检索结果-内蒙古大学图书馆

Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Zheng Zhao Pengfei Yi Jing Dong Rui Liu Mingkai Cheng Dongsheng Zhou Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

Gaze-based methods have been widely applied in human-robot interactions (HRI). However, most methods were often limited to scenarios where the human face and the gazed object should be in the same field of view (FOV), which is difficult to adapt to the complex and changeable typical daily life scene where the service robot is located. To improve the limitation, this paper proposes a framework capable of completing tasks even if the person and the gazed object are not in the same FOV, requiring only a single RGB camera. First, a cross-FOV gaze estimation algorithm is designed to get the gaze point from images containing a human face captured by an RGB camera. Then a multi-view fusion decision model is employed to drive the robot to turn to the gaze point and identify the object according to the gaze point. Finally, a robot can complete tasks using this information. Experiments under conditions of different lighting, different scenes and different users on the NAO robot show the effectiveness of our method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Neural Network-Based Terminal Sliding Mode Controller design for Manipulator Systems 1

引用

UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Qu, Lufeng Fan, Qiming Kang, Yang Yang, Jun Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China School of Intelligent Manufacturing Tianjin Sino-German University of Applied Technology Tianjin300350 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

In the context of nonlinear and uncertain manipulator systems, achieving fast and accurate desired trajectories for each joint of the manipulator is crucial. Our study aims to improve current control methods by combining sliding mode control and neural network techniques. Specifically, we leverage the neural networks’ approximation capabilities to estimate both the modeling error and external disturbance in our research. These estimates are then integrated into the synovial control as additional gains. This approach allows for the sliding mode controller’s switch gain to adapt to changing modeling error and external disturbance, thereby avoiding the vibration production typically associated with traditional sliding mode controllers. It can be observed from the simulation results that the combination of the proposed nonsingular terminal sliding mode controller and neural network achieves fast and precise tracking of desired trajectories. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

TICNet: Three-Branch Real-Time Semantic Segmentation Network with Intensive Compensation of Railway Track Scenes 5

TICNet: Three-Branch Real-Time Semantic Segmentation Network...

引用

5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Bai, Yiwen Yang, Lu Zhang, Lei Song, Yajing School of Transportation Science and Engineering Beihang University Beijing102206 China School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Tianjin300384 China Traffic Control Technology Co. Ltd. Beijing China

ISBN: (纸本)9798331530334

With the rapid development of railway traffic system, real-time semantic segmentation plays a crucial role in railway track scene monitoring. However, most of the existing methods use lightweight convolution to reduce the computational complexity, which results in reduced accuracy. To solve this problem, this paper proposes a real-time semantic segmentation network called TICNet, which is specifically used for accurate segmentation of railway track scenes. TICNet uses a novel three-branch structure designed for efficient feature extraction and accurate segmentation. Results on the RailSem19 dataset show that TICNet has a segmentation accuracy of 62.36% mIoU with an inference speed of 81.2 FPS, which is significantly better than many existing methods. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Remolding Semantic Focus with Dual Attention Mechanism for Aspect-based Sentiment Analysis 8

Remolding Semantic Focus with Dual Attention Mechanism for A...

引用

8th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Li, Xingda Bao, Yanwei Hu, Min Ren, Fuji Hefei University of Technology Hefei Anhui230009 China Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China University of Electronic Science and Technology of China Chengdu Chengdu611730 China

ISBN: (纸本)9781665477352

Aspect-based sentiment analysis (ABSA) is an NLP task that classify fine-grained sentiment towards one specific aspect from the same text. While attention mechanism has achieved great success, attaching aspects to abstract sentiment remains challenging. In this paper, we propose dual attention mechanism, a novel method to re-weight the distribution of attention between stack BERT layers, in prompt learning way with pretrained language model BERT. Specifically, after obtaining the most attractive words, the method raises weight of other possible corresponding words and makes model consider more comprehensively. To introduce more aspect information, we classify the sentiment in improved prompt learning way. Note that the overfitting using BERT on ABSA, we utilize the approach of staged loss that restrict the training not too small. Finally, the experiment results demonstrate the effectiveness and the stability of dual attention and provide a good insight of attention mechanism. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Safety-Aware Human-Lead Vehicle Platooning by Proactively Reacting to Uncertain Human Behaving

arXiv

引用

arXiv 2024年

作者： Hu, Jia Wang, Shuhan Zhang, Yiming Wang, Haoran Liu, Zhilong Cao, Guangzhi Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai201804 China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Cao’an Road Shanghai China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Dazhuo Intelligent Technology Company Shanghai China ZDrive.ai Shanghai China

Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the reliability of perception and decision-making. However, state-of-the-art HL-CACC technology still has a great limitation on driving safety due to the lack of considering the leading human driver’s uncertain behavior. In this study, a HL-CACC controller is designed based on Stochastic Model Predictive Control (SMPC). It is enabled to predict the driving intention of the leading Connected Human-Driven Vehicle (CHV). The proposed controller has the following features: i) enhanced perceived safety in oscillating traffic;ii) guaranteed safety against hard brakes;iii) computational efficiency for real-time implementation. The proposed controller is evaluated on a PreScan&Simulink simulation platform. Real vehicle trajectory data is collected for the calibration of the simulation. Results reveal that the proposed controller: i) improves perceived safety by 19.17% in oscillating traffic;ii) enhances actual safety by 7.76% against hard brakes;iii) is confirmed with string stability. The computation time is approximately 3.2 milliseconds when running on a laptop equipped with an Intel i5-13500H CPU. This indicates the proposed controller is ready for real-time implementation. © 2024, CC BY.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

Accurate MTPA Control Based on Ferrari Method for IPMSM with Partial Derivative Term Calculation 27

Accurate MTPA Control Based on Ferrari Method for IPMSM with...

引用

27th International Conference on Electrical Machines and Systems, ICEMS 2024

作者： Li, Yiyang Gu, Xin Wang, Zhiqiang Guo, Liyan Chen, Wei Li, Xinmin Li, Chen School of Electronics and Information Engineering Tiangong University Tianjin China School of Electrical Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin China National Local Joint Engineering Research Center of Electrical Machine System Design and Manufacturing China Zhejiang University Advanced Electrical Equipment Innovation Center Hangzhou China

ISBN: (纸本)9784886864406

Even if the exact motor parameters are known, the MTPA strategy cannot obtain precise maximum torque per ampere (MTPA) points under large current amplitude because the partial derivative of inductance to current is ignored in the modeling process. In view of this, the MTPA mathematical model considering the partial derivative of inductance to current is established in this paper, and the MTPA point tracking error caused by ignoring the partial derivative in the modeling process based on Ferrari method is analyzed. Then, an offline-online combined method for calculating the partial derivative term is proposed to compensate the MTPA tracking error. On this basis, an MTPA control strategy based on Ferrari method considering the partial derivative of inductance to current is proposed. Experiment to verify the feasibility and effectiveness of the proposed method. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

Hierarchically porous Cu current collector with lithiophilic CuxO interphase towards high-performance lithium metal batteries

引用

Journal of Energy Chemistry 2021年第7期30卷 292-299页

作者： Yaya Wang Zexu Zhao Wei Zeng Xingbo Liu Lei Wang Jian Zhu Bingan Lu State Key Laboratory for Chemo/Biosensing and Chemometrics and College of Chemistry and Chemical EngineeringHunan Key Laboratory of Two-Dimensional MaterialsHunan UniversityChangsha 410082HunanChina State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan Provincial Key Laboratory of Intelligent Laser ManufacturingHunan UniversityChangsha 410082HunanChina School of Materials and Engineering Hunan UniversityChangsha 410082HunanChina School of Physics and Electronics Hunan UniversityChangsha 410082HunanChina

Lithium metal is one of the most promising anode materials for next-generation electrochemical energy storage due to low electrochemical potential and high specific ***,large volume change and uncontrollable formation of lithium dendrite during cycling severely hinder the practical application of rechargeable Li metal ***,we report a hierarchically porous Cu covered with lithiophilic CuxO(HPCu-CuxO) via femtosecond laser strategy in about 2 min as current collector for highperformance Li metal *** precisely tunable pore volume and depth as well as lithiophilic CuxO interphase,the HPCu-CuxO not only guides homogeneous Li nucleation,resulting in a smooth and dendrite-free lithium surface,but also provides space to alleviate the volume expansion of Li metal anode,achieving excellent structure ***,highly stable Coulombic efficiency and ultralow overpotential of 15 mV even up to 1000 h were achieved at the current density of 1 mA cm^(-2).Moreover,the resultant Li@HPCu-CuxO//LiFePO_(4) full battery delivered outstanding cycle stability and rate *** results offer a pathway toward high-energy-density and safe rechargeable Li metal batteries.

