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

1,189 篇 会议
36 篇 期刊文献

馆藏范围

1,225 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

769 篇 工学
- 689 篇 计算机科学与技术...
- 347 篇 软件工程
- 234 篇 电气工程
- 94 篇 信息与通信工程
- 73 篇 控制科学与工程
- 28 篇 生物工程
- 26 篇 网络空间安全
- 24 篇 机械工程
- 22 篇 光学工程
- 20 篇 安全科学与工程
- 16 篇 仪器科学与技术
- 12 篇 生物医学工程（可授...
- 9 篇 交通运输工程
- 8 篇 建筑学
- 6 篇 电子科学与技术（可...
142 篇 管理学
- 100 篇 管理科学与工程(可...
- 54 篇 工商管理
- 35 篇 图书情报与档案管...
116 篇 理学
- 66 篇 数学
- 32 篇 生物学
- 27 篇 物理学
- 16 篇 系统科学
- 13 篇 统计学（可授理学、...
- 10 篇 化学
12 篇 医学
- 11 篇 临床医学
- 7 篇 基础医学(可授医学...
10 篇 法学
- 6 篇 社会学
9 篇 经济学
- 8 篇 应用经济学
7 篇 文学
- 7 篇 新闻传播学
6 篇 教育学
- 6 篇 教育学
3 篇 军事学
1 篇 农学

主题

119 篇 computer archite...
105 篇 cloud computing
90 篇 enterprise archi...
84 篇 distributed comp...
71 篇 business
69 篇 unified modeling...
63 篇 object detection
59 篇 web services
49 篇 computational mo...
46 篇 analytical model...
41 篇 information syst...
40 篇 object oriented ...
40 篇 service oriented...
40 篇 organizations
38 篇 context
34 篇 ontologies
31 篇 companies
30 篇 software
30 篇 collaboration
29 篇 computer science

机构

11 篇 university of tw...
7 篇 federal universi...
5 篇 institute of arc...
5 篇 univ st gallen i...
5 篇 rhein westfal th...
5 篇 reutlingen univ ...
5 篇 inesc id informa...
5 篇 bizzdesign ensch...
5 篇 digital enterpri...
4 篇 novay enschede
4 篇 department of co...
4 篇 secure business ...
4 篇 department of in...
4 篇 department of in...
4 篇 department of au...
4 篇 royal inst techn...
4 篇 tata consultancy...
3 篇 htw aalen busine...
3 篇 chinese academy ...
3 篇 chair of softwar...

作者

16 篇 johnson pontus
14 篇 schmidt rainer
13 篇 van sinderen mar...
13 篇 almeida joao pau...
11 篇 matthes florian
10 篇 ekstedt mathias
10 篇 lagerstrom rober...
9 篇 florian matthes
8 篇 christian m. sch...
8 篇 rainer schmidt
8 篇 atkinson colin
8 篇 schweda christia...
8 篇 kutvonen lea
8 篇 guizzardi gianca...
7 篇 nurcan selmin
7 篇 colin atkinson
7 篇 franke ulrik
7 篇 zimmermann alfre...
6 篇 quartel dick
6 篇 sandkuhl kurt

语言

1,215 篇 英文
9 篇 其他
1 篇 丹麦文

检索条件"任意字段=9th IEEE International Enterprise Distributed Object Computing Conference"

共 1225 条记录，以下是41-50 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：

ALATS: Analysis of localization algorithms in terrestrial surveillance bots 9

ALATS: Analysis of localization algorithms in terrestrial su...

引用

9th ieee international conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Moorthy, Vaishnavi Shah, Darshil Kapoor, Shresth Tb, Srinivas Lal, Abhay Srm Institute of Science and Technology School of Computing Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350334395

Effective surveillance is crucial for optimal security and autonomy. It has led to the development of various surveillance systems, such as smart cameras, bots, and drones, with diverse applications. To achieve an efficient surveillance system for multiple use cases, a proposed solution involves the creation of a terrestrial bot. this Surveillance Bot is a mobile unit designed to monitor the environment, detect objects (including human intruders) using smart cameras, and provide crucial data like temperature and humidity for remote surveillance. the bot employs simultaneous localization and mapping (SLAM) techniques, specifically the bug2 algorithm, to navigate autonomously and avoid obstacles. object detection and human intruder detection algorithms are implemented, considering factors like accuracy, video feed lag, and algorithm robustness. Experimental results demonstrate the bot's high accuracy in object detection (mean average precision of 33.7) and its frame rate performance. For continuous video recording and analysis, the bot utilizes the YOLOv7 real-time object identification algorithm, enhancing its surveillance capabilities by enabling real-time tracking of identified objects. the combination of YOLOv7 with the bot's autonomous movement and mapping abilities creates a powerful and efficient surveillance solution. the proposed work aims to develop a smart surveillance robot suitable for military and civil applications, ensuring comprehensive surveillance. © 2023 ieee.

