检索结果-内蒙古大学图书馆

IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Siyu Chen Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People’s Republic of China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan People’s Republic of China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowadays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and inspiration for the future work.

关键词： Knowledge engineering Correlation Transfer learning Neural networks Task analysis Information technology Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

Observer-Based Robust Containment Control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

引用

第三十九届中国控制会议

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunicationsand Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Containment control multi-agent system input saturation input additive disturbance

来源：评论

学校读者我要写书评

暂无评论

The Research of the Accessibility and Transitivity of Topological Group

The Research of the Accessibility and Transitivity of Topolo...

引用

International Conference on Virtual Reality and intelligent Systems (ICVRIS)

作者： Ji Zhan-Jiang Shi Wei School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Wuzhou Vocational College China

The accessibility and transitivity have an important significance in terms of theory and application. The concept of the accessibility and transitivity are introduced in this paper. We study their dynamical properties. The following result are obtained: (l)Let (K , G ) be the hyperspace of (X , G ). Then ( X , G ) is accessible if and only if (K , G ) is accessible; (2) Let (K , G) be the hyperspace of (X , G). Then (X , G ) is transitive if and only if (K , G ) is transitive. These results enriched the theory of the accessibility and transitivity of topological group.

关键词： Virtual reality intelligent systems

来源：评论

学校读者我要写书评

暂无评论

A survey on negative transfer

arXiv

引用

arXiv 2020年

作者： Zhang, Wen Deng, Lingfei Zhang, Lei Wu, Dongrui the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China the School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

—Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces the learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to handle it. However, there does not exist a systematic survey on the formulation of NT, the factors leading to NT, and the algorithms that mitigate NT. This paper fills this gap, by first introducing the definition of NT and its factors, then reviewing about fifty representative approaches for overcoming NT, according to four categories: secure transfer, domain similarity estimation, distant transfer, and NT mitigation. NT in related fields, e.g., multi-task learning, lifelong learning, and adversarial attacks, are also discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

A visual kinematics calibration method for manipulator based on nonlinear optimization

arXiv

引用

arXiv 2020年

作者： Peng, Gang Wang, Zhihao Yang, Jin Li, Xinde Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China IEEE senior member School of Automation South East University Nanjing China

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera's parameters affects the calibration accuracy of the kinematics of the robot arm. Therefore, this paper proposes a method for calibrating the geometric parameters of a kinematic model of a manipulator based on monocular vision. Firstly, the classic Denavit-Hartenberg (D-H) modeling method is used to establish the kinematic parameters of the manipulator. Secondly, nonlinear optimization and parameter compensation are performed. The three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Recurrent Network for Single image Deraining 8

Multi-Scale Recurrent Network for Single Image Deraining

引用

8th International Conference on Digital Home, ICDH 2020

作者： Li, Youxing Lan, Rushi Sun, Long Li, Ji Luo, Xiaonan Pang, Cheng Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China

ISBN: (纸本)9781728192345

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks. © 2020 IEEE.

关键词： Rain

来源：评论

学校读者我要写书评

暂无评论

Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

引用

第三十九届中国控制会议

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Electrical and Information Engineering Zhengzhou University of Light Industry School of Mechanical & Electrical Engineering Wuhan Institute of Technology

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant（LDTI） systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Parity space Model mismatch detection LDTV systems model-based residual generation

来源：评论

学校读者我要写书评

暂无评论

Semantic Segmentation Network with Local Multi-Scale Attention for Locating Traffic Sign

Semantic Segmentation Network with Local Multi-Scale Attenti...

引用

International Conference on intelligent Computation Technology and Automation, ICICTA

作者： Shun Zhong Zhuoyuan Zheng Peipei Yang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728142845

ISBN: (纸本)9781728142852

Semantic segmentation network is able to detect multi-scale traffic sign effectively. However, probabilities that different scale features participate in decision are equal in the network. In this work, we propose a local multi-scale attention module based on channel-attention, which extracts local features generated by dilated convolution and weights scale features. We demonstrate that adopting local multi-scale attention module on a semantic segmentation network achieves a better performance for locating multi-scale traffic signs than state-of-art convolutional networks by the experimental results on a Chinese traffic sign dataset.

关键词： Semantics Convolution Feature extraction image segmentation Task analysis Kernel Training

来源：评论

学校读者我要写书评

暂无评论

Adversarial refinement network for human motion prediction

arXiv

引用

arXiv 2020年

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong Hong Kong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2020, CC-BY.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

SRGNet: A GRU Based Feature Fusion Network for image Denoising

SRGNet: A GRU Based Feature Fusion Network for Image Denoisi...

引用

2019 International Symposium on intelligent Signal processing and Communication Systems, ISPACS 2019

作者： Wang, Wenhao Pang, Cheng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781728130385

image denoising is an essential pretreatment for most of image processing pipelines, which has been extensively studied for several decades. Recently, convolutional neural networks with skip connections show promising performances on image denoising due to their discriminative feature modeling and utilizing of features from former layers. However, they only apply coarse feature fusion strategies like pixel-wise addition or concatenation which are insufficient in image denoising. In this work, we propose a novel network architecture to exploit finer feature fusion. Specifically, a module based on gate recurrent unit is introduced into the architecture, fusing features from different layers and adopting finer feature selection at the same time. Experiments on multiple challenging datasets show the effectiveness of the proposed network. © 2019 IEEE.

关键词： image denoising

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：