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检索条件"主题词=Detection Algorithms"

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A State Partition Particle Filter based Approach for detection of Switching Attack

A State Partition Particle Filter based Approach for Detecti...

引用

IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Seema Yadav Nand Kishor Shubhi Purwar Vetrivel Subramaniam Rajkumar Alexandru Ştefanov Department of Electrical Engineering Motilal Nehru National Institute of Technology Prayagraj India Department of Engineering Østfold University College Fredrikstad Norway Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

The increasing risk of cyber-physical attacks (CPAs) on power infrastructure has led to need for reliable detection technologies. As the landscape of cyber threats evolves, it becomes imperative to continually update and enhance attack detection techniques. This research investigates the formulation of detection algorithm, via combination of State Partition Particle Filter (SP-PF) theories for power system security. The proposed approach applies intelligent partitioning of the state space so as to be accurately represented with fewer particles. This reduction in computational demand enhances the algorithm’s efficiency, making it more practical for real-time applications. The detection algorithm based on SP-PF is tested against switching attacks (SAs) launched on the governor and excitation systems associated with the generator. The RTDS platform is utilized for conducting real-time simulations of IEEE 9-bus power network in order to demonstrate the efficacy of proposed SP-PF based detection SA in real-time.

关键词： Switches Reliability theory Real-time systems Particle filters Generators Smart grids Power system reliability Resource management Power system security detection algorithms

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A Real-Time Video detection Algorithm for UAV Based on YOLOv8

A Real-Time Video Detection Algorithm for UAV Based on YOLOv...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Xin Nie Qili Wu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Aiming at the common problem of small target detection in UAV real-time video surveillance, this paper proposes a UAV video detection algorithm based on improved YOLOv8 framework. By embedding a feature enhancement module specifically for small targets in the original YOLOv8 architecture, the algorithm effectively improves the recognition degree of the network for small targets. In order to avoid the excessive expansion of network parameters, the header network is simplified, the convolutional layer is removed and the number of channels is reduced to maintain the computational efficiency of the algorithm. Then this paper introduces the Channel Space Attention Mechanism module (CSAM), which makes the network pay more attention to the small target-dense region on the feature map by optimizing the weight distribution, thus enhancing the pertinence of target detection. In addition, a location-based label assignment strategy (LB-SimOTA) is proposed, which assigns different weights according to the intersection ratio (IoU) between the prediction frame and the real frame to optimize the overall quality of the network prediction frame. Experimental results on the VisDrone2019 dataset containing many small targets show that compared with the benchmark model YOLOv8-S, the proposed algorithm improves the accuracy of vehicle and pedestrian detection tasks by 8.44% and 8.12%, respectively, and achieves a frame rate of up to 90 FPS while maintaining the detection speed.

关键词： Accuracy Pedestrians Target recognition Object detection Streaming media Autonomous aerial vehicles Feature extraction Video surveillance Real-time systems detection algorithms

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An Improved SSD Deep Network-Based Algorithm for Eye State detection

An Improved SSD Deep Network-Based Algorithm for Eye State D...

引用

International Conference on Electronic Engineering and Informatics (EEI)

作者： Weikun Dai Jianbin Xiong Qi Wang Xiangjun Dong School of Automation Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Aiming at the problem of poor real-time performance in current eye state detection algorithms, a deep learning algorithm-improved single-shot multibox detector (SSD) with both real-time performance and detection accuracy is proposed. Firstly, the lightweight network MobileNetV2 replaces the original feature extraction network VGG16 to improve the detection speed. Then, spatial and channel squeeze-excitation network (scSENet) attention module is introduced to enhance the spatial features and channel features of the eye to improve the detection accuracy. The proposed method is tested on the public data set Closed Eyes in the Wild (CEW), achieving a mAP value of 96.12%, which is higher than current mainstream methods. The experimental results show that the algorithm has high accuracy and efficient detection speed in the task of eye state detection.

关键词： Deep learning Accuracy Detectors Feature extraction Real-time systems detection algorithms Informatics

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detection Methodology for Multistage Voltage Sag Based on Residual Analysis

Detection Methodology for Multistage Voltage Sag Based on Re...

