Science.gov

Sample records for machine learning tools

  1. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    ERIC Educational Resources Information Center

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  2. An iterative learning control method with application for CNC machine tools

    SciTech Connect

    Kim, D.I.; Kim, S.

    1996-01-01

    A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one of the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.

  3. The use of machine learning and nonlinear statistical tools for ADME prediction.

    PubMed

    Sakiyama, Yojiro

    2009-02-01

    Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future. PMID:19239395

  4. Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.

    PubMed

    Tao, L; Zhang, P; Qin, C; Chen, S Y; Zhang, C; Chen, Z; Zhu, F; Yang, S Y; Wei, Y Q; Chen, Y Z

    2015-06-23

    In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties. PMID:26037068

  5. Of Genes and Machines: Application of a Combination of Machine Learning Tools to Astronomy Data Sets

    NASA Astrophysics Data System (ADS)

    Heinis, S.; Kumar, S.; Gezari, S.; Burgett, W. S.; Chambers, K. C.; Draper, P. W.; Flewelling, H.; Kaiser, N.; Magnier, E. A.; Metcalfe, N.; Waters, C.

    2016-04-01

    We apply a combination of genetic algorithm (GA) and support vector machine (SVM) machine learning algorithms to solve two important problems faced by the astronomical community: star-galaxy separation and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in the process of which we avoid overfitting. We apply our method to star-galaxy separation in Pan-STARRS1 data. We show that our method correctly classifies 98% of objects down to {i}{{P1}}=24.5, with a completeness (or true positive rate) of 99% for galaxies and 88% for stars. By combining colors with morphology, our star-galaxy separation method yields better results than the new SExtractor classifier spread_model, in particular at the faint end ({i}{{P1}}\\gt 22). We also use our method to derive photometric redshifts for galaxies in the COSMOS bright multiwavelength data set down to an error in (1+z) of σ =0.013, which compares well with estimates from spectral energy distribution fitting on the same data (σ =0.007) while making a significantly smaller number of assumptions.

  6. Of Genes and Machines: Application of a Combination of Machine Learning Tools to Astronomy Data Sets

    NASA Astrophysics Data System (ADS)

    Heinis, S.; Kumar, S.; Gezari, S.; Burgett, W. S.; Chambers, K. C.; Draper, P. W.; Flewelling, H.; Kaiser, N.; Magnier, E. A.; Metcalfe, N.; Waters, C.

    2016-04-01

    We apply a combination of genetic algorithm (GA) and support vector machine (SVM) machine learning algorithms to solve two important problems faced by the astronomical community: star–galaxy separation and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in the process of which we avoid overfitting. We apply our method to star–galaxy separation in Pan-STARRS1 data. We show that our method correctly classifies 98% of objects down to {i}{{P1}}=24.5, with a completeness (or true positive rate) of 99% for galaxies and 88% for stars. By combining colors with morphology, our star–galaxy separation method yields better results than the new SExtractor classifier spread_model, in particular at the faint end ({i}{{P1}}\\gt 22). We also use our method to derive photometric redshifts for galaxies in the COSMOS bright multiwavelength data set down to an error in (1+z) of σ =0.013, which compares well with estimates from spectral energy distribution fitting on the same data (σ =0.007) while making a significantly smaller number of assumptions.

  7. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning

    SciTech Connect

    Zhu Xiaofeng; Ge Yaorong; Li Taoran; Thongphiew, Danthai; Yin Fangfang; Wu, Q Jackie

    2011-02-15

    Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy {approx}80% in prediction and effectiveness in improving ART planning quality. Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

  8. Gaussian Process Regression as a machine learning tool for predicting organic carbon from soil spectra - a machine learning comparison study

    NASA Astrophysics Data System (ADS)

    Schmidt, Andreas; Lausch, Angela; Vogel, Hans-Jörg

    2016-04-01

    Diffuse reflectance spectroscopy as a soil analytical tool is spreading more and more. There is a wide range of possible applications ranging from the point scale (e.g. simple soil samples, drill cores, vertical profile scans) through the field scale to the regional and even global scale (UAV, airborne and space borne instruments, soil reflectance databases). The basic idea is that the soil's reflectance spectrum holds information about its properties (like organic matter content or mineral composition). The relation between soil properties and the observable spectrum is usually not exactly know and is typically derived from statistical methods. Nowadays these methods are classified in the term machine learning, which comprises a vast pool of algorithms and methods for learning the relationship between pairs if input - output data (training data set). Within this pool of methods a Gaussian Process Regression (GPR) is newly emerging method (originating from Bayesian statistics) which is increasingly applied to applications in different fields. For example, it was successfully used to predict vegetation parameters from hyperspectral remote sensing data. In this study we apply GPR to predict soil organic carbon from soil spectroscopy data (400 - 2500 nm). We compare it to more traditional and widely used methods such as Partitial Least Squares Regression (PLSR), Random Forest (RF) and Gradient Boosted Regression Trees (GBRT). All these methods have the common ability to calculate a measure for the variable importance (wavelengths importance). The main advantage of GPR is its ability to also predict the variance of the target parameter. This makes it easy to see whether a prediction is reliable or not. The ability to choose from various covariance functions makes GPR a flexible method. This allows for including different assumptions or a priori knowledge about the data. For this study we use samples from three different locations to test the prediction accuracies. One

  9. Machine tool locator

    DOEpatents

    Hanlon, John A.; Gill, Timothy J.

    2001-01-01

    Machine tools can be accurately measured and positioned on manufacturing machines within very small tolerances by use of an autocollimator on a 3-axis mount on a manufacturing machine and positioned so as to focus on a reference tooling ball or a machine tool, a digital camera connected to the viewing end of the autocollimator, and a marker and measure generator for receiving digital images from the camera, then displaying or measuring distances between the projection reticle and the reference reticle on the monitoring screen, and relating the distances to the actual position of the autocollimator relative to the reference tooling ball. The images and measurements are used to set the position of the machine tool and to measure the size and shape of the machine tool tip, and examine cutting edge wear. patent

  10. Applying Machine Learning Tools to the Identification of Foreshock Transient Events

    NASA Astrophysics Data System (ADS)

    Beyene, F.; Murr, D.

    2015-12-01

    Our previous research attempted to establish the relationship between foreshock transient events and transients in the ionosphere observed with ground magnetometers. This earlier work relied on foreshock transient event lists that were generated by a visual survey of the THEMIS data near the bowshock/foreshock. Our aim is to extend our earlier work, and the overall understanding of foreshock transients, by employing machine learning tools to identify foreshock transient events. Successful application of these tools would allow use to survey much more data. We first present results of automated classification of THEMIS data into the three primary regions of solar wind, magnetosheath, and magnetosphere. We then present our initial results of training an SVM classifier using the human generated event list and applying it to a more extensive data set.

  11. Machine Tool Software

    NASA Technical Reports Server (NTRS)

    1988-01-01

    A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.

  12. Machine learning techniques as a helpful tool toward determination of plaque vulnerability.

    PubMed

    Cilla, Myriam; Martínez, Javier; Peña, Estefanía; Martínez, Miguel Ángel

    2012-04-01

    Atherosclerotic cardiovascular disease results in millions of sudden deaths annually, and coronary artery disease accounts for the majority of this toll. Plaque rupture plays main role in the majority of acute coronary syndromes. Rupture has been usually associated with stress concentrations, which are determined mainly by tissue properties and plaque geometry. The aim of this study is develop a tool, using machine learning techniques to assist the clinical professionals on decisions of the vulnerability of the atheroma plaque. In practice, the main drawbacks of 3-D finite element analysis to predict the vulnerability risk are the huge main memories required and the long computation times. Therefore, it is essential to use these methods which are faster and more efficient. This paper discusses two potential applications of computational technologies, artificial neural networks and support vector machines, used to assess the role of maximum principal stress in a coronary vessel with atheroma plaque as a function of the main geometrical features in order to quantify the vulnerability risk. PMID:22287230

  13. Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.

    PubMed

    Jia, Lei; Yarlagadda, Ramya; Reed, Charles C

    2015-01-01

    Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find "hot spots" in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html) is a public database that consists of thousands of protein mutants' experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG) and melting temperature change (dTm) were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models. PMID:26361227

  14. Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools

    PubMed Central

    Jia, Lei; Yarlagadda, Ramya; Reed, Charles C.

