检索结果-内蒙古大学图书馆

Cutting force signal pattern recognition using hybrid neural network in end milling

中国有色金属学会会刊：英文版 2009年第B09期19卷 209-214页

作者： Song-Tae SEONG Ko-Tae JO Young-Moon LEE Research and Development Department Gyeongbuk Hybrid Technology Institute School of Mechanical Engineering Yeongnam University School of Mechanical Engineering Kyungpo ok National University

Under certain cutting conditions in end milling, the signs of cutting forces change from positive to negative during a revolution of the tool. The change of force direction causes the cutting dynamics to be unstable which results in chatter vibration. Therefore, cutting force signal monitoring and classification are needed to determine the optimal cutting conditions and to improve the efficiency of cut. Artificial neural networks are powerful tools for solving highly complex and nonlinear problems. It can be divided into supervised and unsupervised learning machines based on the availability of a teacher. Hybrid neural network was introduced with both of functions of multilayer perceptron (MLP) trained with the back-propagation algorithm for monitoring and detecting abnormal state, and self organizing feature map (SOFM) for treating huge datum such as image processing and pattern recognition, for predicting and classifying cutting force signal patterns simultaneously. The validity of the results is verified with cutting experiments and simulation tests.

关键词：切削力信号混合神经网络模式识别铣床人工神经网络切削条件信号监测监督学习

来源：评论

学校读者我要写书评

暂无评论

image Size, Color Depth, Age variant on Convolution Neural Network 1

Image Size, Color Depth, Age variant on Convolution Neural N...

引用

1st Indonesian Association for pattern recognition international conference, INAPR 2018

作者： Pranoto, Hady Budiharto, Widodo Hendric Spits Warnars, Harco Leslie Matsuo, Tokuro Heryadi, Yaya Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781538694220

Facial recognition as of biometric authentication used in the field of security, military, finance and daily use is become a trend or famous, because of its natural and not intrusive nature. Many methods for face recognition such as holistic learning, the use of local features, shallow learning and deep learning, some methods are susceptible to variations in pose change, illumination, expression and age variation. state of the art of face recognition today is a deep learning technique that delivers high accuracy. In this paper author replicate an face recognition using deep learning architecture called OpenFace Convolutional Neural Network. In this research author make variation on the size of image, color dept and age, and see how that factor impact on accuracy of face recognition in that architecture. As the result from the research, the accuracy of a model depends on the image size, color depth, and age variation, but in OpenFace CNN that recognition still provides fairly good accuracy when reducing the size of image and color depth, as long as the image can still be detected on the landmark facial, so the alignment process can be done on the face image. © 2018 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Non-regularized state Preserving Extreme learning machine for Natural Scene Classification

引用

1st international conference on Computer Vision and image processing (CVIP)

作者： Sidike, Paheding Alom, Md. Zahangir Asari, Vijayan K. Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (纸本)9789811021046;9789811021039

Scene classification remains a challenging task in computer vision applications due to a wide range of intraclass and interclass variations. A robust feature extraction technique and an effective classifier are required to achieve satisfactory recognition performance. Herein, we propose a nonregularized state preserving extreme learning machine (NSPELM) to perform scene classification tasks. We employ a Bag-of-Words (BoW) model for feature extraction prior to performing the classification task. The BoW feature is obtained based on a regular grid method for point selection and Speeded Up Robust Features (SURF) technique for feature extraction on the selected points. The performance of NSPELM is tested and evaluated on three standard scene category classification datasets. The recognition accuracy is compared with the standard extreme learning machine classifier and it shows that the proposed NSPELM algorithm yields better accuracy.

关键词： Scene classification Non-regularization Extreme learning machine Bag-of-words

来源：评论

学校读者我要写书评

暂无评论

Factors Influencing the Predictive Performance of the LstM Model on stock Prices and Its Application in Forecasting US Technology Sector stock Prices - Sample Time Span, Time Window Length, Feature Selection, and Prediction Days 24

Factors Influencing the Predictive Performance of the LSTM M...

