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

7,756 篇 会议
86 篇 期刊文献
55 册 图书

馆藏范围

7,897 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,781 篇 工学
- 2,010 篇 计算机科学与技术...
- 1,390 篇 软件工程
- 653 篇 控制科学与工程
- 502 篇 信息与通信工程
- 493 篇 电气工程
- 251 篇 机械工程
- 205 篇 仪器科学与技术
- 186 篇 电子科学与技术（可...
- 136 篇 生物医学工程（可授...
- 120 篇 生物工程
- 91 篇 光学工程
- 79 篇 网络空间安全
- 78 篇 动力工程及工程热...
- 67 篇 化学工程与技术
- 64 篇 力学（可授工学、理...
- 51 篇 交通运输工程
- 50 篇 材料科学与工程（可...
1,045 篇 理学
- 678 篇 数学
- 285 篇 统计学（可授理学、...
- 217 篇 物理学
- 178 篇 系统科学
- 146 篇 生物学
- 79 篇 化学
667 篇 管理学
- 545 篇 管理科学与工程(可...
- 199 篇 工商管理
- 161 篇 图书情报与档案管...
128 篇 医学
- 99 篇 临床医学
- 80 篇 基础医学(可授医学...
68 篇 法学
- 67 篇 社会学
49 篇 经济学
- 48 篇 应用经济学
33 篇 教育学
20 篇 农学
19 篇 军事学
10 篇 文学
2 篇 艺术学

主题

1,890 篇 computational mo...
1,023 篇 measurement
440 篇 computer archite...
423 篇 neural networks
413 篇 predictive model...
347 篇 computer science
297 篇 accuracy
286 篇 biological syste...
259 篇 analytical model...
240 篇 noise measuremen...
225 篇 real-time system...
222 篇 computer simulat...
219 篇 training
216 篇 brain modeling
216 篇 mathematical mod...
213 篇 feature extracti...
187 篇 power system mod...
182 篇 data models
181 篇 neurons
173 篇 temperature meas...

机构

20 篇 school of comput...
17 篇 department of el...
15 篇 school of electr...
14 篇 budapest univ te...
13 篇 chitkara univers...
13 篇 school of comput...
12 篇 department of co...
11 篇 department of co...
10 篇 college of compu...
10 篇 college of compu...
9 篇 tsinghua univers...
8 篇 department of el...
8 篇 department of co...
7 篇 school of comput...
7 篇 graphic era deem...
7 篇 school of comput...
7 篇 school of comput...
7 篇 peng cheng labor...
7 篇 school of contro...
6 篇 college of compu...

作者

11 篇 towsley don
10 篇 varró dániel
9 篇 wei wang
8 篇 liu alex x.
8 篇 anon
7 篇 liu yang
7 篇 wei li
7 篇 wang jia
6 篇 li zhang
6 篇 byung-ju yi
6 篇 reiner r. dumke
6 篇 yang liu
5 篇 deepak banerjee
5 篇 chunyan miao
5 篇 li li
5 篇 t.m. khoshgoftaa...
5 篇 rizzi antonello
5 篇 j.c. principe
5 篇 sakorn mekruksav...
5 篇 bergmann gábor

语言

7,704 篇 英文
168 篇 其他
28 篇 中文
5 篇 俄文

检索条件"任意字段=Joint International Conference on Measurement and Modeling of Computer Systems"

共 7897 条记录，以下是1041-1050 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：

From Data to Decisions: Enterprise-Level Domain-Specific Graph Retrieval-Augmented Generation systems for Advanced Question Answering

From Data to Decisions: Enterprise-Level Domain-Specific Gra...

