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

6,452 篇 会议
925 篇 期刊文献
2 篇 学位论文
1 册 图书

馆藏范围

7,380 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,162 篇 工学
- 1,138 篇 电气工程
- 909 篇 计算机科学与技术...
- 678 篇 信息与通信工程
- 263 篇 控制科学与工程
- 193 篇 仪器科学与技术
- 160 篇 机械工程
- 160 篇 软件工程
- 119 篇 电子科学与技术（可...
- 107 篇 生物医学工程（可授...
- 98 篇 交通运输工程
- 83 篇 测绘科学与技术
- 81 篇 航空宇航科学与技...
- 30 篇 土木工程
- 28 篇 生物工程
- 19 篇 环境科学与工程（可...
- 19 篇 网络空间安全
- 15 篇 水利工程
- 14 篇 光学工程
265 篇 理学
- 80 篇 系统科学
- 50 篇 地球物理学
- 49 篇 物理学
- 34 篇 数学
- 32 篇 生物学
- 24 篇 地理学
169 篇 医学
- 110 篇 临床医学
- 27 篇 基础医学(可授医学...
160 篇 管理学
- 151 篇 管理科学与工程(可...
44 篇 农学
- 26 篇 作物学
- 17 篇 农业资源与环境
35 篇 文学
- 35 篇 新闻传播学
12 篇 艺术学
6 篇 军事学
5 篇 经济学
4 篇 教育学
3 篇 法学

主题

7,380 篇 detection algori...
773 篇 detectors
766 篇 feature extracti...
632 篇 algorithm design...
585 篇 object detection
532 篇 image edge detec...
397 篇 testing
372 篇 robustness
333 篇 cameras
307 篇 computer science
292 篇 accuracy
286 篇 monitoring
280 篇 data mining
273 篇 computational mo...
254 篇 image segmentati...
235 篇 signal to noise ...
235 篇 clustering algor...
200 篇 change detection...
196 篇 pixel
195 篇 electrocardiogra...

机构

17 篇 school of electr...
15 篇 beijing universi...
13 篇 department of el...
13 篇 department of el...
12 篇 department of el...
11 篇 institute for in...
10 篇 national mobile ...
9 篇 department of el...
9 篇 wireless theorie...
8 篇 department of el...
8 篇 mit lincoln labo...
8 篇 graduate school ...
8 篇 department of au...
8 篇 institute of com...
7 篇 beijing key labo...
7 篇 school of electr...
7 篇 department of el...
7 篇 department of el...
7 篇 department of el...
7 篇 southeast univ n...

作者

13 篇 wei li
13 篇 bin wu
9 篇 dacheng yang
9 篇 ming chen
9 篇 h. wang
9 篇 r. raheli
8 篇 xin liu
8 篇 jaeseok kim
8 篇 shixin cheng
8 篇 keiichi tamura
8 篇 v.k. garg
8 篇 j. mcnames
8 篇 xin zhang
7 篇 lei wang
7 篇 a. schaum
7 篇 hajime kitakami
7 篇 hui zhao
7 篇 giulio colavolpe
7 篇 hui li
7 篇 yong li

语言

7,309 篇 英文
36 篇 中文
30 篇 其他
4 篇 西班牙文
1 篇 土耳其文

检索条件"主题词=detection Algorithms"

共 7380 条记录，以下是641-650 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：

A Modified CA-CFAR Multi-human detection Algorithm in Complex Environment Using Radar

A Modified CA-CFAR Multi-human Detection Algorithm in Comple...

引用

Electronic Engineering and Information Systems (EEISS), International Conference on

作者： Zhihuan Liang Yanghao Jin Buge Liang Jinjun Mo School of Information and Communication Guilin University of Electronic Technology Guilin China Academy of Advanced Interdisciplinary Research Xidian University Xi'an Shaanxi School of Automation Central South University Changsha China

ISBN: (数字)9798350351033

ISBN: (纸本)9798350351040

With a rather strong environment clutter and multi-target interference, while indoor monitoring using UWB radar in complex scenarios, the current CA-CFAR algorithm would inevitably introduce the variance peaks leading to the false detection results in multi-target detection. This paper proposed a Modified CA-CFAR based on the Sliding Window DC Removal and Adaptive Threshold method, using Sliding Window DC Removal to eliminate the static environment clutter to avoid the previous variance peaks involved by replacing the MTI and achieving the Adaptive Threshold through echo statistic estimation to reduce the false detection results. The effectiveness of the proposed algorithm has been verified through comparative analysis based on radar-measured data.