    2015-01-01

    Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find “hot spots” in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html) is a public database that consists of thousands of protein mutants’ experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG) and melting temperature change (dTm) were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models. PMID:26361227

  15. SKYNET: an efficient and robust neural network training tool for machine learning in astronomy

    NASA Astrophysics Data System (ADS)

    Graff, Philip; Feroz, Farhan; Hobson, Michael P.; Lasenby, Anthony

    2014-06-01

    We present the first public release of our generic neural network training algorithm, called SKYNET. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. SKYNET uses a `pre-training' method to obtain a set of network parameters that has empirically been shown to be close to a good solution, followed by further optimization using a regularized variant of Newton's method, where the level of regularization is determined and adjusted automatically; the latter uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimize using standard backpropagation techniques. SKYNET employs convergence criteria that naturally prevent overfitting, and also includes a fast algorithm for estimating the accuracy of network outputs. The utility and flexibility of SKYNET are demonstrated by application to a number of toy problems, and to astronomical problems focusing on the recovery of structure from blurred and noisy images, the identification of gamma-ray bursters, and the compression and denoising of galaxy images. The SKYNET software, which is implemented in standard ANSI C and fully parallelized using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.

  16. Introduction to machine learning.

    PubMed

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods. PMID:24272434

  17. Diamond machine tool face lapping machine

    DOEpatents

    Yetter, H.H.

    1985-05-06

    An apparatus for shaping, sharpening and polishing diamond-tipped single-point machine tools. The isolation of a rotating grinding wheel from its driving apparatus using an air bearing and causing the tool to be shaped, polished or sharpened to be moved across the surface of the grinding wheel so that it does not remain at one radius for more than a single rotation of the grinding wheel has been found to readily result in machine tools of a quality which can only be obtained by the most tedious and costly processing procedures, and previously unattainable by simple lapping techniques.

  18. Machine Learning and Radiology

    PubMed Central

    Wang, Shijun; Summers, Ronald M.

    2012-01-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

  19. Development of a State Machine Sequencer for the Keck Interferometer: Evolution, Development and Lessons Learned using a CASE Tool Approach

    NASA Technical Reports Server (NTRS)

    Rede, Leonard J.; Booth, Andrew; Hsieh, Jonathon; Summer, Kellee

    2004-01-01

    This paper presents a discussion of the evolution of a sequencer from a simple EPICS (Experimental Physics and Industrial Control System) based sequencer into a complex implementation designed utilizing UML (Unified Modeling Language) methodologies and a CASE (Computer Aided Software Engineering) tool approach. The main purpose of the sequencer (called the IF Sequencer) is to provide overall control of the Keck Interferometer to enable science operations be carried out by a single operator (and/or observer). The interferometer links the two 10m telescopes of the W. M. Keck Observatory at Mauna Kea, Hawaii. The IF Sequencer is a high-level, multi-threaded, Hare1 finite state machine, software program designed to orchestrate several lower-level hardware and software hard real time subsystems that must perform their work in a specific and sequential order. The sequencing need not be done in hard real-time. Each state machine thread commands either a high-speed real-time multiple mode embedded controller via CORB A, or slower controllers via EPICS Channel Access interfaces. The overall operation of the system is simplified by the automation. The UML is discussed and our use of it to implement the sequencer is presented. The decision to use the Rhapsody product as our CASE tool is explained and reflected upon. Most importantly, a section on lessons learned is presented and the difficulty of integrating CASE tool automatically generated C++ code into a large control system consisting of multiple infrastructures is presented.

  20. Paradigms for machine learning

    NASA Technical Reports Server (NTRS)

    Schlimmer, Jeffrey C.; Langley, Pat

    1991-01-01

    Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

  1. Application of Machine Learning tools to recognition of molecular patterns in STM images

    NASA Astrophysics Data System (ADS)

    Maksov, Artem; Ziatdinov, Maxim; Fujii, Shintaro; Kiguchi, Manabu; Higashibayashi, Shuhei; Sakurai, Hidehiro; Kalinin, Sergei; Sumpter, Bobby

    The ability to utilize individual molecules and molecular assemblies as data storage elements has motivated scientist for years, concurrent with the continuous effort to shrink a size of data storage devices in microelectronics industry. One of the critical issues in this effort lies in being able to identify individual molecular assembly units (patterns), on a large scale in an automated fashion of complete information extraction. Here we present a novel method of applying machine learning techniques for extraction of positional and rotational information from scanning tunneling microscopy (STM) images of π-bowl sumanene molecules on gold. We use Markov Random Field (MRF) model to decode the polar rotational states for each molecule in a large scale STM image of molecular film. We further develop an algorithm that uses a convolutional Neural Network combined with MRF and input from density functional theory to classify molecules into different azimuthal rotational classes. Our results demonstrate that a molecular film is partitioned into distinctive azimuthal rotational domains consisting typically of 20-30 molecules. In each domain, the ``bowl-down'' molecules are generally surrounded by six nearest neighbor molecules in ``bowl-up'' configuration, and the resultant overall structure form a periodic lattice of rotational and polar states within each domain. Research was supported by the US Department of Energy.

  2. Slide system for machine tools

    DOEpatents

    Douglass, Spivey S.; Green, Walter L.

    1982-01-01

    The present invention relates to a machine tool which permits the machining of nonaxisymmetric surfaces on a workpiece while rotating the workpiece about a central axis of rotation. The machine tool comprises a conventional two-slide system (X-Y) with one of these slides being provided with a relatively short travel high-speed auxiliary slide which carries the material-removing tool. The auxiliary slide is synchronized with the spindle speed and the position of the other two slides and provides a high-speed reciprocating motion required for the displacement of the cutting tool for generating a nonaxisymmetric surface at a selected location on the workpiece.

  3. Slide system for machine tools

    DOEpatents

    Douglass, S.S.; Green, W.L.

    1980-06-12

    The present invention relates to a machine tool which permits the machining of nonaxisymmetric surfaces on a workpiece while rotating the workpiece about a central axis of rotation. The machine tool comprises a conventional two-slide system (X-Y) with one of these slides being provided with a relatively short travel high-speed auxiliary slide which carries the material-removing tool. The auxiliary slide is synchronized with the spindle speed and the position of the other two slides and provides a high-speed reciprocating motion required for the displacement of the cutting tool for generating a nonaxisymmetric surface at a selected location on the workpiece.

  4. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

    NASA Astrophysics Data System (ADS)

    Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

    2011-03-01

    RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

  5. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights

    PubMed Central

    Pasolli, Edoardo; Truong, Duy Tin; Malik, Faizan; Waldron, Levi

    2016-01-01

    Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the “healthy” microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly

  6. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.

    PubMed

    Pasolli, Edoardo; Truong, Duy Tin; Malik, Faizan; Waldron, Levi; Segata, Nicola

    2016-07-01

    Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the "healthy" microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly

  7. Machine tool evaluation and machining operation development

    SciTech Connect

    Morris, T.O.; Kegg, R.

    1997-03-15

    The purpose of this CRADA was to support Cincinnati Milacron`s needs in fabricating precision components, from difficult to machine materials, while maintaining and enhancing the precision manufacturing skills of the Oak Ridge Complex. Oak Ridge and Cincinnati Milacron personnel worked in a team relationship wherein each contributed equally to the success of the program. Process characterization, control technologies, machine tool capabilities, and environmental issues were the primary focus areas. In general, Oak Ridge contributed a wider range of expertise in machine tool testing and monitoring, and environmental testing on machining fluids to the defined tasks while Cincinnati Milacron personnel provided equipment, operations-specific knowledge and shop-floor services to each task. Cincinnati Milacron was very pleased with the results of all of the CRADA tasks. However, some of the environmental tasks were not carried through to a desired completion due to an expanding realization of need as the work progressed. This expansion of the desired goals then exceeded the time length of the CRADA. Discussions are underway on continuing these tasks under either a Work for Others agreement or some alternate funding.

  8. Tool grinding machine

    DOEpatents

    Dial, Sr., Charles E.

    1980-01-01

    The present invention relates to an improved tool grinding mechanism for grinding single point diamond cutting tools to precise roundness and radius specifications. The present invention utilizes a tool holder which is longitudinally displaced with respect to the remainder of the grinding system due to contact of the tool with the grinding surface with this displacement being monitored so that any variation in the grinding of the cutting surface such as caused by crystal orientation or tool thickness may be compensated for during the grinding operation to assure the attainment of the desired cutting tool face specifications.

  9. Improved tool grinding machine

    DOEpatents

    Dial, C.E. Sr.

    The present invention relates to an improved tool grinding mechanism for grinding single point diamond cutting tools to precise roundness and radius specifications. The present invention utilizes a tool holder which is longitudinally displaced with respect to the remainder of the grinding system due to contact of the tool with the grinding surface with this displacement being monitored so that any variation in the grinding of the cutting surface such as caused by crystal orientation or tool thicknesses may be compensated for during the grinding operation to assure the attainment of the desired cutting tool face specifications.