关键词： Lithium metal batteries Femtosecond laser strategy Hierarchically porous structure Lithiophilic CuxO interphase

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

来源：评论

学校读者我要写书评

暂无评论

Segmenting Anything in the Dark via Depth Perception

引用

IEEE Transactions on Multimedia 2025年 27卷 2975-2986页

作者： Liu, Peng Deng, Jinhong Duan, Lixin Li, Wen Lv, Fengmao University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China UESTC Shenzhen Institute for Advanced Study Shenzhen518110 China University of Electronic Science and Technology of China Sichuan Provincial Key Laboratory for Human Disease Gene Study Center for Medical Genetics Department of Laboratory Medicine Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital Chengdu610032 China Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education School of Computing and Artificial Intelligence Chengdu611756 China

Image segmentation under low-light conditions is essential in real-world applications, such as autonomous driving and video surveillance systems. The recent Segment Anything Model (SAM) exhibits strong segmentation capability in various vision applications. However, its performance could be severely degraded under low-light conditions. On the other hand, multimodal information has been exploited to help models construct more comprehensive understanding of scenes under low-light conditions by providing complementary information (e.g., depth). Therefore, in this work, we present a pioneer attempt that elevates a unimodal vision foundation model (e.g., SAM) to a multimodal one, by efficiently integrating additional depth information under low-light conditions. To achieve that, we propose a novel method called Depth Perception SAM (DPSAM) based on the SAM framework. Specifically, we design a modality encoder to extract the depth information and the Depth Perception Layers (DPLs) for mutual feature refinement between RGB and depth features. The DPLs employ the cross-modal attention mechanism to mutually query effective information from both RGB and depth for the subsequent feature refinement. Thus, DPLs can effectively leverage the complementary information from depth to enrich the RGB representations and obtain comprehensive multimodal visual representations for segmenting anything in the dark. To this end, our DPSAM maximally maintains the instinct expertise of SAM for RGB image segmentation and further leverages on the strength of depth for enhanced segmenting anything capability, especially for cases that are likely to fail with RGB only (e.g., low-light or complex textures). As demonstrated by extensive experiments on four RGBD benchmark datasets, DPSAM clearly improves the performance for the segmenting anything performance in the dark, e.g., +12.90% mIoU and +16.23% mIoU on LLRGBD and DeLiVER, respectively. © 1999-2012 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Target-background feature blocks and aberrance repressed correlation filters for real-time UAV tracking

引用

Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Jia, Yunwei Liu, Bo Liu, Zhenzhong Wang, Tianyang Yv, Kaiying Wang, Shuo Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

In recent years, Unmanned Aerial Vehicle (UAV) tracking technology has been widely applied in fields such as agriculture and aviation. However, due to limitations in UAV computational resources and battery life, deep learning-based tracking algorithms are difficult to deploy directly. Therefore, efficient and robust tracking methods based on Discriminative Correlation Filters (DCF) have become the mainstream for UAV tracking. The traditional DCF method updates the model with a fixed step size, making it prone to contamination, while the boundary effect further limits its tracking capability. To address the issue of model contamination, an appearance model update and masking mechanism was designed, introducing feature confidence and overall confidence thresholds to evaluate the quality of features and images. Features and images with poor quality are masked, and the learning rate is reduced to prevent model contamination. This paper proposes the Target-Background Feature Block and Aberrance Repressed Correlation Filter (TBFBARCF), which introduces target and background regularization terms into the spatial filter to mitigate boundary effects and incorporates an aberrance repression regularization term into the temporal aberrance repressed filter to reduce response map aberrance. The two filters mutually constrain each other and are jointly trained. Experiments conducted on three challenging datasets, OTB100, UAV123, and UAVDT, demonstrate that the TBFBARCF tracker outperforms 11 other DCF-based trackers. Its average precision and average success rate reach 0.756 and 0.62, respectively, and it operates at a speed of 36.6 frames per second (FPS) on a CPU platform. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Drones

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：