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检索条件"主题词=detection algorithms"

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Voltage detection Algorithm for Characteristics of Grid-Forming Converters Under Voltage Drop Events

Voltage Detection Algorithm for Characteristics of Grid-Form...

引用

Power Energy Systems and Applications (ICoPESA), International Conference on

作者： Guoping Huang Qiao Shi Junfeng Liu Feng Liao Foshan Power Supply Bureau of Guangdong Power Grid Co. Foshan Guangdong School of Automation and Science and Engineering South China University of Technology Guangzhou Guangdong

ISBN: (数字)9798350351668

ISBN: (纸本)9798350351675

Clarifying the impact and output voltage of the grid- forming converter under grid voltage drop events is a key step in voltage drop management. Focusing on the grid-forming converter with a control structure based on virtual synchronous generator technology and the current limiting section, this paper explores the operating mechanism of the grid-forming converter during voltage drops and the characteristic waveforms and variations of the output voltage under grid voltage drop events are obtained. Aiming at the waveform characteristics of the output voltage, a voltage detection algorithm based on the coordinate-free transform method and morphological filters is proposed, which eliminates the intermediate section of constructing the virtual three-item voltage, and can directly separate the direct flow of the voltage signals. The morphological filters can better extract the cosine periodic fluctuation and change components in the characteristic waveforms of the voltage. Finally, a detection model is built using Simulink, the model verifies the feasibility and effectiveness of the new detection algorithm combined coordinate- free transform method with the morphological filter, as well as its high adaptability to the characteristic waveforms of the grid- forming converter.

关键词： Adaptation models Filters Fluctuations Limiting Focusing Transforms Synchronous generators detection algorithms Voltage control Grid forming

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Research on Apple Fruit detection Algorithm Based on Improved YOLOV8

Research on Apple Fruit Detection Algorithm Based on Improve...

引用

Algorithm, Image Processing and Machine Vision (AIPMV), International Conference on

作者： Yuyang Zhang Jing Jiang Shan Fu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

To address the issues of complex background interference, multi-scale targets and poor detection accuracy of overlapping targets in apple image target detection, an apple fruit detection algorithm CGW-YOLOv8 based on improved YOLOv8 is proposed. Firstly, the CBAM attention module is introduced based on the YOLOv8 detection model to enhance the saliency of the fruit in complex environments and reduce the computational cost of the network. Secondly, GFPN effectively combines feature information of different resolutions, so that the model can achieve higher detection accuracy when dealing with multi-scale targets. Finally, WIoU_v3 is used to assign different weights to each feature point to improve the matching accuracy between the predicted box and the real box, while significantly improving the positioning accuracy. The test results on the apple dataset show that this method achieves an average accuracy of 87.3 %, with an mAP improvement of 3.5% compared to the original YOLOv8 algorithm. The frame rate FPS is 357 frames per second, achieving real-time detection.

关键词： Accuracy Target recognition Computational modeling Machine vision Object detection Feature extraction Prediction algorithms Real-time systems detection algorithms Optimization

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Complex Environment Road Object detection Algorithm Based on Improved YOLOv5s

Complex Environment Road Object Detection Algorithm Based on...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Hu Da Department of Scientific Research Gansu Computing Center Lanzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

A improved YOLOv5s (You Only Look Once 5s) algorithm is intended to dispose of the issues of low detection accuracy, low coverage, and large computational complexity of road object detection algorithms in complex environments. To improve the applicability of the algorithm in various complex scenarios, we draw the CBAM attention mechanism into the CBS module, which is the Backbone benchmark network to enhance the ability to obtain tiny target features and upgrade the recognition rate of tiny targets. By optimizing the CIo U loss function and the alpha IoU loss function, this paper use the alpha IoU loss function to substitute the traditional loss function of YOLOv5s model, this enhancement improves model training accuracy and hasten the convergence rate of the exercise process. The conclusion shows that the improved YOLOv5s algorithm has an accuracy of 94.06%, a recall rate is 92.33%, and an average accuracy is 95.38%. It is superior to other mainstream detection algorithms such as YOLOv5s and SSD, and can achieve road object detection in complex environments. The research results suggest that the improved YOLOv5s algorithm has improved practical efficiency, especially for complex scene object detection, which has important research significance.

