadt Germany

ISBN: (纸本)9781467351461

Wireless sensor networks (WSN) are increasingly used to support critical applications - especially in enterprise settings. If the sensor data collected through the network is incorrect, such applications cannot run reliably. Thus, detecting the occurrence of abnormal sensor values is crucial. In this paper we develop three decentralized, lightweight data anomaly detection mechanisms that can be run directly on sensor nodes. These algorithms are evaluated with a real dataset to which we added plausible attacks. Further, they are compared to standard centralized anomaly detection mechanisms.

关键词： Wireless sensor networks Temperature measurement Temperature sensors Classification algorithms Complexity theory Standards detection algorithms

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Matrix Based Community Evolution Events detection in Online Social Networks

Matrix Based Community Evolution Events Detection in Online ...

引用

IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Yang Sun Junhua Tang Li Pan Jianhua Li School of Information Security Engineering Shanghai Jiao Tong University Shanghai China School of Electrical and Electronic Information Engineering Shanghai Jiao Tong University Shanghai China

In recent years, the evolution of online social networks has become an important research topic in online social network analysis. An important approach to this problem is to detect community evolution events so as to understand the evolution of the whole network. Considering the huge amount of data in large social networks, an efficient and scalable community evolution detection algorithm is necessary. In this paper, we focus on community evolution events detection in dynamic social networks. First, we divide the Facebook and DBLP data set into a series of snapshots and apply the Louvain algorithm to find the communities in each snapshot. Then, we propose a light weight evolution events detection algorithm to find the community evolution patterns between adjacent snapshots, which statistically show the evolution trend of the entire network. Simulation results show that our algorithm can effectively detect the community evolution events in online social networks.

关键词： Correlation Event detection Merging Heuristic algorithms Facebook detection algorithms

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An Improved Crack detection Method Based on Pyramid Split Attention

An Improved Crack Detection Method Based on Pyramid Split At...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Zexu Du Guoliang Zhang Peng Wu Xiangquan Zhang Weizhou Wang Zhiyuan Wang Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory Global Energy Interconnection Research Institute co. Ltd Beijing China State Grid Gansu Electric Power Company Lanzhou City China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin China

ISBN: (纸本)9781665402682

A small leak will sink a great ship. The existence of various cracks poses a huge threat to the quality and safety of engineering construction. For manual inspection of cracks, various complex cracks have high false detections or missed detections. Therefore, we proposed a crack detection algorithm based on attention mechanism to automatically locate cracks to improve the safety of the engineering construction. By improving the CSP layer in the backbone of YOLOv5, it introduces an attention mechanism called pyramid split attention (PSA) to focus on the importance of different channels to make the network better learn the crack features. In addition, during the testing process, we use TTA to synthesize the network detection performances at different scales to get the final detection results more accurately. Finally, the F1 score of our network reached a high level on the RDDC2020 dataset.

关键词： Technological innovation Convolution Manuals Inspection Safety Marine vehicles detection algorithms

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detection algorithm of non-Gaussian character of sample1 distribution and its implementation in radar data processing

Detection algorithm of non-Gaussian character of sample1 dis...

引用

Signal Processing Symposium (SPS)

作者： Igor Prokopenko Nadiya Babanska Institute of Air Navigation National Aviation University Kyiv Ukraine

An article is dedicated to creation of locally-optimal robust algorithm of non-Gaussian signal detection on the base of sampling statistical analysis and to investigation of the algorithm effectiveness.

关键词： Noise detection algorithms Signal processing algorithms Radar detection Data processing Decision making

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Real-time fire and flame detection in video

Real-time fire and flame detection in video

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： N. Dedeoglu B.U. Toreyin U. Gudukbay A.E. Cetin Bilkent University Ankara Turkey

The paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker are detected by analyzing the video in the wavelet domain. Periodic behavior in flame boundaries is detected by performing a temporal wavelet transform. Color variations in fire are detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire-colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.

关键词： Fires Wavelet analysis Wavelet domain Frequency Layout Wavelet transforms detection algorithms Cameras Monitoring Motion analysis

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Synchronous optical CDMA systems with mark-detection algorithm: APD noise and thermal noise

Synchronous optical CDMA systems with mark-detection algorit...

