检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Pang, Jichao Liu, Dan Tang, Xiaoqing Chen, Yan Huang, Qiang Arai, Tatsuo Liu, Xiaoming Key Laboratory of Biomimetc Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Mechatronical Engineerin Beijing Institute of Technology Beijing100081 China The University of Electro-Communications Center for Neuroscience and Biomedical Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350372601

In recent years, micromanipulator with dual end-effectors called microhand has been widely used in industrial and biological fields. Whether the tip positions of the two end-effectors of the microhand can be accurately aligned seriously affects the precise grasping of the microhand. However, few researchers have presented practical ways for precisely aligning the tip positions of a 6-DOF microhand's two end- effectors. This paper proposes a novel, inexpensive, lightweight, robust, and easy-to-use microhand dual end-effectors clamping device that can precisely and quickly align the two end-effectors of a 6-DOF microhand. First, we describe the microhand dual end-effectors clamping device's lightweight structural design. After that, we simplify the microhand dual end-effectors clamping device model and create a mathematical model to determine the relationship between the end-effectors' tip position and the rotation angles of screw No.1, screw No.2, screw No.3 of the microhand dual end-effectors clamping device. Through experimentation, we are able to attain precise alignment of the two end-effectors tip positions of the 6-DOF microhand, so we confirm that our mathematical model is true. Finally, we use the impulse response of PZT in the microhand's lower module to confirm the stability of the microhand dual end- effectors clamping device. We believe that the proposed the microhand dual end-effectors clamping device has great potential in industrial and biological applications as a universal tool for precise alignment of the tip positions of the two end-effectors of a 6-DOF microhand. © 2024 IEEE.

关键词： Clamping devices

来源：评论

学校读者我要写书评

暂无评论

A semantic segmentation method for UAV image based on improved BiseNetv2 5

A semantic segmentation method for UAV image based on improv...

引用

5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Guo, Fengjuan Han, Sen Zhao, Jiahang Han, Jiale Xin, Jing Shaanxi Key Laboratory of Integrated and Intelligent Navigation The 20th Research Institute of China Electronics Technology Corporation Shaanxi Xi’an710068 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9781510668621

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for "Human", the IoU of TBiseNetv2 is about 4.3% higher than that of the popular UNetFormer. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Pylon Reconstruction Based on NeRF

Pylon Reconstruction Based on NeRF

引用

International Conference on Electronics Information and Emergency Communication

作者： Zijian Hu Chenxing Wang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

In this paper, we propose a pylon reconstruction method based on Neural Radiance Fields (NeRF) technology. The advantage of this method lies in its ability to reconstruct pylons from images, with a multi-view model maintaining good performance even under complex lighting and occlusion conditions. We demonstrate the process of using NeRF to reconstruct electric power towers through experiments. During the experimental process, we collect images of electric power towers from different environments and angles, then apply NeRF to process these images, using deep learning to learn and generate a network model capable of expressing the scene. The experimental results show that our method can complete the reconstruction of scenes with only image inputs, and the reconstructed models have a certain accuracy in terms of geometric details and textures.

关键词： Surface reconstruction Image segmentation Image resolution Shape Poles and towers Lighting Reconstruction algorithms

来源：评论

学校读者我要写书评

暂无评论

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT images via a Deep Learning Architecture

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT ...

引用

2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Tang, Xiaoyu Chai, Lin Song, Chengze School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China School of Sino-french Engineering Institute Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781837242689

Computer-aided diagnosis has emerged as a crucial trend in modern medicine. Recently, CT-based automatic detection algorithms for lung infectious diseases suffer from issues of low accuracy and complex architecture. In this paper, three MoB-NetV2 models (MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL) for the detection of COVID-19 in CTs are proposed. the MoB-NetV2 model is proposed by inserting batch normalization layers between bottlenecks in MobileNetV2 and substituting the fully connected layer with the Net-1 model (SNN, ELM, RVFL), i.e. MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL. After comparison, it is found that the proposed MoB-NetV2-RVFL achieves an average accuracy of 93.42% on a private dataset comprising of 2392 images, while the precision is reached at 97.33%. Furthermore, it is demonstrated that our MoB-NetV2-RVFL outperforms other existing algorithms in the literature. High classification accuracy and precision and low computing amount are achieved, thereby enhancing the potential application of the model in the field of medical image-aided diagnosis of COVID-19 virus. © The Institution of Engineering & Technology 2024.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Anomaly-Aware Semantic Self-Alignment Framework for Video-Based Person Re-Identification

Anomaly-Aware Semantic Self-Alignment Framework for Video-Ba...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhidan Ran Xiaobo Lu Wei Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