引用

international conference on Big Data and Smart Computing (BIGCOMP)

作者： Sandeep Varma Shivam Shivam Sarun Natarajan Ankita Banerjee Sourodeep Roy ZS Associates Pune India Rockwell Automation Pune India

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Retrieval-Augmented Generation (RAG) is aimed at improving the functionality of large language model (LLM) applications by incorporating specific data. This may include searching for relevant materials or files concerning a particular issue or search provided as background information to the LLM. Some prominent architectures like the Vanilla RAG architecture focuses primarily on retrieval of textual data, primarily utilizing vector databases, thus neglecting the structural intricacies of textual data, resulting in a critical gap in the generation process. To address this gap, we have introduced SAGE-QA (Scalable Advanced Graph RAG for Enterprise Question Answering), which significantly enhances both the retrieval and generation processes by emphasizing the importance of topological information for reasoning tasks on a variety of data sources. For our use case, SAGE-QA significantly outperforms current state-of-the-art RAG methods with its novel retrieval mechanism, which effectively mitigates hallucinations, and acts as a sustainable contextually relevant application.

关键词： measurement Databases Soft sensors Large language models Computational modeling Retrieval augmented generation computer architecture Question answering (information retrieval) Vectors Data models

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection for Multivariate Time Series Based on Contrastive Learning and Autoformer

Anomaly Detection for Multivariate Time Series Based on Cont...

引用

international conference on computer Supported Cooperative Work in Design

作者： Xuwen Shang Jue Zhang Xingguo Jiang Hong Luo Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Nowadays, due to the increasingly complex and huge data flow, anomaly detection has become an important technology to ensure the high security and reliability of the systems. It helps identify unusual observations in the data, thereby protecting safety and reducing risk. However, in the field of multivariate temporal anomaly detection, there are still some difficulties in spatio-temporal correlation mining of variables, generalization ability in dealing with complex temporal sequences, and feature learning under different granularity. Therefore, we propose an unsupervised anomaly detection method for multivariate time series based on contrastive learning and Autoformer autoencoder. In order to fully explore the spatio-temporal correlation of time series data, this paper uses Graph Attention Network and Auto-Correlation to jointly learn spatio-temporal correlation, and obtain positive and negative pairs through time-domain and frequency-domain data augmentation. Finally, the Autoformer autoencoder is used for anomaly detection in a predictive manner. In the loss function section, a joint loss function is formed by introducing contrastive learning losses at the time step and time window granularity, combined with prediction loss, to improve the learning effect. This paper investigates the performance of the proposed method through experiments on the SMAP and MSL public datasets, and achieves better results than baseline methods.

关键词： Correlation Computational modeling Time series analysis Contrastive learning computer architecture Stability analysis Safety

来源：评论

学校读者我要写书评

暂无评论

Sign Language Recognition using Deep Learning

Sign Language Recognition using Deep Learning

引用

Pervasive Computational Technologies (ICPCT), international conference on

作者： Bablu Kumar Shabanam Kumari Tarun Kumar Central University of Rajasthan Bandar Sindri Ajmer Rajasthan India G. L. Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Sign language recognition is extremely pivotal for filling the void existing between those deaf or hard of hearing, and the rest of mankind. computer vision advancement through deep learning techniques has been followed successfully with remarkable success across images. This research paper presents a comprehensive study on the application of CNN for sign language recognition. The primary objective is to develop an accurate and efficient system to recognize sign language gestures and translate them into corresponding text or speech. The proposed approach comprises a deep learning model trained as 3000 images collected from various datasets and online resources. The proposed deep learning model is trained in such way that it achieves the seem less efficiency on real time images that contains sign language on different scales and poses. The various outperformed metrics such as precision, recall and f1-score are also recorded. The model achieves 0.995%, 0.994%, and 0.992% accuracy during the testing session.

