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检索条件"主题词=detection algorithms"

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Topology Mapping and Connectivity detection for Autonomous Mobile Robot Systems

Topology Mapping and Connectivity Detection for Autonomous M...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Jingfu Hou Ziyan Zhao Shixin Liu Dawei Yang College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China KUKA China Robotic and Industrial Automation Foshan China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Autonomous Mobile Robots (AMRs) are continuously improving the efficiency of logistics and material handling in smart manufacturing plants. However, it is still a very challenging task for AMR systems to create suitable and reliable maps with high robustness. AMR places stringent requirements on matching the accuracy of the map with the accuracy of the robot’s task execution. In this paper, we propose a way to create maps for AMRs. A laser sensor is used to obtain a SLAM base map and construct a topology map on top of it. It can effectively cope with complex factory environments and support complex tasks. In addition, this paper proposes a topological map connectivity detection algorithm based on Tarjan’s algorithm for detecting the correctness of AMR maps. The algorithm is improved based on the obtained algorithm. The results show that the improved algorithm can verify the correctness of AMR maps in a shorter time.

关键词： Accuracy Simultaneous localization and mapping Materials handling Robustness Production facilities Topology Mobile robots detection algorithms Autonomous robots Smart manufacturing

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A Compressed Post-Processing Algorithm for Online Data detection Model Based on Knowledge Distillation

A Compressed Post-Processing Algorithm for Online Data Detec...

引用

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

作者： Xin Zhang Fengru Jia Yinfei Xu Zhigang Xing Hangzhou Electric Power Equipment Manufacturing Co. Ltd Hangzhou China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

This paper puts forward a compressed post-processing algorithm for the online data detection model based on knowledge distillation. By employing a new model to purify the eigenvalue of the input original image. The online detection model for the surface quality of workpieces is trained. The scaling factor in the BN layer is utilized to conduct sparse training of the local network space. And both channel simplify and layer simplify are carried out on the sparse model. YOLOV4 lacks the accuracy of location detection and identification is enhanced, and the autoregressive processing of compressed data is improved through the online data detection algorithm based on knowledge distillation. The simulation results indicate that without the knowledge distillation method, the optimal accuracy of the model is 96.05% after 100 iterations, which is 1.29% lower than that before compression. After 90 passes, the accuracy of the model attains 97.41%, which is essentially in line with that before compression. The end of tests reveal that the model condensing can accurately simplify the accuracy of model and guarantee the accuracy for the surface quality of workpieces.

关键词： Training Knowledge engineering Accuracy Image coding Computational modeling Simulation Data models Eigenvalues and eigenfunctions Data mining detection algorithms

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Multi-Terrain Radar HRRP Target detection Using Contrastive Learning

Multi-Terrain Radar HRRP Target Detection Using Contrastive ...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Xinyang Wang Mingchen Yuan Jiaqi Liu Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As radar resolution improves, high-resolution radars have significantly enhanced target detection performance through the generation of high-resolution range profiles (HRRPs). However, when the range resolution is much smaller than the target, the target’s energy is dispersed across multiple range cells, leading to poor detection performance with traditional energy-based detection algorithms. Furthermore, existing detection algorithms are highly sensitive to clutter characteristics and exhibit poor adaptability to varying scenarios. To address these issues, this paper proposes a Constant False Alarm Rate (CFAR) HRRP detection algorithm based on contrastive learning for multiple terrains. The algorithm improves the contrastive learning loss function to achieve well-clustered representations of clutter samples during training. Test samples are evaluated by calculating the Mahalanobis distance between them and the training samples, and compared against detection thresholds to accomplish target detection. Testing results show that the proposed algorithm achieves superior detection performance across multiple terrains. Furthermore, to reach a 90% detection probability, the proposed method requires a signal-to-clutter ratio (SCR) reduction of 1 to 4 dB compared to the competing methods.

关键词： Training Energy resolution Radar detection Object detection Contrastive learning Radar detection algorithms Clutter Signal to noise ratio Testing

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DCAPT-Net: A Multi-Scale Ship detection Algorithm for SAR Images

DCAPT-Net: A Multi-Scale Ship Detection Algorithm for SAR Im...