  10. Machine Learning in Medicine.

    PubMed

    Deo, Rahul C

    2015-11-17

    Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

  11. Deformation Twin Nucleation and Growth Characterization in Magnesium Alloys Using Novel EBSD Pattern Analysis and Machine Learning Tools

    NASA Astrophysics Data System (ADS)

    Rampton, Travis M.

    Deformation twinning in Magnesium alloys both facilitates slip and forms sites for failure. Currently, basic studies of twinning in Mg are facilitated by electron backscatter diffraction (EBSD) which is able to extract a myriad of information relating to crystalline microstructures. Although much information is available via EBSD, various problems relating to deformation twinning have not been solved. This dissertation provides new insights into deformation twinning in Mg alloys, with particular focus on AZ31. These insights were gained through the development of new EBSD and related machine learning tools that extract more information beyond what is currently accessed. The first tool relating to characterization of deformed and twinned materials focuses on surface topography crack detection. The intensity map across EBSD images contains vital information that can be used to detect evolution of surface roughness and crack formation, which typically occurs at twin boundaries. The method of topography recovery resulted in reconstruction errors as low as 2% over a 500 microm length. The method was then applied to a 3 microm x 3 microm area of twinned Tantalum which experienced topographic alterations. The topography of Ta correlated with other measured changes in the microstructure. Additionally, EBSD images were used to identify the presence of cracks in Nickel microstructures. Several cracks were identified on the Ni specimen, demonstrating that cracks as thin as 34 nm could be measured. A further EBSD based tool developed for this study was used to identify thin compression twins in Mg; these are often missed in a traditional EBSD scan due to their size relative to the electron probe. This tool takes advantage of crystallographic relationships that exist between parent and twinned grains; common planes that exist in both grains lead to bands of consistent intensity as a scan crosses a twin. Hence, twin boundaries in a microstructure can be recognized, even when

  12. Tool wear monitoring by machine learning techniques and singular spectrum analysis

    NASA Astrophysics Data System (ADS)

    Kilundu, Bovic; Dehombreux, Pierre; Chiementin, Xavier

    2011-01-01

    This paper explores the use of data mining techniques for tool condition monitoring in metal cutting. Pseudo-local singular spectrum analysis (SSA) is performed on vibration signals measured on the toolholder. This is coupled to a band-pass filter to allow definition and extraction of features which are sensitive to tool wear. These features are defined, in some frequency bands, from sums of Fourier coefficients of reconstructed and residual signals obtained by SSA. This study highlights two important aspects: strong relevance of information in high frequency vibration components and benefits of the combination of SSA and band-pass filtering to get rid of useless components (noise).

  13. Machine Tool Operation, Course Description.

    ERIC Educational Resources Information Center

    Denny, Walter E.; Anderson, Floyd L.

    Prepared by an instructor and curriculum specialists, this course of study was designed to meet the individual needs of the dropout and/or hard-core unemployed youth by providing them skill training, related information, and supportive services knowledge in machine tool operation. The achievement level of each student is determined at entry, and…

  14. Stacked Extreme Learning Machines.

    PubMed

    Zhou, Hongming; Huang, Guang-Bin; Lin, Zhiping; Wang, Han; Soh, Yeng Chai

    2015-09-01

    Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. It provides a unified solution that can be used directly to solve regression, binary, and multiclass classification problems. In this paper, we propose a stacked ELMs (S-ELMs) that is specially designed for solving large and complex data problems. The S-ELMs divides a single large ELM network into multiple stacked small ELMs which are serially connected. The S-ELMs can approximate a very large ELM network with small memory requirement. To further improve the testing accuracy on big data problems, the ELM autoencoder can be implemented during each iteration of the S-ELMs algorithm. The simulation results show that the S-ELMs even with random hidden nodes can achieve similar testing accuracy to support vector machine (SVM) while having low memory requirements. With the help of ELM autoencoder, the S-ELMs can achieve much better testing accuracy than SVM and slightly better accuracy than deep belief network (DBN) with much faster training speed. PMID:25361517

  15. MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development.

    PubMed

    Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

    2015-01-01

    Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

  16. Standardized Curriculum for Machine Tool Operation/Machine Shop.

    ERIC Educational Resources Information Center

    Mississippi State Dept. of Education, Jackson. Office of Vocational, Technical and Adult Education.

    Standardized vocational education course titles and core contents for two courses in Mississippi are provided: machine tool operation/machine shop I and II. The first course contains the following units: (1) orientation; (2) shop safety; (3) shop math; (4) measuring tools and instruments; (5) hand and bench tools; (6) blueprint reading; (7)…

  17. Machine tools and fixtures: A compilation

    NASA Technical Reports Server (NTRS)

    1971-01-01

    As part of NASA's Technology Utilizations Program, a compilation was made of technological developments regarding machine tools, jigs, and fixtures that have been produced, modified, or adapted to meet requirements of the aerospace program. The compilation is divided into three sections that include: (1) a variety of machine tool applications that offer easier and more efficient production techniques; (2) methods, techniques, and hardware that aid in the setup, alignment, and control of machines and machine tools to further quality assurance in finished products: and (3) jigs, fixtures, and adapters that are ancillary to basic machine tools and aid in realizing their greatest potential.

  18. Machine Learning for Biological Trajectory Classification Applications

    NASA Technical Reports Server (NTRS)

    Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

    2002-01-01

    Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

  19. Chip breaking system for automated machine tool

    DOEpatents

    Arehart, Theodore A.; Carey, Donald O.

    1987-01-01

    The invention is a rotary selectively directional valve assembly for use in an automated turret lathe for directing a stream of high pressure liquid machining coolant to the interface of a machine tool and workpiece for breaking up ribbon-shaped chips during the formation thereof so as to inhibit scratching or other marring of the machined surfaces by these ribbon-shaped chips. The valve assembly is provided by a manifold arrangement having a plurality of circumferentially spaced apart ports each coupled to a machine tool. The manifold is rotatable with the turret when the turret is positioned for alignment of a machine tool in a machining relationship with the workpiece. The manifold is connected to a non-rotational header having a single passageway therethrough which conveys the high pressure coolant to only the port in the manifold which is in registry with the tool disposed in a working relationship with the workpiece. To position the machine tools the turret is rotated and one of the tools is placed in a material-removing relationship of the workpiece. The passageway in the header and one of the ports in the manifold arrangement are then automatically aligned to supply the machining coolant to the machine tool workpiece interface for breaking up of the chips as well as cooling the tool and workpiece during the machining operation.

  20. Model-based machine learning

    PubMed Central

    Bishop, Christopher M.

    2013-01-01

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  1. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  2. Machine Learning in Systems Biology

    PubMed Central

    d'Alché-Buc, Florence; Wehenkel, Louis

    2008-01-01

    This supplement contains extended versions of a selected subset of papers presented at the workshop MLSB 2007, Machine Learning in Systems Biology, Evry, France, from September 24 to 25, 2007. PMID:19091048

  3. Machine learning in systems biology.

    PubMed

    d'Alché-Buc, Florence; Wehenkel, Louis

    2008-01-01

    This supplement contains extended versions of a selected subset of papers presented at the workshop MLSB 2007, Machine Learning in Systems Biology, Evry, France, from September 24 to 25, 2007. PMID:19091048

  4. Web Mining: Machine Learning for Web Applications.

    ERIC Educational Resources Information Center

    Chen, Hsinchun; Chau, Michael

    2004-01-01

    Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining applications…

  5. Topics in Machine Learning for Astronomers

    NASA Astrophysics Data System (ADS)

    Cisewski, Jessi

    2016-01-01

    As astronomical datasets continue to increase in size and complexity, innovative statistical and machine learning tools are required to address the scientific questions of interest in a computationally efficient manner. I will introduce some tools that astronomers can employ for such problems with a focus on clustering and classification techniques. I will introduce standard methods, but also get into more recent developments that may be of use to the astronomical community.

  6. Machine Shop. Student Learning Guide.

    ERIC Educational Resources Information Center

    Palm Beach County Board of Public Instruction, West Palm Beach, FL.

    This student learning guide contains eight modules for completing a course in machine shop. It is designed especially for use in Palm Beach County, Florida. Each module covers one task, and consists of a purpose, performance objective, enabling objectives, learning activities and resources, information sheets, student self-check with answer key,…

  7. National Machine Tool Partnership (NMTP) FY 1998

    SciTech Connect

    1997-12-01

    The Department of Energy (DOE) Defense Programs (DP) National Machine Tool Partnership (NMTP) program has been active since February 1993. The NMTP program is an element of the DP Technology Partnership Program. The NMTP has assisted the Association of Manufacturing Technology (AMT) in the formulation of a technology roadmap for the machine tool industry. This roadmap has been developed to provide a clearer step-by-step plan for technology development and implementation to help close the gap between user requirements and industry implementation. The document outlines a suggested path for the development of technologies for the machine tool industry. The plan details the technology issues or needs analysis facing the machine tool industry. In a parallel effort, the NMTP has prepared a needs analysis of machine tool related technologies needed in various DP laboratory weapons core programs, including the Advanced Design and Production Technologies (ADaPT) initiative.

