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

2,738 篇 会议
1,840 篇 期刊文献
28 册 图书

馆藏范围

4,606 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,755 篇 工学
- 1,370 篇 计算机科学与技术...
- 1,095 篇 软件工程
- 562 篇 信息与通信工程
- 558 篇 电子科学与技术（可...
- 557 篇 电气工程
- 540 篇 控制科学与工程
- 261 篇 生物工程
- 260 篇 机械工程
- 260 篇 光学工程
- 235 篇 动力工程及工程热...
- 228 篇 生物医学工程（可授...
- 196 篇 化学工程与技术
- 184 篇 仪器科学与技术
- 164 篇 材料科学与工程（可...
- 117 篇 土木工程
- 108 篇 力学（可授工学、理...
- 103 篇 交通运输工程
- 86 篇 航空宇航科学与技...
1,602 篇 理学
- 695 篇 数学
- 581 篇 物理学
- 301 篇 生物学
- 200 篇 统计学（可授理学、...
- 191 篇 系统科学
- 190 篇 化学
573 篇 管理学
- 428 篇 管理科学与工程(可...
- 195 篇 工商管理
- 160 篇 图书情报与档案管...
213 篇 医学
- 186 篇 临床医学
- 158 篇 基础医学(可授医学...
- 101 篇 药学(可授医学、理...
92 篇 法学
77 篇 经济学
48 篇 教育学
34 篇 农学
11 篇 文学
8 篇 军事学
6 篇 艺术学
1 篇 哲学

主题

86 篇 computer science
67 篇 control systems
67 篇 accuracy
63 篇 optimization
63 篇 feature extracti...
61 篇 machine learning
59 篇 computational mo...
56 篇 laboratories
53 篇 neural networks
52 篇 deep learning
50 篇 data mining
50 篇 design engineeri...
49 篇 hardware
46 篇 computer archite...
45 篇 testing
44 篇 predictive model...
44 篇 delay
43 篇 monitoring
43 篇 training
42 篇 bandwidth

机构

46 篇 school of optica...
44 篇 the pillar of in...
40 篇 the school of co...
37 篇 the school of au...
32 篇 school of comput...
31 篇 singapore univer...
31 篇 department of el...
30 篇 school of automa...
30 篇 shenzhen city ke...
28 篇 pillar of inform...
26 篇 school of system...
25 篇 state key labora...
23 篇 college of compu...
22 篇 department of el...
22 篇 distributed mult...
22 篇 department of sy...
21 篇 tianjin key labo...
21 篇 school of electr...
20 篇 department of el...
20 篇 school of automa...

作者

93 篇 niyato dusit
80 篇 xiong zehui
45 篇 kang jiawen
39 篇 han zhu
38 篇 du hongyang
33 篇 yuen chau
27 篇 shen xuemin
25 篇 mei-ling shyu
24 篇 shu-ching chen
22 篇 mao shiwen
21 篇 chen shu-ching
21 篇 olaf stursberg
20 篇 ji zhen
20 篇 kim dong in
18 篇 quek tony q.s.
17 篇 zhen ji
17 篇 xu minrui
17 篇 poor h. vincent
16 篇 kang li
16 篇 yang yang

语言

4,286 篇 英文
219 篇 其他
85 篇 中文
7 篇 朝鲜文
6 篇 日文
1 篇 德文
1 篇 法文

检索条件"机构=Electrical Engineering and Computer Science System Design"

共 4606 条记录，以下是81-90 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：

Autonomous Drone Control for Wireless Charging Using Power Lines 18

Autonomous Drone Control for Wireless Charging Using Power L...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Wang, Pengkai Kim, Jonghoek Mutahira, Husna Muhammad, Mannan Saeed Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sejong University Dept. of Defense System Engineering Seoul Korea Republic of Sogang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331011

The limited autonomy of flight has long been considered a significant constraint in drone systems. In the context of drone inspections of power lines, this study focuses on a drone equipped with a coil designed for autonomous battery charging. Positioned atop the drone is a charging coil, and on the upper surface, there is an upward-facing camera with a restricted field-of-view (FOV), serving the crucial role of aligning the drone with power lines. This research introduces a wireless power transfer strategy to facilitate the self-charging process of the drone while maintaining its position beneath power lines. To enable the drone's autonomous approach to power lines, a method is devised to guide the drone from below. As the drone approaches the power lines, it monitors the current induced by these lines and gradually adjusts its altitude until a sufficiently high induced current is achieved. The proposed approach ensures safety, as the drone avoids direct contact with high-voltage power lines, setting it apart from other existing methods that require such contact. To the best of our knowledge, this study offers a novel approach to precisely controlling drone movements, and establishing reliable wireless power transfer from power lines. The effectiveness of the developed wireless transfer strategy has been verified through MATLAB simulations. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

AI-Assisted Markerless Activity Tracking system for Supporting Aging and Wellness

AI-Assisted Markerless Activity Tracking System for Supporti...

