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检索条件"主题词=detection algorithms"

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Experience With a Physics-Based Anomaly detection Algorithm to Detect DC Faults at Utility PV Plants

Experience With a Physics-Based Anomaly Detection Algorithm ...

引用

IEEE Conference Photovoltaic Specialists (PVSC)

作者： Scott Sheppard Steven Koskey Corson Teasley Christopher Perullo Daniel Fregosi Wayne Li Turbine Logic Atlanta GA USA Electric Power Research Institute Charlotte NC USA

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

String, combiner, and tracker faults comprise a significant reduction in PV plant capacity. A method is presented to detect these faults using data typically monitored at utility-scale plants. The detection algorithm has been validated using aerial infrared data and the accuracy is presented across a range of sites, conditions, and scenarios, including automated versus manual model configuration and satellite versus ground-based weather data.

关键词： Photovoltaic systems Satellites Accuracy Manuals Data models detection algorithms Monitoring Anomaly detection Meteorology

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detection of Pantograph Angle Deficiency Based on Optimized YOLOv8n Model

Detection of Pantograph Angle Deficiency Based on Optimized ...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yifeng Wang Hu Sheng Na Geng Wenqiang Li School of Computer and Communication Engineering Dalian Jiaotong University Dalian China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

A optimized YOLOv8 detection algorithm, UIB-YOLOv8, is proposed to address the problems of high missed and false detection rates in pantograph fault detection, as well as difficulty in identifying pantograph angles in environments with insufficient lighting. A key point detection algorithm based on a high-resolution feature pyramid network is used to extract the position of the pantograph angle. The UIB module is introduced into the backbone network to enhance feature extraction in the image. The MQA attention mechanism is combined to reduce the number of model parameters and improve detection speed. Finally, a Focal Loss loss function is added before the result output to reduce the problem of imbalanced sample categories. The experimental results show that UIB-YOLOv8 improves MAP by 2.2%, reduces parameter count by 26.35%, and reduces floating-point computation by 24.36% compared to YOLOv8 algorithm. Especially in low light conditions, the accuracy of identifying pantograph angle is improved by 4.8%.

关键词： Fault diagnosis Deep learning Computer vision Machine learning algorithms Computational modeling Fault detection Lighting Object detection Feature extraction detection algorithms

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Application of HHT Algorithm to ECG QRS detection

Application of HHT Algorithm to ECG QRS Detection

引用

International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Bochen Chen Dan Ye Bo Jiang Lifeng Lin Bingli Jiao School of Electronics Peking University Beijing China General Administration of Customs Beijing China Guangzhou Nursing Home Guangzhou China

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

Electrocardiogram (ECG) signals are the most important tool in the diagnosis of cardiovascular diseases, and the QRS complexes are the most significant waveforms within ECG signals, representing the critical steps of the heart's pumping function. This study integrates the Hilbert-Huang Transform (HHT) into conventional ECG detection algorithms, which improves the real-time detection method of QRS complexes based on traditional differential thresh-old techniques. The effectiveness of the proposed method is validated using simulation results from the MIT-BIH ECG dataset provided by the Massachusetts Institute of Technology. The results indicate that the QRS complexes detection rate of the HHT-based ECG monitoring algorithm is significantly higher than that of traditional methods, thereby confirming the superiority of this approach.

关键词： Heart Filtering Simulation Noise reduction Transforms Electrocardiography Real-time systems detection algorithms Monitoring Testing

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Lightweight Multi-task Hyperdimensional Computing Framework Driven by Binary Neural Network for Sleep Apnea detection

Lightweight Multi-task Hyperdimensional Computing Framework ...

引用

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Tian Chen Yi Liu Guanzheng Liu Changhong Wang School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Sleep apnea syndrome (SAS) is a prevalent sleep disorder that impacts neurocognitive and cardiovascular health. The need for effective long-term management of SAS in everyday conditions has led to the adoption of wearable devices based on photoplethysmography (PPG) technology as a viable monitoring solution. However, the computational demands of current detection algorithms often surpass the capabilities of low-power, miniaturized wearable devices. To overcome this challenge, this study introduces a novel, lightweight detection algorithm for SAS that utilizes hyperdimensional computing. This algorithm is a multi-task hyperdimensional computing framework that is enhanced by binary neural network and incorporates metric learning. The proposed model demonstrates inference performance with an accuracy of $\mathbf{7 5. 7 2 \%}$, sensitivity of 73.74%, and specificity of $\mathbf{7 6. 4 3 \%}$. Compared to conventional machine learning approaches, this model also offers better performance on embedded platforms, achieving up to $25 \times$ reduction in memory usage, $39 \times$ decrease in latency, and 64× lower energy consumption on an ARM CortexM4 processor.