关键词： Deep learning measurement Training Sign language Accuracy Translation Computational modeling Speech recognition Real-time systems Testing

来源：评论

学校读者我要写书评

暂无评论

Dual Active Bridge Converter Output Current Ripple Prediction in EV Battery Chargers Through Multimodal Adaptive Spatio-Temporal Graph Neural Network

Dual Active Bridge Converter Output Current Ripple Predictio...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Abhay Chaturvedi Mudit Mittal Swapnil Uttamrao Deokar Rachit Adhvaryu G. Satish Natrayan L Department of Electronics & Communication Engineering GLA University Mathura Uttar Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Departement of Mechanical Engineering Smt. Kashibai Navale College of Engineering Pune Maharashtra Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The prediction of converter output current ripple in Electric Vehicle (EV) battery chargers stands as a vital condition to maintain stable charging with safe battery operation by reducing voltage fluctuations and delivering steady currents. The prediction of converter output current ripple faces difficulties because the complex dynamics of converter behavior changes according to various load conditions and environmental factors. This measurement inaccuracy threatens the capability to make optimal power transfers and reduces loss management ability thereby affecting the life span and operational performance of the EV battery. This research presents a framework to predict the amount of current oscillation in chargers for EV battery systems powered by Dual Active Bridge (DAB) Converter. The proposed technique utilizes Multimodal Adaptive Spatio-Temporal Graph Neural Network (MASTGNN) as its method. The proposed method aims at both enhancing current ripple prediction accuracy and optimizing converter performance together with improving EV battery lifespan during charging. A MAST-GNN process predicts output current ripples in DAB converters used for EV battery charging through modeling which evaluates the behavior changes of voltage and current and power when operational parameters vary. Implementation of the proposed method in MATLAB will undergo comparison testing with CNN as well as BESO-ANFIS and ANN together with other existing methods. By achieving an accuracy of 97% in current ripple prediction and maintaining a high converter efficiency of 98.5%, the proposed MASTGNN method outperforms traditional approaches without compromising the stability and performance of the EV battery charging process.

关键词： Battery chargers Accuracy Adaptive systems Switched reluctance motors Electric vehicles Real-time systems Loss measurement Graph neural networks Batteries Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

Monitoring Effluent Water Quality in Wastewater Treatment Process Based on Multi-model Online Soft Sensing Method

Monitoring Effluent Water Quality in Wastewater Treatment Pr...

引用

international conference on Image, Vision and Computing (ICIVC)

作者： Zhaozhao Zhang Juan Wu Lei Yang Yingqin Zhu College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China Departmen of the Control Automatic CINVESTAV-IPN(National Polytechnic Institute) Mexico city Mexico

For the biochemical reaction stage of the wastewater treatment process, which involves a complex microbial reaction process, there are many parameters that are difficult to monitor online. Therefore, using a multi-model online soft measurement method is a key but challenging problem. In this paper, advanced machine learning technology is applied, and a multi-model online soft sensing method is adopted to address the difficulty of online monitoring of effluent quality parameters in the sewage treatment process. Specifically, the method uses an online subtraction clustering algorithm to divide real-time working condition data samples, and according to the distribution of real-time working condition data samples in the space, the fuzzy strategy is adopted to assign the real-time working condition data of the corresponding subspace to different submodels for learning. Finally, the final soft measurement results are obtained by dynamically integrating the outputs of each submodel. The actual operation data of a sewage treatment plant were used to detect the ammonia nitrogen in the effluent water of the sewage treatment process. Experimental results show that the proposed method is able to self-organize multi-model soft sensor model driven by real-time condition data, and the multi-model soft sensor model constructed by this method has improved the modeling accuracy, modeling speed, and the ability to track real-time conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Doomed Run Prediction in Physical Design by Exploiting Sequential Flow and Graph Learning 40

Doomed Run Prediction in Physical Design by Exploiting Seque...

引用

40th IEEE/ACM international conference on computer Aided Design (ICCAD)

作者： Lu, Yi-Chen Nath, Siddhartha Khandelwal, Vishal Lim, Sung Kyu Georgia Inst Technol Sch ECE Atlanta GA 30332 USA Synopsys Inc Mountain View CA USA Synopsys Inc Hillsboro OR USA