引用

2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Abedi, Hajar Ansariyan, Ahmad Morita, Plinio P Wong, Alexander Boger, Jennifer Shaker, George University of Waterloo Electrical and Computer Engineering Waterloo Canada University of Waterloo School of Public Health Sciences Waterloo Canada University of Waterloo System Design Engineering Waterloo Canada

ISBN: (纸本)9798350328264

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters. © 2023 IEEE.

关键词： activity recognition aging mm-wave radar personalized care sequential deep learning

来源：评论

学校读者我要写书评

暂无评论

Study on Multi-Motor Synchronization Control for Nuclear Island Main Equipment Transporter Based on Improved Virtual Line-Shafting Structure 11th

Study on Multi-Motor Synchronization Control for Nuclear Isl...

引用

11th Frontier Academic Forum of electrical engineering, FAFEE 2024

作者： Sheng, Beining Song, Beibei An, Yanbo Du, Hua Cui, Shumei School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

ISBN: (纸本)9789819788231

The existing drive method of the main equipment transporter of the nuclear island makes the transporter have frequent jamming problems when the transporter going straight and turning, which affects the safety during the transporter of the main equipment. Therefore, it is necessary to design an on-board multi-motor synchronous system with differential synchronization function. Although the traditional virtual line-shafting structure has the ability of same-speed synchronization and differential synchronization, there is a large synchronization error. For this reason, this paper proposes a multi-motor synchronous control method based on the improved virtual line-shafting structure, which can effectively solve the inherent synchronous delay problem of the virtual line-shafting. First, analyze and research the combination method of virtual line-shafting structure and deviation coupling structure, and designs the electronic differential to be able to adapt to the requirements of same-speed and differential synchronization. Then the influence of virtual line-shafting parameters on the system is analyzed using the root locus method, and a simplified parameter model of the virtual line-shafting is established, on the basis of which an improved virtual line-shafting control method with low synchronous delay is proposed. The results show that this method can significantly reduce the synchronization error compared with the traditional virtual line-shafting. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Root loci

来源：评论

学校读者我要写书评

暂无评论

Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

引用

Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Landscape Patterns in Mining Cities Influenced by Extraction and Terrain Features

Landscape Patterns in Mining Cities Influenced by Extraction...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Madhavan, K. Babu, J.Jai Jaganath Karthikeyan, P. Centre for System Design Chennai Institute of Technology Department of Computer Science & Engineering Tamil Nadu Chennai600 069 India Centre for System Design Chennai Institute of Technology Department of Biomedical Engineering Tamil Nadu Chennai600 069 India

ISBN: (纸本)9798350348910

The combined influence of mining activities and the underlying terrain gradient has caused significant transformations in the landscape patterns of mining cities. The natural topography being altered by mining operations can result in a distinct evolution in the spatial arrangement and ecological dynamics of these cities. Spatial heterogeneities are influenced by factors such as extraction techniques, waste disposal methods, and infrastructure development. The inherent terrain gradient heavily influences the distribution of mining sites and urban expansion patterns. This research underscores the intricate interplay between human-induced activities and natural topographical features in shaping the landscape of mining cities. The development of resilient communities in areas that are evolving due to mining requires a clear understanding of these dynamics for sustainable urban planning, environmental conservation, and community development. © 2023 IEEE.

关键词： Waste disposal

来源：评论

学校读者我要写书评

暂无评论

SyREC: A Symbolic-Regression-Based Ensemble Combiner 36

SyREC: A Symbolic-Regression-Based Ensemble Combiner

引用

36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Fong, Kei Sen Motani, Mehul Department of Electrical and Computer Engineering Singapore College of Design and Engineering Singapore Institute of Data Science Singapore N.1 Institute for Health Singapore National University of Singapore Singapore

