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检索条件"任意字段=15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014"

共 118 条记录，以下是61-70 订阅

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Retrieval of Manufacturing Knowledge Using Machine learning - A Review 15

Retrieval of Manufacturing Knowledge Using Machine Learning ...

引用

15th international conference on Manufacturing Research / 32nd National conference on Manufacturing Research

作者： Ostermeyer, Emeric Danjou, Christophe Durupt, Alexandre Duigou, Julien L. E. Univ Technol Compiegne Dept Mech Engn UMR Roberval 7337 Compiegne France ETS Dept Automated Prod Montreal PQ Canada

ISBN: (纸本)9781614997924;9781614997917

data-driven models, and maturating data-treatment tools, now allow for automation in a wide range of domains. the data-rich domain that is NC machining presents a strong potential for automation, in this regard, and the smart factory calls for knowledge capitalisation in manufacturing. the field of machining saw numerous data formats, and is still led by heterogeneous sources. To exploit this massive amount of data, capitalisation of information from those sources is compulsory. Such system should allow aggregation of different sources and formats, and accommodate for information about diverse elements regarding manufacturing Parts, tools, machines... In this paper, automated process planning generation methods are reviewed. the localisation and formalisation of data in manufacturing documents is considered, and emphasis is made on linking trajectories and features, to define strategies. Attempts to use machine learning techniques in manufacturing are reviewed, and the lack thereof in manufacturing knowledge retrieval is underlined. then, a framework is proposed to realise knowledge retrieval from legacy programs, and several locks and improvement possibilities are identified.

关键词： CAPP-CAM Manufacturing knowledge Machine learning intelligent Manufacturing Systems

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Machine learning based Continuous Knowledge engineering for Additive Manufacturing 15

Machine Learning based Continuous Knowledge Engineering for ...

引用

15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Ko, Hyunwoong Witherell, Paul Ndiaye, Ndeye Y. Lu, Yan Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore NIST 100 Bur Dr Gaithersburg MD 20899 USA

ISBN: (纸本)9781728103563

Additive manufacturing (AM) assisted by a digital twin is expected to revolutionize the realization of high-value and high-complexity functional parts on a global scale. Machine learning (ML) introduced in digitized AM provides potential to transform AM data into knowledge continuously and automatically;hence AM products will be designed and manufactured with improved quality. Conventional product development systems, however, fail to fully adopt ML algorithms on the increasingly available AM data for knowledge acquisition. To address the limitation, this paper proposes an algorithmic framework for constructing AM knowledge automatically and continuously from data. the proposed algorithm develops predictive models to correlate process parameters with part structure and properties using ML algorithms on design, process control, in-situ monitoring, and ex-situ measurement AM data. Based on the predictive models, the algorithm constructs prescriptive rules necessary for decision-making in AM product developments. the constructed rule knowledge is stored in a knowledge base by the algorithm for further AM knowledge query and constructions. then, the algorithm provides feedback to a knowledge-query formulation phase, which forms the algorithm into a closed loop. through the algorithm, AM knowledge can self-evolve continuously, while reflecting dynamically generated AM data in an automated and autonomous manner. A case study is presented to illustrate the proposed algorithm and a software prototype is developed to demonstrate the algorithm capability.

关键词： Predictive models Prediction algorithms Decision making Knowledge based systems Software algorithms data mining

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Graph-based clustering based on cutting sets

Graph-based clustering based on cutting sets

引用

international conference on Intelligence engineering Systems

作者： Buza, K. Kis, P.B. Buza, A. Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Dept. of Math. College of Dunaujvaros Dunaujvaros Hungary Dept. of IT College of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9781424489565

One of the most prominent challenges in data mining is the clustering of databases containing many categorical attributes. Representation of such data in continuous, Euclidean space usually does not reflect the true segments of data. As a crucial consequence, clustering algorithms working in continuous, Euclidean space may produce segmentations of poor quality. An alternative direction explores graph-based representation of data. In this paper, we show that graph-based data representation is well suitable for the case of categorical attributes. In particular, we offer the following contributions: i) we propose and analyze a flexible graph-based genetic clustering algorithm, where the ideal clusters can be characterized using external cluster quality functions, called kernels, ii) we study kernels, and define the crucial property of effective kernels, iii) we introduce a framework for distributed data-oriented graph clustering computations. In contrast of the complexity of our problem, which turns out to be NP-hard in our analysis, experiments show that in case of well clusterable data, our algorithm has attractive scalability properties. We also perform experiments on real medical data that provides us with further evidence about the practical applicability of our approach. © 2011 IEEE.