ISBN: (纸本)9781665445078

Modern designs are increasingly reliant on physical design (PD) tools to derive full technology scaling benefits of Moore's Law. Designers often perform power, performance, and area (PPA) exploration through parallel PD runs with different tool configurations. Efficient exploration of PPA is mission-critical for chip designers who are working with stringent time-to-market constraints and finite compute resources. Therefore, a framework that can accurately predict a "doomed run" (i.e., will not meet the PPA targets) at early phases of the PD flow can provide a significant productivity boost by enabling early termination of such runs. Multiple QoR metrics can be leveraged to classify successful or doomed PD runs. In this paper, we specifically focus on the aspect of timing, where our goal is to identify the PD runs that cannot achieve end-of-flow timing results by predicting the post-route total negative slack (TNS) values in early PD phases. To achieve our goal, we develop an end-to-end machine learning (ML) framework that performs TNS prediction by modeling PD implementation as a sequential flow. Particularly, our framework leverages graph neural networks (GNNs) to encode netlist graphs extracted from various PD phases, and utilize long short-term memory (LSTM) networks to perform sequential modeling based on the GNN-encoded features. Experimental results on seven industrial designs with 5:2 train/test split ratio demonstrate that our framework predicts post-route TNS values in high fidelity within 5.2% normalized root mean squared error (NRMSE) in early design stages (e.g., placement, CTS) on the two validation designs that are unseen during training.

关键词： Training Semiconductor device modeling Productivity measurement Moore's Law Mission critical systems Predictive models

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Learning Framework for Food Ingredient Classification Using ResNet and SMO Classifiers

A Novel Deep Learning Framework for Food Ingredient Classifi...

引用

international conference on Computational Innovations and Engineering Sustainability (ICCIES)

作者： Rahin M A. Devi Gowtham M Sivaraman C Department of Computer Applications(PG) Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Applications Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamil Nadu India

ISBN: (数字)9798331536695

ISBN: (纸本)9798331536701

The research proposes a new deep learning (DL) system that uses ResNet to extract features from food images and Sequential Minimal Optimization (SMO) classifiers for prediction. In addition to having trouble handling complex, high-dimensional datasets, traditional machine learning (ML) methods like Support Vector Machines (SVMs) also have trouble operating in real time. The proposed approach combines ResNet's capacity to learn hierarchical features with SMO's robust classification capabilities, delivering both accuracy and scalability advantages. The proposed method performs better than existing method, according to the testing results, with a maximum accuracy of 93.4%, precision and recall of 91.7% and 92.5%, respectively, whereas the existing system achieves only 85.3% and 78.5%. While it has longer trained times (7.5 hours), it has faster inference speeds (88 milliseconds). Robustness against noisy data (88.7%) and low-quality data (89.5%) contributes to the proposed system's strength, allowing for performance optimization that is excellent for real-time applications such as food safety and nutritional monitoring.

关键词： Training Support vector machines Accuracy Computational modeling Feature extraction Real-time systems Data models Robustness Noise measurement Optimization

来源：评论

学校读者我要写书评

暂无评论

Effects of Meteorological Parameters on Microwave Propagation modeling During Dust Storm

Effects of Meteorological Parameters on Microwave Propagatio...

引用

international conference on Mechatronics (ICOM)

作者： E.I. Eltahir Elfatih A. A. Elsheikh Md Rafiqul Islam Mohamed Hadi Habaebi Aisha Hassan A. Hashim Mosab Hamdan Department of Electrical and Computer Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Electrical Engineering King Khalid University(KKU) Abha Saudi Arabia Walton Institute for Information and Communication Systems Science South East Technological University Waterford Ireland

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Dust storms are complex meteorological events that significantly impact the propagation of microwave signals, presenting challenges to prediction models which often rely on theoretical scattering principles without considering various meteorological parameters. In this study, meteorological data-including pressure, visibility, wind speed, temperature, and humidity-were monitored concurrently with signal levels at a 14.4 GHz microwave link operated in Khartoum, Sudan, over the course of a year. The variations in received signal levels were analyzed in relation to these parameters. Initial theoretical predictions, based on measured visibility and dust characteristics, underestimated the actual attenuation observed. Through detailed analysis, it was observed that humidity directly affects dust particle properties. By reassessing the models to incorporate the influence of humidity and other meteorological factors, the predictive accuracy of microwave signal attenuation during dust storms was significantly increased from 0.096 dB/km to 0.576 dB/km for Rayleigh approximation at the visibility limit of 83 m. This research highlights the importance of including comprehensive meteorological data in prediction models to enhance their accuracy in real-world conditions.

