检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

22 篇 会议
11 篇 期刊文献

馆藏范围

33 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

29 篇 工学
- 15 篇 计算机科学与技术...
- 12 篇 软件工程
- 6 篇 控制科学与工程
- 5 篇 信息与通信工程
- 4 篇 电子科学与技术（可...
- 3 篇 机械工程
- 3 篇 光学工程
- 3 篇 建筑学
- 3 篇 土木工程
- 3 篇 航空宇航科学与技...
- 3 篇 生物医学工程（可授...
- 2 篇 力学（可授工学、理...
- 2 篇 仪器科学与技术
- 1 篇 电气工程
- 1 篇 交通运输工程
- 1 篇 生物工程
19 篇 理学
- 14 篇 数学
- 7 篇 物理学
- 2 篇 系统科学
- 2 篇 统计学（可授理学、...
- 1 篇 生物学
6 篇 管理学
- 3 篇 管理科学与工程(可...
- 2 篇 图书情报与档案管...
- 1 篇 工商管理
- 1 篇 公共管理
1 篇 经济学
- 1 篇 应用经济学
1 篇 医学
- 1 篇 公共卫生与预防医...
1 篇 军事学

主题

3 篇 motion planning
2 篇 socksv5
2 篇 lifting scheme
2 篇 license plates (...
2 篇 multi-homed
2 篇 anisotropic diff...
1 篇 euclidean distan...
1 篇 partial differen...
1 篇 image matching
1 篇 deep space
1 篇 autonomous optic...
1 篇 very large scale...
1 篇 heterogeneous gr...
1 篇 hu invariant mom...
1 篇 private
1 篇 image segmentati...
1 篇 medical imaging
1 篇 wavelet
1 篇 pixel
1 篇 heterogeneous

机构

3 篇 college of life ...
3 篇 shanghai jiao to...
3 篇 institute for pa...
2 篇 state education ...
2 篇 state key lab. f...
2 篇 inst. for patter...
2 篇 guizhou key lab ...
2 篇 hubei university...
2 篇 key laboratory o...
2 篇 institute for pa...
1 篇 state education ...
1 篇 college of life ...
1 篇 school of comput...
1 篇 second artillery...
1 篇 nanchang hangkon...
1 篇 key lab. of educ...
1 篇 artificial intel...
1 篇 beijing institut...
1 篇 assoc. jeunes ch...
1 篇 state commn. res...

作者

4 篇 cai chao
4 篇 ding mingyue
4 篇 tian jinwen
4 篇 liu jian
3 篇 yang yang
3 篇 shen hong-bin
3 篇 wang lin
3 篇 li shidong
2 篇 蔡超
2 篇 张天序
2 篇 丁名跃
2 篇 jinwen tian
2 篇 zhang bao-liang
2 篇 li dehua
2 篇 田玉龙
2 篇 吴伟仁
2 篇 pan renlong
2 篇 jian liu
2 篇 周成平
2 篇 chen xiang

语言

33 篇 英文

检索条件"机构=Guizhou Key Lab of Pattern Recognition and Intelligent Control"

共 33 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

引用

5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

来源：评论

学校读者我要写书评

暂无评论

SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

引用

WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

引用

2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

An intelligent framework for plotting using functions

An intelligent framework for plotting using functions

引用

2012 International Conference on Computer Science and Service System, CSSS 2012

作者： Pan, Feng Guizhou Key Lab of Pattern Recognition and Intelligent Control Guizhou University for Nationalities 550025 Guiyang China School of Computer and Information Engineering Guizhou University for Nationalities 550025 Guiyang China

ISBN: (纸本)9780769547190

In view of the complexity of function mapping coding and the uncertainty of function, this paper proposes that function equation should be separated from the algorithm to design an input program, and then multiple software tools could be utilized to accomplish the complex tasks. This program regards the code generator as the center to dynamically generate graphics code and then calls the execution engine to operate the drawing program. Code generator can automatically generate code for the graphics requirements of any function, which provides a unified mapping framework for the class model of function. The results show that the program is effective and feasible. © 2012 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

An image matching evolutionary algorithm based on Hu invariant moments

An image matching evolutionary algorithm based on Hu invaria...