关键词： Clustering algorithms

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Real-Time Visual-Based Localization for Mobile Robot Using Structured-View Deep learning 15

Real-Time Visual-Based Localization for Mobile Robot Using S...

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15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Hong, Yi-Feng Chang, Yu-Ming Li, Chih-Hung G. Natl Taipei Univ Technol Grad Inst Mfg Technol Taipei 10608 Taiwan

ISBN: (纸本)9781728103563

this paper demonstrates a place recognition and localization method designed for automated guidance of mobile robots. Collecting and annotating sufficient images for a supervised deep learning model is often an exhausting work. Devising an effective visual detection scheme for a mobile robot location detection job in a feature-barren environment such as the indoor corridors of buildings is also quite challenging. To address these issues, a supervised deep learning model for the spatial coordinate detection of a mobile robot is proposed here. Specifically, a novel technique is introduced involving structuring and collaging of the surrounding views obtained by the on-board cameras for the training data preparation. A system linking robot kinematics and image processing provides automatic data annotation, which significantly reduces the need for human work on data preparation. Experimental evidence showed that the precision and recall rates of the location coordinate detection are 0.91 and 0.85, respectively. Also, the detection appeared to be effective over a path width of 0.75 m, which is sufficient to cover the possible deviations from the target path. Furthermore, it took averagely 0.14 s for each visual detection performed by an ordinary PC on-board the mobile robot;thus, real-time navigation using the proposed method is achievable.

关键词： Mobile robots Visualization Robot kinematics Cameras Robot vision systems Real-time systems

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Feature Selection and Interpretable Feature Transformation: A Preliminary Study on Feature engineering for Classification Algorithms 19th

Feature Selection and Interpretable Feature Transformation: ...

引用

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

作者： Tallon-Ballesteros, Antonio J. Tuba, Milan Xue, Bing Hashimoto, Takako Univ Seville Seville Spain Singidunum Univ Belgrade Serbia Victoria Univ Wellington Wellington New Zealand Chiba Univ Commerce Konodai Ichikawa City Chiba Japan

ISBN: (纸本)9783030034962;9783030034955

this paper explores the limitation of consistency-based measures in the context of feature selection. these kinds of filters are not very widespread in large-dimensionality problems. Typically, the number of selected of attributes is very small and the ability to do right predictions is a drawback. the principal contribution of this work is the introduction of a new approach within feature engineering to create new attributes after the feature selection stage. the experimentation on multi-class problems with a feature space in the order of tens of thousands shed light on that some improvements took place with the new proposal. As a final insight, some new relationships were discovered due to the combined application of feature selection and feature transformation. Additionally, a new measure for classification problems which relates the number of features and the number of classes or labels is also proposed.

关键词： Classification Feature engineering Feature selection data mining Feature discovery Feature transformation

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Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks 15

Automating Visual Inspection of Lyophilized Drug Products Wi...

引用

15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Tsay, Calvin Li, Zheng Genentech Inc 1 DNA Way San Francisco CA 94080 USA Univ Texas Austin McKetta Dept Chem Engn Austin TX 78712 USA

ISBN: (纸本)9781728103563

In the increasingly automated world of biotech manufacturing, the inspection of lyophilized drug products remains a cumbersome and manual process, relying on human operators to inspect finished vials for glass defects, contaminants, and other possible issues. Automating this procedure is challenging due to "fogging" on glass vials and low occurrence of defective samples for training decision models. In this work, we apply deep neural networks to automated classification of lyophlized product vials via computer vision. the proposed approach utilizes shared convolutional layers to account for multiple images acquired of the same vial at various rotations, and transfer learning is examined as a tool to partially overcome the lack of defective data in industrial manufacturing applications. the method is tested using a real-world industrial product as a representative case study. We find that 85-90% of defects can be detected from a single camera angle, demonstrating the potential of multi-input neural networks in lyophilized drug product inspection. Moreover, the results suggest that transfer learning enhances the generalization ability of learned models, even when using data designed for a very different task.

