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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68642 条记录，以下是191-200 订阅

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Human-Robot Pose Tracking Based on CNN with Color and Geometry Aggregation 16th

Human-Robot Pose Tracking Based on CNN with Color and Geo...

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16th international conference on Social Robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. The variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. The iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. The DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. This algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. The algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Numerical Investigation on Mechanical-Electrical-Thermal Characteristics of Contact Pairs with Rough Surface

Numerical Investigation on Mechanical-Electrical-Thermal Cha...

引用

international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Wu, Chengchuang Zhou, Yu Zhang, Chao Ren, Wanbin School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819620494

Contact resistance and contact spot temperature rise directly determine the electrical contact reliability of electrical switches. This paper establishes an electrical contact simulation model which can take into account the surface morphology of contacts. The real contact spot morphology, contact spot temperature rise, and possible melting area are calculated and extracted using commercial software COMSOL Multiphysics. And the evolution of real contact area in the mechanical loading process is given in detail. Through simulation, the relationships between contact force, peak value of inrush current, and the temperature rise in the contact area were established. The experimental results demonstrate the feasibility of the proposed simulation method. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Enterprise software

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PP-DETR: Progressive Proposal Detection Transformer for 3D Visual Grounding 24

PP-DETR: Progressive Proposal Detection Transformer for 3D V...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Li, Jinyuan Zhu, Duxin Liu, Zhuangzhi Luo, Yang Su, Jinhe Cai, Guorong Wu, Yundong School of Computer Engineering Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

3D Visual Grounding (3D VG) has become increasingly important in applications such as robotics and human-computer interaction. However, accurately localizing target objects in complex 3D scenes based on natural language descriptions remains a significant challenge. While current methods based on DETR have shown notable improvements in the field of 3D VG, they struggle to achieve precise localization in the presence of multiple objects. To address these challenges, we propose Progressive Proposal Detection Transformer for 3D VG (PP-DETR). We introduce a progressive proposal generation strategy into the DETR-based 3D VG model which is named Progressive Category-Constrained Sampler(PCCS). After thoroughly integrating point clouds features with natural language features, our method generates object queries through a stepwise sampling of point clouds features. These queries are then decoded to produce the final bounding boxes for the target objects. PCCS reduces the interference from irrelevant proposals, enhancing the model’s focus on relevant objects and thereby improving overall localization accuracy. Extensive experiments conducted on the ScanRefer dataset demonstrate that our model achieves an overall accuracy of 54.06% on the overall0.25 metric, surpassing the baseline EDA by 0.23%. In more complex multi-object scenarios, our approach yields improvements of 0.45% and 0.49% in the multi0.25 and multi0.5 metrics, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Deep Convolutional Neural Networks for Autism Spectrum Disorders 3rd

Deep Convolutional Neural Networks for Autism Spectrum Disor...

引用

3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Patel, Drashti Mittal, Shreyan Rahevar, Mrugendrasinh Patel, Abhishek Department of Computer Science and Engineering CSPIT Charotar University of Science and Technology Gujarat Changa India U & P U Patel Department of Computer Engineering CSPIT Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9789819743582

Autism Spectrum Disorder (ASD) stands as a complex and heterogeneous neurodevelopmental condition, encompassing a range of challenges in social interaction, communication, and behavior. Timely and accurate detection of ASD is crucial to initiate tailored interventions and support, optimizing developmental outcomes for affected individuals. This study delves into a novel avenue for ASD detection, leveraging the capabilities of Deep Convolutional Neural Networks (Deep CNN) applied to textual data. The model training and evaluation stages incorporate established metrics, assessing accuracy, precision, recall, and F1-score. The significance of this research extends beyond its technical prowess. By harnessing the power of deep-learning, this approach bridges the gap between cutting-edge technology and clinical practice. The implications of this study reverberate across multidisciplinary domains, from healthcare to machine learning, underscoring the potential for artificial intelligence to positively impact the lives of those affected by ASD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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An Intrusion Detection Scheme Based on Sequential Tri-Way Decisions of Multiple Granularity for Intelligent Connected Vehicles

An Intrusion Detection Scheme Based on Sequential Tri-Way De...

