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

7,070 篇 会议
48 篇 期刊文献
39 册 图书

馆藏范围

7,157 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,912 篇 工学
- 4,098 篇 计算机科学与技术...
- 2,521 篇 软件工程
- 1,046 篇 信息与通信工程
- 789 篇 控制科学与工程
- 663 篇 生物工程
- 640 篇 电气工程
- 424 篇 生物医学工程（可授...
- 285 篇 交通运输工程
- 281 篇 机械工程
- 226 篇 仪器科学与技术
- 226 篇 安全科学与工程
- 206 篇 建筑学
- 197 篇 动力工程及工程热...
- 185 篇 光学工程
- 166 篇 化学工程与技术
- 154 篇 土木工程
1,900 篇 理学
- 1,065 篇 数学
- 718 篇 生物学
- 442 篇 统计学（可授理学、...
- 285 篇 物理学
- 252 篇 系统科学
- 140 篇 化学
1,152 篇 管理学
- 656 篇 管理科学与工程(可...
- 582 篇 图书情报与档案管...
- 316 篇 工商管理
408 篇 医学
- 354 篇 临床医学
- 298 篇 基础医学(可授医学...
- 188 篇 药学(可授医学、理...
244 篇 教育学
- 243 篇 教育学
211 篇 法学
- 197 篇 社会学
119 篇 农学
111 篇 经济学
50 篇 文学
7 篇 艺术学
5 篇 军事学

主题

556 篇 machine learning
456 篇 deep learning
264 篇 training
253 篇 data mining
235 篇 data models
220 篇 feature extracti...
215 篇 accuracy
184 篇 support vector m...
178 篇 predictive model...
138 篇 neural networks
137 篇 machine learning...
130 篇 computational mo...
121 篇 reinforcement le...
107 篇 convolutional ne...
103 篇 real-time system...
93 篇 forecasting
90 篇 artificial intel...
89 篇 learning systems
87 篇 transfer learnin...
86 篇 big data

机构

69 篇 chitkara univers...
17 篇 school of electr...
15 篇 computer science...
14 篇 harbin engineeri...
13 篇 graphic era hill...
12 篇 building civil a...
12 篇 uttaranchal inst...
12 篇 computer science...
11 篇 university of ch...
10 篇 school of electr...
10 篇 school of comput...
10 篇 graphic era deem...
10 篇 school of electr...
10 篇 yonsei univ dept...
9 篇 wuhan university...
9 篇 univ chinese aca...
9 篇 school of inform...
8 篇 school of remote...
7 篇 university of sa...
7 篇 microsoft resear...

作者

17 篇 kukreja vinay
15 篇 vinay kukreja
13 篇 yin hujun
12 篇 liu yang
12 篇 corchado emilio
11 篇 mehta shiva
10 篇 shiva mehta
10 篇 hujun yin
10 篇 cho sung-bae
9 篇 notsu akira
9 篇 silva catarina
9 篇 alhajj reda
7 篇 ksieniewicz pawe...
7 篇 hammer barbara
7 篇 zhang byoung-tak
7 篇 wozniak michal
7 篇 hussain abir
7 篇 soubra hassan
7 篇 huang de-shuang
7 篇 camacho david

语言

6,794 篇 英文
352 篇 其他
28 篇 中文
1 篇 西班牙文
1 篇 俄文

检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7157 条记录，以下是861-870 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：

Progressive Neural Networks for Continuous Classification of Retinal Optical Coherence Tomography Images 11

Progressive Neural Networks for Continuous Classification of...

引用

11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Wang, Di Huang, Kun Chen, Qiang Nanjing University of Science and Technology School of Computer Science and Engineering NanJing China

ISBN: (纸本)9798350345346

Deep learning models have demonstrated a great effectiveness on the classification of retinal lesions in optical coherence tomography images. However, the performance of these models deteriorates significantly when classifying images from different devices compared to the training data, which greatly limits their practicality. To address the issue of poor performance of the model in real-world applications, we propose a method of multi-granularity feature space constraint by studying samples from different devices as domain incremental tasks, to maintain the stability and plasticity of the model, thereby making the model having good generalization performance. Experimental results demonstrate that the proposed method effectively maintains performance on both new and old datasets. Compared to some state-of-the-art baselines focusing on natural images, the proposed method in this paper achieves higher average accuracy and lower forgetting rate on publicly available medical image datasets. © 2023 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

20th EAI international conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2023

20th EAI International Conference on Mobile and Ubiquitous S...