关键词： Automation in life sciences: biotechnology pharmaceutical and health care Computer vision for manufacturing Defect detection Deep learning

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Identification of Individual Glandular Regions Using LCWT and Machine learning Techniques 19th

Identification of Individual Glandular Regions Using LCWT an...

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

作者： Gabriel Garcia, Jose Colomer, Adrian Naranjo, Valery Penaranda, Francisco Sales, M. A. Univ Politecn Valencia Inst Invest & Innovac Bioingn I3B Camino Vera S-N Valencia 46022 Spain Hosp Clin Univ Valencia Serv Anat Patol Valencia Spain

ISBN: (纸本)9783030034931;9783030034924

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. the most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1.468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectively.

关键词： Machine learning Multilayer perceptron Support vector machine Locally constrained watershed transform Gland unit identification Histological prostate image

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Giving voice to the internet by means of conversational agents

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15th international conference on intelligent data engineering and automated learning, ideal 2014

作者： Griol, David De Miguel, Araceli Sanchis Molina, José Manuel Universidad Carlos III de Madrid Avda. de la Universidad 30 28911 Leganés Spain

ISBN: (纸本)9783319108391

In this paper we present a proposal to develop conversational agents that avoids the effort of manually defining the dialog strategy for the agent and also takes into account the benefits of using current standards. In our proposal the dialog manager is trained by means of a POMDP-based methodology using a labeled dialog corpus automatically acquired using a user modeling technique. the statistical dialog model automatically selects the next system response. thus, system developers only need to define a set of files, each including a system prompt and the associated grammar to recognize user responses. We have applied this technique to develop a conversational agent in VoiceXML that provides information for planning a trip. © 2014 Springer international Publishing Switzerland.

关键词： learning systems

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Grouping Parallel Bayesian Network Structure learning Algorithm Based on Variable Ordering 17th

Grouping Parallel Bayesian Network Structure Learning Algori...

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

作者： Qi, Xiaolong Shi, Yinhuan Wang, Hao Gao, Yang Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Yili Normal Univ Dept Elect & Informat Engn Yining 835000 Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Given an ordering constraint of n random variables and the maximum in- degree u for any variable, the search space of Bayesian network structure reduces from n!2(n(n-1)/2) to the 2(n(n-1)/2). Even so, with the increase of the number of variables, the requirement of time cannot be tolerated. In this paper, we present a parallel Bayesian network structure learning algorithm based on variable ordering. the algorithm includes three components: 1. variable grouping: it completes variable partition;2. group learning: it completes independently construct of sub-Bayesian network;3. Between the groups learning: it asynchronously combines sub-Bayesian network in order to get the full Bayesian networks. We theoretically analyzed that time complexity of our algorithm is O(mu(2)nr), where u that is number of parent, In the worst case, u = n, complexity of the algorithm is O(mn(3)r). the empirical results present in term of time complexity grouping parallel algorithm has significance compared with the traditional algorithm.

关键词： Bayesian network Structure learning Parallel learning

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Modelling Survival by Machine learning Methods in Liver Transplantation: Application to the UNOS dataset 20th

Modelling Survival by Machine Learning Methods in Liver Tran...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Guijo-Rubio, David Villalon-Vaquero, Pedro J. Gutierrez, Pedro A. Dolores Ayllon, Maria Briceno, Javier Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci Cordoba Spain Unit Hepatobiliary Surg & Liver Transplantat Cordoba Spain

ISBN: (纸本)9783030336172;9783030336165

the aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this purpose, we consider a database from the United Network for Organ Sharing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of America from November 2004 until June 2015. the dataset contains more than a 74% of censoring, being a challenging and difficult problem. Several methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor characteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation metrics are used, being the concordance index (ipcw) the most suitable for this problem. the results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.

关键词： United Network for Organ Sharing Liver transplant Survival analysis Machine learning

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