检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Guo, Yu Liu, Ryan Wen Qu, Jingxiang Lu, Yuxu Zhu, Fenghua Lv, Yisheng The School of Navigation Wuhan University of Technology Wuhan430063 China The National Engineering Research Center for Water Transport Safety Wuhan430063 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide the vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels, but without knowing the information on identity, position and movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity and dynamic information for the vessels of interest. However, traditional data fusion methods easily suffer from several potential limitations, e.g., asynchronous messages, missing data, random outliers, etc. In this work, we first extract the AIS- and video-based vessel trajectories, and then propose a deep learning-enabled asynchronous trajectory matching method (named DeepSORVF) to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS- and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results under occlusion conditions. To validate the efficacy of our DeepSORVF, we have also constructed a new benchmark dataset (termed FVessel) for vessel detection, tracking, and data fusion. It consists of many videos and the corresponding AIS data collected in various weather conditions and locations. The experimental results have demonstrated that our method is capable of guaranteeing high-reliable data fusion and anti-occlusion vessel tracking. © 2023, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Pilot Study on Coupling Behaviors of Limb Movements and Brain Activation Based on EEG and fNIRS Synchronization Information

A Pilot Study on Coupling Behaviors of Limb Movements and Br...

引用

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Xi Tang Qingge Li Yao Tang Lan Tian Yue Zheng Xiangxin Li Naifu Jiang Peng Shang Guanglin Li Liang Peng Peng Fang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems & the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Motion is one of the basic physiological functions of human beings. However, many brain diseases such as stroke may cause different degrees of motor dysfunctions for patients. As a commonly used rehabilitation method, active and passive exercise training may enhance patients’ neuromuscular functions and recover their motor abilities. It is known that limb movements are strongly coupled with brain activation but there is currently insufficient exploration on the coupling behaviors from the perspective of informatics. In this study, the coupling relationship between limb movements and brain activation was preliminarily studied based on three healthy subjects. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals were synchronously collected during lower limb movements, and time-frequency analysis (TFA) and transfer entropy (TE) analysis were performed to quantitatively study the brain activation behaviors. In the experiments, a desynchronization phenomenon of μ rhythm in EEG was observed during exercise states, and the experimental results demonstrate the activation rule of motor and prefrontal cortexes upon limb movements. Calculations show that there exists a bidirectional flow of information between EEG and cerebral oxygen metabolism signals, but with a difference between different directions. This work may support the rehabilitation for patients with motor dysfunctions with a guidance of quantitative indicators and also benefit the exploration on neuroscience.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Differential-Evolution-Based Generative Adversarial Networks for Edge Detection

Differential-Evolution-Based Generative Adversarial Networks...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Wenbo Zheng Chao Gou Lan Yan Fei-Yue Wang School of Software Engineering Xi'an Jiaotong University The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Since objects in natural scenarios possess various scales and aspect ratios, learning the rich edge information is very critical for vision-based tasks. Conventional generative adversarial networks (GANs) based methods for edge detection don't perform so well due to model collapse. In order to capture rich edge information and avoid model collapse as much as possible, we consider the learning of GANs as an evolutionary optimization and propose a novel method termed as differential-evolution-based generative adversarial networks (DEGAN) for richer edge detection. In particular, built upon GANs structure, we introduce an improved differential evolutionary algorithm to refine the input of generator, with fitness function evaluated by the discriminator. Experimental results on the well-known BSDS500 and NYUD benchmarks indicate our proposed DEGAN can achieve state-of-the-art performance while retaining a fast speed and validate its simplicity, effectiveness, and efficiency. The high quality of our results on edge detection with proposed DEGAN may promise to make other vision-based tasks work better.