关键词： Temperature measurement Microwave integrated circuits Storms Atmospheric modeling Wind speed Humidity Predictive models

来源：评论

学校读者我要写书评

暂无评论

Quantitative Analysis of Error Propagation in Hindi-English Cascaded Speech-to-Speech Translation Models

Quantitative Analysis of Error Propagation in Hindi-English ...

引用

Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Mahendra Gupta Maitreyee Dutta Chandresh Kumar Maurya Dept. of Computer Science and Engineering NITTTR Chandigarh Chandigarh India Dept. of IMEE NITTTR Chandigarh Chandigarh India Dept. of Computer Science and Engineering IIT Indore Indore India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Speech-to-Speech Translation (S2ST) plays a crucial role in reducing language barriers and enabling seamless communication between people from different linguistic backgrounds. Traditional S2ST systems typically rely on a cascaded design, where components like Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) work together in sequence to complete the translation process. Cascaded models are widely used S2ST models and are prone to error propagation (EP) across the pipeline, significantly impacting translation quality. EP is discussed in various literature. However, a comprehensive quantitative analysis is not available, particularly for low-resource languages like Hindi and English. This work presents a detailed quantitative study of EP in Hindi-English cascaded S2ST models, bridging this critical research gap. This study utilizes both text-based and textless evaluation metrics such as BLEU, Translation Edit Rate (TER), and BLASER score for translation accuracy to quantify the impact of EP at various stages of the pipeline on the FLEURES dataset. The result analysis shows that due to EP, the translation quality of the S2ST model decreases in BLEU score of 11.55 for English→Hindi and BLEU score of 12.16 for Hindi→English. Similarly, reference-based BLASER decreases by 0.61 and 0.45 for English→Hindi and Hindi→English, respectively.

关键词： measurement Analytical models Translation Accuracy Statistical analysis Computational modeling Pipelines Linguistics Text to speech Machine translation

来源：评论

学校读者我要写书评

暂无评论

Catboost3D: A Novel CatBoost-based Approach for Efficient Classification of 3D Models

Catboost3D: A Novel CatBoost-based Approach for Efficient Cl...

引用

Computing, Internet of Things and Microwave systems (ICCIMS), international conference on

作者： Mohcine Bouksim Mohamed Amine Madani Fatima Rafii Zakani Khadija Arhid Taoufiq Gadi Mohamed Aboulfatah SmartICT Lab National School of Applied Sciences Mohammed First University Oujda Morocco Engineering Sciences Laboratory LSI National School of Applied Sciences Mohammed First University Oujda Morocco Lab. of Applied Sciences SOVIA team National School of Applied Sciences University of Abdelmalek Essaâdi. Al Hoceïma Morocco Lab. of Processes Signals Industrial Systems and Computer science Higher School of Technology Cadi Ayyad University Safi Morocco MISI Laboratory Faculty of Science and Technology University of Hassan I. Settat Morocco

ISBN: (数字)9798350351736

ISBN: (纸本)9798350351743

The classification of 3D models consists in grouping similar objects in predefined classes. This task is challenging and requires results that align with human perception. This paper introduces a new classification method for 3D models, using the CatBoost classifier, a machine learning algorithm that has shown good results in various domains. Our approach addresses the challenge of accurately categorizing 3D models without needing a large database for training and using the 3D models directly without passing by intermediate representation. We trained the CatBoost classifier using the dihedral angle, shape index, and shape diameter function to create a model able to classify each query object to the correct class. We demonstrate, through extensive experimentation, that our method not only accurately classifies 3D models into their respective categories but also effectively handles the complexity and variability inherent in 3D data.

关键词： measurement Training Solid modeling Three-dimensional displays Shape Databases Computational modeling Semantics Robustness Indexes

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 << < 101 102 103 104 105 106 107 108 109 110 > >>

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：