引用

1st international conference on image processing machine learning and pattern recognition

作者： Xu, Zhengyue Fudan Univ Sch Econ Shanghai Peoples R China

ISBN: (纸本)9798400707032

In recent years, the increasing global economic uncertainties have made financial markets more complex, posing challenges to stock price forecasting using traditional time series models. Therefore, optimizing the LstM (Long Short-Term Memory) model and enhancing its prediction accuracy is of great significance for maintaining financial market stability. This paper primarily investigates the effects of four factors-sample time span, time window length, feature selection, and prediction days-on the predictive performance of the LstM model. The study selects five representative stocks from the U.S. technology sector and employs the controlled variable method to separately alter these four factors. The variations in the model's mean squared error (MSE) were analyzed, and the underlying causes were hypothesized. The findings are as follows: First, an excessively small or large sample time span leads to a decline in the model's predictive ability;second, an overly long time window length results in reduced predictive performance;third, the impact of feature selection on the model's predictive ability varies significantly across different stocks;fourth, increasing the number of prediction days leads to a rise in the model's mean squared error;fifth, among these four factors, the sample time span has the most significant impact on the model's predictive ability.

关键词： Neural networks Deep learning stock price prediction LstM model

来源：评论

学校读者我要写书评

暂无评论

recognition of Speed Limits on Speed-Limit Signs by Using machine learning 24

Recognition of Speed Limits on Speed-Limit Signs by Using Ma...

引用

24th international conference on Mechatronics and machine Vision in Practice (M2VIP)

作者： Miyata, Shigeharu Kindai Univ Sch Engn Dept Robot Takayaumenobe 1 Higashihiroshima 7392116 Japan

ISBN: (纸本)9781509065462

This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using Hue, Saturation and Value (HSV) color spaces for extracting the speed limit numbers on the identified speed-limit signs, and finally (3) a method for recognition of the extracted numbers using a neural network. The method of traffic sign recognition previously proposed by the author consisted of extracting geometric shapes from the sign and recognizing them based on their aspect ratios. This method cannot be used for the numbers on speed-limit signs because the numbers all have the same aspect ratios. This study focuses on considering the following three points. (1) Make it possible to detect only the speed-limit sign in an image of scenery using a single process focusing on the local patterns of speed limit signs. (2) Make it possible to separate and extract the two-digit numbers on a speed-limit sign in cases when the two-digit numbers are incorrectly extracted as a single area due to the light environment. (3) Make it possible to identify the numbers using a neural network by focusing on three feature quantities. This study also used the proposed method with still images in order to validate it.

关键词： pattern recognition image processing computer vision traffic sign machine learning local binary pattern neural network

来源：评论

学校读者我要写书评

暂无评论

Comprative study with Analysis of OCR Algorithms and Invention Analysis of Character recognition Approched Methodologies 1

Comprative Study with Analysis of OCR Algorithms and Inventi...

引用

1st IEEE international conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Henge, Santosh Kumar Rama, B. Kakatiya Univ Dept Comp Sci Warangal Telangana State India

ISBN: (纸本)9781467385879

OCR is the most active, interesting evaluation invention of text cum character processing recognition and pattern based image recognition. In present life OCR has been successfully using in finance, legal, banking, health care and home need appliances. The OCR consists the different levels of processing methods like as image Pre Acquisition, Classification, Post-Acquisition, Pre-Level processing, Segmented processing, Post-Level processing, Feature Extraction. The many researchers are proposed various levels of different methodologies and approaches in different versions of languages with help of modern and traditional technologies. This paper expressed the detail study and analysis of various character recognition methods and approaches: in details like as flow and type of approached methodology was used, type of algorithm has built with support of technology has implemented background of the proposed methodology and invention best outcomes flow for the each methodology. This paper and also expressed the main objectives and ideology of various OCR algorithms, like as neural networks algorithm, structural algorithm, support vector algorithm, statistical algorithm, template matching algorithm along with how they classified, identified, rule formed, inferred for recognition of characters and symbols.