关键词： Image edge detection Generators Gallium nitride Training Generative adversarial networks Evolutionary computation Evolution (biology)

来源：评论

学校读者我要写书评

暂无评论

ModuleNet: Knowledge-inherited Neural Architecture Search

arXiv

引用

arXiv 2020年

作者： Chen, Yaran Gao, Ruiyuan Liu, Fenggang Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Beihang Univercity Beijing China Beijing Institute of Technology Beijing China

—Although Neural Architecture Search (NAS) can bring improvement to deep models, they always neglect precious knowledge of existing models. The computation and time costing property in NAS also means that we should not start from scratch to search, but make every attempt to reuse the existing knowledge. In this paper, we discuss what kind of knowledge in a model can and should be used for new architecture design. Then, we propose a new NAS algorithm, namely ModuleNet, which can fully inherit knowledge from existing convolutional neural networks. To make full use of existing models, we decompose existing models into different modules which also keep their weights, consisting of a knowledge base. Then we sample and search for new architecture according to the knowledge base. Unlike previous search algorithms, and benefiting from inherited knowledge, our method is able to directly search for architectures in the macro space by NSGA-II algorithm without tuning parameters in these modules. Experiments show that our strategy can efficiently evaluate the performance of new architecture even without tuning weights in convolutional layers. With the help of knowledge we inherited, our search results can always achieve better performance on various datasets (CIFAR10, CIFAR100) over original architectures. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

ACP based 3D Emergency Drills system for Petrochemical Plants

ACP based 3D Emergency Drills System for Petrochemical Plant...

引用

World Congress on Intelligent control and Automation

作者： Sifeng Jing Changjian Cheng Gang Xiong Xiwei Liu Xiuqin Shang Weixing Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Dongguan Research Institute of CASIA Cloud Computing Industrial Technology Innovation and Incubation Center Chinese Academy of Sciences

ISBN: (纸本)9781467313971

To further enhance emergency management skills of an organisation's emergency response personnel, emergency response training, especially 3D emergency drill, is currently becoming more and more important in the petrochemical sector. So, a novel 3D emergency drills system is designed and developed based on ACP approach, which can be used for mocking emergency response plan drills and evaluating the plan. A case study reveals that the performance of the system is good and the system can meet the needs of emergency response training and optimizing emergency response plan in petrochemical plants.

关键词： ACP Approach Artificial systems Computational Experiments Parallel Execution Mock-Drill Emergency Response Training

来源：评论

学校读者我要写书评

暂无评论

A Hybrid of Hard and Soft Attention for Person Re-Identification

A Hybrid of Hard and Soft Attention for Person Re-Identifica...

引用

Chinese Automation Congress (CAC)

作者： Xuesong Li Yating Liu Kunfeng Wang Yong Yan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China State Grid Zhejiang Electric Power Company LTD Hangzhou China

Existing pedestrian re-identification methods based on deep learning have achieved good results under constrained conditions. However, there exist some challenges including large human pose variations, viewpoint changes, severe occlusions and imprecise detection of persons. So we present a Hard/Soft hybrid Attention Network (HSAN) that combines pose information and attention mechanism to deal with the challenges. Our model includes two main parts: Pose-guided Hard Attention (PHA) and Regional Soft Attention (RSA). PHA uses the keypoints generated by pose estimation to enhance the foreground information, and RSA is learned to eliminate the background clutter. We extract reliable features and locate discriminative regions by using these two modules to handle occlusions, pose changes and background noises. We conduct a lot of experiments on public datasets including DukeMTMC-ReID, Market-1501, and CUHK03, and the results show that our method achieves stateof-the-art performance.