引用

20th EAI international conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2023

ISBN: (纸本)9783031639883

the proceedings contain 65 papers. the special focus in this conference is on Mobile and Ubiquitous Systems: Computing, Networking and Services. the topics include: High-Performance Features in Generalizable Fingerprint-Based Indoor Positioning;SCORE: Scalable Contact Tracing over Uncertain Trajectories;faultBit: Generic and Efficient Wireless Fault Detection Using the Internet of things;deepHeteroIoT: Deep Local and Global learning over Heterogeneous IoT Sensor data;Federated Reinforcement learning for automated LoRaWAN Management in Industrial IoT;a Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications;LOADHoC: Towards the Automatic Local Distribution of Computation Using Existing IoT Devices;e-Go Bicycle intelligent Speed Adaptation System for Catching the Green Light;FedGCS: Addressing Class Imbalance in Long-Tail Federated learning;FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated learning;RADEAN: A Resource Allocation Model Based on Deep Reinforcement learning and Generative Adversarial Networks in Edge Computing;a Stream data Service Framework for Real-Time Vehicle Companion Discovery;KS-Autoformer: An Autoformer-Based SOC Prediction Framework for Electric Vehicles;deep Reinforcement learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks;securing Wireless Communication in Critical Infrastructure: Challenges and Opportunities;cross-User Activity Recognition via Temporal Relation Optimal Transport;selfAct: Personalized Activity Recognition Based on Self-Supervised and Active learning;a Novel Method for Wearable Activity Recognition with Feature Evolvable Streams;let’s Vibrate with Vibration: Augmenting Structural engineering with Low-Cost Vibration Sensing;research on data Drift and Class Imbalance in Android Malware Detection;Reputation-Based Dissemination of Trustworthy Information in VANETs;reputation System

关键词：

来源：评论

学校读者我要写书评

暂无评论

Construction of crack image dataset using active learning 11th

Construction of crack image dataset using active learning

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Shu, J.P. Li, J. Jin, Z.F. College of Civil Engineering and Architecture Zhejiang University Hangzhou China Center for Balance Architecture Zhejiang University Hangzhou China The Architectural Design & Research Institute of Zhejiang University Co. Ltd Hangzhou China

ISBN: (纸本)9781032345314

Crack detection is important for inspection of bridges and automated crack segmentation based on deep learning is an essential solution. However, the construction of crack image dataset is laborious and time-consuming, becoming an obstacle to developing new deep learning methods. the study aims to develop a method that can automatically select a small portion of the most valuable crack images for human annotation from a large pool, without annotating all images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of steel box girders of one bridge, which contains 500 images of 320 x 320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acting as unlabelled images. the proposed acquisition function is compared with Query-By-Committee (QBC), Entropy, and Core-set. the advantages of the proposed method are verified on U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. the results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images.. © 2022 the Author(s).

关键词： Box girder bridges

来源：评论

学校读者我要写书评

暂无评论

PROTEIN REPRESENTATION learning BY GEOMETRIC STRUCTURE PRETRAINING 11

PROTEIN REPRESENTATION LEARNING BY GEOMETRIC STRUCTURE PRETR...

引用

11th international conference on learning Representations, ICLR 2023

作者： Zhang, Zuobai Xu, Minghao Jamasb, Arian Chenthamarakshan, Vijil Lozano, Aurélie Das, Payel Tang, Jian Mila - Québec AI Institute Canada Université de Montréal Canada University of Cambridge United Kingdom IBM Research United States HEC Montréal Canada CIFAR AI Canada

learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://***/DeepGraphlearning/GearNet. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Next-Gen Crystal Structure Classification: Harnessing Deep learning and Machine learning Fusion 11th

Next-Gen Crystal Structure Classification: Harnessing Deep L...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Mahalekshmi, A. Chellam, Heren Rani Anna Government College for Women Affiliated to Manonmaniam Sundaranar University Tamil Nadu Tirunelveli India Department of Computer Science Rani Anna Government College for Women Affiliated to Manonmaniam Sundaranar University Tamil Nadu Tirunelveli India

ISBN: (纸本)9789819717231

Crystal structure classification is the task of assigning a crystal structure to a given set of experimental data. this is a challenging task, as there are over 2 lacs known crystal structures, and the experimental data can be noisy and incomplete. In this paper, we propose a deep machine learning (DML) fusion approach for automatic crystal structure classification. Our approach uses a RESNET with Naive Bayes Classifier (RNB-NET) to learn features from the experimental data. the neural network is then trained on a dataset of known crystal structures. We evaluate our approach on a dataset of more than five thousand crystal structures. Our approach achieves an accuracy of 95%, which is significantly better than the accuracy of previous methods. Our approach is a promising new method for automatic crystal structure classification. It is fast, accurate, and can be used to classify crystal structures from noisy and incomplete data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crystal structure

来源：评论

学校读者我要写书评

暂无评论

Visual Navigation for Obstacle Avoidance Using Deep Reinforcement learning with Policy Optimization 11

Visual Navigation for Obstacle Avoidance Using Deep Reinforc...