  8. Gaussian processes for machine learning.

    PubMed

    Seeger, Matthias

    2004-04-01

    Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided. PMID:15112367

  9. Game-powered machine learning.

    PubMed

    Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

    2012-04-24

    Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

  10. Game-powered machine learning

    PubMed Central

    Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

    2012-01-01

    Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

  11. Machine learning methods in chemoinformatics

    PubMed Central

    Mitchell, John B O

    2014-01-01

    Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

  12. Method for machining steel with diamond tools

    DOEpatents

    Casstevens, J.M.

    1984-01-01

    The present invention is directed to a method for machine optical quality finishes and contour accuracies of workpieces of carbon-containing metals such as steel with diamond tooling. The wear rate of the diamond tooling is significantly reduced by saturating the atmosphere at the interface of the workpiece and the diamond tool with a gaseous hydrocarbon during the machining operation. The presence of the gaseous hydrocarbon effectively eliminates the deterioration of the diamond tool by inhibiting or preventing the conversion of the diamond carbon to graphite carbon at the point of contact between the cutting tool and the workpiece.

  13. Method for machining steel with diamond tools

    DOEpatents

    Casstevens, John M.

    1986-01-01

    The present invention is directed to a method for machining optical quality inishes and contour accuracies of workpieces of carbon-containing metals such as steel with diamond tooling. The wear rate of the diamond tooling is significantly reduced by saturating the atmosphere at the interface of the workpiece and the diamond tool with a gaseous hydrocarbon during the machining operation. The presence of the gaseous hydrocarbon effectively eliminates the deterioration of the diamond tool by inhibiting or preventing the conversion of the diamond carbon to graphite carbon at the point of contact between the cutting tool and the workpiece.

  14. Speed-Selector Guard For Machine Tool

    NASA Technical Reports Server (NTRS)

    Shakhshir, Roda J.; Valentine, Richard L.

    1992-01-01

    Simple guardplate prevents accidental reversal of direction of rotation or sudden change of speed of lathe, milling machine, or other machine tool. Custom-made for specific machine and control settings. Allows control lever to be placed at only one setting. Operator uses handle to slide guard to engage or disengage control lever. Protects personnel from injury and equipment from damage occurring if speed- or direction-control lever inadvertently placed in wrong position.

  15. Numerically Controlled Machine Tools and Worker Skills.

    ERIC Educational Resources Information Center

    Keefe, Jeffrey H.

    1991-01-01

    Analysis of data from "Industry Wage Surveys of Machinery Manufacturers" on the skill levels of 57 machining jobs found that introduction of numerically controlled machine tools has resulted in a very small reduction in skill levels or no significant change, supporting neither the deskilling argument nor argument that skill levels increase with…

  16. Machine Tool Series. Duty Task List.

    ERIC Educational Resources Information Center

    Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This task list is intended for use in planning and/or evaluating a competency-based course to prepare machine tool, drill press, grinding machine, lathe, mill, and/or power saw operators. The listing is divided into six sections, with each one outlining the tasks required to perform the duties that have been identified for the given occupation.…

  17. Refrigerated cutting tools improve machining of superalloys

    NASA Technical Reports Server (NTRS)

    Dudley, G. M.

    1971-01-01

    Freon-12 applied to tool cutting edge evaporates quickly, leaves no residue, and permits higher cutting rate than with conventional coolants. This technique increases cutting rate on Rene-41 threefold and improves finish of machined surface.

  18. Vibration absorber modeling for handheld machine tool

    NASA Astrophysics Data System (ADS)

    Abdullah, Mohd Azman; Mustafa, Mohd Muhyiddin; Jamil, Jazli Firdaus; Salim, Mohd Azli; Ramli, Faiz Redza

    2015-05-01

    Handheld machine tools produce continuous vibration to the users during operation. This vibration causes harmful effects to the health of users for repeated operations in a long period of time. In this paper, a dynamic vibration absorber (DVA) is designed and modeled to reduce the vibration generated by the handheld machine tool. Several designs and models of vibration absorbers with various stiffness properties are simulated, tested and optimized in order to diminish the vibration. Ordinary differential equation is used to derive and formulate the vibration phenomena in the machine tool with and without the DVA. The final transfer function of the DVA is later analyzed using commercial available mathematical software. The DVA with optimum properties of mass and stiffness is developed and applied on the actual handheld machine tool. The performance of the DVA is experimentally tested and validated by the final result of vibration reduction.

  19. Applications of Machine Learning in Information Retrieval.

    ERIC Educational Resources Information Center

    Cunningham, Sally Jo; Witten, Ian H.; Littin, James

    1999-01-01

    Introduces the basic ideas that underpin applications of machine learning to information retrieval. Describes applications of machine learning to text categorization. Considers how machine learning can be applied to the query-formulation process. Examines methods of document filtering, where the user specifies a query that is to be applied to an…

  20. Machine learning phases of matter

    NASA Astrophysics Data System (ADS)

    Carrasquilla, Juan; Stoudenmire, Miles; Melko, Roger

    We show how the technology that allows automatic teller machines read hand-written digits in cheques can be used to encode and recognize phases of matter and phase transitions in many-body systems. In particular, we analyze the (quasi-)order-disorder transitions in the classical Ising and XY models. Furthermore, we successfully use machine learning to study classical Z2 gauge theories that have important technological application in the coming wave of quantum information technologies and whose phase transitions have no conventional order parameter.

  1. Upgrading the capabilities of existing machine tools for precision machining

    SciTech Connect

    Barkman, W.E.

    1982-05-01

    A number of two-axis turning machines at the Oak Ridge Y-12 Plant have undergone upgrading as a means of meeting the needs for parts with tolerances that were more restrictive than the capability of the basic machine. The level of upgrading has ranged from changing a single machine characteristic to doing a complete overhaul of the slides, drives, spindle, and control system. The features available for the up-grading process include: tool setters, air bearing spindles and slides, pressurized oil bearing slides, electric dc torque motor drives, linear motor slide drives, eddy current spindle drives, laser feedback, vibration-isolation machine platforms, and computer numerical control (CNC) systems. Actual case histories are presented which show the levels of performance achieved with the various modifications. A discussion of the advantages and disadvantages of the various options is included.

  2. Learning Extended Finite State Machines

    NASA Technical Reports Server (NTRS)

    Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard

    2014-01-01

    We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

  3. Learning Machine Learning: A Case Study

    ERIC Educational Resources Information Center

    Lavesson, N.

    2010-01-01

    This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

  4. The Higgs Machine Learning Challenge

    NASA Astrophysics Data System (ADS)

    Adam-Bourdarios, C.; Cowan, G.; Germain-Renaud, C.; Guyon, I.; Kégl, B.; Rousseau, D.

    2015-12-01

    The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying to τ+τ- together with background events were made available to the public through the website of the data science organization Kaggle (kaggle.com). Participants attempted to identify the search region in a space of 30 kinematic variables that would maximize the expected discovery significance of the signal process. One of the primary goals of the Challenge was to promote communication of new ideas between the Machine Learning (ML) and HEP communities. In this regard it was a resounding success, with almost 2,000 participants from HEP, ML and other areas. The process of understanding and integrating the new ideas, particularly from ML into HEP, is currently underway.

  5. Paradigms for Realizing Machine Learning Algorithms.

    PubMed

    Agneeswaran, Vijay Srinivas; Tonpay, Pranay; Tiwary, Jayati

    2013-12-01

    The article explains the three generations of machine learning algorithms-with all three trying to operate on big data. The first generation tools are SAS, SPSS, etc., while second generation realizations include Mahout and RapidMiner (that work over Hadoop), and the third generation paradigms include Spark and GraphLab, among others. The essence of the article is that for a number of machine learning algorithms, it is important to look beyond the Hadoop's Map-Reduce paradigm in order to make them work on big data. A number of promising contenders have emerged in the third generation that can be exploited to realize deep analytics on big data. PMID:27447253

  6. Machine Learning of Maritime Fog Forecast Rules.

    NASA Astrophysics Data System (ADS)

    Tag, Paul M.; Peak, James E.