引用

International Conference on Big Data and Information Analytics (BigDIA)

作者： Haosheng Song Lin Wang Luran Mi Hua Yu Jingwei Gan The 54th Research Institute of CETC Shijiazhuang China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Within the recent timeframe, the detection of ships in SAR images has become a critical task for many countries. To tackle the issue of multi-scale ship detection, this study presents DCAPT-Net, a new ship detection algorithm. This algorithm enhances multi-level feature fusion by introducing a novel dense connection attention pyramid structure. Furthermore, it incorporates a dedicated small object detection layer and a four-head Transformer structure detection head to improve the representation of small targets. Collectively, these innovations achieve effective multi-scale ship detection. Studies performed using the High Resolution SAR Images Dataset (HRSID) and the SAR Ship detection Dataset (SSDD) highlight the DCAPT -Net's superior performance in identifying ship targets. Additionally, ablation studies confirm the efficacy of the network improvements introduced.

关键词： Technological innovation Head Image resolution Object detection Big Data Transformers Radar polarimetry Marine vehicles detection algorithms

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Faking Fluent: Unveiling the Achilles’ Heel of Multilingual Deepfake detection

Faking Fluent: Unveiling the Achilles’ Heel of Multilingual...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Rishabh Ranjan Bikash Dutta Mayank Vatsa Richa Singh Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

With the rapid advancement of deep learning techniques, the generation of audio deepfakes has achieved remarkable realism across various languages and accents. However, the effectiveness of audio deepfake detection models in diverse linguistic environments remains a crucial area of investigation. This paper presents the first empirical study on the robustness of current audio deepfake detection algorithms across different languages and accents. We evaluate whether these models maintain their effectiveness across varied linguistic domains or perform better in specific language contexts. Our comprehensive analysis examines state-of-the-art audio deepfake detection models trained on the ASVspoof 2019 and BhashaBluff datasets, assessing their performance across four diverse datasets: three representing similar-language variations (Speech Accent Archive, Svarah, and the UK English Accent Dataset) and one representing a different language (Vaani). Our results and supporting analysis indicate that while current models perform well on benchmark datasets, their ability to generalize across diverse linguistic conditions is limited. We identify potential vulnerabilities in existing models when faced with unfamiliar languages or accents, highlighting the need for more inclusive and adaptable detection systems. Our results highlight the need to enhance the robustness of audio deepfake detection across the global linguistic spectrum and emphasize the importance of developing models capable of effectively identifying synthetic speech, regardless of language or accent.

关键词： Deep learning Deepfakes Analytical models Adaptation models Visualization Phonetics Robustness Security Speech synthesis detection algorithms

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Lightweight Tea Shoots detection Algorithm Based on Deep Learning

Lightweight Tea Shoots Detection Algorithm Based on Deep Lea...

引用

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

作者： Haoran Wu Jingui Zhang Yongjun Yang Daoxiong Gong Faulty of Information Technology Beijing University of Technology Beijing China Liangshitang Sub-District Office Agricultural Comprehensive Service Center Shaoyang China Hunan Tea Research Institute Hunan Academy of Agricultural Sciences Changsha China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In machine tea picking, the complex tea garden environment and frequent occlusions require an algorithm that can accurately identify tea shoots while being deployable on resource-limited mobile devices. To address this, a lightweight YOLO-TD (You Only Look Once-Tea Detect) tea shoots detection algorithm based on improved YOLOv8 is proposed. The algorithm uses lightweight C2f-faster modules for feature extraction, reducing model complexity while enhancing central feature extraction capabilities. Inner-SIoU is employed as the loss function to optimize training and improve sensitivity to small tea shoots. Additionally, the G-Head structure with a dual-branch feature extraction design is used. Experimental results show that the proposed algorithm achieves a mean average precision (mAP) of 82.20%, reduces the Giga Floating-point Operations Per Second (GFLOPS) by 45.24%, accurately identifies key points of tea shoots, and effectively detects occluded tea shoots. This improved algorithm offers a robust solution for tea shoot detection in machine tea picking.

关键词： Location awareness Training Automation Sensitivity Feature extraction Mobile handsets Magnetic heads Object recognition detection algorithms Robots

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Network Topology detection Method Based on BGP Protocol and Simulation Verification

Network Topology Detection Method Based on BGP Protocol and ...

