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检索条件"任意字段=16th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2015"

共 86 条记录，以下是41-50 订阅

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Development of an intelligent automated polishing system 16

Development of an intelligent automated polishing system

引用

16th international conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2016

作者： Kalt, E. Monfared, R.P. Jackson, M.R. EPSRC Centre for Innovative Manufacturing in Intelligent Automation Loughborough University United Kingdom

ISBN: (纸本)9780956679086

In high-value manufacturing sectors, many manufacturing processes are still performed manually, such as polishing operations for small metallic parts. Increasing volume, the need for consistency in quality, and health and safety issues are some of the reasons for industry to search urgently for alternative solutions for manual polishing processes. this article reports the development of an intelligent automated polishing system to achieve consistent surface quality and removal of superficial defects from high-value components, such as those used in aerospace industry. the article reports an innovative method to capture manual polishing processes by skilled operators. the captured polishing parameters are then used to develop and control a robotic polishing system that can adopt various polishing patterns. A brief summary of existing fully and semi- automated polishing systems and their inadequacy for industrial applications are discussed. the need for building automation system based on manual operations are explained and a systematic data capturing process for a specific aerospace-based component is defined. the development of the process capturing device is explained, the data analysis and interpretations are discussed and the migration from manual operation to an automated polishing system is reported. Further detailed information is given in relation with combining data from various sensors and building of an automated system based on learning from manual operations. the research results are also briefly discussed and conclusions are drawn regarding applicability of automated systems for highly skilled manual operations.

关键词： Aerospace industry

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Sparse Non-negative Matrix Factorization with Generalized Kullback-Leibler Divergence 17th

Sparse Non-negative Matrix Factorization with Generalized Ku...

引用

17th international conference on intelligent data engineering and automated learning (ideal)

作者： Chen, Jingwei Feng, Yong Liu, Yang Tang, Bing Wu, Wenyuan Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Automated Reasoning & Cognit Chongqing 400714 Peoples R China Chongqing Jiaotong Univ Coll Informat Sci & Engn Chongqing 400074 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Non-negative Matrix Factorization (NMF), especially with sparseness constraints, plays a critically important role in data engineering and machine learning. Hoyer (2004) presented an algorithm to compute NMF with exact sparseness constraints. the exact sparseness constraints depends on a projection operator. In the present work, we first give a very simple counterexample, for which the projection operator of the Hoyer (2004) algorithm fails. After analysing the reason geometrically, we fix this bug by adding some random terms and show that the fixed one works correctly. Based on the fixed projection operator, we propose another sparse NMF algorithm aiming at optimizing the generalized Kullback-Leibler divergence, hence named SNMF-GKLD. Experimental results show that SNMF-GKLD not only has similar effects with Hoyer (2004) on the same data sets, but is also efficient.

关键词： Non-negative Matrix Factorization Projection operator Generalized Kullback-Leibler divergence

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Stop wasting time: On predicting the success or failure of learning for industrial applications

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8th international conference on intelligent data engineering and automated learning

作者： Smith, J. E. Tahir, M. A. Univ W England Sch Comp Sci Bristol BS16 1QY Avon England

ISBN: (纸本)9783540772255

the successful application of machine learning techniques to industrial problems places various demands on the collaborators. the system designers must possess appropriate analytical skills and technical expertise, and the management of the industrial or commercial partner must be sufficiently convinced of the potential benefits that they are prepared to invest in money and equipment. Vitally, the collaboration also requires a significant investment in time from the end-users in order to provide training data from which the system can (hopefully) learn. this poses a problem if the developed Machine learning system is not sufficiently accurate, as the users and management;may view their input as wasted effort, and lose faith with the process. In this paper we investigate techniques for making early predictions of the error rate achievable after further interactions. In particular we show how decomposing the error in different components can lead to useful predictors of achievable accuracy, but;that this is dependent on the choice of an appropriate sampling methodology.

关键词： learning systems

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A First Approach to Face Dimensionality Reduction through Denoising Autoencoders 19th

A First Approach to Face Dimensionality Reduction Through De...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Pulgar, Francisco J. Charte, Francisco Rivera, Antonio J. del Jesus, Maria J. Univ Jaen Dept Comp Sci Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783030034931;9783030034924

the problem of high dimensionality is a challenge when facing machine learning tasks. A high dimensional space has a negative effect on the predictive performance of many methods, specifically, classification algorithms. there are different proposals that arise to mitigate the effects of this phenomenon. In this sense, models based on deep learning have emerged. In this work, denoising autoencoders (DAEs) are used to reduce dimensionality. To verify its performance, an experimentation is carried out where the improvement obtained with different types of classifiers is verified. the classification method used are: kNN, SVM, C4.5 and MLP. the test for kNN and SVM show a better predictive performance for all datasets. the executions for C4.5 and MLP reflect improvements only in some cases. the execution time is lower for all tests. In addition, a comparison between DAEs and PCA, a classical method of dimensionality reduction, is performed, obtaining better results with DAEs in most cases. the conclusions reached open up new lines of future work.

关键词： Classification Deep learning Autoencoders Denoising autoencoders Dimensionality reduction High dimensionality

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Freight Vehicle Travel Time Prediction Using Sparse Gaussian Processes Regression with Trajectory data 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Li, Xia Bai, Ruibin Univ Nottingham Ningbo China Ningbo 315100 Zhejiang Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. the results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. the overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. the computational complexity of SGPR models is O(mn(2)), and it does not require lengthy model fitting process as SVR and ANN. this makes GP models more practicable for real-world practice in large-scale transportation data analyses.

