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

4,133 篇 会议
73 篇 期刊文献
10 册 图书

馆藏范围

4,216 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,927 篇 工学
- 2,145 篇 计算机科学与技术...
- 1,308 篇 软件工程
- 712 篇 信息与通信工程
- 478 篇 电气工程
- 447 篇 光学工程
- 397 篇 生物医学工程（可授...
- 350 篇 生物工程
- 287 篇 控制科学与工程
- 203 篇 电子科学与技术（可...
- 188 篇 机械工程
- 187 篇 仪器科学与技术
- 128 篇 测绘科学与技术
- 120 篇 化学工程与技术
- 111 篇 材料科学与工程（可...
- 99 篇 土木工程
- 89 篇 建筑学
- 82 篇 航空宇航科学与技...
- 81 篇 环境科学与工程（可...
- 71 篇 安全科学与工程
- 68 篇 网络空间安全
1,307 篇 理学
- 518 篇 数学
- 510 篇 物理学
- 384 篇 生物学
- 173 篇 统计学（可授理学、...
- 139 篇 化学
487 篇 管理学
- 285 篇 图书情报与档案管...
- 222 篇 管理科学与工程(可...
445 篇 医学
- 364 篇 临床医学
- 187 篇 基础医学(可授医学...
- 134 篇 药学(可授医学、理...
71 篇 农学
64 篇 法学
24 篇 经济学
23 篇 教育学
12 篇 艺术学
10 篇 军事学
7 篇 文学
1 篇 哲学

主题

281 篇 image reconstruc...
233 篇 image segmentati...
214 篇 feature extracti...
187 篇 deep learning
180 篇 data mining
170 篇 image processing
133 篇 image classifica...
126 篇 training
116 篇 convolutional ne...
109 篇 accuracy
106 篇 data models
92 篇 image recognitio...
85 篇 image enhancemen...
81 篇 remote sensing
73 篇 image analysis
72 篇 computer vision
68 篇 image color anal...
65 篇 machine learning
63 篇 computational mo...
61 篇 visualization

机构

13 篇 chitkara univers...
12 篇 chitkara univ in...
6 篇 school of comput...
6 篇 school of data a...
5 篇 chitkara univers...
5 篇 graphic era deem...
5 篇 uttaranchal univ...
4 篇 beijing institut...
4 篇 china academy of...
4 篇 chennai
4 篇 wuhan univ sch r...
4 篇 center for earth...
4 篇 vishwakarma inst...
4 篇 univ chinese aca...
4 篇 computer science...
4 篇 geomatics engine...
4 篇 cccc big data te...
4 篇 university of ch...
4 篇 sun yat sen univ...
4 篇 waseda universit...

作者

8 篇 li jonathan
7 篇 wang cheng
7 篇 zhang wei
6 篇 sharma neha
6 篇 li li
6 篇 wang wei
6 篇 wang yan
6 篇 li yan
6 篇 chen yen-wei
5 篇 wang jing
5 篇 gupta rupesh
5 篇 b. solaiman
5 篇 m.s. bouhlel
5 篇 rueckert daniel
5 篇 zhang jun
4 篇 vibhute amol d.
4 篇 zhang lei
4 篇 gill kanwarparta...
4 篇 xie bin
4 篇 chen chen

语言

4,108 篇 英文
85 篇 其他
32 篇 中文
2 篇 法文
1 篇 泰文

检索条件"任意字段=2nd Conference on Image Reconstruction from Incomplete Data"

共 4216 条记录，以下是181-190 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：

Design Of An Improved Dish Recognition Algorithm Based On image Retrieval 2

Design Of An Improved Dish Recognition Algorithm Based On Im...

引用

2nd International conference on Machine Vision, image Processing and Imaging Technology, MVIPIT 2024

作者： Yu, Yi Liu, Jie Sebial, Archival Canillo, Angie Ceniza Jiangxi Vocational College of Industry and Engineering China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (纸本)9798331543037

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification *** breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly redu

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer image Classification using Convolutional Neural Networks 2

Breast Cancer Image Classification using Convolutional Neura...

引用

2nd IEEE International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Ravi Teja, S. Joshi, Sujata Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798331504960

This study aimed to develop and evaluate a deep learning-based classification model for breast ultrasound images. The primary objectives were to classify ultrasound images into three categories: benign, malignant, and normal. Breast cancer detection using ultrasound imaging is challenging due to class imbalance and insufficient annotations in datasets. This research focuses on enhancing classification accuracy by leveraging a pre-trained ResNet-50 model. Methods included data augmentation to address class imbalance and the integration of masks overlayed on ultrasound images to emphasize regions of interest. These enhancements significantly improved the model’s ability to distinguish between benign, malignant, and normal breast tissue. The model achieved an accuracy of 98%, up from an initial 86% on unseen data, demonstrating the impact of detailed annotations and fine-tuning. The results underscore the potential of AI-driven models in improving diagnostic accuracy. Future work involves incorporating additional data sources and validating the model across diverse clinical settings, with the goal of advancing early detection and personalized treatment strategies in breast cancer care. © 2024 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

An Improved Depth image Clustering Algorithm Based on Spatial Information 2

An Improved Depth Image Clustering Algorithm Based on Spatia...