    1996-05-01

    In recent years, the field of artificial intelligence has contributed significantly to the science of meteorology, most notably in the now familiar form of expert systems. Expert systems have focused on rules or heuristics by establishing, in computer code, the reasoning process of a weather forecaster predicting, for example, thunderstorms or fog. In addition to the years of effort that goes into developing such a knowledge base is the time-consuming task of extracting such knowledge and experience from experts. In this paper, the induction of rules directly from meteorological data is explored-a process called machine learning. A commercial machine learning program called C4.5, is applied to a meteorological problem, forecasting maritime fog, for which a reliable expert system has been previously developed. Two detasets are used: 1) weather ship observations originally used for testing and evaluating the expert system, and 2) buoy measurements taken off the coast of California. For both datasets, the rules produced by C4.5 are reasonable and make physical sense, thus demonstrating that an objective induction approach can reveal physical processes directly from data. For the ship database, the machine-generated rules are not as accurate as those from the expert system but are still significantly better than persistence forecasts. For the buoy data, the forecast accuracies are very high, but only slightly superior to persistence. The results indicate that the machine learning approach is a viable tool for developing meteorological expertise, but only when applied to reliable data with sufficient cases of known outcome. In those instances when such databases are available, the use of machine learning can provide useful insight that otherwise might take considerable human analysis to produce.

  7. Sine-Bar Attachment For Machine Tools

    NASA Technical Reports Server (NTRS)

    Mann, Franklin D.

    1988-01-01

    Sine-bar attachment for collets, spindles, and chucks helps machinists set up quickly for precise angular cuts that require greater precision than provided by graduations of machine tools. Machinist uses attachment to index head, carriage of milling machine or lathe relative to table or turning axis of tool. Attachment accurate to 1 minute or arc depending on length of sine bar and precision of gauge blocks in setup. Attachment installs quickly and easily on almost any type of lathe or mill. Requires no special clamps or fixtures, and eliminates many trial-and-error measurements. More stable than improvised setups and not jarred out of position readily.

  8. Advanced machine tools, loading systems viewed

    NASA Astrophysics Data System (ADS)

    Kharkov, V. I.

    1986-03-01

    The machine-tooling complex built from a revolving lathe and a two-armed robot designed to machine short revolving bodies including parts with curvilinear and threaded surfaces from piece blanks in either small-series or series multiitem production is described. The complex consists of: (1) a model 1V340F30 revolving lathe with a vertical axis of rotation, 8-position revolving head on a cross carriage and an Elektronika NTs-31 on-line control system; (2) a gantry-style two-armed M20-Ts robot with a 20-kilogram (20 x 2) load capacity; and (3) an 8-position indexable blank table, one of whose positions is for initial unloading of finished parts. Subsequently, machined parts are set onto the position into which all of the blanks are unloaded. Complex enclosure allows adjustment and process correction during maintenance and convenient observation of the machining process.

  9. Machine-Tool Technology Instructor's Sourcebook.

    ERIC Educational Resources Information Center

    Tammer, Anthony M.

    This document lists and annotates commercial and noncommercial resources pertaining to machine-tool technology. Following an introduction that explains how the document came to be written, the subjects of succeeding chapters are (1) periodicals; (2) associations; (3) audiovisual resources, including a subject index; (4) publishers, including a…

  10. Introducing Machine Learning Concepts with WEKA.

    PubMed

    Smith, Tony C; Frank, Eibe

    2016-01-01

    This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information. PMID:27008023

  11. ATST telescope mount: telescope of machine tool

    NASA Astrophysics Data System (ADS)

    Jeffers, Paul; Stolz, Günter; Bonomi, Giovanni; Dreyer, Oliver; Kärcher, Hans

    2012-09-01

    The Advanced Technology Solar Telescope (ATST) will be the largest solar telescope in the world, and will be able to provide the sharpest views ever taken of the solar surface. The telescope has a 4m aperture primary mirror, however due to the off axis nature of the optical layout, the telescope mount has proportions similar to an 8 meter class telescope. The technology normally used in this class of telescope is well understood in the telescope community and has been successfully implemented in numerous projects. The world of large machine tools has developed in a separate realm with similar levels of performance requirement but different boundary conditions. In addition the competitive nature of private industry has encouraged development and usage of more cost effective solutions both in initial capital cost and thru-life operating cost. Telescope mounts move relatively slowly with requirements for high stability under external environmental influences such as wind buffeting. Large machine tools operate under high speed requirements coupled with high application of force through the machine but with little or no external environmental influences. The benefits of these parallel development paths and the ATST system requirements are being combined in the ATST Telescope Mount Assembly (TMA). The process of balancing the system requirements with new technologies is based on the experience of the ATST project team, Ingersoll Machine Tools who are the main contractor for the TMA and MT Mechatronics who are their design subcontractors. This paper highlights a number of these proven technologies from the commercially driven machine tool world that are being introduced to the TMA design. Also the challenges of integrating and ensuring that the differences in application requirements are accounted for in the design are discussed.

  12. Data Mining and Machine Learning in Astronomy

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.; Brunner, Robert J.

    We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

  13. Machine learning of user profiles: Representational issues

    SciTech Connect

    Bloedorn, E.; Mani, I.; MacMillan, T.R.

    1996-12-31

    As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research described here focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (ER) problem.

  14. Machine learning research 1989-90

    NASA Technical Reports Server (NTRS)

    Porter, Bruce W.; Souther, Arthur

    1990-01-01

    Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base.

  15. Machine learning in motion control

    NASA Technical Reports Server (NTRS)

    Su, Renjeng; Kermiche, Noureddine

    1989-01-01

    The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

  16. Machine learning in sedimentation modelling.

    PubMed

    Bhattacharya, B; Solomatine, D P

    2006-03-01

    The paper presents machine learning (ML) models that predict sedimentation in the harbour basin of the Port of Rotterdam. The important factors affecting the sedimentation process such as waves, wind, tides, surge, river discharge, etc. are studied, the corresponding time series data is analysed, missing values are estimated and the most important variables behind the process are chosen as the inputs. Two ML methods are used: MLP ANN and M5 model tree. The latter is a collection of piece-wise linear regression models, each being an expert for a particular region of the input space. The models are trained on the data collected during 1992-1998 and tested by the data of 1999-2000. The predictive accuracy of the models is found to be adequate for the potential use in the operational decision making. PMID:16530383

  17. Defect classification using machine learning

    NASA Astrophysics Data System (ADS)

    Carr, Adra; Kegelmeyer, L.; Liao, Z. M.; Abdulla, G.; Cross, D.; Kegelmeyer, W. P.; Ravizza, F.; Carr, C. W.

    2008-10-01

    Laser-induced damage growth on the surface of fused silica optics has been extensively studied and has been found to depend on a number of factors including fluence and the surface on which the damage site resides. It has been demonstrated that damage sites as small as a few tens of microns can be detected and tracked on optics installed a fusion-class laser, however, determining the surface of an optic on which a damage site resides in situ can be a significant challenge. In this work demonstrate that a machine-learning algorithm can successfully predict the surface location of the damage site using an expanded set of characteristics for each damage site, some of which are not historically associated with growth rate.

  18. Defect Classification Using Machine Learning

    SciTech Connect

    Carr, A; Kegelmeyer, L; Liao, Z M; Abdulla, G; Cross, D; Kegelmeyer, W P; Raviza, F; Carr, C W

    2008-10-24

    Laser-induced damage growth on the surface of fused silica optics has been extensively studied and has been found to depend on a number of factors including fluence and the surface on which the damage site resides. It has been demonstrated that damage sites as small as a few tens of microns can be detected and tracked on optics installed a fusion-class laser, however, determining the surface of an optic on which a damage site resides in situ can be a significant challenge. In this work demonstrate that a machine-learning algorithm can successfully predict the surface location of the damage site using an expanded set of characteristics for each damage site, some of which are not historically associated with growth rate.

  19. An investigation of chatter and tool wear when machining titanium

    NASA Technical Reports Server (NTRS)

    Sutherland, I. A.

    1974-01-01

    The low thermal conductivity of titanium, together with the low contact area between chip and tool and the unusually high chip velocities, gives rise to high tool tip temperatures and accelerated tool wear. Machining speeds have to be considerably reduced to avoid these high temperatures with a consequential loss of productivity. Restoring this lost productivity involves increasing other machining variables, such as feed and depth-of-cut, and can lead to another machining problem commonly known as chatter. This work is to acquaint users with these problems, to examine the variables that may be encountered when machining a material like titanium, and to advise the machine tool user on how to maximize the output from the machines and tooling available to him. Recommendations are made on ways of improving tolerances, reducing machine tool instability or chatter, and improving productivity. New tool materials, tool coatings, and coolants are reviewed and their relevance examined when machining titanium.