关键词： Travel time prediction Machine learning Gaussian Processes Sparse approximation Trajectory data Freight vehicle

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Applying Cost-Sensitive Classifiers with Reinforcement learning to IDS 19th

Applying Cost-Sensitive Classifiers with Reinforcement Learn...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Blanco, Roberto Cilla, Juan J. Briongos, Samira Malagon, Pedro Moya, Jose M. Univ Politecn Madrid ETSI Telecomunicac Integrated Syst Lab Ave Complutense 30 Madrid 28040 Spain Univ Politecn Madrid Ctr Computat Simulat Campus Montegancedo Madrid 28660 Spain

ISBN: (纸本)9783030034931;9783030034924

When using an intrusion detection system as protection against certain kind of attacks, the impact of classifying normal samples as attacks (False Positives) or attacks as normal traffic (False Negatives) is completely different. In order to prioritize the absence of one kind of error, we use reinforcement learning strategies which allow us to build a cost-sensitive meta-classifier. this classifier has been build using a DQN architecture over a MLP. While the DQN introduces extra effort during the training steps, it does not cause any penalty on the detection system. We show the feasibility of our approach for two different and commonly used datasets, achieving reductions up to 100% in the desired error by changing the rewarding strategies.

关键词： Intrusion detection Cost-sensitive classification Reinforcement learning

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Visualizing MOOC User Behaviors: A Case Study on XuetangX 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Zhang, Tiantian Yuan, Bo Tsinghua Univ Grad Sch Shenzhen Div Informat Intelligent Comp Lab Shenzhen 518055 Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

the target of KDD CUP 2015 is to use the MOOC (Massive Open Online Course) user dataset provided by XuetangX to predict whether a user will drop a course. However, despite of the encouraging performance achieved, the dataset itself is largely not well investigated. To gain an in-depth understanding of MOOC user behaviors, we conduct two case studies on the dataset containing the information of 79,186 users and 39 courses. In the first case study, we use visualization techniques to show that some courses are more likely to be simultaneously enrolled than others. Furthermore, a set of association rules among courses are discovered using the Apriori algorithm, confirming the practicability of using historical enrollment data to recommend courses for users. Meanwhile, clustering analysis reveals the existence of clear grouping patterns. In the second case study, we examine the influence of two user factors on the dropout rate using visualization, providing valuable guidance for maintaining student learning activities.

关键词： User behavior Visualization Association rule Clustering MOOC

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Communication Skills Personal Trainer Based on Viola-Jones Object Detection Algorithm 19th

Communication Skills Personal Trainer Based on Viola-Jones O...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Pardo Pertierra, Alvaro Gil Gonzalez, Ana B. Teira Lafuente, Javier de Luis Reboredo, Ana Univ Salamanca Dept Comp & Automat Fac Sci Plaza Merced S-N Salamanca 37008 Spain Univ Salamanca Philosophy Fac Salamanca Spain

ISBN: (纸本)9783030034931;9783030034924

the paper presents a personal training system that allows the user to use non-verbal communication techniques that help him improve his way of speaking in public. the tool is a software solution that implements algorithms to identify elements of non-verbal communication (NVC), such as the positions of the head, hands and trunk of the users making use of cascading classifiers. It presents the developed technology and the different characteristics of the application that is an important part of the treatment in artificial vision techniques in the detection of NVC and its training.

关键词： Oratorical coach Artificial vision Viola-Jones algorithm AdaBoost Non-verbal communication (NVC) learning environment

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Triaxial Accelerometer Located on the Wrist for Elderly People's Fall Detection 17th

Triaxial Accelerometer Located on the Wrist for Elderly Peop...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Collado Villaverde, Armando R-Moreno, Maria D. Barrero, David F. Rodriguez, Daniel Univ Alcala Dept Automat Crta Madrid Barcelona Madrid Spain Univ Alcala Dept Ciencias Computac Crta Madrid Barcelona Alcala De Henares Spain

ISBN: (纸本)9783319462578;9783319462561

the loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. the popularization of smartwatches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smartwatch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. the results show high accuracy and robust classifiers able to detect falls.

关键词： Fall detection Accelerometer Machine learning Classification Supervised learning Care for the elderly

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Measuring the Impact of the international Relationships of the Andalusian Universities Using Dimensions database 19th

Measuring the Impact of the International Relationships of t...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Garcia-Sanchez, P. Cobo, M. J. Univ Cadiz Dept Comp Sci & Engn Cadiz Spain

ISBN: (纸本)9783030034962;9783030034955

Researchers usually have been inclined to publish papers with close collaborators: same University, region or even country. However, thanks to the advancements in communication technologies, members of international research networks can cooperate almost seamlessly. these networks usually tend to publish works with more impact than the local counterparts. In this paper, we try to demonstrate if this assumption is also valid in the region of Andalusia (Spain). the *** database is used to obtain the articles where at least one author is from an Andalusian University. the publication list is divided into 4 geographical areas: local (only one affiliation), regional (only Andalusian affiliations), national (only Spanish affiliations) and international (any affiliation). Results show that the average number of citations per paper increases as the author collaboration networks increases geographically.

关键词： Bibliometric analysis international collaboration Andalusian universities Dimensions.ai

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