引用

2nd International conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Yan, Li Su, Shan Hu, Xiao Zhou, Yuquan Chen, Changjun Wuhan University School of Geodesy and Geomatics Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798350331417

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed. © 2023 IEEE.

关键词： Depth image clustering K-Means RGB-D datasets Spatial information

来源：评论

学校读者我要写书评

暂无评论

Leveraging Pre-trained ResNet-18 with Transfer Learning for Yoga Posture Classification 2

Leveraging Pre-trained ResNet-18 with Transfer Learning for ...

引用

2nd IEEE International conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Aruna, M. Kaneriya, Gruhit Jain, Prashuk Srm Institute of Science and Technology Computing TechnologiesSchool of Computing Kattankulathur Chennai India

ISBN: (纸本)9798350372892

This research paper explores the application of pre-trained ResNet-18 with transfer learning for the classification of yoga postures. The study utilizes a dataset comprising images of various yoga poses taken from Kaggle. Through fine-tuning, the pre-trained model achieved impressive training accuracy of 9 8 %. Furthermore, on unseen data, the model maintained accuracy of 9 4. 9 3 %. Advantages and disadvantages of the methodology are discussed, along with experimental setup details and metrics evaluation. The findings underscore the efficacy of leveraging pre-trained models and transfer learning techniques for complex image classification tasks in specialized domains like yoga postures. However, a notable limitation arises in the difficulty of removing noise during model implementation, leading to a decrease in confidence scores. For future work, the project aims to address this limitation by focusing on noise removal techniques while maintaining accuracy and confidence scores, thus enhancing the robustness of the classification model. © 2024 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Biologically Inspired image Processing Techniques for Improved Resolution in FMRI data 2

Biologically Inspired Image Processing Techniques for Improv...

引用

2nd IEEE International conference on Emerging Research in Computational Science, ICERCS 2024

作者： Sanjana, S. Sri Kiruthika, C. Kanimozhi, M. Karpagam Academy of Higher Education Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798331534950

Functional Magnetic Resonance Imaging (fMRI) is a popular neuro-imaging technology that detects brain activity by monitoring blood oxygenation levels. However, fMRI suffers from limitations in temporal resolution due to the slow hemodynamic response, leading to challenges in accurately capturing rapid neural processes. To address this issue, this work proposed the use of Particle Swarm Optimization (PSO) method to enhance temporal resolution in fMRI data analysis. PSO is a nature-inspired optimization algorithm that efficiently explores high-dimensional search spaces to find optimal solutions. This approach refines the temporal resolution by identifying optimal time points and reducing noise, which enhances the interpretability of neural dynamics. Experimental results demonstrate that PSO-based temporal resolution enhancement improves the detection of cognitive and motor-related brain activity compared to conventional methods. This approach provides a significant step forward in fMRI analysis, offering better insights into fast neural events and opening avenues for more accurate brain-computer interfaces and clinical diagnostics. © 2024 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Deep Learning in image Classification Using Modified ResNet50 and Texture Features for Lung Cancer Detection 2

Deep Learning in Image Classification Using Modified ResNet5...

引用

2nd International conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Banjarnahor, Evander Pangaribuan, Jefri Junifer Barus, Okky Putra Romindo, Romindo Universitas Pelita Harapan Informatics Department Tangerang Indonesia Universitas Pelita Harapan Information Systems Department Tangerang Indonesia

ISBN: (纸本)9798350351613

According to WHO data, in 2020 there were 30,843 deaths from lung cancer in Indonesia. Some of the factors that lead to lung cancer are smoking, air pollution, and an unhealthy diet. Early detection with a lung CT scan is critical in finding treatment alternatives and increasing the patient's life expectancy. Screening and diagnosis by Pulmonologist takes a long time, but can be accelerated with computational techniques and reduce the *** study proposes a deep learning-based lung cancer detection method. Researchers compared the performance of ResNet50, ResNet50V2, and a modified model that combines ResNet50V2 with GLCM texture features. The models were simulated to classify lung CT scan images into normal, benign, or malignant. from the simulation results, it was found that the ResNet50V2 with GLCM achieved the highest accuracy at 82%, 82% sensitivity, and 90% specificity with processing each step for 71.32 seconds on 15% testing data. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

image Classification for Pneumonia and the Normal Based on Convolutional Neural Network 2

Image Classification for Pneumonia and the Normal Based on C...