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International Conference on Harmonics and Quality of Power

作者： Shunyi Li Ying Wang Huaxi Yu Xianyong Xiao Haishan He College of Elec. Engr. Sichuan University Chengdu China

ISBN: (数字)9798350382563

ISBN: (纸本)9798350382570

The monitoring of voltage sags is a crucial prerequisite for mitigating their harmful effects. With the energy transition in power systems and the integration of a large number of distributed generations, multi-stage voltage sags (MSVS) are becoming increasingly common, causing more severe and profound impacts on the grid and users. Currently, there is a lack of effective description and detection methods for MSVS, posing challenges to their statistics, management, and prevention. Due to the varying residual voltages and multiple transition moments of MSVS, traditional methods struggle to effectively detect them. To address these difficulties, this study proposed a novel detection method based on residual analysis, tailored for the special characteristics of MSVS. First, relevant terms for MSVS were introduced, providing a foundation for the monitoring and management of MSVS. Secondly, a detection algorithm based on residual analysis is proposed, achieving accurate identification of MSVS and precise detection of their mutation moments. The effectiveness and accuracy were validated with data from a 10kV distribution network. The proposed method lays the foundation for improving the power quality of the existing energy transition system.

关键词： Accuracy Voltage fluctuations Prevention and mitigation Power quality Distribution networks Power system harmonics Harmonic analysis Power grids detection algorithms Monitoring

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Seizure detection Based on Vagus Nerve Activity: Translation to an Implantable Setting

Seizure Detection Based on Vagus Nerve Activity: Translation...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Michel-Antony Ngan Yamb Javier Chávez Cerda Louis Vande Perre Riëm El Tahry Antoine Nonclercq BEAMS department Université Libre de Bruxelles Brussels Belgium Institute of Neuroscience Université Catholique de Louvain Brussels Belgium

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Epilepsy is a chronic disease that affects fifty million people worldwide. One-third of patients are not responding to antiepileptic drugs. For those patients, if surgery is impossible, vagus nerve stimulation (VNS) can be offered as an adjunctive treatment. As on-demand stimulation improves VNS efficacy, closed-loop VNS – i.e., stimulating at the occurrence of a seizure – emerges as a promising solution for those patients. However, a suitable biomarker for seizure detection is required. Besides, to be clinically valuable, computations related to such a biomarker should be low enough to be embedded in an implant. This work explores the use of vagus nerve electroneurogram (VENG) as a biomarker and compares the performances of various seizure detection algorithms. We analyzed data from six rats that exhibited acute seizures induced by pentylenetetrazol (PTZ), along with three control rats infused with a saline solution. All seizures could be detected with no false positives, even without requiring large computations. This work opens ways to reduce the computation power necessary for VENG-based seizure detection, aiming at embedding a seizure detection algorithm in an implant. Further work should address whether the performance of low computation power algorithms is maintained in a chronic setting, i.e., in the presence of movement artifacts.

关键词： Drugs Surgery Epilepsy Implants Rats detection algorithms

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Research on the detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. The algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. The experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks detection algorithms Cervical cancer

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Near-Optimal detection of Zak-OTFS Signals in Doubly-Selective Channels Using Mixed Gibbs Sampling

Near-Optimal Detection of Zak-OTFS Signals in Doubly-Selecti...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Vineetha Yogesh A. Chockalingam Department of ECE Indian Institute of Science Bangalore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recently, there has been an increasing interest in the emerging Zak transform based orthogonal time frequency space (OTFS), termed Zak-OTFS, owing to 1) its increased resilience to high Dopplers (compared to the multicarrier version of OTFS (MC-OTFS) that has been widely studied in the past) due to its non-fading attribute, and 2) its structured input-output relation through twisted convolution operation, which renders the input-output relation to be predictable. Zak-OTFS research is in its nascent stages and several aspects of Zak-OTFS remain open for investigation. In this paper, we investigate one such aspect, namely, signal detection in Zak-OTFS. Early papers on Zak-OTFS have considered only minimum mean square error (MMSE) detection, whose performance is far from optimum. In this work, we consider an approximate maximum a posteriori probability (MAP) detection algorithm based on mixed Gibbs sampling (MGS), where sampling is done on a mixed distribution (a weighted mixture of the target distribution and uniform distribution). The mixing with uniform distribution helps to alleviate the stalling problem witnessed in sampling purely from the target distribution. The MGS algorithm is shown to achieve better performance compared to MMSE detection as well as message passing (MP) detection, which is a widely adopted detection scheme in MC-OTFS research, and achieves near optimal performance. The closeness to optimum maximum-likelihood (ML) performance is established using a lower bound on the ML performance obtained using reactive tabu search.