引用

International Conference on Big Data and Information Analytics (BigDIA)

作者： Rui Xuan Fei Wang Shuhui Chen the School of Computer (of National University of Defense Technology) Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

With the development of network technology, the network topology structure becomes more and more complex. The AS-level topology in the Internet reflects the logical connection relationship among the autonomous systems in the network, and is the foundation of the Internet of Things management, network detection and traffic monitoring. The mainstream AS-level topology identification method at home and abroad is based on the BGP path AS path attribute information to detect the connection relationship between AS and achieve network topology restoration. In this paper, a topology detection method based on the traffic information is designed, and the establishment of network environment and the simulation verification of network topology detection method are realized based on the EVE-NG simulation platform.

关键词： Protocols Network topology Telecommunication traffic Big Data Routing Real-time systems Topology Internet of Things detection algorithms Monitoring

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T-YOLOv5: A Traffic Sign detection Algorithm Based on Multilayer Feature Fusion

T-YOLOv5: A Traffic Sign Detection Algorithm Based on Multil...

引用

IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Yuechong Zhang Qiaomei Zhang Jinhang Tian Dapeng Liu Haiying Liu Lixia Deng School of Information and Automation Engineering Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Wuzhou Electric Co. Ltd Weifang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In recent years, assisted driving has become a hot research topic, and traffic sign detection, as an essential part of assisted driving, has also received great attention. This article addresses the issues of lighting impact and distance between traffic signs and detection equipment in the field of traffic sign detection. A feature extraction module based on multi-layer feature semantic fusion is proposed to improve the algorithm's ability to extract deep features while retaining shallow semantic features and positional information. Through experimental verification, the proposed T-YOLOv5 algorithm has improved by 5.1% compared to the original algorithm in mAP0.5, with good performance improvement.

关键词： YOLO Measurement Location awareness Hands Semantics Lighting Feature extraction Nonhomogeneous media Data mining detection algorithms

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Community detection Algorithm based on Graph Capsule Networks

Community Detection Algorithm based on Graph Capsule Network...

引用

Image Processing (ASIP), Asia Symposium on

作者： Ran Yang Xiaoyan Zheng Yiwen Shen Zhiya Zhou School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China Qingxian No.1 Middle School in Cangzhou City Information Technology Engineering Association China

ISBN: (数字)9798350376159

ISBN: (纸本)9798350376166

GNN (Graph Neural Network) is a type of neural network model specifically designed to handle graph-structured data. Compared to traditional neural network models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNN aims to process data with nodes and edges to capture the relationships and structure among nodes in a graph. However, existing GNNs have limited capabilities in obtaining hierarchical graph representations, which are crucial for graph classification. To address the limitations of GNNs, this paper proposes an improved graph capsule network that incorporates a data disentanglement module to recover the underlying independent factors from observed mixed data, thus unraveling the heterogeneous factors behind node embeddings. By extracting node features in the form of capsules and utilizing routing mechanisms, important information at the graph level can be captured. This allows for the decomposition of the feature representation of each node into independent representations corresponding to different factors or attributes, enabling a better understanding and utilization of the diverse features of nodes and enhancing the effectiveness and capabilities of graph analysis and learning.

关键词： Recurrent neural networks Asia Routing Feature extraction Graph neural networks Data models Data mining Convolutional neural networks detection algorithms

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Bad Data detection Algorithm for Power System Based on K-Prototypes Clustering and Broad learning

Bad Data Detection Algorithm for Power System Based on K-Pro...

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Intelligent Power and Systems (ICIPS), International Conference on

作者： Junye Li Tiantian Cai Jie Shao China Southern Power Grid Digital Grid Research Institute Co. Ltd. Guangzhou China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

In order to solve the problem of low identification rate of bad data in power grid state estimation, this paper proposes a bad data detection method based on K-prototypes clustering and broad learning. Firstly, the K-prototypes clustering algorithm is improved by increasing the adaptive part of the algorithm and selecting the best class. Secondly, the broad learning algorithm is used to compare the predicted quantity and the modified quantity measurement of state estimation, and the two algorithms are organically combined through the algorithm joint parameters to screen out bad data and improve the recognition rate and operation speed. Finally, through the simulation analysis of IEEE-30 bus case, the results show that compared with common detection methods, the proposed algorithm can effectively improve the identification rate and operation speed of bad data.

关键词： Deep learning Accuracy Clustering algorithms Prediction algorithms Power grids Hybrid power systems State estimation detection algorithms Long short term memory

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