引用

2nd IEEE International conference on Electrical Engineering, Big data and Algorithms, EEBDA 2023

作者： Rui, Zhang Ying, Cao Fangzhe, Wang Xintong, Bao Toe, Teoh Teik Harbin Institute of Technology Weihai Weihai China Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781665462532

Our model focused on distinguishing pneumonia X-ray images from normal ones. Based on VGGNet16, some layers were reduced in our model;meanwhile, the dropout function and batch normalization were added to avoid the overfitting. Adam was used as the optimizer, and Binary Cross Entropy was used as the loss function. Aiming to evaluate the model, we used different metrics based on the confusion matrix to analyze its performance, including the recall of model, the precision and the F1 score. Our model's final accuracy reached 86.54%. © 2023 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Research on Prefabricated Building Decision Support System Using Artificial Intelligence and data Processing 2

Research on Prefabricated Building Decision Support System U...

引用

2nd IEEE International conference on image Processing and Computer Applications, ICIPCA 2024

作者： Wang, Liyan Institute of Civil Engineering Shandong University of Engineering and Vocational Technology Jinan China

ISBN: (纸本)9798350360240

This paper studies the intelligent aided decision making based on Web and building information modeling. Then, based on the network global information network and functional components, this paper builds a network BIM platform. Then, the new idea of energy cost increase control based on life cycle of prefabricated buildings is studied. By analyzing the cost increase of the whole process of prefabricated house construction, the corresponding increment index is obtained. The project cost is predicted by using grey system method based on the building information model. By integrating the increment index with the cost growth index, a dynamic model of cost growth is established. By using this model, the correlation rules among various cost growth indices are obtained. This method is improved to control the increase of energy consumption cost in the whole construction process. The decision results of this method are verified from the perspectives of shock resistance and value index. It is proved that the theory and algorithm proposed in this project are feasible. © 2024 IEEE.

关键词： artificial neural network building structure selection intelligent decision support Prefabricated building Web-BIM

来源：评论

学校读者我要写书评

暂无评论

Monitoring of CO2 Sequestration through Seismic Inversion using Simulated Annealing: Insight from Synthetic data Analysis 2

Monitoring of CO2 Sequestration through Seismic Inversion us...

引用

2nd EAGE/Aqua Foundation Indian Near Surface Geophysics conference and Exhibition

作者： Hema, G. Maurya, S.P. Department of Geophysics Banaras Hindu University Varanasi221005 India

ISBN: (纸本)9789462824904

The long-term safety of geologic carbon storage can be monitored using a variety of geophysical approaches. Monitoring CO2 migration in a geologic carbon storage site is generally carried out using the seismic method. In the present study, a seismic inversion based on simulated annealing is used to monitor CO2 migration, providing more detailed information. Seismic modeling can be used to image and monitor the fluid flow effects and leak detection in the reservoir. The Gassman fluid substitution was carried out to calculate the volume of the CO2 plume for the post-injection case. A 2D seismic model was generated to simulate the CO2 injection scenario. In the present study, synthetic data was used to check the reliability of the algorithm used in the case of the global optimization inversion technique. After performing the inversion analysis, a decrease in the impedance values is observed at the injection site. The inverted section shows very clear CO2 information which cannot be estimated from the interpretation of seismic data alone. © 2023 2nd EAGE/Aqua Foundation Indian Near Surface Geophysics conference and Exhibition. All rights reserved.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Completion Method of Dissolved Gas Detection data in Oil of Extra-High Voltage DC Converter Transformer 2

Completion Method of Dissolved Gas Detection Data in Oil of ...

引用

2nd IEEE International conference on image Processing and Computer Applications, ICIPCA 2024

作者： Zhang, Chenchen Ma, Huan Zhang, Haibin Huang, Xingxing State Grid Anhui Ultra High Voltage Company Anhui Province Hefei China Hefei University of Technology Anhui Province Hefei China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

This study proposes a new complementary method for dissolved gas data in oil of ultra-high voltage DC converter transformers based on Markov model by taking Markov theory as the basis and giving full consideration to the transfer characteristics between all the states of the system at adjacent time points. The method converts the time series of dissolved gas data in oil into a Markov chain of transfers between different states, and calculates the complementary values of dissolved gas data in oil using forward and reverse state transfer matrices. At the same time, a comprehensive evaluation system for the quality of dissolved gas data in oil is established from the perspective of data mining, evaluating the effect of data complementarity from multiple perspectives and synthesizing the evaluation results from each perspective based on the D-S evidence fusion theory to arrive at a comprehensive evaluation result. In the experiment of completing 25 groups of random missing values in the dissolved gas data of UHV DC converter transformer oil, the results show that the similarity between the completed data and the actual values can reach 99.999%. The effect of missing data of 15 groups of extreme value points and jump points is further verified, and after comprehensive evaluation, the similarity between the completed data and the actual values can reach 98.956%. The validation results show that the proposed method can accurately complete the missing values of dissolved gases in DC converter transformer oil without changing the data characteristics, which is conducive to improving the accuracy of the condition assessment method for UHV DC converter transformers. © 2024 IEEE.

关键词： Markov chains

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422页 << < 15 16 17 18 19 20 21 22 23 24 > >>

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：