关键词： Support Vector machine (SVM) Optical Character recognition (OCR) Artificial Neural Network (ANN) pattern Matching Algorithms (PMA) stastical Algorithm (SstA) strctural Algorithm (SA) Freeman Chain Code (FCC) Digitized-Retrievable Rorm (DRF) Feedforwardneural Network (FINN) Feedback-neural Network (FbNN) Top-down-hierarchical-histogram (THH) Mirror image learning (MIL)

来源：评论

学校读者我要写书评

暂无评论

The research of image processing algorithms in AGVS

The research of image processing algorithms in AGVS

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Zhang, Jun-You Wang, Shu-Feng ShanDong Univ Technol Coll Transportat & Vehicle Engn Zibo 255049 Peoples R China

ISBN: (纸本)9781424410651

Because of its flexibilily and reliability, computer vision guidance is applied into Automated Guided Vehicle System on express highway. Taken landmark as guided line symbol, based on road video image acquisition and processing, the guided line can be identified and the input information for guidance control can be obtained. Since common technology of image processing troubles actual application, algorithms are studied emphatically. According to the characteristics of vision guidance on express highway, some algorithms are improved.

关键词： AGVS guided line image processing

来源：评论

学校读者我要写书评

暂无评论

Hand-Drawn Symbol recognition in Immersive Virtual Reality Using Deep Extreme learning machines 1st

Hand-Drawn Symbol Recognition in Immersive Virtual Reality U...

引用

1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Cecotti, Hubert Boumedine, Cyrus Callaghan, Michael Ulster Univ Fac Comp & Engn Magee CampusNorthland Rd Derry BT48 7JL Londonderry North Ireland

ISBN: (纸本)9789811048593;9789811048586

Typical character and symbol recognition systems are based on images that are drawn on paper or on a tablet with actual physical contact between the pen and the surface. In this study, we investigate the recognition of symbols that are written while the user is immersed inside a room scale virtual reality experience using a consumer grade head-mounted display and related peripherals. A novel educational simulation was developed consisting of a virtual classroom with whiteboard where users can draw symbols. A database of 30 classes of hand-drawn symbols created from test subjects using this environment is presented. The performance of the symbol recognition system was evaluated with deep extreme learning machine classifiers, with accuracy rates of 94.88% with a single classifier and 95.95% with a multiple classifier approach. Further analysis of the results obtained support the conclusion that there are a number of challenges and difficulties related to drawing in this type of environment given the unique constraints and limitations imposed by virtual reality and in particular the lack of physical contact and haptic feedback between the controller and virtual space. Addressing the issues raised for these types of interfaces opens new challenges for both human-computer interaction and symbol recognition. Finally, the approach proposed in this paper creates a new avenue of research in the field of document analysis and recognition by exploring how texts and symbols can be analyzed and automatically recognized in virtual scenes of this type.

关键词： Virtual reality Symbol recognition Extreme learning machine Multiple classifier systems

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potency of Neural Networks: A Thorough Examination of Deep learning Techniques 1

Exploring the Potency of Neural Networks: A Thorough Examina...

引用

1st international conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Tyagi, Manisha Panipat Institute of Engineering & Technology Haryana Panipat Samalkha132102 India

ISBN: (纸本)9798350392111

Deep learning stands as a revolutionary paradigm in artificial intelligence (AI), demonstrating its prowess across diverse areas, such as image recognition, natural language processing, and autonomous systems. This review article offers a comprehensive exploration of various deep learning techniques, shedding light on their distinctive features, applications, and recent advancements. From classic architectures to cutting-edge innovations, we delve into the diverse landscape of neural networks that have reshaped the field of artificial intelligence. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Complex and Composite Graphical Symbol recognition and Retrieval: A Quick Review 1st

Complex and Composite Graphical Symbol Recognition and Retri...

引用

1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Santosh, K. C. Univ South Dakota Dept Comp Sci 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9789811048593;9789811048586

One of the key difficulties in graphics recognition domain is to work on complex and composite symbol recognition, retrieval and spotting. This paper covers a quick view on complex and composite symbol recognition, which is inspired by real-world industrial problem. Considering it as a pattern recognition problem, three different approaches: statistical, structural and syntactic are taken into account. It includes fundamental concepts or techniques and research standpoints or directions derived by a real-world application.

关键词： Symbol recognition Complex and composite symbols statistical structural and syntactic approaches Document processing

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：