引用

international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Yongzhong Wang, Qi Li, Yuheng Zhang, Shipeng School of Automotive Engineering Nantong Institute of Technology Nantong226010 China Internet of Things and Big Data Major of Macao Polytechnic University Macao China School of Computer Science Jiangsu University of Science and Technology Zhenjiang212003 China

ISBN: (纸本)9789819639762

With the rapid developing technology of the Internet of Things, intelligent connected vehicles are facing more and more serious cybersecurity threats and challenges. With the developing of cybersecurity technology for intelligent connected vehicles it has posed a comprehensive challenge to the existing cybersecurity technologies. Intrusion detection is an important research field of cybersecurity. An intrusion detection scheme based on sequential tri-way decisions of multiple granularities (S3WD-MG) is proposed for Intelligent Connected Vehicles. The S3WD-MG method obtains feature sets of multi-granularities through the auto-encoder of deep learning. This method combines multiple granularities and sequential tri-way decisions to make decisions by the most appropriate for network behavior through by the decision threshold. The experiment results on NSL-KDD show that S3WD-MG method does have a good performance in intrusion detection. Compared with other models, our proposed S3WD-MG method has a higher detection’s rate and the robustness of algorithm is stronger. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Network intrusion

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Design and implementation of infant crying monitoring and analysis system based on deep learning 24

Design and implementation of infant crying monitoring and an...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Huang, Jiayuan Li, Linfeng Gong, Fan Wang, Ziyan Zhan, Huijiong Guangzhou Institute of Science and Technology Guangdong Guangzhou China

ISBN: (纸本)9798400710094

Aiming at the problems that the design functions of intelligent baby monitoring products on the market are too single, such as only equipped with basic environmental monitoring functions and low accuracy of crying recognition caused by only sound sensors for baby cry monitoring, a baby monitoring system based on deep learning and multi-sensor combination is designed. The system uses Jetson Nano B01 as the model deployment terminal, combined with the single-chip microcomputer equipped with sensors and K210 visual processing unit to realize the functions of infant crying monitoring, environment and sign data detection, and based on neural network design. The function of emotional analysis of infant crying is expected to’ translate’ the potential needs and health signals of infant crying. Finally, through the analysis of the algorithm data and the actual test of the system, it is shown that the system can meet the design requirements and achieve the target function, which better solves the shortcomings of the current intelligent infant monitoring products and improves the intelligent level of infant monitoring products. © 2024 Copyright held by the owner/author(s).

关键词： Microcomputers

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Construction and Visual Validation of Low-Carbon Development Evaluation System for Urban Agglomerations 41st

Construction and Visual Validation of Low-Carbon Developme...

引用

41st computer Graphics international conference, CGI 2024

作者： Wang, Yanru Wang, Song Wang, Hesong Long, Hao Hu, Hao School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9783031818059

Evaluating the urban of low-carbon development (LCDU) is crucial for promoting sustainable urban development. Previous evaluation works are weak in exploring the influence of spatial patterns of urban agglomerations on LCDU and analyzing the differences among urban agglomerations, focusing on the selection of indicator sets rather than the establishment of a complete evaluation system. To address the lack of feature mining at the granularity of urban agglomerations in the traditional Entropy Weight-Topsis algorithm (EWT), this paper proposes the EWT deformation. Building upon this, we construct a low-carbon development evaluation system (LCDE). To demonstrate the validity of LCDE, we develop a LCDVis system with data from 62 Chinese cities as the example and design visual encoding, including high-dimensional feature portrait and feature river map to enhance the interpretability of the evaluation results. Furthermore, we take three case studies and some positive feedback from users and experts that demonstrate the effectiveness of our methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-carbon

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DM-Vis: A Graph-Based Data Reconnaissance System for Multi-domain Urban Data 41st

DM-Vis: A Graph-Based Data Reconnaissance System for Multi...

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41st computer Graphics international conference, CGI 2024

作者： Wang, Hesong Wang, Song Wang, Yanru Long, Hao School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9783031818059

As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain data analysis falls short for complex tasks, necessitating multi-source data integration to offer decision-makers a holistic view of interconnections and trends. However, challenges arise in effectively representing data associations and swiftly identifying relevant datasets. This paper proposes techniques to integrate multi-domain urban data relationships and facilitate the acquisition of target datasets. We introduce a method to transform data entities into association graphs and a data extraction method to aid users in understanding data relationships and acquiring target datasets. Using these methods, we developed DM-Vis, a data reconnaissance visual analysis system, to facilitate data integration and exploration from multiple sources. Real-data case studies and positive feedback from users and experts validate the utility and reliability of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data extract data integration data reconnaissance multi-domain data query embedding

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Interference-Based Reliability and Capacity Analysis for IEEE 802.11 Broadcast Ad-Hoc Networks on the Highway 11

Interference-Based Reliability and Capacity Analysis for IEE...

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11th international conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In IEEE 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in IEEE 802.11 broadcast ad-hoc networks on highways. Based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Markov processes

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