关键词： Accuracy Radar detection Estimation Object detection Clutter detection algorithms Ultra wideband radar

来源：评论

学校读者我要写书评

暂无评论

A Novel Lightweight Disease detection Algorithm Used in Ground-Penetrating-Radar

A Novel Lightweight Disease Detection Algorithm Used in Grou...

引用

Annual Conference of Industrial Electronics Society

作者： Huanming Gan Hongjin Hu Guang-Zhong Cao Yu Lei Yanliang Du Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots College of Mechatronics and Control Engineering Shenzhen University Shenzhen China National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment (Shenzhen) College of Civil and Transportation Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

To solve the problems of excessive parameters in deep learning models of Ground Penetrating Radar (GPR) disease detection, this paper proposes a lightweight disease detection algorithm based on YOLOv8. A lightweight model is obtained by replacing its backbone network with the GhostHGNetV2 network. The SimAM attention mechanism module is used to improve the ability to perceive different levels of feature representation. Additionally, the WIOU is used as the loss function to improve the convergence and accuracy of the algorithm detection performance. Experimental results demonstrate that the proposed algorithm reduces the parameters of original networks by 25.3%, model size by 28.8%, and computational complexity (GFLOPs) by 20.3%. The effectiveness of the proposed lightweight disease detection algorithm is verified.

关键词： Industrial electronics Accuracy Attention mechanisms Ground penetrating radar Object detection Feature extraction detection algorithms Robots Diseases Convergence

来源：评论

学校读者我要写书评

暂无评论

Improved Change Point detection via Bias Reduction in Parameters Estimation

Improved Change Point Detection via Bias Reduction in Parame...

引用

European Signal Processing Conference (EUSIPCO)

作者： Anthony Torre Abigael Taylor Dominique Poullin Thierry Chonavel DEMR ONERA Palaiseau France IMT Atlantique Brest France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The recognition of unknown radar signals requires the implementation of detection algorithms in order to extract signals of interest. This paper presents an implementation of the Cumulative Sum (CuSum) algorithm for radar applications and the segmentation of unknown received signals. The study focuses on the parameters estimation of the signal distribution with an evaluation of the bias of several estimators along with the proposition of a correction factor to obtain a more robust estimator. Simulations and real data experiments validate the algorithm's capacity to retrieve both the onset and end of unknown waveforms.

关键词： Parameter estimation Magnetic resonance imaging Signal processing algorithms Radar detection Europe Data models detection algorithms Spectrogram Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Mold detection Algorithm Based on Improved YOLOv8n

Mold Detection Algorithm Based on Improved YOLOv8n

引用

Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Zhiping Luo Yilai Zhang Chao Li Kangping Luo School of Information Engineering Jingdezhen Ceramic University Jingdezhen China Jiangxi Vocational and Technical College of Information Application Jiujiiang China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

To address the issues of low detection efficiency and accuracy, as well as the large parameters and slow speed of existing ceramic mold detection models in the ceramic product field, a lightweight ceramic mold detection algorithm based on YOLOv8n is proposed. Our improvements include: Firstly, data augmentation is performed using the GSES method on the 13KCeramics dataset. Secondly, StarNet is employed as the new backbone network to replace the original network in YOLOv8. Subsequently, the Agent attention module is added to some structures of the backbone and neck networks to enhance the model's expressive ability. Finally, Scale and Location Sensitive (SLS) loss is used to replace the original loss function. These improvements enable the model to better learn the scale and position information of ceramic molds during training, thereby further improving the accuracy of object detection. Results show that the improved algorithm achieves an increase of approximately 8.59%, 9.48%, and 0.07 in mAP@50, mAP@50-95, and F1 score, respectively, compared to the original YOLOv8n, reaching 98.7%, 98.12%, and 0.97. The FPS reaches 161 frames, an increase of approximately 13.8%, and the number of parameters is reduced by about 1.1M. These results demonstrate the significant effectiveness of the improved model.