关键词： YOLO Training Accuracy Attention mechanisms Target recognition Roads Benchmark testing detection algorithms Optimization Convergence

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detection of Low Rate DDoS with Adversarial Attacks by Enhanced Generative Adversarial Networks

Detection of Low Rate DDoS with Adversarial Attacks by Enhan...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Tzu-Yi Hsiao Shih-Hsuan Yang National Taipei University of Technology Taipei Taiwan Computer Science and Information Engineering Department National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Internet of Things is vulnerable to cybersecurity attacks because it is typically designed with minimal security considerations. The Low-Rate Distributed Denial-of-Service (LR-DDoS) attacks evade detection by injecting a small portion of malicious packets that persistently lurk within the IoT network. Recently, adversarial LR-DDoS attacks further complicate machine learning based security protection. In this study, an improved detection algorithm based on a generative adversarial network is proposed against LR-DDoS. To learn the temporal characteristics, LR-DDoS time-series features are taken as a part of the generator's input and Long Short-Term Memory modules are incorporated. Two discriminators with adaptive weighting are employed, one for conventional LR-DDoS attacks and the other for the adversarial attacks, to generate plausible LR-DDoS samples. The generated packets are used to train the detection algorithm of an Intrusion detection System. Simulation results show that the proposed method achieves an accuracy of 94% in detecting adversarial LR-DDoS packets, which outperforms the state-of-the-art methods by at least 10%.

关键词： Accuracy Computational modeling Machine learning Generative adversarial networks Feature extraction Transformers Complexity theory Internet of Things detection algorithms Long short term memory

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Non-Parametric Target detection Based on Sparse Representation for Hyperspectral Images

Non-Parametric Target Detection Based on Sparse Representati...

引用

Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Wenhao Wang Jun Liu Danilo Orlando Li Xiao Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Dipartimento di Ingegneria dell’ Informazione Università di Pisa Pisa Italy MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

Target detection is a critical aspect in the practical application of hyperspectral images. Recently, sparse representation has garnered significant attention in this field. The underlying assumption of the sparse representation model is that each pixel can be linearly and sparsely represented by a combination of the prior target and background spectra. However, detection algorithms based on sparse representation heavily rely on preset hyperparameters, which may lead to noticeable performance degradation if not chosen appropriately. To avoid this issue, we propose a non-parametric iterative target detection algorithm that considers uninterested spectra as interference and iteratively estimates target energy while minimizing interference energy. Furthermore, our proposed method enhances the detection performance for multi-pixel targets through appropriate selection of multiple test pixels. Experimental results on real hyperspectral datasets demonstrate the superior performance of our proposed detector over its counterparts.

关键词： Dictionaries Sparse approximation Object detection Interference Detectors Iterative algorithms Sparse matrices Security detection algorithms Hyperspectral imaging

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Endoscopic Lesion detection Based on Improved SSD

Endoscopic Lesion Detection Based on Improved SSD

引用

Robotics, Control and Automation Engineering (RCAE), International Conference on

作者： Tao Wu Xiaohui Liu Lisheng Wei Ziqiang Shao Xiaoxue Huang Shengbo Zhu School of Electrical Engineering Anhui Polytechnic University Wuhu China Shanghai Oushuo Intelligent Packaging Technology Co. Ltd China Anhui Polytechnic University Wuhu China School of Business Shanghai Jian Qiao University China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Aiming at the problem of low accuracy and speed of lesion detection by lightweight model, an endoscopic lesion detection algorithm based on improved SSD is proposed. Firstly, the VGG backbone network is replaced with a lighter MobileNetV2 network to reduce the consumption of computing resources. Secondly, the CBAM attention mechanism is proposed to refine the shallow input features, so as to comprehensively capture the key information of the features. Finally, the improved SmoothL1 loss function is used to balance the accuracy and robustness of the model. Experiments show that the average accuracy of the improved model is improved, the parameter amount is 1/6 of the original model, the calculation amount GFLOPS is reduced by 59 G, and the frame rate per second FPS is increased by 96 frames/s. Compared to other algorithms, there is an improvement in average accuracy, the model is more lightweight, and it consumes fewer computing resources, indicating the effectiveness of the improved algorithm in the detection of endoscopic lesions.