关键词： Pose estimation Task analysis Noise measurement Feature extraction Reliability Machine learning

来源：评论

学校读者我要写书评

暂无评论

LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

arXiv

引用

arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Multi-view adaptive fusion network for 3D object detection

arXiv

引用

arXiv 2020年

作者： Wang, Guojun Tian, Bin Zhang, Yachen Chen, Long Cao, Dongpu Wu, Jian State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510275 China Lab University of Waterloo Canada College of Automotive Engineering Jilin University China

3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both modalities without information loss and interference. To solve this issue, we propose a single-stage multi-view fusion framework that takes LiDAR Birds-Eye View, LiDAR Range View and Camera View images as inputs for 3D object detection. To effectively fuse multi-view features, we propose an Attentive Pointwise Fusion (APF) module to estimate the importance of the three sources with attention mechanisms which can achieve adaptive fusion of multi-view features in a pointwise manner. Besides, an Attentive Pointwise Weighting (APW) module is designed to help the network learn structure information and point feature importance with two extra tasks: foreground classification and center regression, and the predicted foreground probability will be used to reweight the point features. We design an end-to-end learnable network named MVAF-Net to integrate these two components. Our evaluations conducted on the KITTI 3D object detection datasets demonstrate that the proposed APF and APW module offer significant performance gain and that the proposed MVAF-Net achieves state-of-the-art performance in the KITTI benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Parallel Learning Based Foundation Model for Networked Traffic Signal control

Parallel Learning Based Foundation Model for Networked Traff...

引用

International Conference on Intelligent Transportation

作者： Chen Zhao Xingyuan Dai Yuanyuan Chen Yilun Lin Yisheng Lv Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanghai AI Laboratory Shanghai China Macao Institute of Systems Engineering Macau University of Science and Technology Macao China

Networked Traffic Signal control (NTSC) is a fundamental component of Intelligent Transportation systems (ITS) and the broader vision of smart city development. While a plethora of intelligent strategies have been developed, the Sim2Real challenge often impedes their full realization. In response, this paper introduces the Parallel Learning-based Adaptive Network for Traffic Signal control (PLANT) as a foundation model for NTSC. We employ the Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate a wide range of artificial scenarios for robust PLANT training. Further, the Transformer-based Cooperation Mechanism (TCM) is integrated as the primary learner within PLANT, facilitating effective capture of traffic dynamics and knowledge accumulation. This knowledge is readily transferable to real-world applications through meticulous fine-tuning, equipping PLANT to adapt and evolve in alignment with shifting transportation paradigms. Our empirical study on the Hangzhou road network demonstrates PLANT's superiority over both traditional and emerging DRL-based approaches, emphasizing its viability as a potential foundation model for NTSC.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Numerical and Experimental Research on Electromagnetic-Thermal and Leakage Defects Characteristics of the High Voltage Cable Terminal

SSRN

引用

SSRN 2023年

作者： Liu, Xingmou Gu, Yan Li, Lei Yang, Yongming Cheng, Peng Jadoon, Ammad Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Electrical Engineering Chongqing University Chongqing400044 China State Grid Sichuan Electric Power Company Chengdu Power Supply Company Chengdu610000 China NFC-Institute of Engineering &Technology Multan Pakistan

Thermal and insulation performance of the High Voltage Cable Terminal (HVCT) could effectively improve the reliability and economy of power energy transmission. In this paper, a numerical model of the electromagnetic heat flow field was proposed to analyze the heat transfer and temperature characteristics of HVCT under oil leakage defects. This model was the first validated using operation data from the actual equipment and the relative error was less than 1.51%. The effects of oil leakage fault on the global and local temperature distribution of the HVCT were then investigated under different load currents. In addition, the local temperature rise effect of different ambient temperatures on the HVCT oil leakage defect was also considered. Finally, combined with reference indicators such as oil level, temperature, and temperature gradient, a qualitative and quantitative evaluation of the oil leakage fault of HVCT was performed. The results showed that the temperature of the core, surface, and their maximum temperature gradient were linearly related to the ambient temperature. However, the temperature of the core at the oil leak exceeded the normal temperature by up to 7.2 °C with 1000A load current, and the temperature gradient change reaches 681.3 °C/m, which indicated that the convection heat dissipation effect of the oil-deficient part was poor, and the heat accumulates near the cable core. Therefore, the numerical model and analytical results presented in this paper provided an important method for online temperature monitoring and fault analysis of HVCTs. © 2023, The Authors. All rights reserved.

关键词： Thermal gradients

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：