引用

3rd International Conference on Image Analysis and Signal Processing, IASP 2011

作者： Zhang, Ruliang Wang, Lin Key Laboratory of Pattern Recognition and Intelligent Control Guizhou University for Nationalities Guiyang China

ISBN: (纸本)9781612848808

This paper presents an image matching evolutionary algorithm (called IMEA algorithm) based on Hu invariant moments. First, the population is initialized. A group of searched subgraphs is constructed. Second, the fitness function based on Hu invariant moments is designed. The seven Hu invariant moments of the template image and the searched subgraph are calculated. The Euclidean distance of Hu invariant moments is used to measure similarity between the template image and the searched subgraph. The template image and the searched subgraph are matched if these Euclidean distances are less than the set threshold. Finally, a new searched subgraph is constructed by means of a new evolutionary strategy. The new searched subgraph replaces the searched subgraph whose value of the fitness function is maximum. Experimental results demonstrate the great robustness and efficiency of the IMEA algorithm. © 2011 IEEE.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

An Image Matching Evolutionary Algorithm Based on Hu Invariant Moments

An Image Matching Evolutionary Algorithm Based on Hu Invaria...

引用

2011 International Conference on Image Analysis and Signal Processing(2011第三届图像分析与信号处理国际会议 IASP 2011)

作者： Ruliang Zhang Lin Wang Key Laboratory of Pattern Recognition and Intelligent Control Guizhou University for NationalitiesGuiyang China

ISBN: (纸本)9781612848792

关键词： Hu invariant moments evolutionary algorithm euclidean distance image matching

来源：评论

学校读者我要写书评

暂无评论

License plate character recognition based on Gaussian-Hermite moments

License plate character recognition based on Gaussian-Hermit...

引用

2nd International Workshop on Education Technology and Computer Science, ETCS 2010

作者： Ma, Xiaojuan Pan, Renlong Wang, Lin Guizhou Key Lab of Pattern Recognition and Intelligent Control Guizhou University for Nationalities Guiyang China

ISBN: (纸本)9780769539874

Character recognition plays an important role in the automatic license plate recognition (ALPR) system. In this paper, we propose a new method to recognize the license plates characters by using 2D Gaussian-Hermite moments (GHMs) of different orders with 231 GHMs features as the input vector of BP neural network. The system worked under variable illumination, variable size of plate and dynamic backgrounds. The experimental results demonstrate robust and efficient of our method. © 2010 IEEE.

关键词： License plates (automobile)

来源：评论

学校读者我要写书评

暂无评论

An efficient method for skew correction of license plate

An efficient method for skew correction of license plate

引用

2nd International Workshop on Education Technology and Computer Science, ETCS 2010

作者： Pan, Renlong Ma, Xiaojuan Wang, Lin Guizhou Key Lab. of Pattern Recognition and Intelligent Control Guizhou University for Nationalities Guiyang China

ISBN: (纸本)9780769539874

Character segmentation and recognition are important steps in the automatic license plate recognition (ALPR) system. The skew license plate has a great influence on the accurate character segmentation and recognition. To solve the problem, an efficient approach for skew correction of license plate is proposed. First, the plate image is divided into a set of 5x5 non-overlapping blocks. The local orientation of each black is estimated by gradients of pixels in the block. The horizontal incline angle of license plate is detected by the local maximum of the direction angle histogram. The plate image is rotated according to this angle. Then, the vertical distortion of license plate image was corrected by the single-character projection method. The experimental results demonstrate the great robustness and efficiency of proposed method. © 2010 IEEE.

关键词： License plates (automobile)

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共4页 << < 1 2 3 4 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：