关键词： Location awareness Accuracy Automation Computational modeling Robustness Mobile handsets Lesions detection algorithms Robots Optimization

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Improved YOLOv8 detection Algorithm of Contraband in X-Ray Security Inspection Image

Improved YOLOv8 Detection Algorithm of Contraband in X-Ray S...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Zhaoyuan Chen Yan Xu Fang Li Guorong Liu Ping Hu Xiaodi Zhang College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Personnel safety is particularly important in public places where security checks are required, such as airports, train stations and subway stations. In view of the features of disorderly placement of items, blocking of contraband and intentional hiding in X-ray security inspection images, this research proposes an improved YOLOv8 network model combined with Mixup data augmentation, Biformer dual-layer routing attention modules, and C2f_f modules to assist security personnel in conducting efficient security inspections. At the input stage, the Mixup data augmentation method is employed to enhance the model's ability to learn to detect obstructed and concealed prohibited items. The Biformer dual-layer routing attention modules are introduced into the backbone network to enhance the model's focus on foreground information and improve its recognition ability. The C2f_f modules are incorporated into the backbone network and Neck section to improve the model's feature representation capability and enhance overall network performance. Testing on the Easy, Hard, and Hidden datasets of PIDray yields mAPs of 85.2%, 86.2%, and 68.3% respectively. Experimental results demonstrate an increased detection accuracy of 2.3%, 1.4%, and 2.2% respectively on the three prohibited item recognition test sets for the improved YOLOv8 model.

关键词： Accuracy Inspection Data augmentation Routing Data models Neck Security Personnel detection algorithms X-ray imaging

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YOLOv7-FDASR: Improved YOLOv7 for Fall detection with Convolutional Attention Module and Super-Resolution

YOLOv7-FDASR: Improved YOLOv7 for Fall Detection with Convol...

引用

Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Xijun Lu Chengde Lin School of Artificial Intelligence Guilin University of Electronic Technology Gulin China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Object detection methods are important in human fall detection tasks. However, fall detection suffers from complex scene in reality, varying camera viewpoints, and low camera resolution, which poses a huge challenge to generalized object detectors. To solve these problems, we propose a super-resolution fall detector with an attention mechanism based on YOLOv7 (YOLOv7-FDASR). We introduce CBAM modules to enhance network expressiveness and refine network features. In the Head section, we introduce an independently designed lightweight super-resolution module for improving the image resolution and obtaining more feature information at high frequencies. Finally, WIoU is used as a loss function to effectively prevent low-quality object recognition from biasing the ideal recognition predictions in reality. Extensive experiments on real datasets, Fall-D and Falling, demonstrate the effectiveness and superiority of our proposed method in terms of detection performance. The achieved Average Precisions (APs) are $57.9 \%$ and $57.1 \%$ on the two datasets, respectively, representing an improvement of approximately ${5 \%}$ to $9 \%$ over the baseline (YOLOv7).

关键词： Attention mechanisms Head Superresolution Detectors Object detection Cameras Object recognition High frequency Fall detection detection algorithms

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Explosion smoke and dust detection algorithm based on improved YOLOv8

Explosion smoke and dust detection algorithm based on improv...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Xuesong Luo Tonghui Qian Xingzhou Chen Wenyu Xiong Institute of Intelligent Manufacturing Jianghan University Wuhan China Wuhan Donglv Technology Co. Ltd Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In response to the low accuracy, slow detection speed, and missed detections of the blasting smoke and dust detection algorithm, this paper proposes the YOLOv8-AIES algorithm. By introducing C2f-AgileBlock to reduce parameters and computational complexity, using iRMB-EMA hybrid attention model to enhance feature extraction, and optimizing feature fusion with SlimNeck, the detection speed of the model is improved, missed detections are reduced, while maintaining lightweight and fast response. The experimental results show that YOLOv8-AIES performs well in mAP@0.5 On the Recall, it reached 94.9% and 93.9%, respectively, with a significant decrease in parameter and computational complexity, making it suitable for blast smoke and dust detection in practical scenarios.

关键词： Information science Accuracy Computational modeling Feature extraction Explosions detection algorithms Computational complexity

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detection of Small Changes in Power Systems with Hardware-in-Loop Testing

Detection of Small Changes in Power Systems with Hardware-in...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Hosur, Sanjay Duan, Dongliang Univ Wyoming Dept Elect & Comp Engn Laramie WY 82071 USA

ISBN: (纸本)9781665405072

In this paper, the subspace based output only detection algorithm proposed in our previous work [1]-[3] is applied to detect small changes in power systems. In this work, the disturbances considered are the ones that cause the operating point of power systems to change gradually or subtly over time, which are difficult to be captured by most existing detection tools. The simulation is carried out using the Opal-RT Real Time Digital Simulator (RTDS). The IEEE 39-bus system is simulated with this hardware in the loop testing platform. The changes studied are slow load rampings in the system. Results show that these small changes can be well detected by our algorithm.

关键词： Real-time systems Hardware Power systems Delays detection algorithms Testing Load modeling

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