关键词： Training Adaptation models Accuracy Shape Semantics Object detection Data augmentation Robustness Ceramics detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on Illegal Behavior detection Algorithm of Underground Mine Workers Based on Improved YOLOv8

Research on Illegal Behavior Detection Algorithm of Undergro...

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Nan Liu Gang Huang Daxing Xu Lu Zhang School of Electronic and Information Engineering Anhui Jianzhu University He Fei China School of Electronic and Information Engineering Qu Zhou University Qu Zhou China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Aiming at the problems of poor detection effect and weak generalization ability of illegal production of workers in mines, a mine worker illegal target detection algorithm based on improved YOLOv8 is proposed. In view of the problem of large scale changes of targets in mines, a unified self-attention mechanism detection head (DyHead) is introduced to replace the original detection head, which greatly improves the feature expression ability of the detection head. Experimental results show that on the public dataset VOC2012, after adding DyHead to yolov8, the detection accuracy is improved by 1.5%, on the self-built dataset, after adding DyHead, the detection accuracy is improved by 2.3 %. This shows that compared with the original model, the improved algorithm after adding DyHead not only greatly improves the model detection ability, but also has good generalization performance. Compared with the mainstream algorithm, the algorithm proposed in this paper has lower model complexity and higher detection accuracy, which is suitable for target detection in complex environments in mines.

关键词： Accuracy Computational modeling Production Feature extraction Boosting Complexity theory detection algorithms Buried object detection Load modeling

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Models for Malware Family detection

Deep Learning Models for Malware Family Detection

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Jenna Snead Krishnendu Ghosh Department of Computer Science College of Charleston South Carolina USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Malware attacks have been rising, and the response for detection and mitigation have fallen short to counter the threats. The advances in artificial intelligence have not yielded a comprehensive solution in early detection of mal ware and its variants, making this a critical gap. Our work leverages on the biology-inspired analysis of malware trace sequences and clustering based on trace similarity computation. The malware traces extracted from the community detection algorithms are assumed to represent malware families precisely. The deep learning framework uses the most prototypical features from the extracted traces for prediction of mal ware families. In this work, deep learning models in the form of artificial neural networks (ANNs) are evaluated and experimental results are presented. The results elucidate the deep learning framework for malware family detection across different variations of detection and training splits.

关键词： Deep learning Training Prevention and mitigation Digital forensics Artificial neural networks Feature extraction Malware Phylogeny Security detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Traffic Sign Recognition Algorithm Based on Improved Real-Time detection Transformer

Traffic Sign Recognition Algorithm Based on Improved Real-Ti...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Rui Zhang Minhan Zhang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Accurate detection of traffic signs is of great significance in the fields of road safety, intelligent transportation, and autonomous driving. This paper proposes the MY-DETR traffic sign detection method, which incorporates three innovative modules: EDB-SOP, AIFI-HiLo, and Inner-IoU. The EDB-SOP enhances robustness to small targets, occlusion, and complex backgrounds through multi-branch feature extraction and spatial optimization pooling technology. The AIFI-HiLo module integrates low-frequency and high-frequency attention mechanisms, optimizing the processing of multi-scale and multi-frequency features, and improving detection accuracy and robustness. The Inner-IoU loss function focuses on the inner overlapping area of the bounding box, improving the accuracy of traditional IoU in cases of low overlap. Experimental results show that MY-DETR significantly outperforms some mainstream algorithms on multiple traffic sign datasets, especially in terms of m $A$ P50 and mAP50-95, providing a more efficient and accurate traffic sign detection solution for complex scenarios.