  20. Adaptive Learning Systems: Beyond Teaching Machines

    ERIC Educational Resources Information Center

    Kara, Nuri; Sevim, Nese

    2013-01-01

    Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its…

  1. Machine learning for medical images analysis.

    PubMed

    Criminisi, A

    2016-10-01

    This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. PMID:27374127

  2. Probabilistic machine learning and artificial intelligence.

    PubMed

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery. PMID:26017444

  3. Probabilistic machine learning and artificial intelligence

    NASA Astrophysics Data System (ADS)

    Ghahramani, Zoubin

    2015-05-01

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  4. Automatic tool path generation for finish machining

    SciTech Connect

    Kwok, Kwan S.; Loucks, C.S.; Driessen, B.J.

    1997-03-01

    A system for automatic tool path generation was developed at Sandia National Laboratories for finish machining operations. The system consists of a commercially available 5-axis milling machine controlled by Sandia developed software. This system was used to remove overspray on cast turbine blades. A laser-based, structured-light sensor, mounted on a tool holder, is used to collect 3D data points around the surface of the turbine blade. Using the digitized model of the blade, a tool path is generated which will drive a 0.375 inch diameter CBN grinding pin around the tip of the blade. A fuzzified digital filter was developed to properly eliminate false sensor readings caused by burrs, holes and overspray. The digital filter was found to successfully generate the correct tool path for a blade with intentionally scanned holes and defects. The fuzzified filter improved the computation efficiency by a factor of 25. For application to general parts, an adaptive scanning algorithm was developed and presented with simulation results. A right pyramid and an ellipsoid were scanned successfully with the adaptive algorithm.

  5. Machine vision systems using machine learning for industrial product inspection

    NASA Astrophysics Data System (ADS)

    Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

    2002-02-01

    Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

  6. Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

    NASA Astrophysics Data System (ADS)

    Goyal, Manish Kumar; Burn, Donald H.; Ojha, C. S. P.

    2012-05-01

    Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046-2065 and 2081-2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T max and T min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081-2100.

  7. Web-based machine tool condition monitoring

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Morteza; Victory, J. L.

    2000-12-01

    This paper looks at the advantages of using the Internet, as the basis for the implementation of low-cost condition monitoring systems, in the manufacturing industry. A model based condition monitoring system, is presented where a number of machining stations dispersed at different physical locations can be inspected via Internet access and the signals from the process analyzed in a dedicated condition monitoring center. Incentive for the new approach to the system health monitoring, logging and surveillance are presented. These extend into advantages of using model-based techniques and the need for an appropriate mathematical model of the machine tool. Finally, the data acquisition and communication system to be used in this application for Internet access will be explained.

  8. Circular machine design techniques and tools

    SciTech Connect

    Servranckx, R.V.; Brown, K.L.

    1986-04-01

    Some of the basic optics principles involved in the design of circular accelerators such as Alternating Gradient Synchrotrons, Storage and Collision Rings, and Pulse Stretcher Rings are outlined. Typical problems facing a designer are defined, and the main references and computational tools are reviewed that are presently available. Two particular classes of problems that occur typically in accelerator design are listed - global value problems, which affect the control of parameters which are characteristic of the complete closed circular machine, and local value problems. Basic mathematical formulae are given that are considered useful for a first draft of a design. The basic optics building blocks that can be used to formulate an initial machine design are introduced, giving only the elementary properties and transfer matrices only in one transverse plane. Solutions are presented for some first-order and second-order design problems. (LEW)

  9. Photonic Neurocomputers And Learning Machines

    NASA Astrophysics Data System (ADS)

    Farhat, Nabil H.

    1990-05-01

    The study of complex multidimensional nonlinear dynamical systems and the modeling and emulation of cognitive brain-like processing of sensory information (neural network research), including the study of chaos and its role in such systems would benefit immensely from the development of a new generation of programmable analog computers capable of carrying out collective, nonlinear and iterative computations at very high speed. The massive interconnectivity and nonlinearity needed in such analog computing structures indicate that a mix of optics and electronics mediated by judicial choice of device physics offer benefits for realizing networks with the following desirable properties: (a) large scale nets, i.e. nets with high number of decision making elements (neurons), (b) modifiable structure, i.e. ability to partition the net into any desired number of layers of prescribed size (number of neurons per layer) with any prescribed pattern of communications between them (e.g. feed forward or feedback (recurrent)), (c) programmable and/or adaptive connectivity weights between the neurons for self-organization and learning, (d) both synchroneous or asynchroneous update rules be possible, (e) high speed update i.e. neurons with lisec response time to enable rapid iteration and convergence, (f) can be used in the study and evaluation of a variety of adaptive learning algorithms, (g) can be used in rapid solution by fast simulated annealing of complex optimization problems of the kind encountered in adaptive learning, pattern recognition, and image processing. The aim of this paper is to describe recent efforts and progress made towards achieving these desirable attributes in analog photonic (optoelectronic and/or electron optical) hardware that utilizes primarily incoherent light. A specific example, hardware implementation of a stochastic Boltzmann learning machine, is used as vehicle for identifying generic issues and clarify research and development areas for further

  10. Multistrategy machine-learning vision system

    NASA Astrophysics Data System (ADS)

    Roberts, Barry A.

    1993-04-01

    Advances in the field of machine learning technology have yielded learning techniques with solid theoretical foundations that are applicable to the problems being encountered by object recognition systems. At Honeywell an object recognition system that works with high-level, symbolic, object features is under development. This system, named object recognition accomplished through combined learning expertise (ORACLE), employs both an inductive learning technique (i.e., conceptual clustering, CC) and a deductive technique (i.e., explanation-based learning, EBL) that are combined in a synergistic manner. This paper provides an overview of the ORACLE system, describes the machine learning mechanisms (EBL and CC) that it employs, and provides example results of system operation. The paper emphasizes the beneficial effect of integrating machine learning into object recognition systems.

  11. Machine Learning and Cosmological Simulations

    NASA Astrophysics Data System (ADS)

    Kamdar, Harshil; Turk, Matthew; Brunner, Robert

    2016-01-01

    We explore the application of machine learning (ML) to the problem of galaxy formation and evolution in a hierarchical universe. Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and (2) quantitatively evaluating the extent of the influence of dark matter halo properties on small-scale structure formation. For our analyses, we use both semi-analytical models (Millennium simulation) and N-body + hydrodynamical simulations (Illustris simulation). The ML algorithms are trained on important dark matter halo properties (inputs) and galaxy properties (outputs). The trained models are able to robustly predict the gas mass, stellar mass, black hole mass, star formation rate, $g-r$ color, and stellar metallicity. Moreover, the ML simulated galaxies obey fundamental observational constraints implying that the population of ML predicted galaxies is physically and statistically robust. Next, ML algorithms are trained on an N-body + hydrodynamical simulation and applied to an N-body only simulation (Dark Sky simulation, Illustris Dark), populating this new simulation with galaxies. We can examine how structure formation changes with different cosmological parameters and are able to mimic a full-blown hydrodynamical simulation in a computation time that is orders of magnitude smaller. We find that the set of ML simulated galaxies in Dark Sky obey the same observational constraints, further solidifying ML's place as an intriguing and promising technique in future galaxy formation studies and rapid mock galaxy catalog creation.

  12. Memristor models for machine learning.

    PubMed

    Carbajal, Juan Pablo; Dambre, Joni; Hermans, Michiel; Schrauwen, Benjamin

    2015-03-01

    In the quest for alternatives to traditional complementary metal-oxide-semiconductor, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model, and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that although both models could lead to useful memristor-based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models. PMID:25602769

  13. Coordinate measurement machines as an alignment tool

    SciTech Connect

    Wand, B.T.

    1991-03-01

    In February of 1990 the Stanford Linear Accelerator Center (SLAC) purchased a LEITZ PM 12-10-6 CMM (Coordinate measurement machine). The machine is shared by the Quality Control Team and the Alignment Team. One of the alignment tasks in positioning beamline components in a particle accelerator is to define the component's magnetic centerline relative to external fiducials. This procedure, called fiducialization, is critical to the overall positioning tolerance of a magnet. It involves the definition of the magnetic center line with respect to the mechanical centerline and the transfer of the mechanical centerline to the external fiducials. To perform the latter a magnet coordinate system has to be established. This means defining an origin and the three rotation angles of the magnet. The datum definition can be done by either optical tooling techniques or with a CMM. As optical tooling measurements are very time consuming, not automated and are prone to errors, it is desirable to use the CMM fiducialization method instead. The establishment of a magnet coordinate system based on the mechanical center and the transfer to external fiducials will be discussed and presented with 2 examples from the Stanford Linear Collider (SLC). 7 figs.