ISBN: (纸本)9798331527235

Symbolic Regression (SR) is the task of finding a concise white-box mathematical expression that fulfills a given machine learning (ML) objective. In this work, we introduce the first-of-its-kind SR-based ensemble combiner (SyREC) for ML which utilizes the quasi-arithmetic mean (parameterized by a function f) as an ensemble combiner and discovers f via SR. Our Sy REC demonstrates advantages over existing ensemble combiners, i.e., averaging methods and meta-learners. Compared to averaging methods, Sy REC allows for increased combiner complexity via exploring a large function class and provides a learning process to tune the ensemble combiner to the specific dataset. Compared to meta-learners, SyREC preserves important ensemble properties, like idempotency, monotonicity and boundedness, which produce predictions more consistent with the base models. We note SyREC produces a white-box ensemble combiner that lies in the intersection of both categories of existing combiners, addressing the weaknesses while capturing the strengths of each category. To evaluate Sy REC, we present experiments on 16 open-source PMLB datasets and commonly used base models, including a large variety of experiments and analyses across different settings: (i) regression and classification tasks, (ii) equally weighted combiners and custom-weighted combiners, (iii) homogeneous and heterogeneous ensembles, (iv) sequential and parallel ensembles. We find that in all these settings, SyREC shows consistent improvement over existing ensembling approaches. This indicates that SyREC is a robust ensemble combiner, which produces explainable and consistent ensemble predictions. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Deep Network Architecture for COVID-19 Detection from Computed Tomography Images

Efficient Deep Network Architecture for COVID-19 Detection f...

引用

2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bhardwaj, Shaurya Goel, Chirag Kumar, Abhimanyu Dubey, Satish Kumar Bhardwaj, Saurabh Srivastava, Vishal Thapar Institute of Engineering and Technology Computer Science & Engineering Patiala India Thapar Institute of Engineering and Technology Electrical & Instrumentation Engineering Patiala India Iit Delhi Sensors Instrumentation and Cyber Physical System Delhi India

ISBN: (纸本)9798331533311

The globally devastating consequence of COVID-19 or Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2) has posed immense threat to the life of living beings. Doctors and scientists throughout the world are working day and night to combat the proliferation or transmission of this deadly disease through advances in terms of technology, finances, data repositories, protective equipment, and many other services. Rapid and efficient detection of COVID-19 reduces the rate of spreading this deadly disease and early treatment improve the recovery rate. In this paper, we proposed a new framework to exploit powerful features extracted from the autoencoder and Gray Level Co-occurence Matrix (GLCM), combined with random forest algorithm for the efficient and fast detection of COVID-19 using computed tomographic images. The model's performance is evident from its 97.78% accuracy, 96.78% recall, and 98.77% specificity. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking 12

Adaptive Confidence Threshold for ByteTrack in Multi-Object ...

引用

12th IEEE International Conference on Control, Automation and Information sciences, ICCAIS 2023

作者： Ma, Linh Van Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Korea Republic of Pusan National University Department of Mechanical-IT Convergence System Engineering Busan Korea Republic of

ISBN: (纸本)9798350328783

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Automated CKD Detection via Image Processing Employing Delta-E Metric and CNN

Automated CKD Detection via Image Processing Employing Delta...

引用

2024 International Conference on system, Computation, Automation and Networking, ICSCAN 2024

作者： Espin Shalo, S.P. Jeeshan, R.J. Gunasekar, S. Pius Agbulu, G. Venkat Ramana, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai602105 India Rajalakshmi Engineering College Department of Computer Science and Design Tamilnadu Chennai602105 India Chinhoyi University of Technology Department of Mechatronics Engineering Zimbabwe Indian Institute of Technology Department of Electrical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Chronic Kidney Disease (CKD) is a global health concern that often progresses silently until advanced stages, making early detection critical. This paper presents a point-of-care system that automates the color comparison between a test strip and a reference strip to detect biomarkers like albumin associated with kidney dysfunction. The system leverages mobile-captured images, applying image processing techniques such as segmentation and color extraction, and compares the test strip's top color with reference colors on the parent strip using the Delta-E metric in the LAB color space. The closest match quantifies the biomarker concentration, providing a reliable diagnostic measure. To enhance diagnostic precision, this research incorporates Convolutional Neural Networks (CNNs) to refine image preprocessing and anomaly detection. The CNN model complements existing methods by mitigating environmental influences like lighting variability and improving feature extraction accuracy. This hybrid approach ensures consistency while adapting to diverse datasets and image conditions. The result is a cost-effective, fast, and accessible diagnostic tool that reduces manual errors and supports early CKD detection in resource-limited settings. Future work will focus on expanding the application of this system to detect other diseases and further optimize the integration of CNNs for enhanced scalability and robustness. © 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

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461页 << < 5 6 7 8 9 10 11 12 13 14 > >>

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：