关键词： Accuracy Transportation Focusing Feature extraction Transformers Robustness Real-time systems Road safety detection algorithms Optimization

来源：评论

学校读者我要写书评

暂无评论

FD-YOLOv8:An Improved Fire detection Model Base on YOLOv8n

FD-YOLOv8:An Improved Fire Detection Model Base on YOLOv8n

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Longxiang Li Zhe Liu Yangbo Ruan Beimin Chen School of Artificial Intelligence Jianghan University WuHan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Fire detection technology plays a crucial role in the field of fire safety. However, traditional fire alarms driven by smoke detectors often fail to report fires in a timely and effective manner. To cope with these challenges, We proposed an improved fire detection algorithm FD-YOLOv8 based on YOLOv8n. In this study, we introduced the OCA positive traffic attention mechanism to optimize the Bottleneck layer, enhancing the model's accuracy in object detection. Additionally, we integrated GSConv, an efficient lightweight convolution, to design an improved C2f-OGBlock module, further enhancing the model's detection capability while reducing parameters and computational costs. Furthermore, we incorporated a highly efficient feature enhancement algorithm, HIFA, into the model, significantly improving its feature extraction capabilities. We compared the improved YOLOv8 model with existing models. Experimental results demonstrate that our improved model achieves superior detection performance, with mAP@50 increasing by 3.2% and mAP@50:90 improving by 1.2% compared to the original model.

关键词： Attention mechanisms Accuracy Computational modeling Object detection Visual systems Feature extraction Scattering parameters detection algorithms Smoke detectors Fire safety

来源：评论

学校读者我要写书评

暂无评论

Research on AI-Based Target detection Technology for Sports Images

Research on AI-Based Target Detection Technology for Sports ...

引用

Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Shujian Zhang Department of Physical Education College of General Education Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

With the rapid development of artificial intelligence, deep learning-based target detection technology has been widely applied in various fields. Sports images, as a special application scenario, present new challenges for target detection due to their complex actions and rapidly changing targets. This paper aims to study AI-based target detection technology for sports images by analyzing and improving existing detection algorithms and proposing an optimized detection algorithm suitable for sports scenarios. First, the paper outlines the basic theories of AI and target detection technology, then analyzes the characteristics of sports images and the current application status of detection algorithms, and finally designs and implements a target detection system for sports images. Experiments validate the proposed algorithm’s effectiveness and superiority in various sports scenarios. This research provides a theoretical foundation and technical support for applications such as sports analysis, intelligent refereeing, and athlete training.

关键词： YOLO Training Performance evaluation Scientific computing Neural networks Lighting Reliability detection algorithms Artificial intelligence Sports

来源：评论

学校读者我要写书评

暂无评论

Research on Road Disease detection Algorithm Based on Improved YOLOv5s

Research on Road Disease Detection Algorithm Based on Improv...

引用

Electronics and Communication, Network and Computer Technology (ECNCT), International Conference on

作者： Xili Yang Hu Sheng Na Geng Wenqiang Li Unit: School of Computer and Communication Engineering Dalian Jiaotong University Dalian Liaoning China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

Addressing the issues of low detection efficiency and high subjectivity in traditional pavement disease detection methods, this study leverages deep learning algorithms in object detection to enhance detection accuracy while maintaining real-time responsiveness. We introduce a pavement disease detection algorithm, YOLOv5s-MBD, which enhances YOLOv5s. Initially, we replace the original CIoU Loss with MPDIoU Loss as the regression loss function, maximizing the DIoU value to improve the network's learning of the position and shape of bounding boxes. Subsequently, we integrate a BiFPN weighted bidirectional pyramid structure, enabling more comprehensive and efficient information propagation across multiple scales, thereby enhancing detection performance at different scales. Lastly, we introduce a decoupled head into the detection head of YOLOv5s to better utilize semantic information between low-level and high-level features, thus enhancing detection accuracy and network convergence speed. The results of the experiment demonstrate that the YOLOv5s-MBD algorithm has improved the mAP @ 0.5 for disease recognition by 5.6%, and 1.6% respectively compared to YOLOv3, YOLOv4, YOLOv5s, and YOLOv8 on the GRDDC2020 dataset, and the recognition accuracy and recall rate have also been improved to varying degrees.

关键词： YOLO Accuracy Head Shape Roads Semantics Real-time systems Neck detection algorithms Diseases

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500页 << < 61 62 63 64 65 66 67 68 69 70 > >>

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：