  14. Alternating minimization and Boltzmann machine learning.

    PubMed

    Byrne, W

    1992-01-01

    Training a Boltzmann machine with hidden units is appropriately treated in information geometry using the information divergence and the technique of alternating minimization. The resulting algorithm is shown to be closely related to gradient descent Boltzmann machine learning rules, and the close relationship of both to the EM algorithm is described. An iterative proportional fitting procedure for training machines without hidden units is described and incorporated into the alternating minimization algorithm. PMID:18276461

  15. In silico machine learning methods in drug development.

    PubMed

    Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

    2014-01-01

    Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases. PMID:25262800

  16. A New Approach to Precision Design for Machine Tools

    NASA Astrophysics Data System (ADS)

    Li, Baodong; Jiao, Aisheng; Yi, Xiangbin; Xu, Yanwei

    Precision of the NC axes is an important aspect of machine tool design. Conventionally, the precision specification of machine tools is empirically determined, resulting in poor designs with insufficient or excessive precision. To provide a cost-effective precision specification for machine tools, an active precision design approach is proposed to generate the specification of the positioning repeatability of NC axes to meet the designated working precision requirements of the machine tools. Finally, the approach is demonstrated and validated through a case study of precision design for a gear milling machine.

  17. Machine Translation-Assisted Language Learning: Writing for Beginners

    ERIC Educational Resources Information Center

    Garcia, Ignacio; Pena, Maria Isabel

    2011-01-01

    The few studies that deal with machine translation (MT) as a language learning tool focus on its use by advanced learners, never by beginners. Yet, freely available MT engines (i.e. Google Translate) and MT-related web initiatives (i.e. Gabble-on.com) position themselves to cater precisely to the needs of learners with a limited command of a…

  18. Machine learning applications in genetics and genomics.

    PubMed

    Libbrecht, Maxwell W; Noble, William Stafford

    2015-06-01

    The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets. PMID:25948244

  19. 13. TOOL ROOM SHOWING W. ROBERTSON MACHINE & FOUNDRY CO. ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    13. TOOL ROOM SHOWING W. ROBERTSON MACHINE & FOUNDRY CO. NO. 5 POWER HACKSAW (FOREGROUND) AND WELLS METAL BAND SAW (BACKGROUND). VIEW SOUTHEAST - Oldman Boiler Works, Office/Machine Shop, 32 Illinois Street, Buffalo, Erie County, NY

  20. Interpolator for numerically controlled machine tools

    DOEpatents

    Bowers, Gary L.; Davenport, Clyde M.; Stephens, Albert E.

    1976-01-01

    A digital differential analyzer circuit is provided that depending on the embodiment chosen can carry out linear, parabolic, circular or cubic interpolation. In the embodiment for parabolic interpolations, the circuit provides pulse trains for the X and Y slide motors of a two-axis machine to effect tool motion along a parabolic path. The pulse trains are generated by the circuit in such a way that parabolic tool motion is obtained from information contained in only one block of binary input data. A part contour may be approximated by one or more parabolic arcs. Acceleration and initial velocity values from a data block are set in fixed bit size registers for each axis separately but simultaneously and the values are integrated to obtain the movement along the respective axis as a function of time. Integration is performed by continual addition at a specified rate of an integrand value stored in one register to the remainder temporarily stored in another identical size register. Overflows from the addition process are indicative of the integral. The overflow output pulses from the second integration may be applied to motors which position the respective machine slides according to a parabolic motion in time to produce a parabolic machine tool motion in space. An additional register for each axis is provided in the circuit to allow "floating" of the radix points of the integrand registers and the velocity increment to improve position accuracy and to reduce errors encountered when the acceleration integrand magnitudes are small when compared to the velocity integrands. A divider circuit is provided in the output of the circuit to smooth the output pulse spacing and prevent motor stall, because the overflow pulses produced in the binary addition process are spaced unevenly in time. The divider has the effect of passing only every nth motor drive pulse, with n being specifiable. The circuit inputs (integrands, rates, etc.) are scaled to give exactly n times the

  1. An introduction to quantum machine learning

    NASA Astrophysics Data System (ADS)

    Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

    2015-04-01

    Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

  2. Machine Learning for Biomedical Literature Triage

    PubMed Central

    Almeida, Hayda; Meurs, Marie-Jean; Kosseim, Leila; Butler, Greg; Tsang, Adrian

    2014-01-01

    This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm. PMID:25551575

  3. New tools for learning.

    PubMed

    Dickinson, D

    1999-01-01

    In the last twenty-five years more has been learned about the human brain than in the past history of mankind. Through the use of new technologies such as PET and CAT scans and functional MRI's, it is now possible to see and learn much about the human brain while it is in the process of thinking. The research of neuroscientists, such as Marian Diamond, has demonstrated that the brain changes physiologically as a result of learning and experience--for better or worse--and that plasticity can continue throughout the lifespan. It appears that there are particular kinds of environments that are most conducive to the development of good mental equipment. They are positive, nurturing, stimulating, and encourage action and interaction. Many of the most effective schools and training programs have created such high-challenge low-threat environments. It is also very clear that intelligence is not a static structure, but an open, dynamic system that can continue to develop throughout life. This understanding is being utilized not only in school systems but in the workplace, where training programs show that even at the adult level people are able to develop their intelligence more fully. Corporations such as Motorola have implemented programs in which they are training their employees, managers, and executives to think, problem-solve and create more effectively using strategies developed by such educational innovators as Reuven Feurstein, J.P. Guilford, and Edward de Bono. A most recent development is in the new kinds of technology that make it possible for people to take responsibility for their own learning as they access and process information through the internet, communicate with experts anywhere in the world, and use software that facilitate higher order thinking and problem-solving. Computers are in no way replacing teachers, but rather these new tools allow them to spend more time being facilitators, mentors, and guides. As a result, teachers and students are able

  4. Advances in Machine Learning and Data Mining for Astronomy

    NASA Astrophysics Data System (ADS)

    Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

    2012-03-01

    Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

  5. Machine learning: Trends, perspectives, and prospects.

    PubMed

    Jordan, M I; Mitchell, T M

    2015-07-17

    Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. PMID:26185243

  6. Experimental investigation of active machine tool vibration control

    NASA Astrophysics Data System (ADS)

    Rojas, J.; Liang, Chen; Geng, Zheng J.

    1996-05-01

    The successful vibration reduction of machine tools during machining process can improve productivity, increase quality, and reduce tool wear. This paper will present our initial investigation in the application of smart material technologies in machine tool vibration control using magnetostrictive actuators and electrorheological elastomer dampers on an industrial Sheldon horizontal lathe. The dynamics of the machining process are first studied, which reveals the complexity in the machine tool vibration response and the challenge to the active control techniques. The active control experiment shows encouraging results. The use of electrorheological elastomer damping device for active/passive vibration control provides significant vibration reduction in the high frequency range and great improvement in the workpiece surface finishing. The research presented in this paper demonstrates that the combination of active and active/passive vibration control techniques is very promising for successful machine tool vibration control.

  7. [Research on infrared safety protection system for machine tool].

    PubMed

    Zhang, Shuan-Ji; Zhang, Zhi-Ling; Yan, Hui-Ying; Wang, Song-De

    2008-04-01

    In order to ensure personal safety and prevent injury accident in machine tool operation, an infrared machine tool safety system was designed with infrared transmitting-receiving module, memory self-locked relay and voice recording-playing module. When the operator does not enter the danger area, the system has no response. Once the operator's whole or part of body enters the danger area and shades the infrared beam, the system will alarm and output an control signal to the machine tool executive element, and at the same time, the system makes the machine tool emergency stop to prevent equipment damaged and person injured. The system has a module framework, and has many advantages including safety, reliability, common use, circuit simplicity, maintenance convenience, low power consumption, low costs, working stability, easy debugging, vibration resistance and interference resistance. It is suitable for being installed and used in different machine tools such as punch machine, pour plastic machine, digital control machine, armor plate cutting machine, pipe bending machine, oil pressure machine etc. PMID:18619302

  8. A Machine Learning Based Framework for Adaptive Mobile Learning

    NASA Astrophysics Data System (ADS)

    Al-Hmouz, Ahmed; Shen, Jun; Yan, Jun

    Advances in wireless technology and handheld devices have created significant interest in mobile learning (m-learning) in recent years. Students nowadays are able to learn anywhere and at any time. Mobile learning environments must also cater for different user preferences and various devices with limited capability, where not all of the information is relevant and critical to each learning environment. To address this issue, this paper presents a framework that depicts the process of adapting learning content to satisfy individual learner characteristics by taking into consideration his/her learning style. We use a machine learning based algorithm for acquiring, representing, storing, reasoning and updating each learner acquired profile.

  9. Tool force evaluation of lathe machined high explosives

    SciTech Connect

    Flowers, G.L.