关键词： object detection

来源：评论

学校读者我要写书评

暂无评论

object Detection from the Images of Hotel Scenario Based on Faster-R-CNN-FFS Model 9

Object Detection from the Images of Hotel Scenario Based on ...

引用

9th ieee international conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Zhang, Jing Zhou, Anqi Zhao, Chuanxiu Huang, Degen Xu, Jian Dongbei University of Finance and Economics Dalian116025 China Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350304428

this paper aims to solve the problem of object detection in images generated by hotel consumers, which have complex backgrounds and various sizes of objects. It proposes an improved Faster R-CNN-FFS model, which improves the Faster R-CNN model from three aspects: first, it uses feature fusion technology based on Resnet-50 to build feature maps, capturing the whole picture semantic and enhancing the detection effect of small objects;second, it adopts Focal loss function instead of cross entropy loss function, increasing the weight ratio of data samples with less quantity, alleviating the problem of imbalanced positive and negative samples of all categories of detecting objects;third, it introduces Soft-NMS mechanism to filter candidate boxes, improving the robustness of object detection model under occlusion scenario. Experimental results show that the recognition performance of the proposed Faster R-CNNFFS model is significantly different from the state-of-the-Art models Retinanet, YOLO v3 and Faster R-CNN, and outperforms Retinanet model significantly. © 2023 ieee.

关键词： object detection

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning for Efficient Detection of Recalled Products 9

Hybrid Deep Learning for Efficient Detection of Recalled Pro...

引用

9th ieee international conference on Big Data computing Service and Applications, BigDataService 2023

作者： Yoo, Hyo-Jae Yoo, Kwan-Hee Korea Consumer Agency Department of Consumer Information Eumseong-gun Korea Republic of Chungbuk National University School of Computer Science Cheongju-si Korea Republic of

ISBN: (纸本)9798350333794

Cases exist in which recalled products in foreign countries are still sold in domestic online marketplaces. For the safety of consumers, the government and related agencies collect information on recalled products from overseas institutions and monitor whether these products are being sold online. this paper proposes a method of finding recalled products online by utilizing a hybrid deep learning model. An object detection model is employed to obtain product images from user screens. these images are utilized as queries to find similar images in a recalled product dataset. the experimental results obtained in this study show that this method can work well for monitoring. the deep learning model and dataset will be improved in future work to enhance the performance of each process. © 2023 ieee.

关键词： object detection

来源：评论

学校读者我要写书评

暂无评论

Robust Pedestrian Detection in Inclement Weather and Occluded Scenarios: A Review 9

Robust Pedestrian Detection in Inclement Weather and Occlude...

引用

9th international conference on Smart computing and Communications, ICSCC 2023

作者： Prasad, Sreedevi R. Pankaj, Dhanya S. College of Engineering Dept. of Cse Kerala Trivandrum India

ISBN: (纸本)9798350314090

Detecting pedestrians is a specialised form of object detection that relies on visual cues to recognize and locate pedestrians. It has practical applications in areas such as artificial intelligence, automated driving, and video surveillance. However, despite notable advancement in the field of deep learning, the task of detecting pedestrians continues to encounter difficulties such as occlusions and poor weather conditions. the objective of this paper is to present a concise overview of recent developments in using deep learning techniques to address these challenges like occlusion and inclement weather in pedestrian detection. Additionally, it presents a compilation of commonly used datasets for detecting pedestrians and an analysis of the miss rates (MR) of chosen approaches utilizing CityPersons dataset, considering different occlusion degrees. Finally, potential scenarios where these strategies could be applied are examined. © 2023 ieee.

关键词： object detection

来源：评论

学校读者我要写书评

暂无评论

An FPGA-based Convolutional Neural Network Accelerator for Edge computing 9

An FPGA-based Convolutional Neural Network Accelerator for E...