关键词： Lower bound Time-frequency analysis Message passing Modulation Detectors Transforms Complexity theory detection algorithms Signal detection Resilience

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Multi-Scale Distribution Network Equipment detection Algorithm Based on Improved YOLOv8

Multi-Scale Distribution Network Equipment Detection Algorit...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Siming He Yong Li Cheng Li Min Xu Xiaofang Yuan Haozhi Xu Sijia Hu Electric Power Research Institute China Southern Power Grid Guangzhou Guangdong China College of Electrical and Information Engineering Hunan University Changsha Hunan China

ISBN: (数字)9798331516611

ISBN: (纸本)9798331516628

This paper proposes a detection algorithm for power distribution network equipment based on an improved YOLOv8 model, to address issues such as low recognition rates and poor detection performance of power distribution network equipment of different scales in the context of power inspection. The improved model introduces an enhanced spatial pyramid module, making multi-scale semantic information fusion more efficient, thereby enhancing the model's semantic representation capability for power distribution network equipment objects of different scales. A feature pyramid network integrating multi-scale location information is proposed to improve the model's localization ability for power distribution network equipment objects of different scales. Additionally, a loss function called Focal SIOU that balances multi-scale objects is introduced, enabling the model to better learn to detect poorly-performing scales of power distribution network equipment objects. Experimental results show that the improved YOLOv8 model exhibits excellent detection performance for equipment of different scales on the power distribution network equipment object detection dataset, with an average detection precision (mAP) increase of 2.4% compared to the original model. The precision for small, medium, and large-scale object detection $(\text{AP}_{\mathrm{S}},\text{AP}_{\mathrm{M}}, \text{AP}_{\mathrm{L}})$ respectively increased by 3.0%, 4.8% and 2.7 % , effectively improving the detection performance for power distribution network equipment objects of all scales.

关键词： Location awareness Electrical engineering Automation Semantics Object detection Distribution networks Inspection Power systems detection algorithms Context modeling

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Improved YOLOv8n Helmet detection Algorithm for Missed and False detection

Improved YOLOv8n Helmet Detection Algorithm for Missed and F...

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

作者： Jing Li Chengjun Qiu Yuangan Wang Song Wang College of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Faculty of Electronics and Information Technology Beibu Gulf University Qinzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Aiming at the missed and false detection of helmet-wearing detection caused by a small target size or target being occluded in complex working scenarios. An improved helmet-wearing detection algorithm based on YOLOv8n is proposed. First, utilizing the BiFPN enhances the performance in detecting small targets and enhances the capability for generalization and feature fusion. Second, the incorporation of the CA attention mechanism into the backbone network aims to minimize the chances of misclassification, and enhance the precision of detection, thus optimizing the model performance. Finally, Introducing the SioU loss function improves the robustness of the detection. It is concluded from the experiments that the average accuracy of helmet-wearing detection improved from 93.0% to 94.9%, and the average accuracy of non-helmet-wearing detection improved from 91.3% to 92.4%, which is 1.9% and 1.1% respectively. The results show that the improved YOLOv8n algorithm can effectively improve the detection accuracy when the target size is small or the occlusion is missing.

关键词： Head Accuracy Attention mechanisms Feature extraction Robustness Safety detection algorithms Cognitive systems

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YOLOv8 Complex Scene Helmet detection Based on Lightweight Improvement

YOLOv8 Complex Scene Helmet Detection Based on Lightweight I...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Junqing Bai Weinan Chen Chenxu Zhang Yunjie Zhou Xi'an Shiyou University Xi'an China Fuzhou University Xiamen China Naval Submarine Academy Qingdao China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

Wearing helmets has been a mandatory guideline for construction sites, mining, drilling and other fields. For different complex scenes, there is a wide demand for efficient and accurate helmet detection, and the current helmet detection algorithms have problems such as large number of parameters and weak timeliness. In this regard, a lightweight Faster-GAMv8 helmet detection algorithm is proposed to improve YOLOv8. By introducing the GAM attention mechanism, the correlation between different channels is effectively captured. The method of using FasterBlock module to replace C2f significantly reduces the model parameters and FLOPs operations. Finally, the inclusion of the Wise-IoU loss function enables a more accurate evaluation of the detection results of small targets. Experiments using the VOCdevkit dataset show that the proposed algorithm achieves a mAP of 91.1% and the floating-point computation is reduced to 7.6MB, which is able to better satisfy the demand for fast detection at industrial sites and provides an effective solution for real-time monitoring in the field of industrial safety.

关键词： Head Accuracy Attention mechanisms Convolution Computational modeling Redundancy Feature extraction Robustness Safety detection algorithms

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