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

116 篇 会议
1 篇 期刊文献

馆藏范围

117 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

84 篇 工学
- 72 篇 计算机科学与技术...
- 41 篇 软件工程
- 21 篇 电气工程
- 21 篇 信息与通信工程
- 13 篇 控制科学与工程
- 12 篇 生物工程
- 11 篇 生物医学工程（可授...
- 9 篇 电子科学与技术（可...
- 8 篇 光学工程
- 6 篇 机械工程
- 5 篇 化学工程与技术
- 4 篇 仪器科学与技术
- 3 篇 石油与天然气工程
- 2 篇 动力工程及工程热...
- 2 篇 轻工技术与工程
- 2 篇 安全科学与工程
- 2 篇 网络空间安全
41 篇 理学
- 25 篇 数学
- 14 篇 生物学
- 6 篇 物理学
- 6 篇 化学
- 6 篇 系统科学
- 5 篇 统计学（可授理学、...
21 篇 管理学
- 12 篇 管理科学与工程(可...
- 9 篇 图书情报与档案管...
- 5 篇 工商管理
7 篇 医学
- 6 篇 基础医学(可授医学...
- 6 篇 临床医学
- 5 篇 药学(可授医学、理...
3 篇 经济学
- 2 篇 应用经济学
1 篇 法学
1 篇 教育学
1 篇 军事学

主题

38 篇 pattern recognit...
20 篇 machine learning
10 篇 support vector m...
8 篇 deep learning
8 篇 feature extracti...
7 篇 training
5 篇 face recognition
5 篇 machine learning...
4 篇 local binary pat...
4 篇 neural networks
4 篇 computer vision
4 篇 convolutional ne...
3 篇 pattern classifi...
3 篇 svm
3 篇 data mining
3 篇 classification
3 篇 emotion recognit...
3 篇 artificial neura...
3 篇 principal compon...
2 篇 covid-19

机构

2 篇 shanghai jiao to...
2 篇 ai school tianhu...
2 篇 vermont internat...
1 篇 p.s.r engineerin...
1 篇 palmerston north...
1 篇 univ lahore dept...
1 篇 dept. of mechani...
1 篇 s.v. university ...
1 篇 guizhou minzu un...
1 篇 dept. of electro...
1 篇 east china jiaot...
1 篇 fr. conceicao ro...
1 篇 democritus unive...
1 篇 graphic era univ...
1 篇 coll engn dept e...
1 篇 univ technol mal...
1 篇 ifet college of ...
1 篇 vjti dept elect ...
1 篇 vjti elect engn ...
1 篇 noida institute ...

作者

3 篇 lu bao-liang
2 篇 ling xufeng
2 篇 yang jie
1 篇 fu jia
1 篇 gupta shagun
1 篇 yu shuyang
1 篇 shuyang yu
1 篇 tan weijie
1 篇 gill abdul aziz
1 篇 deshmukh afif
1 篇 vidhya k.
1 篇 li hua-liang
1 篇 gopalakrishnan s...
1 篇 yong-do choi
1 篇 karthik akkenagu...
1 篇 chitralekha jena
1 篇 jose anand a.
1 篇 wang fudi
1 篇 ananth n.s yoga
1 篇 princess joyce b...

语言

117 篇 英文

检索条件"任意字段=2nd IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2021"

共 117 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：

Experimental comparison of advance control strategies which uses pattern recognition technique for nonlinear system

Experimental comparison of advance control strategies which ...

引用

2010 The 2nd international conference on machine learning and Computing, ICMLC 2010

作者： Bojja, Polaiah Abraham, K. Varadarajan, S. Giri Prasad, M.N. J.N.T. University Ananthapur A..P India Dept. of Mechanical Engg. ASCET Nellore India S.V. University Tirupati India J.N.T. University Pulivendula India

ISBN: (纸本)9780769539775

On-line tool wear estimation in turning is essential for on-line cutting process optimization. In this work, cutting force measurement is used for a reliable on-line flank wear estimation and tool life monitoring. Models for flank wear will be obtained as a function of machining parameters and dynamic cutting forces. The coefficients for flank wear models are obtained by using the experimental results. Then the non-linear dynamic models obtained are calibrated with the actual conditions. These developed models will be used for the simulation of flank wear and using control variable such as cutting speed;the flank wear will be controlled. For model validation, the flank wear is estimated using a nonlinear model. In the present work, an attempt has been made to control the flank wear during turning of on-line cutting process using the Fuzzy Logic Controller and Neural network based on self-tuning of PID controller approaches. Those approaches are treat the material as dynamic system and involve developing state space models from available material behavior model. The evaluation of performance criteria can be compared for those approaches of PI controller with Fuzzy Logic Controller and Neural network based on self-tuning of PID controller. Simulation studies are carried-out for the nonlinear system using MATLAB software. © 2010 ieee.

关键词： Nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

引用

2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 ieee.