Video-based person re-identification (Re-ID) aims at matching the video snippets of the same person across multiple cameras. The ubiquitous appearance misalignment is a critical challenge in video person re-identification. Existing alignment-based methods rely on off-the-shelf human parsing models and cannot handle anomalous appearance information (e.g., obstacles and pedestrian interference) in video sequences. In this paper, we propose Anomaly-Aware Semantic Self-Alignment (ASSA), a novel video-based person Re-ID framework that seeks out body parts without prior human topology information and learns part-based feature representations against anomalous information. The proposed ASSA performs part classifier training and part-aligned representation learning alternately. For the classifier training, we design a Salient Region Extraction module to segment the entire foreground from the background in each input frame. Furthermore, a novel Anomaly-Aware Refinement module is proposed to suppress the influence of anomalous interference. Extensive experiments on three prevalent benchmarks demonstrate the effectiveness and superiority of the proposed framework.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Traffic Signal control for Large-Scale Road Networks Based on Deep Reinforcement with PSR

Traffic Signal Control for Large-Scale Road Networks Based o...

引用

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Zhicheng Zhou Hui Zhang Ya Zhang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper investigates the problem of Traffic Signal control (TSC) in large-scale road networks. In extensive road networks, it is customary to define each intersection as an agent, however, the issue of partial observability is particularly prominent. In this paper, Predictive State Representation (PSR) is employed to address the challenge of partial observability in large-scale multi-agent systems. A Multi-agent Deep Reinforcement Learning (DRL) model based on PSR called PSR-XLight is proposed in Large-Scale TSC systems. Multi-agent PSR is conducted with centralized training and independent filtering which overcome the challenge of prohibitive computations when the number of agents is large. Parameters sharing is adopted between each agent’s PSR model to enhance learning efficiency and facilitate utilization in large-scale multi-agent environments. Each agent undergoes independent DRL training and execution while parameters sharing is adopted. Experiments are conducted on real-world road networks and a large-scale road network comprising 1000 intersections.

关键词： Training Filtering Roads Computational modeling Deep reinforcement learning control systems Observability

来源：评论

学校读者我要写书评

暂无评论

YOLO-MSP: Effective Cigarette Detection YOLO Network for Surveillance Scenario

YOLO-MSP: Effective Cigarette Detection YOLO Network for Sur...

引用

International Conference on systems and Informatics (ICSAI)

作者： Zhiwei Zhang Xiaobo Lu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

This paper presents an new approach to enhance the detection of smoking behavior using object detection neural networks, focusing on the challenge of small object detection, namely cigarettes in video frames. We introduce a Multi-Spatial Pooling (MSP) bottleneck mechanism integrated into a YOLOv8 backbone, which significantly improves the network's ability to discern smaller objects without compromising real-time inference capabilities. This enhancement facilitates the extraction of relevant features across various scales and efficiently handles the computational overhead, thereby boosting the detection accuracy and operational efficiency. Our results demonstrate a substantial improvement in performance metrics over traditional and existing neural network approaches, making this method highly suitable for real-time applications in diverse scenarios.

关键词： YOLO measurement Computational modeling Surveillance Neural networks Focusing Streaming media Feature extraction Real-time systems Informatics

来源：评论

学校读者我要写书评

暂无评论

Automatic Programming system of Painting Robots for Unstructured Workpieces Spraying 36

Automatic Programming System of Painting Robots for Unstruct...

引用

36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Liu, Xinkai Xu, Yunhui Zhou, Bo Fang, Fang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781665437127

Spraying technology has been applied more and more widely in aeronautical manufacturing field. However, aeronautical test workpieces usually have unstructured and complex shapes, which makes the spraying inefficient and the quality of coating cannot be guaranteed. Therefore, this paper designs an automatic programming and simulation system for aeronautical workpiece spraying. And the kinematics and inverse kinematics modeling of NACHI robot, spraying parameters optimization and trajectory planning of the robot are studied in detail. This paper also designs the simulation experiments on typical aeronautical test workpieces, and the result shows that this system can improve the spraying efficiency and coating thickness effect, which has a certain universality and effectiveness. © 2021 IEEE.

关键词： Inverse kinematics

来源：评论

学校读者我要写书评

暂无评论

Visual Explanation of Object Detectors via Saliency Maps 18

Visual Explanation of Object Detectors via Saliency Maps

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xiao, Jin Liu, Wenrui Hu, Xiaoguang Jiang, Hao Wang, Weipeng Yahang, Li Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing China The Third Academy of Casic Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350312201

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

引用

Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：