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检索条件"主题词=detection algorithms"

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Application of the MOD-STL-Louvain Method for Community detection in Manufacturing Industries: A Study on Electricity Consumption

Application of the MOD-STL-Louvain Method for Community Dete...

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IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)

作者： Zhiqiang Lan Guoyao Wu Yongjie Guo Fan Pan Yingxin Shen Jiacheng Wu Yanan Yu Yuan He Yujun Yan State Grid Fujian Marketing Service Center (Metering Center and Integrated Capital Center) Fujian China State Grid Fujian Marketing Service Center (Metering Center and Integrated Capital Center) Xiamen Fujian China Fujian Electric Power Supply Service Co. LTD. Xiamen Fujian China School of Mathematical Sciences Xiamen University Xiame Fujian China School of Mathematical Sciences Xiamen University Xiamen Fujian China

ISBN: (数字)9798331523008

ISBN: (纸本)9798331523015

Electricity consumption data captures the usage patterns across various regions, industries, and enterprises, acting as a crucial indicator of economic activity and growth potential. Therefore, analyzing electricity consumption data is essential for understanding economic development. This paper introduces a novel method, MOD-STL-Louvain, for exploring the clustering patterns of enterprises based on their electricity consumption data. The method combines STL decomposition, complex networks, and the Louvain community detection algorithm to classify enterprises into different communities according to their electricity consumption trends. The method was applied to the electricity consumption data of several industries in a province in Eastern China, and the results showed that it can effectively classify enterprises into different communities, which can be characterized by different electricity consumption trends. This research provides a new perspective for understanding the development status of enterprises in their industries and offers a valuable reference for economic work.

关键词： Economics Manufacturing industries Electric potential Electricity Asia Complex networks Market research detection algorithms

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Potato Disease detection Algorithm Based on Improved YOLOv8

Potato Disease Detection Algorithm Based on Improved YOLOv8

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Computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Peng Zhou Liqing Geng Genghuang Yang Liming Chen School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China Fitow (Tianjin) Detection Technology Co.Ltd Tianjin China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Address the challenges posed by the diverse range of potato fruit defects, which encompass difficulties in detection and limited accuracy, an improved YOLOv8 potato disease detection algorithm is proposed. Firstly, for the lack of spatial information capturing ability of the original YOLOv8 model, the DRB idea is introduced to improve the C2f module, and then the original C2f module is improved by the improved C2f-DRB module. Secondly, for the problem that small targets of potato diseases are not easy to be detected, the CARAFE up-sampling operator is introduced to improve the model's ability of extracting features of the disease, and to enhance the effect of the disease detection. Finally, the original IoU loss function of the model is improved using the Inner IoU loss function, which enhances the generalization ability of the model. Relevant comparison experiments were constructed for ten kinds of diseased potatoes, and the results showed that the improved YOLOv8 algorithm improved 2.4%, 1.7%, and 1.5% in precision, recall, and mAP50, respectively, which proved the proposed algorithm had better performance, and verified the effectiveness and feasibility of the proposed algorithm.

关键词： Computer vision Accuracy Feature extraction detection algorithms Diseases

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Performance study of the acoustic detection algorithm for the local discharge model

Performance study of the acoustic detection algorithm for th...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Qi Yang Deng Song Liuqing Yang Xianyin Mao Huarong Zeng Yao Zhong Meihua Mountain Freezing Climate Disaster and Equipment Safety Observation and Research Station of Guizhou Province Guiyang China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang 550002 Guizhou China Guiyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

This paper designs a set of acoustic detection system based on high-performance 80-channel multi-arm spiral microphone array based on the application of acoustic imaging technology in local discharge detection of power equipment. Through the combination of theoretical analysis, simulation and experimental verification, DAMAS2 (CBF), DAMAS2 and OMP-DAMS. The results show that although CBF algorithm can achieve basic discharge position positioning, its spatial resolution is relatively poor; DAMAS2 algorithm has improved resolution, but still has low computational efficiency. In contrast, OMP-DAMAS algorithm performs excellent in terms of resolution, positioning accuracy and calculation efficiency. Especially in the context of complex environment, it can more accurately identify the local discharge position, and the calculation speed is fast, which is suitable for real-time detection system. The research results of this paper provide an effective acoustic imaging algorithm and its practical scheme for the accurate detection of local discharge of power equipment, which has high application value and promotion prospect.

关键词： Accuracy Array signal processing Acoustic imaging Acoustic arrays Discharges (electric) Microphone arrays Acoustics Spatial resolution detection algorithms Load modeling

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Yoga Pose detection using MobileNetV3+pose comparison and instruction

Yoga Pose Detection using MobileNetV3+pose comparison and in...