关键词： deep learning Image pattern recognition MobileNetV3-small Wheat imperfect grains

来源：评论

学校读者我要写书评

暂无评论

Discrimination of Decision Confidence Levels from EEG Signals 10

Discrimination of Decision Confidence Levels from EEG Signal...

引用

10th international ieee-EMBS conference on Neural Engineering (NER)

作者： Li, Rui Liu, Le-Dian Lu, Bao-Liang Shanghai Jiao Tong Univ Qing Yuan Res Inst Ctr Brain Comp & Machine IntelligenceBrain Sci & Dept Comp Sci & EngnKey Lab Shanghai Educ Commis 800 Dong Chuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Med RuiJin Hosp Ctr Brain Machine Interface & Neuromodulat 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9781728143378

To explore the capability of utilizing electroencephalograms (EEGs) for the measurement of human decision confidence levels, this paper develops a new visual perceptual decision confidence experiment. In this experiment, a visual perceptual decision-making task is performed by 14 participants, and their EEG data are recorded. The problem of measuring decision confidence levels is considered to be a pattern classification task, and two pattern classifiers are trained with differential entropy (DE), power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM), and asymmetry (ASM) features extracted from multichannel EEG data. We compare the performance of these features and find that the DE feature performs better than the others for measuring levels of decision confidence. The experimental results indicate that EEG signals offer good capability for measuring human decision confidence levels. The best performance of our proposed method in measuring five levels of decision confidence reaches an accuracy of 49.14% and F1-score of 45.07%, and for the extreme levels of decision confidence, the recognition accuracy reaches 91.28%, with an average F1-score of 88.92%. Topographic maps are also used to depict the neural patterns of EEG signals, suggesting that the posterior parietal cortex and occipital cortex might be sensitive brain areas for indicating decision confidence.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Realization of Security System Using Facial recognition and Arduino Keypad Door Lock System 1

引用

2nd international conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2020

作者： Lenka, Rasmita Shubham, Nishant Sinha, Nishant Gupta, Rohit KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9789811587528

ISBN: (纸本)9789811587511

In today’s world which is full of technological and unseen errors security is one of the major issues that should not be over seen. The technological and modern perspective has to be used to resolve the modern day problems. This project here in is based on Open CV face detection module to feature a face recognition system to identify and recognize the face of a person using certain facial feature which is again integrated with the traditional keypad pin input for ensuring the overall security throughout. It can be used as an access control system that is by registering the staffs, students, employee or officials of an organization with their faces, and later which can be used to recognize the people by capturing the images of the faces, the system show cases the accurate recognition which happens to become more accurate as time passes due to machine learning algorithm which enables constant learning of the system. The system is implemented on desktop using web camera or mounted camera;it first captures the image using the web camera or mounted camera and then applies machine learning algorithm to chalk out the features that could be used for the prediction at the time of implementation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Plant recognition System based on Leaf Image 2

Plant Recognition System based on Leaf Image

引用

2nd international conference on Intelligent Computing and Control Systems (ICICCS)

作者： Raut, Swati P. Bhalchandra, A. S. Govt Coll Engn Dept Elect Aurangabad Maharashtra India

ISBN: (纸本)9781538628423

Nature has huge members of plants identifying them and classifying them is important task for botanists. Usually plants are recognized by leaf and its characteristics like shape, texture, vein structure, color etc. A system is developed which recognizes plants automatically based on leaf structure using image processing. Moreover evolutionary changes are also taking place in plants and it has impact on identification and classification. Image data base and related information is stored on cloud. Shape measure include area, perimeter, ratio of MajorAxisLength and MinorAxisLength, vein structure indicating angle of sub veins with major vein, texture are the major feature used. Botanists visit remote places, jungles where the plants need to be identified and classified. So an attempt is made to develop an automatic identification system where in an image of leaf is captured by any smart phone, uploaded on cloud, where image available and complete data base is trained. Captured image is processed features will be extracted and gives to classifies the plant classification result will be transmitted back to smart phone and related information.

关键词： leaf pattern machine learning digital signature

来源：评论

学校读者我要写书评

暂无评论

Deep learning-Powered Face recognition Attendance System: A Novel Approach to Automated Prediction 2

Deep Learning-Powered Face Recognition Attendance System: A ...

引用

2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sornalakshmi, M. Abinash, M.J. Gopalakrishnan, S. Kasthuri, R. Nivedhitha Harini, C.M. Balamurugan, K. Department of Computer Science PG Arulmigu Kalasalingam College of Arts and Science Tamilnadu India Arulmigu Kalasalingam College of Arts and Science Tamilnadu India

ISBN: (纸本)9798350340235

Taking attendance is one of the most critical activities that must be completed on a regular basis at colleges, universities, organizations, schools, and companies. The bulk of the time, it is done manually, such as by name or roll number. The primary purpose of this work is to develop a Face recognition-based attendance system that will automate this human procedure. This research work fits the standards for modernizing the way attendance is handled, as well as the time management criteria. This gadget is put in the classroom and is programmed with student information such as name, roll number, class, sec, and photos. Open Computer Vision (CV) is used to extract the photos. The understudy can move toward the machine before the beginning of the connected example, and it will start taking photos and contrasting them with the passing dataset. The camera and processor board in this task were a Logitech C270 web camera and a NVIDIA Jetson Nano Engineer pack. The image is changed as follows: Initial, a Haar Cascade classifier is utilized to recognize faces, trailed by the LBPH (Neighborhood Twofold Example Histogram) Calculation, histogram information is considered in contrast to a preset dataset, and the gadget consequently distinguishes participation. A Succeed document is made and refreshed consistently with data from the pertinent class educator. The accompanying work summarizes the fundamental elements of a facial recognition -based attendance system. © 2023 ieee.