引用

9th international conference on Robotics and Automation Engineering, ICRAE 2024

作者： Li, Tengfei Gu, Shenshen Shanghai University School of Mechatronic Engineering and Automation Shanghai China

ISBN: (纸本)9798331518301

Convolutional Neural Networks (CNNs) are a powerful tool in computer vision, excelling at tasks like image classification and object detection. the need for low latency and strict power consumption in real-world applications during forward inference makes deploying neural networks on resource-constrained hardware challenging. this paper tackles deployment challenges by proposing Quantization-Aware Filter Pruning (QAFP), significantly reducing model size with minimal accuracy loss. Leveraging the strengths of convolutional computations, parameters are stored in blocks and optimized loop circuits are designed to maximize resource utilization on hardware. this work proposes an FPGA-based CNN accelerator that significantly improves the speed and power efficiency of CNN inference compared to traditional processors. the accelerator was evaluated on a ZYNQ XC7Z020 platform utilizing a compressed VGG16 model for validation. Notably, the compressed model achieved a high accuracy of 93.5 % while reducing the single-inference fixed-point operation count to 3.1 G (from original VGG16) and the parameter count to 1.1 M. this results in an impressive effective computing performance of 41 Giga Operations Per Second (GOP/s) while maintaining low power consumption at just 1.1 W, making it highly suitable for resource-constrained applications. © 2024 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive reppoint: An efficient approach for orientated object detection in remote sensing imagery 9

Adaptive reppoint: An efficient approach for orientated obje...

引用

9th ieee international conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Zhan, Yuhao Zhong, Jiandan Du, Yongjun Wei, Qiaomu Chen, Chao Fu, Lin Li, Luying Chengdu University of Information Technology Chengdu China Chengdu China

ISBN: (纸本)9798350304428

With the improvement of the remote sensing technology, more and more remote sensing imagery data can be obtained easily. therefore, the object detection in remote sensing imagery becomes an important task in computer vision area. However, the object of remote sensing imagery are presented with multi-scales, arbitrary orientation and complex background, which make the task becomes challenging. Currently, the mainstream object detection methods of multi-scales and oriented objects, such as RepPoint, which focus on learning a set of adaptive points and generating the point set to the bounding box (with angle information) of the objects. However, the diversity of categories and complex background always make the learned points (in adaptive points set) deviating outside the ground-Truth bounding box, which will yield inaccurate location of the objects and decrees the detection result. To deal with this problem, this paper proposes a spatial constraint method that can enforce penalties on outlier points within the point set. Firstly, a reasonable evaluation metric is proposed for quantifying the distance between the outlier points and the ground-Truth;Secondly, an spatial constraint parameter is defined for the objects in different scales. Extensive testing was performed on the challenging DOTA and HRSC 2016 datasets. According to the results, the proposed method demonstrated a performance improvement of 1.84% and 0.4% for the respective datasets. © 2023 ieee.

关键词： object detection

来源：评论

学校读者我要写书评

暂无评论

Prayer Hall Vacancy Detection 9

Prayer Hall Vacancy Detection

引用

9th ieee international conference on computing, Engineering and Design, ICCED 2023

作者： Razak, Muhammad Naqib Syahmi Bin Ab Mohamad, Muhammad Imran Bin Handayani, Dini Oktarina Dwi Attarbashi, Zainab S. Afrizal, Sandra Hakiem Suryady, Zeldi International Islamic University Malaysia Kuliyyah of Information and Communication Technology Selangor Malaysia Public Health Master Programs Faculty of Health Sciences and Technology Universitas Binawan Indonesia Malaysia

ISBN: (纸本)9798350370126

the issue of overcrowding in prayer rooms located in public areas, such as shopping centers, educational institutions, and other public spaces during prayer times, is a matter of concern nowadays. this condition has the potential to induce discomfort and adversely impact public health issues due to the increased risk of disease transmission, particularly respiratory illnesses. To address this issue, this paper presents a proof-of-concept system that uses computer vision techniques to detect, track, and count the number of people entering and exiting a prayer hall and subsequently count the total number of vacancies. the proposed system is based on the YOLOv5 object detection algorithm the Centroid Tracker object tracking algorithm and an existing object counting method. A custom dataset of 1,993 images of people entering and exiting a prayer hall was collected, cleaned, and annotated for use in training the system. the results of a sample video inference showed an average people detection mAP score of 98.9% and a people counting accuracy of 40% running on a Macbook Pro with an Ml chip. the counting results were displayed on a web app platform hosted on the Heroku cloud application platform. © 2023 ieee.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Small object Detection in Drone Scenes Based on Improved YOLO 9

Small Object Detection in Drone Scenes Based on Improved YOL...