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Information Technology and Contemporary Sports (TCS), International Conference on

作者： Xinyan Xie School of Internationa Education Guangxi University of Science and Technology Liu Zhou China

ISBN: (数字)9798331529864

ISBN: (纸本)9798331529871

In the contemporary era, the ancient practice of yoga has witnessed a remarkable resurgence, transcending cultural and geographical boundaries to become a global phenomenon. The ascent of yoga as a global wellness practice underscores the need for accurate yoga pose execution, particularly among novices and those practicing without direct supervision. Leveraging recent advancements in pose detection algorithms, this study proposes a novel framework utilizing MobileNetV3 and Convolutional Neural Networks (CNNs) to detect and classify yoga poses in real-time, coupled with immediate instructional feedback [4] . This integration aims to enhance practitioners’ experience by ensuring correct pose alignment, thereby making yoga more accessible and maximizing its health benefits. A comprehensive dataset, inclusive of diverse yoga styles and practitioner demographics, underpins the system’s development, ensuring broad applicability and effectiveness.

关键词： Accuracy Real-time systems Cultural differences Convolutional neural networks Information technology detection algorithms Sports

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Accurate detection of Superspreaders on Multi-Pipeline Switches

Accurate Detection of Superspreaders on Multi-Pipeline Switc...

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International Conference on Computer and Communications (ICCC)

作者： Cenman Wang Bowen Zheng Fan Yang Ye Tian Anhui Key Laboratory on High Performance Computing School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In various network monitoring tasks, the detection of superspreaders has become increasingly crucial. However, latest superspreader detection algorithms and data structures assume the target switch with a single pipeline, while state-of-the-art programmable switches actually comprise multiple independent pipelines. In current method for deploying super-spreader detection applications on multi-pipeline switches, a separate measurement data structure instance is employed for each pipeline. However, under multi-path routing, this simplistic approach leads to lower detection accuracy due to hash collisions. To address this issue, in this paper, we present PipelineAlloc, a HyperLogLog-based superspreader detection system designed for multi-pipeline switches. In order to reduce hash collisions in measurement data structures, PipelineAlloc assigns an egress pipeline as the monitor pipeline for each source IP address. PipelineAlloc introduces novel measurement data structures in the ingress pipelines for temporarily storing statistics of the source and destination IP addresses of packets, and uses packets to carry these measurement data to egress pipelines, which serve as the monitor pipelines. The HyperLogLog-based measurement data structures in egress pipelines are updated using data carried by packets. Experiments driven by real-world traffic traces show that PipelineAlloc outperforms existing solutions in terms of detection accuracy.

关键词： Accuracy Scalability Current measurement Pipelines Switches Data structures Routing IP networks detection algorithms Monitoring

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PDAST: Paradigm Towards Weather Domain Adaptation for 3D detection Based on Density Aware Pooling and Self-Training

PDAST: Paradigm Towards Weather Domain Adaptation for 3D Det...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Yonghui Huang Xi Zhang Chuan Hu Yanhao Liu Yakang Wang School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Most 3D detection algorithms are developed and validated on datasets collected during clear weather, while performance significantly deteriorates under severe weather conditions such as snowy days. To address this problem, we propose PDAST, an unsupervised pipeline for weather domain adaptation. PDAST involves point density dealing (PDD), a data augmentation method, and a density-aware pooling module (PDV), both of which are plug-and-play modules to enhance algorithmic robustness in snowy weather conditions along with the self-training [1] strategy. The data augmentation method PDD randomly samples and perturbs the ground truth point database in the point cloud. Meanwhile, the PDV module combines voxel features and density-based positional encoding features as inputs to a multi-head self-attention module, facilitating the capture of global features comprising density features. In this paper, we validate the effectiveness of PDAST on a typical voxel-based 3D detection baseline, SecondIOU [2], and conduct experiments on STF [3] datasets with clear and snowy datasets. Our approach PDAST achieves a performance enhancement of around 14.9% ~ 25.5% for $AP_{3D}$ (Average Precision Of 3D Boundingboxes) and 17.5% ~ 29.3% for $AP_{AOS}$ (Average Precision Of Orientation Similarity) compared to the baseline detector on clear-to-snowy conditions. Furthermore, PDAST's performance in unsupervised clear-to-snowy conditions matches or exceeds the baseline model fully supervised on snowy conditions. Our PDAST alleviates the performance degradation caused by severe weather, such as snowy days, and is of great significance for all-weather perception.