关键词： Training Computational modeling Neural networks Multitasking Sleep apnea Wearable devices Synthetic aperture sonar Integrated circuit modeling detection algorithms Monitoring

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A Distributed Critical Node detection Algorithm for UAV Swarm Networks

A Distributed Critical Node Detection Algorithm for UAV Swar...

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Computing, Communication, Perception and Quantum Technology (CCPQT), International Conference on

作者： Zihang He Haosong Gou Xiong Wu Gaoyi Zhang Pengfei Du Daosen Zhai School of Electronics and Information Northwestern Polytechnical University Xi'an China Sichuan Co. Ltd. China Mobile Group Chengdu China School of Physics and Electronics Hunan University Changsha China Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Xihua University Chengdu China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

Unmanned aerial vehicle (UAV) swarm can be widely used in cooperative detection and strike across wide geographic areas. To support the collaboration of multiple UAVs, a connected network topology is essential. Thus, critical nodes, whose removal will disconnect the network into two or more separate components, will play an important role in UAV swarm networks. This paper investigate the distributed critical node detection algorithm for UAV swarm networks. Firstly, we analyze the logical relationship between critical node, robustness, and k-hop subgraph. Guided by the analysis, we propose a distributed critical node detection algorithm by using the geometrical relationship of neighbors and the k-hop local topology. Abundant simulation results indicate that the proposed algorithms can strike a good balance between accuracy and overhead and greatly outperform the global detection algorithms especially in large-scale networks. Besides, the simulation study provides some useful design guidance for robust topology control of UAV swarm networks.

关键词： Accuracy Quantum computing Network topology Simulation Quantum mechanics Collaboration Autonomous aerial vehicles Robustness Topology detection algorithms

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Video Matting Tamper detection Based on Compressed Tracking

Video Matting Tamper Detection Based on Compressed Tracking

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Zhiwen Chen Xiling Yang Computer Science Wuhan Railway Vocational College Of Technology Wuhan China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In the digital era, the authenticity of video content has become a significant concern, especially given the rapid advancements in video technology and the increasing potential for tampering. This poses risks to information reliability and impacts areas like legal proceedings, media integrity, and personal privacy. This paper introduces a novel video matting tamper detection method that employs compressed tracking to ascertain the authenticity of video subjects. The method involves several precise steps: initially, edge detection highlights potential tampering by outlining object boundaries. Feature analysis then examines elements like color and texture for anomalies, and target tracking monitors object movements over time to identify inconsistencies. A final comparison of these results with known authentic characteristics helps pinpoint tampering signs. Extensive testing has proven the method’s effectiveness in detecting various tampering types accurately, offering a reliable tool for verifying video authenticity and contributing valuable insights to the field of information security.

关键词： Technological innovation Privacy Target tracking Image edge detection Media Reliability Object recognition detection algorithms Monitoring Testing

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Crack detection Algorithm Based on Improved YOLOv11n Network

Crack Detection Algorithm Based on Improved YOLOv11n Network

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

作者： Yuxin He Ailing Ding School of Information Engineering Changan University Xi'an China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

The detection algorithms for various engineering structure cracks often suffer from missed detections, false positives, and low accuracy. This paper proposes an improved crack detection model based on Yolov11, integrating RFCBAM and Triplet Attention mechanisms. Experimental results show that this model performs better in detection compared to the original model and those improved by a single mechanism. The metrics for Precision (P), Recall (R), mAP 50 , and mAP 50−95 all reach high levels.

关键词： YOLO Computer science Attention mechanisms Accuracy Sensitivity Target recognition Data mining detection algorithms Monitoring Intelligent control

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A framework for automatic analysis of sheep ruminating behavior based on keypoint detection

A framework for automatic analysis of sheep ruminating behav...