引用

9th international conference on Image, Vision and computing, ICIVC 2024

作者： Cao, Xu Zhang, Xizheng Hou, Yingjun Lu, Zhangyu Liu, Ruoyuan Qin, Wei School of Information Science and Engineering Hunan Institute of Engineering Xiangtan China

ISBN: (纸本)9798350385991

In the field of target detection, small target detection is one of the challenges, especially in the field of drone scene detection where the requirements for small target detection are higher. To address this issue, this paper proposed a drone scene target detection model based on improved yolov5 to improve the detection ability of the model in multi-scale complex scenarios. this paper adopts the latest YOLOv5 version. Using Conv instead of Focus slicing operation at the input end and SPPF instead of SPP pooled operation, which greatly improves backbone feature extraction ability. In the Neck layer, the CBAM attention mechanism was incorporated to make the model pay more attention to important information. In order to improve the model's sensitivity to small targets, this paper added a small size prediction layer to the Head layer. the experimental results show that the model has achieved remarkable results on the VisDrone2019 data set. the mAP@50 increased by 4.7%, mAP@75 by 2.9%, mAP@50:95 increased by 2.8%, Precision increased by 3.8%, and Recall increased by 4.6%. © 2024 ieee.

关键词： Target drones

来源：评论

学校读者我要写书评

暂无评论

Potholes Detection Using Yolo Algorithms and their Integration Into Gazebo Simulation

Potholes Detection Using Yolo Algorithms and Their Integrati...

引用

9th international conference on Control, Robotics and Cybernetics, CRC 2024

作者： Hassan Tanveer, M. Fatima, Zainab Rehman, Tanazzah Siddique, Moaaz Shah, Arbaz Voicu, Razvan Cristian Farrukh Shahid, M. Kennesaw State University Department of Robotics and Mechatronics Engineering Marietta30060 United States NED University of Engineering and Technology Department of Software Engineering Karachi Pakistan FAST University of computing and Emerging Science Department of AI and Data Science Karachi Pakistan

ISBN: (纸本)9798331517489

Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. this study shows using a computer vision technique called You Only Look Once (YOLO) to find potholes on roads. YOLO is proficient and efficient for realtime object detection. the researchers want to see which version of YOLO is more effective in object analysis in pictures and videos. We'll be comparing YOLOv5, YOLOv7, and YOLOv8, thereby also setting up the system in Gazebo simulated environment. By carefully analyzing the results, we aim to understand the advantages and disadvantages of each YOLO version. this will help us see the most useful ones for finding potholes when managing and maintaining roads. © 2024 ieee.

关键词： object detection

来源：评论

学校读者我要写书评

暂无评论

Implementation of Single Shot Multibox Detector (SSD) Algorithm for object Detection 9

Implementation of Single Shot Multibox Detector (SSD) Algori...

引用

9th international conference on Business and Industrial Research, ICBIR 2024

作者： Sengaphone, Phongsavanh De Leon, Juan Miguel Augusto, Gerardo L. Gonzaga, Jeremias A. Chua, Joseph Aldrin Lim, Laurence A. Gan Concepcion, Ronnie Bandala, Argel A. Naguib, Raouf Mechanical Engineering Dept Manila Philippines Manufacturing Engineering and Management Dept Manila Philippines Electronics and Communications Engineering Dept Manila Philippines Mathematics Computer Science and Engineering Dept Liverpool United Kingdom

ISBN: (纸本)9798350383010

this study presents an innovative implementation of the Single Shot Multibox Detector (SSD) algorithm for real-time object detection on the Raspberry Pi 4. It harnesses the synergies of Python programming, the OpenCV computer vision library, and the SSD algorithm's adaptability. Python, chosen for its versatility, is the programming language, while OpenCV forms the backbone for implementing the finely tuned SSD algorithm. the resulting modular script, designed for real-time object detection, incorporates optimization techniques such as frame skipping and region of interest cropping to enhance Raspberry Pi 4 performance. the system demonstrates promising results, showcasing accurate and responsive object detection in resource-constrained environments. the Raspberry Pi 4's versatility makes it the solution for diverse edge computing applications, including surveillance, robotics, and intelligent environments. Computational constraints prompted the adoption of optimizations, while identified challenges illuminate future research directions exploring advanced algorithms and considering more potent hardware variants. this study contributes significantly to the intersection of computer vision and edge computing, underscoring the successful implementation of the SSD algorithm on the Raspberry Pi 4 and encapsulating technical achievements, practical implications, and a roadmap for future advancements in real-time object detection. © 2024 ieee.

关键词： Python

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123页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：