关键词： Face recognition Local Binary pattern Histogram Open Computer Vision

来源：评论

学校读者我要写书评

暂无评论

Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings

Objective Depression Detection Using EEG and Eye Movement Si...

引用

45th Annual international conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Liu, Luyu Peng, Dan Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Like Comp & Machine Intelligence Dept Comp Sci & Engn Key LabShanghai Educ Commiss Intelligent Interac 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Med RuiJin Mihoyo Lab Clin Neurosci Ctr 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9798350324471

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings-20 positive and 20 negative-as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively;b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments;c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs;d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

关键词： Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

Crime prediction: pattern recognition and prediction of collusive bidding 2

Crime prediction: Pattern recognition and prediction of coll...

引用

2021 2nd international conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2021

作者： Zhai, Yiming Cheng, Junqing Ding, Ning Fan, Zhenyu Lu, Yang Public Security Behavioral Science Lab People's Public Security University of China Beijing China College of Investigation People's Public Security University of China Beijing China

Bidding is an effective means to promote fair competition in the market and optimize the allocation of social resources. However, with the increasing incidence of bid-rigging crimes, public resource regulatory agencies and public security agencies are facing new challenges. On the premise of fully studying the current trend of China's domestic collusive bidding crimes, this paper summarized the abnormal characteristics of four categories of companies suspected of bid rigging, and selected seven representative subcategories of data based on actual conditions, and set thresholds for these data. Then machine learning methods were utilized to build a pre-warning model for collusion bidding. The results show that the use of machine learning methods to predict the bid collusion can achieve a maximum accuracy of 95%. The research can not only improve the early warning, monitoring, analysis and judgment capabilities of the economic crime investigation department, but ensure the benign operation of bidding, and protect the sustained and rapid development of the social economy. © Published under licence by IOP Publishing Ltd.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Research on Technology of Eye State recognition Based on Gabor Wavelet Transform and pattern Classification

Research on Technology of Eye State Recognition Based on Gab...

引用

ICMLCA 2021;2nd international conference on machine learning and Computer Application

作者： Yanjun Feng Jun Liu Kai Song

Fatigue driving can be detected through human eye state recognition. A methodology was presented in this paper for recognizing the state of human eyes by using Gabor Wavelet Transform (GWT) and pattern recognition. Firstly, human face was detected by skin color Gaussian model. Then the position of eyes was located by combining Gabor filtering and integral projection. After that, the feature vector was constructed by extracting the amplitudes of key points in the Gabor amplitude image. Finally, prognostic classification was implemented with a Support Vector machine (SVM) classifier. The experimental results indicated that this eye state recognition method makes full use of direction and scale characteristics of GWT and reduces the dimension of feature vector, which not only simplifies the algorithm, but also has a high rate of recognition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DouGNN: An End-to-End Deep learning Framework for Predicting Individual Behaviors from fMRI Data 2

DouGNN: An End-to-End Deep Learning Framework for Predicting...

引用

2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Cao, Qumei Wen, Xuyun MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798350331417

Predicting individual behavior from functional connectivity (FC) using machine learning is a critical research topic in neuroscience. While various models have been proposed, they mainly focus on designing behavior prediction methods, overlooking the influence of upstream FC network construction approach on downstream tasks. In this paper, leveraging graph neural networks (GNNs), we introduce an end-to-end deep learning framework named DouGNN, aimed at learning individual behavior from fMRI data. Through joint training of two GNNs, DouGNN innovatively integrates individualized cortical parcellation (for FC network construction) and individual behavior prediction into a unified optimization model. By integrating individualized cortical parcellation, the model gains the ability to dynamically modify the subject's cortical parcellation and the downstream tasks. This enhancement enables the subsequently constructed FC networks to better represent individual-specific features and task-related characteristics, thereby significantly improving the performance of individual behavior prediction. Moreover, the generated individual-specific, task-aware cortical parcellations from this process can also help us in understanding the relationship between FC and behaviors. We conducted three representative behavior prediction experiments on the publicly available HCP dataset. The results demonstrate that DouGNN outperforms existing methods in all behavior prediction tasks, achieving superior performance and generating more functionally homogeneous individualized cortical parcellations. © 2023 ieee.

关键词： Behavior prediction functional magnetic resonance imaging graph neural network individualized cortical parcellation

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12页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：