引用

International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Haotian Zhang Yuan Ma Yinghan Shi Yongliang Qiao Meili Wang College of Information Engineering Northwest A&F University Yangling China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture Yangling China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling China Australian Institute for Machine Learning (AIML) The University of Adelaide Australia

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Ruminating behaviour is a critical indicator of sheep health and welfare, with abnormal rumination strongly associated with gastric ailments in sheep. Timely monitoring and analysing ruminating behaviour are imperative for assessing sheep health status and optimizing feed composition. However, deploying existing ruminant monitoring algorithms on sheep farms presents challenges due to the hypermobility of the sheep’s head and the complexity of farm environments. This study proposes a real-time analysis framework based on the RTMPose keypoint detection method for sheep rumination behaviour. The framework employs keypoint detection to precisely analyze various aspects of sheep rumination, including the number of ruminating chews, ruminating time, chewing rate, ruminating rate, and ruminating time rate. Additionally, we constructed a dataset for sheep rumination behaviour to train the framework’s object detection and keypoint detection algorithms. Experiments were conducted using fifteen challenging *** demonstrate that the ruminating behaviour analysis framework based on key points achieves acceptable with 94.56% accuracy in the number of ruminating chews, 97.49% accuracy in ruminating time, and 97.99% accuracy in chewing rate, with 72.9M parameters. The proposed method can be flexibly deployed on handheld monitoring devices, presenting a viable approach for automatic identification and analysis of rumination behaviour using computer vision technology.

关键词： Accuracy Mechatronics Costs Object detection Streaming media Real-time systems Object recognition Feeds detection algorithms Monitoring

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Object detection in High-Resolution Aerial Images Using Single Shot Multi-Box Detector

Object Detection in High-Resolution Aerial Images Using Sing...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： S. Gobinath Muntather Almusawi G Shalini Bollipelly Pruthviraj Goud K Sudheer Kumar Department of Information Technology V.S.B. Engineering College Karur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology Anurag University Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In recent decades, the detection of objects plays a vital role in High-Resolution Aerial Images and extensive range applications such as environmental managing, urban development and military applications. The obj ect detection in high resolution aerial images has improved with advanced technologies. However, there is still challenging task due to various appearances and scales of the obj ects. A Single Shot Multi-Box Detector Method (SSDM) is proposed in this paper for higher detection precision of Objects in High-Resolution Aerial Images. The Histogram Equalization (HE) is used for augmenting the images of RSOD dataset and Otsu method is used for segmentation process. Next, feature extraction is carried out by Gray Level Co-occurrence Matrix (GLCM) method. And lastly the proposed SSDM method is used for detecting the obj ects in high resolution aerial images. The proposed SSMD technique is evaluated using the RSOD dataset and achieved 99.7% of precision, 97.3% of recall and 99.1 % of map respectively. The proposed SSDM method outperformed when compared with existing methods such as KCFS-YOLOv5 and LAR-YOLOv8.

关键词： YOLO Image segmentation Histograms Image resolution Databases Detectors Network security Data science Feature extraction detection algorithms

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Adaptive Scene Change Safety Helmet Wearing detection Algorithm

Adaptive Scene Change Safety Helmet Wearing Detection Algori...

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Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Xiangming Qi Yuhan Li School of Software Liaoning Technical University Huludao China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Wearing a safety helmet is a basic measure to reduce or avoid accident losses. However, factors such as changes in lighting and target occlusion can cause instability in various test result indicators. To address this issue, a helmet-wearing detection algorithm based on adaptive scene changes is proposed. Taking YOLOv8n as the baseline, the SE attention mechanism is embedded in the basic unit C2f to adjust channel weights, capturing more details and enhancing the ability for multi-scale feature extraction and fusion. The BiFormer attention mechanism is embedded at the front end of the detection head, utilizing dual-layer routing attention to improve target feature capture, enhance detection head performance, and strengthen the algorithm's adaptive scene change capability. The baseline algorithm's loss function, CIoU, is replaced with Inner IoU, focusing more on overlapping areas within the bounding box. This improves detection accuracy, accelerates convergence, and enhances generalization ability. Additionally, the Lion optimizer is used to optimize the algorithm, improving gradient descent performance, reducing memory consumption and computational complexity, and increasing detection speed. Ablation experiments and comparative experiments on the self-made Adaptive Scene dataset show that, compared with the baseline algorithm, Precision, Recall, and mAP are improved by 3.1%, 5.3%, and 4.4 %, respectively, demonstrating the superiority of the proposed algorithm.

关键词： Training Head Attention mechanisms Feature extraction Routing Loss measurement Robustness Safety Classification algorithms detection algorithms

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