引用

11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Parsbin, Ali Akraminia, Mahdi Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran Niroo Research Institute Mechanical Rotary Equipment Research Department Tehran Iran

ISBN: (纸本)9798350308105

In order for mobile robots to navigate through indoor environments without colliding, they must be able to detect and avoid obstacles. In recent years, memory-based Deep Reinforcement learning approaches have gained increasing popularity as they enable robots to navigate more safely as a result of a long sequence of information acquired over time. As the indoor environment has a partial observability problem due to its complexity, obtaining sufficient information from its properties remains an ongoing challenge. To retain relevant information about the structure of the environment over time, LSTM has been incorporated into a network architecture to implement a memory-based Deep Reinforcement learning method. As part of this method, LiDAR data are fused with grayscale images captured by a monocular camera to obtain the information necessary to identify obstacles, and the final features are used to develop an effective obstacle avoidance policy. According to the assessment results, the approach demonstrated remarkable performance in terms of average accumulated reward and success rate. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

HIERARCHICAL SLICED WASSERSTEIN DISTANCE 11

HIERARCHICAL SLICED WASSERSTEIN DISTANCE

引用

11th international conference on learning Representations, ICLR 2023

作者： Nguyen, Khai Ren, Tongzheng Nguyen, Huy Rout, Litu Nguyen, Tan Ho, Nhat Department of Statistics and Data Sciences The University of Texas at Austin AustinTX78712 United States Department of Computer Science The University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Mathematics University of California Los Angeles Los AngelesCA90095 United States

Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of dimensionality. the value of sliced Wasserstein distance is the average of transportation cost between one-dimensional representations (projections) of original measures that are obtained by Radon Transform (RT). Despite its efficiency in the number of supports, estimating the sliced Wasserstein requires a relatively large number of projections in high-dimensional settings. therefore, for applications where the number of supports is relatively small compared with the dimension, e.g., several deep learning applications where the mini-batch approaches are utilized, the complexities from matrix multiplication of Radon Transform become the main computational bottleneck. To address this issue, we propose to derive projections by linearly and randomly combining a smaller number of projections which are named bottleneck projections. We explain the usage of these projections by introducing Hierarchical Radon Transform (HRT) which is constructed by applying Radon Transform variants recursively. We then formulate the approach into a new metric between measures, named Hierarchical Sliced Wasserstein (HSW) distance. By proving the injectivity of HRT, we derive the metricity of HSW. Moreover, we investigate the theoretical properties of HSW including its connection to SW variants and its computational and sample complexities. Finally, we compare the computational cost and generative quality of HSW with the conventional SW on the task of deep generative modeling using various benchmark datasets including CIFAR10, CelebA, and Tiny ImageNet. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Plug and Play Wireless intelligent Temperature Sensing Device and Status Evaluation for Distribution Transformers 11

Plug and Play Wireless Intelligent Temperature Sensing Devic...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Wu, Tianyi Xu, Xiangyi Gao, Kai State Grid Shanghai Electric Power Company Shanghai Electric Power Research Institute Shanghai China

ISBN: (纸本)9798350368345

A wireless temperature sensing device with a fast installation structure for distribution transformers is proposed in this paper. the device adopts wireless surface acoustic wave technology and differential structure design. In order to prevent device detection circuit failure or degradation, a method based on indirect standard source is proposed for calibrating the circuit error. At the same time, in order to improve the accuracy of frequency estimation for sensor feedback signals, frequency estimation algorithm based on singular value decomposition of feedback signals is carried out. By taking into account key factors such as current, temperature, three-phase imbalance, and equipment service life, combined with the historical operating status of the equipment. A scoring method is adopted to comprehensively evaluate the working status of the distribution transformer. through testing and on-site practical application, it has proved that the intelligent temperature sensing device for distribution transformers can effectively monitor the temperature of transformer joints and upload monitoring data and assessing results to the distribution IoT management platform. © 2024 IEEE.

关键词： Frequency estimation

来源：评论

学校读者我要写书评

暂无评论

20th EAI international conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2023

20th EAI International Conference on Mobile and Ubiquitous S...

引用

20th EAI international conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2023

ISBN: (纸本)9783031639913

关键词：

来源：评论

学校读者我要写书评

暂无评论

Federated learning for Getting the IoT Arrangement of Smart City Against Digital threats 11

Federated Learning for Getting the IoT Arrangement of Smart ...

引用

11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Bhardwaj, Vikash Shekhar Saini, Rakesh Kumar Dewan Vs Institute of Engineering & Technology Department of Electrical Engineering Meerut250001 India Dewan Vs Institute of Engineering & Technology Department of Electronics & Communication Engineering Meerut250001 India School of Computing Dit University Uttarakhand Dehradun248001 India

ISBN: (纸本)9798350318159

IoT gadgets have the potential to attach real components with instruments, computer software, hardware, as well as the Web network aimed at information exchange. IoT uses remote management to shrewdly increase efficiency and profitability, but the risk to security and protection grows. Digital risks are expanding gradually, resulting in insufficient levels of safety and categorization. A few IoT flaws are revealed when programmers use the Web, necessitating additional security measures for the smart city's IoT devices. Reduced IoT strings are necessary for effective interruption identification frameworks (IIFs). AI computations then use a large and complex dataset to provide accurate findings. the output of AI might apply in identifying irregularities among IoT system architectures. In this paper seven information parameters from the TON-IoT telemetry dataset together with a few AI-classifiers as well as a Federated-learning prototype in detection of interruption. By means of the Indoor Regulator, GPS Tracker, Carport Entryway, and Modbus datasets, the suggested IDS was able to achieve an exactness of 99.99%. © 2023 IEEE.

关键词： Smart city

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 << < 83 84 85 86 87 88 89 90 91 92 > >>

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：