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检索条件"主题词=detection algorithms"

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Topology Error detection for Defensive Islanding of Power Grids

Topology Error Detection for Defensive Islanding of Power Gr...

引用

IEEE General Meeting Power& Energy Society

作者： Abdelrahman Abdelkader Mohammed Benidris Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Resilience improvement through defensive islanding requires multiple simultaneous topological changes after an event to exclude damaged components and transform the grid into power balanced islands. Topology error identification is challenging under such conditions given the limited availability of measurements and uncertainty of events. This paper proposes a new approach for machine learning-aided topology error detection and identification for defensive islanding. The approach combines conventional data error detection with a data driven model to distinguish error types and identify the erroneous component. The topology check is done on two different levels. In the lower level (decoupled islands), a state estimation problem is solved for error detection in each of the smaller grids. Solving decoupled small systems requires less time and computation power, which is essential for the time sensitive post-disaster task at hand. The identification step, however, is at the high level (connected grid) due to the possibility of the topology error being in the links between the islands. Approach implementation is possible through numerous State Estimation (SE) and topology validation approaches. In this work, a DC SE is performed since the concern here is the connectivity rather than accurate power flow calculations. A neural network model is used to validate topology considering data and system uncertainties. A case study utilizing the proposed method is performed on different topological variations of the IEEE 118-Bus system. Scarcity and uncertainty of data are also considered. The results show promising potential of the proposed algorithm.

关键词： Accuracy Uncertainty Islanding Network topology Measurement uncertainty Termination of employment Topology State estimation detection algorithms Resilience

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Integrated Vision-Aided Precision Navigation and Obstacle detection Sensing Pipeline for UAM Approach and Landing

Integrated Vision-Aided Precision Navigation and Obstacle De...

引用

Conference on Digital Avionics Systems (DASC)

作者： Enrico Miccio Paolo Veneruso Roberto Opromolla Giancarmine Fasano Carlo Tiana Giacomo Gentile Dept. of Industrial Engineering University of Naples Federico II Naples Italy Vision Systems Collins Aerospace Portland OR USA Applied Research and Technology Collins Aerospace Rome Italy

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Enabling obstacles detection capabilities onboard manned or unmanned aircraft during the landing phase using vision sensors is a challenging task. In fact, detecting and tracking static and moving objects such as other vehicles which typically lie below the horizon is hindered by the cluttered background and the ownship motion. Motion-based detection approaches (such as the ones exploiting homography) may attain a reasonable performance in flat scenarios, but encounter challenges in three-dimensional environments due to the highly variable distance of imaged features. This paper explores obstacle detection algorithms for landing considering both motion-based algorithms using homography, and appearance-based techniques built on Convolutional Neural Networks (CNNs), with the idea of combining them. The obstacle detection function is conceived to complement a previously developed precision navigation system for landing, exploiting the same vision sensors. Different approaches are considered for static and moving obstacles. The pipeline has been validated through both synthetic and flight-testing data showing promising results for future more structured integrations.

关键词： Navigation Tracking Pipelines Vision sensors Aerospace electronics Feature extraction Convolutional neural networks detection algorithms Aircraft

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Local Community detection from Noisy Graph Streams

Local Community Detection from Noisy Graph Streams

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Han Yang Lingxiang Wang Qingren Wang Huabin Wang Yiwen Zhang Hong Zhong Dengcheng Yan School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The demand for real-time graph analysis has encouraged the research of online community detection methods. However, these methods often assume clean data, which is uncommon in reality. How to further improve the performance and robustness of online community detection for real-world networks has garnered the great attention. In this paper, we propose NgsLCD, an algorithm that identifies valid edges in noisy streams to expand communities and mitigate noise effects. It also uses a node-community similarity metric to filter out incorrectly added nodes. Tests on real networks show NgsLCD reduces noise impact and maintains robust detection performance across varying noise rate.

关键词： Image edge detection Noise Filtering algorithms Robustness Real-time systems Noise measurement detection algorithms Streams Software engineering Noise level

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A survey on the modern research on deep learning-based object detection

A survey on the modern research on deep learning-based objec...

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Computer Vision and Machine Intelligence (CVMI), International Conference on

作者： T Selvamuthukumar P Dhanalakshmi K Vijayalakshmi R Abinaya Annamalai University Chidambaram Tamilnadu India SR Gudlavalleru Engineering College Vijayawada Andhra Pradesh India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Detecting objects remains a vital and challenging aspect within computer vision applications. Substantial progress in detecting objects is made by enhancing how objects are represented and by utilizing deep models of neural networks. This paper delivers a broad survey on the advancements on object detection within the deep learning era in recent years. Object detection algorithms are generally branded into two broad types centered on their training approach: single-stage detection algorithms and two-stage detection algorithms, each utilizing unique methods for pinpointing objects in images. We examine the underlying rationale of these algorithms by conducting experiments to compare various aspects, including quality metrics, trade-offs between speed/accuracy, and modelling practices. This study also provides a comparative assessment of leading convolutional neural networks (CNNs) used in detecting objects, emphasizing each model’s strengths/limits and presenting key conclusions. To foster a deeper understanding, we also identify areas for future research and potential advancements in object detection models within the domain of deep learning.