关键词： Point cloud compression Degradation Three-dimensional displays Pipelines Imaging Detectors Data augmentation Robustness detection algorithms Meteorology

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Adaptive Malware detection In Windows Portable Executables (Wpes)

Adaptive Malware Detection In Windows Portable Executables (...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： A Sathish N.Babu M Bhavani K Divya B Jhoshna T Hemavathi Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andra Pradesh India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

It's crucial to comprehend the characteristics of malware variants that enable them to elude malware detection methods as they are created with the intention of avoiding detection by means of a variety of evasion strategies. It is done by a framework designed to generate undetectable malware samples on Windows executable files by incorporating various techniques such as adding benign sections and obfuscating code. Creating undetectable malware variants using a framework can successfully evaded numerous anti-malware programs, showing significantly higher evasiveness than both the original malware and a previously suggested solution. By examining various perturbations and their efficacy, our results contribute to the improvement of malware detection algorithms by providing a better knowledge of the categories to which more evasive malware variants may belong and how they might be created. To create anti-malware programmed that are capable of successfully identifying malware varieties that have not been observed before, experimental findings and observations would be helpful.

关键词： Technological innovation Codes Social networking (online) Computer hacking Perturbation methods Market research Malware Computational efficiency detection algorithms Monitoring

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Real-Time R-Wave detection Algorithm for ECG Signals Based on Feature Enhancement

Real-Time R-Wave Detection Algorithm for ECG Signals Based o...

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Electronics and Communication, Network and Computer Technology (ECNCT), International Conference on

作者： Sikai Guo Huibin Wang Ke Xu Wenping Chen College of Information Science and Engineering Hohai University HHU Nanjing China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

With advancing technology, real-time monitoring of electrocardiogram (ECG) signals has transitioned from clinical settings to daily life. To meet the growing demand for real-time R-wave detection, this paper proposes a feature-enhanced algorithm. The signal undergoes low-pass filtering followed by first-order backward differentiation and cumulative multiplication to enhance R-wave detection. Adaptive thresholding and a retrospective missed detection mechanism are then applied. Experimental validation using MIT-BIH and NSTDB databases shows high P+ and Se rates of 99.80% and 99.70%, 98.28% and 99.90%, respectively. Compared to existing methods, the algorithm offers high detection accuracy, simplicity, and real-time capability, effectively meeting the requirements for real-time R-wave detection scenarios.

关键词： Sensitivity Filtering Noise Interference Electrocardiography Feature extraction Prediction algorithms Real-time systems detection algorithms Monitoring

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YOLO-GCEF: A Novel detection Algorithm for Timber Transport Trucks in Forest Farms

YOLO-GCEF: A Novel Detection Algorithm for Timber Transport ...

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Conference on Vehicle Control and Intelligence (CVCI)

作者： Suzheng Wu Ziwei Wang Ang Li Xin Bai Ruiqi Fang Guodong Yin School of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Vehicle detection technology is essential for automatic monitoring and AI-assisted driving systems. Detecting timber transport trucks presents several challenges, including a lack of datasets and complex environment. To address these issues, this paper constructs a new dataset called Timber Transport Trucks (TTT) dataset and proposes a novel vehicle detection method: YOLO-GCEF. This method incorporates the GC attention module into the backbone network to obtain critical information for vehicle detection tasks, thereby improving detection accuracy. Additionally, EWF loss is proposed, which combines Efficient IOU loss(EIOU), weighted bounding box regression loss, and Focal Loss. We conducted some experiments on the TTT dataset. The experimental results show that YOLO-GCEF can achieve 90.3% mAP.

关键词： Accuracy Vehicle detection Forestry detection algorithms Monitoring

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Weak Signal detection Algorithm Based on Multi-coupled Chaotic Oscillator

Weak Signal Detection Algorithm Based on Multi-coupled Chaot...

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IEEE/OES China Ocean Acoustics (COA)

作者： Sikai Xie Wentie Yang Zuoshuai Wang Yidong Xu Yantai Research Institute of Harbin Engineering University Yantai China Hubei Key Laboratory of Marine Electromagnetic Detection and Control Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

Weak signal detection technology is essential for underwater target detection systems that rely on underwater acoustics. This paper focuses on enhancing the performance of such detection systems. We propose a multi-coupled chaotic oscillator based on the concept of coupling oscillators and analyze its nonlinear dynamic characteristics using phase trajectory diagrams and bifurcation diagram. Subsequently, we develop a comprehensive detection algorithm utilizing this model and refine the system with a variable scale approach. Finally, we created a simulation model to compare the performance of detection algorithms, which includes both single Duffing model and dual-coupled Duffing model, with the existing MCVPD detection algorithm.

关键词： Couplings Oceans Object detection Bifurcation Trajectory detection algorithms Underwater acoustics Oscillators Signal detection Signal to noise ratio

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