检索结果-内蒙古大学图书馆

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An optimization Strategy for Deep Neural Networks Training

An optimization Strategy for Deep Neural Networks Training

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Tingting Wu Peng Zeng Chunhe Song State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665464697

Learning rate is one of the essential hyperparameters influencing the training process and the accuracy of deep neural networks. However, until now, it is challenging to determine an optimal learning rate. A large learning rate can accelerate the training process but may bring instability in the training process and miss the global optimum. In contrast, a small learning rate would be in a stable training process, but the training speed would be slow, and the training process is easy to fall into local optimum. In this paper, first, the impact of the learning rate is analyzed. It is found that a learning rate schedule should consist of two stages to take into account the speed and accuracy of the training process simultaneously. Based on this consideration, this paper proposes an improvement strategy of learning rate schedules: a two-stage integration strategy of a large fixed learning rate and a rapid decay learning rate. Second, the proposed strategy is applied to optimize a series of widely used learning rate settings. Extensive experiments on CIFAR-10 and CIFAR-100 datasets with VGG19, ResNets, ResNext, DenseNets, SeNet, and some other models demonstrate that the proposed strategy optimizes the learning rate settings, the performance of trained models is enhanced.

关键词： Training Deep learning Measurement Schedules Computational modeling Neural networks Predictive models

来源：评论

学校读者我要写书评

暂无评论

Learning Disentangled Representations and Temporal-Correlation Dynamics for Robotic Anomaly Diagnosis

Learning Disentangled Representations and Temporal-Correlati...

引用

IEEE International Conference on robotics and Biomimetics

作者： Dong Liu Hongmin Wu Kezheng Sun Yisheng Guan Biomimetic and Intelligent Robotics Lab (BIRL) School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665481106

Anomalous diagnosis is valuable for reducing potential damages in long-term autonomy robot manipulation tasks, especially in Human-robot collaboration scenarios. Deep learning-based methods have been widely investigated for robot anomaly diagnosis, which can effectively encode complex dynamics from multi-modal sensory data. However, the lacking of enough anomalous samples and the fusion of high-dimensional and modality correlation as well as time-dependent is still a challenging problem. In this paper, a novel framework is introduced to generate synthetic anomaly samples for data augmentation by learning the disentangled representation with sequential disentangled variational autoencoder (sDVAE), and a temporal-correlation VAE (tcVAE) model for robot anomaly diagnosis by learning the temporal correlation features of multimodal anomalies. To evaluate the proposed methods, 115 original anomalous samples from 7 representative anomalies that are first recorded on a self-developed human-robot kitting task. Results indicate that the proposed methods show the best performance of the highest precision (97%), f1-score (95%), and accuracy (93%) with synthetic samples across all baseline methods.

关键词： Learning systems Correlation Biomimetics Biological system modeling Collaboration Robot sensing systems Data models

来源：评论

学校读者我要写书评

暂无评论

Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

引用

International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Tao Jiang Enzhi Zhu Sidong Wu Linshuai Zhang Shuoxin Gu Lin Xu Qian Wang Xianggui Tang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Salient Object Detection in Traffic Scene through the TSOD10K Dataset

arXiv

引用

arXiv 2025年

作者： Qiu, Yu Sun, Yuhang Mei, Jie Xiao, Lin Xu, Jing College of Information Science and Engineering Hunan Normal University Changsha410081 China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases College of Artificial Intelligence Nankai University Tianjin300350 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Unlike SOD in natural scene images (NSI-SOD), which prioritizes visually distinctive regions, TSOD emphasizes the objects that demand immediate driver attention due to their semantic impact, even with low visual contrast. This dual criterion, i.e., bridging perception and contextual risk, re-defines saliency for autonomous and assisted driving systems. To address the lack of task-specific benchmarks, we collect the first large-scale TSOD dataset with pixel-wise saliency annotations, named TSOD10K. TSOD10K covers the diverse object categories in various real-world traffic scenes under various challenging weather/illumination variations (e.g., fog, snowstorms, low-contrast, and low-light). Methodologically, we propose a Mamba-based TSOD model, termed Tramba. Considering the challenge of distinguishing inconspicuous visual information from complex traffic backgrounds, Tramba introduces a novel Dual-Frequency Visual State Space module equipped with shifted window partitioning and dilated scanning to enhance the perception of fine details and global structure by hierarchically decomposing high/low-frequency components. To emphasize critical regions in traffic scenes, we propose a traffic-oriented Helix 2D-Selective-Scan (Helix-SS2D) mechanism that injects driving attention priors while effectively capturing global multi-direction spatial dependencies. We establish a comprehensive benchmark by evaluating Tramba and 22 existing NSI-SOD models on TSOD10K, demonstrating Tramba’s superiority. Our research establishes the first foundation for safety-aware saliency analysis in intelligent transportation systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

arXiv

引用

arXiv 2024年

作者： Liu, Yu Zhang, Yuexin Li, Kunming Qiao, Yongliang Worrall, Stewart Li, You-Fu Kong, He Shenzhen518055 China Department of Mechanical Engineering City University of Hong Kong Hong Kong School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Australian Centre for Field Robotics The University of Sydney NSW2006 Australia Australian Institute for Machine Learning The University of Adelaide SA5005 Australia Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering crossdataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme. © 2024, CC BY.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

SLAM-Based Joint Calibration of Differential RSS Sensor Array and Source Localization

SLAM-Based Joint Calibration of Differential RSS Sensor Arra...

引用

Annual Conference of Industrial Electronics Society

作者： Linya Fu Xu Qiao Shoudong Huang Guoqiang Mao Zhiyun Lin Youfu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern Uni. of Sci. and Tech. Shenzhen China Robotics Institute Uni. of Technology Sydney Sydney Australia State Key Laboratory of Integrated Services Networks Xidian University China Department of Mechanical Engineering City University of Hong Kong China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern Uni. of Sci. and Tech. Shenzhen China

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://***/SUSTech2022/DRSS-sensor-array-calibration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

arXiv

引用

arXiv 2021年

作者： Barišić, Antonella Petric, Frano Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb Croatia

This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder's path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV's motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results. © 2021, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Design and Development of a Rapidly Deployable Low-Cost Tensegrity In-Pipe Robot

Design and Development of a Rapidly Deployable Low-Cost Tens...

引用

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Yixiang Liu Xiaolin Dai Kai Guo Jiang Wu Rui Song Jie Zhao Yibin Li School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Mechanical Transmissions Chongqing University Chongqing China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Co. Ltd Hangzhou China Engineering Research Center of Intelligent Unmanned System of Ministry of Education School of Control Science and Engineering Shandong University Jinan China School of Mechanical Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid members and flexible cables, which has the advantages of simple structure, good flexibility, deformability, and impact resistance. Inspired by this structure, we design a novel worm-like tensegrity robot for different pipe environments, which can be manufactured rapidly at low cost. Firstly, a robotic module based on the tensegrity structure is designed inspired by the motion patterns of worm-like organisms. Then, the design process of the module is presented based on the mathematical analysis of the deformation. Finally, a prototype of the tensegrity robot is developed using simple and low-cost parts in less than an hour. To test the motion performance, load performance, and inspection capability of the tensegrity robot, we designed a series of experiments on horizontal pipes, vertical pipes, elbows, and steel pipes. Experimental results show that the worm-like tensegrity robot is simple in structure, easy to manufacture, low in cost, and good in performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

引用

System Reliability and Safety Engineering (SRSE), International Conference on

作者： Ruiqi Li Kai Wang Peng Zeng Jiwei Cao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical Engineering Harbin Institute of Technology China

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed.

关键词：

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：