关键词： Deep learning Surveys Training Measurement Computer vision Reviews Neural networks Object detection Object recognition detection algorithms

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Research on Kiwifruit detection Algorithm Based on Faster-YOLOv8n

Research on Kiwifruit Detection Algorithm Based on Faster-YO...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Dan Zhao Jiaxing Wang Wei Shan College of Physics and Electronic Information Huaibei Normal University Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

In view of the complex structure of YOLOv8n model and the large amount of parameters and calculations of the existing kiwifruit detection algorithm, it is difficult to deploy in the terminal. This paper proposes a lightweight kiwifruit detection algorithm model Faster-YOLOv8n based on YOLOv8n. That is, all C2f modules in the YOLOv8n model are merged with the FasterNet lightweight network structure to form a new C2f-Faster module to reduce the consumption of computing resources. The experimental results show that the average detection accuracy mAP of the Faster-YOLOv8n algorithm on the self-made kiwifruit dataset collected in this paper is 96.9 %. Compared with the YOLOv8n algorithm, the model size is reduced by 32 %,the calculation amount is reduced by 22.2 %, the parameter quantity is reduced by 29.6 %, and the detection speed is increased by 26.7 %.Therefore, the detection method proposed in this paper reduces the consumption of computing resources and provides a theoretical reference for the lightweight detection of kiwifruit.

关键词： YOLO Accuracy Image recognition Fuses Computational modeling Production detection algorithms Artificial intelligence

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Community detection and Centrality Measures in Alzheimer’s Disease Network Analysis

Community Detection and Centrality Measures in Alzheimer’s ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Yaswanth Chowdary Thotakura Kalyan Kumar Doppalapudi Srinivas Reddy Gopavarapu Geetha Siva Srinivas Gollapalli Jaya Lakshmi Tangirala Department of CSE SRM University-AP India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This project intricately explores network analysis in the context of Alzheimer’s disease, emphasizing community detection and centrality as a pivotal facet in data research on complex networks. Community structure, an essential component of complex networks, has progressively emerged as a focus of study interest. The community detection algorithm is useful for examining the network’s true structure. The paper highlights the importance of community identification strategies in understanding network behavior and topology analysis by classifying them into non-overlapping and overlapping methods. Many ways are looked at, such as those built on different factors separation into parts, group linking, tag movement and gradual steps partitioning. The process of finding out what sicknesses, like Alzheimer’s disease someone has also highlights the advantages of knowing and working with many different abilities, skills working as a group to fix issues, and sharing what we know in a community. It present a way to look at community splits using a measure called modularity. This focuses on how it can be used to check layout, steer refinement, and comprehend network dynamics. In this paper, we look into measures of importance, especially coreness centrality. We also talk about core-periphery structures, k-shell decomposition, and extensions like the all-around score and hybrid centrality measures, which provide useful tools for determining influential nodes in complex networks.

关键词： Heart Social networking (online) Shape Layout Network analyzers Complex networks Social factors Topology Alzheimer's disease detection algorithms

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Analyzing GNN Models for Community detection Using Graph Embeddings: A Comparative Study

Analyzing GNN Models for Community Detection Using Graph Emb...

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International Conference on Computing and Networking Technology (ICCNT)

作者： V V Devesh Kumar S Sreesankar Krishnpriya Dinesan Pranav B Nair L R Deepthi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Graphs are effective models for illustrating complex connections in various contexts, including social networks, citation networks, and biological systems. In this study, the performance of Graph Neural Networks(GNN) for two main tasks is being explored: Node classification and Community detection. Community detection on complex networks is an emerging area that helps to understand the underlying structures and functions of interconnected systems across various domains. The structural and semantic information are captured using the various GNN models. This paper uses four GNN models for node classification: GCN, GAT, GraphSAGE, and Deep Graph Infomax. The embeddings of these models are clustered to detect the communities by using spectral clustering, k-means clustering, and the Louvain algorithm. The GNN model’s performance was assessed in terms of computational complexity, accuracy of clustering, and modularity. The results demonstrate that GNN models, particularly GCN, yield competitive performance in community detection tasks on citation networks. The effectiveness of each model varies across different datasets and community detection algorithms.