引用

International Conference on Communication Technology (ICCT)

作者： Li Ou Wu Jiangxing Lan Julong National Digital Switching System Engineering and Technological Research and Development Center Zhengzhou China

A new mark-detection algorithm for synchronous optical CDMA using modified prime sequence codes is proposed. Performance analysis shows that the bit error probability (BER) is significantly improved when using the mar... 详细信息

关键词： Optical noise Multiaccess communication Decoding Optical signal processing Optical receivers Interference Signal processing algorithms Performance analysis detection algorithms Error probability

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Target detection and Anti-Clutter Using Energy Priority Idea Based on 77-79 GHz Radar

Target Detection and Anti-Clutter Using Energy Priority Idea...

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Jiadong Pu Shaonan Li Chao Wang Zhiquan Zhou Xiang Feng School of information science and Engineering Harbin Institute of Technology Weihai Shandong China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In this paper, to accurately establish sea clutter model, we propose a 3-partitioned random multiplicative mechanism using multifractal theory to tackle the high-frequency radar sea clutter, which could extract feature factors from sea clutter data and verifies the multifractal features. Furthermore, we present a clutter energy priority SVD-OP (CEP-SVD-OP) based detection algorithm. In which, echo signals of the selected distance units are combined into a signal space, and processed by orthogonal projection idea. Then, the clutter suppression is performed again for all the distance units on the individual pulses after orthogonal projection. The singular values are utilized to determine the signal subspace and clutter subspace. After eliminating the clutter subspace, the signal is reconstructed using the Hankel matrix, and the detection-flow can be achieved. Simulations has demonstrated that CEP-SVD-OP algorithm obtains some superior performance in sea clutter suppression and target detection.

关键词： Radar clutter Signal processing algorithms Sea measurements Radar detection Object detection Millimeter wave radar Feature extraction Fractals Clutter detection algorithms

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A feature-based filled pause detection system for Dutch

A feature-based filled pause detection system for Dutch

引用

IEEE Workshop on Automatic Speech Recognition and Understanding

作者： F. Stouten J.P. Martens ELIS Ghent University Ghent Belgium

Nowadays, automatic speech recognizers have become quite good in recognizing well prepared fluent speech (e.g. news readings). However, the recognition of unprepared or spontaneous speech is still problematic. Some important reasons for this are that spontaneous speech is less articulated, exhibits a high speaking rate and usually contains a lot of disfluencies. The latter occur when the speaker needs time to think about the continuation of his discourse, or when he needs to change/correct his last utterance. Although there are different types of disfluencies (interruptions, corrections, repetitions, etc.) the most common ones are filled pauses. They can take the form of an interjection like /uh/ or /uhm/, or an abnormal lengthening of one syllable of a word. In this paper we propose a new method for detecting such fillers prior to the speech recognition. Tests show that it is possible to improve the recognition accuracy by just removing the detected filled pauses from the recognizer input.

关键词： Computer vision Automatic speech recognition Search engines Detectors Databases Speech recognition Testing Indexing detection algorithms

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A Robust Graph-Based Algorithm for detection and Characterization of Anomalies in Noisy Multivariate Time Series

A Robust Graph-Based Algorithm for Detection and Characteriz...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Haibin Cheng Pang-Ning Tan Christopher Potter Steven Klooster Michigan State University Lansing MI USA NASA Ames Research Center Moffett Field CA USA California State University CA USA

detection of anomalies in multivariate time series is an important data mining task with potential applications in medical diagnosis, ecosystem modeling, and network traffic monitoring. In this paper, we present a robust graph-based algorithm for detecting anomalies in noisy multivariate time series data. A key feature of the algorithm is the alignment of kernel matrices constructed from the time series. The aligned kernel enables the algorithm to capture the dependence relationship between different time series and to support the discovery of different types of anomalies (including subsequence-based and local anomalies). We have performed extensive experiments to demonstrate the effectiveness of the proposed algorithm. We also present a case study that shows the utility of applying our algorithm to detect ecosystem disturbances in Earth science data.

关键词： Robustness Kernel Ecosystems Vegetation Data mining Geoscience Telecommunication traffic detection algorithms Earth Conferences

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