关键词： Analytical models Social networking (online) Computational modeling Image edge detection Semantics Complex networks Interconnected systems Graph neural networks detection algorithms Context modeling

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An Adaptive Step detection Algorithm for Smartwatch with Deep Learning-based Human Activity Recognition

An Adaptive Step Detection Algorithm for Smartwatch with Dee...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Sohee Park Jae Hong Lee Chan Gook Park Dept. of Aerospace Engineering Seoul National University Seoul Republic of Korea Dept. of Aerospace Engineering/ASRI Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

As the use of smartwatches continues to grow, improving the accuracy of tracking for smartwatch users has become increasingly important. Smartwatches are worn on the wrist, which means the sensors may detect free and independent hand movements while walking. This creates challenges for pedestrian dead reckoning (PDR) using the inertial sensors embedded in smartwatches because the data may lose information on walking characteristics. Therefore, it is essential to identify the user’s walking state first, whether moving or stationary, and then conduct precise step detection, which is a critical factor in reducing errors in PDR. In this study, we propose an adaptive step detection algorithm tailored to four categorized user activity states: walking, running, hand movements while standing, and hand movements while walking. We conducted human activity recognition (HAR) with these four categories to identify the user’s state and decide the appropriate method for step detection. To enable implementation on mobile devices, we propose a lightweight deep learning-based HAR network utilizing a grouped convolutional layer. The proposed network reduces the number of trainable parameters by approximately one-third compared to conventional CNN structures while achieving $\mathbf{9 6. 4 5 \%}$ accuracy in activity classification. For adaptive step detection, we utilize gyroscope data for normal walking, accelerometer data for running, and walking frequency obtained via fast Fourier transform (FFT) from normal walking data for segments of walking interspersed with hand movements. By applying these tailored step detection methods to each walking scenario, we enhance step detection accuracy, thereby improving smartwatch user position and distance estimation in overall PDR.

关键词： Legged locomotion Accelerometers Wrist Accuracy Fast Fourier transforms Convolution Angular velocity Gyroscopes Human activity recognition detection algorithms

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Fire detection Algorithm Based on Improved YOLOv8

Fire Detection Algorithm Based on Improved YOLOv8

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Neuromorphic Computing (ICNC), International Conference on

作者： Xue Qian Du Jiang Xiong Li Ling Zhang Lang Liu College of Computer Science and Engineering Chongqing Three Gorges University Chongqing China College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

Fire identification plays a crucial role in the realm of fire safety, serving as a vital component for the early detection and swift response to fire incidents. Effective fire identification is essential in minimizing the damage and losses associated with fires, as it enables timely interventions. we introduce a novel fire detection method that employs RHE-YOLOv8, aiming to enhance the accuracy of fire identification and localization. By leveraging this advanced approach, we seek to optimize the response speed of fire identification systems, ultimately facilitating more precise recognition and pinpointing of fire sources. In this study, the YOLOv8 model incorporates the RepGhost module to decrease its floating-point computations, thus achieving a more lightweight structure. Furthermore, the seamless integration of the Hyb-ECA attention mechanism into the network's neck structure has notably boosted the model's recognition accuracy, all while maintaining a low computational footprint. Experimental findings demonstrate that these enhancements can elevate the average precision (mAP) of the YOLOv8 model by 2.5%. Simultaneously, the model undergoes a decrease in floating-point operations (GFLOPs) of 13.6%. These enhancements significantly improve both the precision and effectiveness of fire detection, demonstrating their efficacy in real-world fire situations.

关键词： Location awareness Accuracy Target recognition Neuromorphic engineering Computational modeling Object detection Lightweight structures Neck detection algorithms Optimization

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Airport Runway Conflict detection And Visual Simulation

Airport Runway Conflict Detection And Visual Simulation

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Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Tao Song Siyuan Zhang Xiaoyun Shen Siming Yan Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Technical Support Department Chongqing Sub-bureau of Southwest China Reginal ATMB Chongqing China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

In order to prevent runway intrusion events due to the incorrect deployment of aircraft by controllers with insufficient situational awareness, a 3D visual simulation system for runway conflict detection is designed. By analyzing different types of conflict scenarios of aircraft arrival and departure, the runway conflict models are constructed, and for different conflict models, the spatial distance method is used to determine whether runway incursion of the aircraft occurs. Then the combination of spatial distance and time threshold are used for the conflict alarm. The runway protection area is constructed, the aircraft motion model of the airport surface is established based on the data of airport surveillance data. The visual simulation of the arrival and departure conflict detection algorithm is realized on the Unreal Engine 4 platform. The experimental results show that according to the arrival and departure conflict detection, when aircrafts are close to each other and less than the encounter time threshold, the system can detect the runway conflict in real time and give the alarm.

关键词： Visualization Solid modeling Analytical models Three-dimensional displays Atmospheric modeling Airports Aircraft manufacture Aircraft detection algorithms Engines

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