    1980-04-01

    The purpose of this study was to develop a better understanding of the effects of machining properties upon tool forces encountered during lathe machining of high explosives, in order to optimize machining conditions for mechanical properties test specimens. Monetary considerations dictated that the tooling either already exist or be fabricated in-house using limited machine shop capability. The design chosen which fit between the tool holder and the tool post and interfaced to existing signal conditioners was easily fabricated. The study evaluated all forces on the cutter during machining of two types of high explosives at four cutter radii, four feed rates, three depths of cut and two cutting speeds. The study pointed out design problems, instrumentation drift, tool chatter and detection levels. It also showed that the type of high explosive was more significant than first thought toward influencing tool force levels.

  10. Extreme Learning Machines for spatial environmental data

    NASA Astrophysics Data System (ADS)

    Leuenberger, Michael; Kanevski, Mikhail

    2015-12-01

    The use of machine learning algorithms has increased in a wide variety of domains (from finance to biocomputing and astronomy), and nowadays has a significant impact on the geoscience community. In most real cases geoscience data modelling problems are multivariate, high dimensional, variable at several spatial scales, and are generated by non-linear processes. For such complex data, the spatial prediction of continuous (or categorical) variables is a challenging task. The aim of this paper is to investigate the potential of the recently developed Extreme Learning Machine (ELM) for environmental data analysis, modelling and spatial prediction purposes. An important contribution of this study deals with an application of a generic self-consistent methodology for environmental data driven modelling based on Extreme Learning Machine. Both real and simulated data are used to demonstrate applicability of ELM at different stages of the study to understand and justify the results.

  11. Introduction to machine learning for brain imaging.

    PubMed

    Lemm, Steven; Blankertz, Benjamin; Dickhaus, Thorsten; Müller, Klaus-Robert

    2011-05-15

    Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. PMID:21172442

  12. Learning in brains and machines.

    PubMed

    Poggio, T; Shelton, C R

    2000-01-01

    The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we sketch some of our work over the last ten years in the area of supervised learning, focusing on three interlinked directions of research: theory, engineering applications (that is, making intelligent software) and neuroscience (that is, understanding the brain's mechanisms of learning). PMID:11198239

  13. An Expert Machine Tools Selection System for Turning Operation

    NASA Astrophysics Data System (ADS)

    Tan, C. F.; Khalil, S. N.; Karjanto, J.; Wahidin, L. S.; Chen, W.; Rauterberg, G. W. M.

    2015-09-01

    The turning machining process is an important process in the manufacturing industry. It is important to select the right tool for the turning process so that the manufacturing cost will be decreased. The main objective of this research is to select the most suitable machine tools with respect to user input requirement. The selection criteria are based on rule based expert system and multi-criteria weighted average method. The developed system consists of Knowledge Acquisition Module, Machine Tool Selection Module, User Interface Module and Help Module. The system capable of selecting the most suitable machine along with its full specification and ranks the machines based on criteria weighted. The main benefits from using the system is to reduce the complexity in the decision making for selecting the most appropriate machine tools to suit one requirement in the turning process for manufacturing industry.

  14. Market for multiaxis laser machine tools

    NASA Astrophysics Data System (ADS)

    Ream, Stanley L.

    1991-03-01

    While it's true that this is an exciting topic, it niay be more exciting than profitable, but it certainly has captured the attention of a lot of us laser folks, and it keeps growing almost because it wants to. First of all let me comment briefly with a word from our sponsor that GE Fanuc is one of the several ways the Fanuc laser product gets into the United States. We market it, GM Fanuc also markets it, and of course it shows up on Japanese machine tool built products. The information in this little presentation came from discussions with you folks wherever possible. In some cases I was unable to make contact with the horse's mouth as it were, but we got roundabout information so it's not gospel, but it's close. We've also had some updated information at the show here updated rumors maybe that suggest that some of the numbers may be high or low. I think in the aggregate it's not too far off.

  15. Information Model for Machine-Tool-Performance Tests

    PubMed Central

    Lee, Y. Tina; Soons, Johannes A.; Donmez, M. Alkan

    2001-01-01

    This report specifies an information model of machine-tool-performance tests in the EXPRESS [1] language. The information model provides a mechanism for describing the properties and results of machine-tool-performance tests. The objective of the information model is a standardized, computer-interpretable representation that allows for efficient archiving and exchange of performance test data throughout the life cycle of the machine. The report also demonstrates the implementation of the information model using three different implementation methods.

  16. Recent Advances in Predictive (Machine) Learning

    SciTech Connect

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  17. Application of accelerated tool life tests to machining of titanium

    SciTech Connect

    Stagner, R.T.

    1980-09-01

    The tool life of several commercial C-2 grade cutting tools used in machining titanium was estimated using two experimental techniques, the quick facing test and the multipass facing test. Comparisons among the tools tested were made statistically by analyzing differences in regression equations derived from test data. Tool life end points were determined by operator judgement, tool force analysis, and tool wear measurement. Of the ten tools tested, nine had the same life under the test conditions.

  18. Distributed fuzzy learning using the MULTISOFT machine.

    PubMed

    Russo, M

    2001-01-01

    Describes PARGEFREX, a distributed approach to genetic-neuro-fuzzy learning which has been implemented using the MULTISOFT machine, a low-cost form of personal computers built at the University of Messina. The performance of the serial version is hugely enhanced with the simple parallelization scheme described in the paper. Once a learning dataset is fixed, there is a very high super linear speedup in the average time needed to reach a prefixed learning error, i.e., if the number of personal computers increases by n times, the mean learning time becomes less than 1/n times. PMID:18249882

  19. Diagnostic Tools for Learning Organizations.

    ERIC Educational Resources Information Center

    Moilanen, Raili

    2001-01-01

    The Learning Organization Diamond Tool was designed for holistic analysis of 10 learning organization elements at the individual and organizational levels. A test in 25 Finnish organizations established validity. Comparison with existing tools showed that differences derive from their different purposes. (Contains 33 references.) (SK)

  20. Graphite fiber reinforced structure for supporting machine tools

    DOEpatents

    Knight, Jr., Charles E.; Kovach, Louis; Hurst, John S.

    1978-01-01

    Machine tools utilized in precision machine operations require tool support structures which exhibit minimal deflection, thermal expansion and vibration characteristics. The tool support structure of the present invention is a graphite fiber reinforced composite in which layers of the graphite fibers or yarn are disposed in a 0/90.degree. pattern and bonded together with an epoxy resin. The finished composite possesses a low coefficient of thermal expansion and a substantially greater elastic modulus, stiffness-to-weight ratio, and damping factor than a conventional steel tool support utilized in similar machining operations.

  1. Study on machining mechanism of nanotwinned CBN cutting tool

    NASA Astrophysics Data System (ADS)

    Chen, Junyun; Jin, Tianye; Wang, Jinhu; Zhao, Qingliang; Lu, Ling

    2014-08-01

    The latest developed nanotwinned cubic boron nitride (nt-CBN) with isotropic nano-sized microstructure possesses an extremely high hardness (~100GPa Hv), very large fracture toughness (>12Mpa m1/2) and excellent high temperature stability. Thus nt-CBN is a promising tool material to realize ultra-precision cutting of hardened steel which is widely used in mold insert of optical and opto-electrical mass products. In view of its hard machinability, the machining mechanism is studied in this paper. Three feasible methods of mechanical lapping, laser machining as well as ion beam sputtering are applied to process nt-CBN. The results indicate that among the three kinds of methods, mechanical lapping not only can achieve the highest machining accuracy because of material removing at ductile mode completely, but also has satisfactory high material removal rate. Thus mechanical lapping method is appropriate to finish machining of nt-CBN cutting tool. Moreover, laser machining method can be only used in contour machining or rough machining of cutting tool as worse machined surface quality. With regard to ion beam sputtering method, the material remove rate is too low in spite of high machining accuracy. Additionally, no phase transition was found in any machining process of nt-CBN.

  2. Modeling of cumulative tool wear in machining metal matrix composites

    SciTech Connect

    Hung, N.P.; Tan, V.K.; Oon, B.E.

    1995-12-31

    Metal matrix composites (MMCs) are notoriously known for their low machinability because of the abrasive and brittle reinforcement. Although a near-net-shape product could be produced, finish machining is still required for the final shape and dimension. The classical Taylor`s tool life equation that relates tool life and cutting conditions has been traditionally used to study machinability. The turning operation is commonly used to investigate the machinability of a material; tedious and costly milling experiments have to be performed separately; while a facing test is not applicable for the Taylor`s model since the facing speed varies as the tool moves radially. Collecting intensive machining data for MMCs is often difficult because of the constraints on size, cost of the material, and the availability of sophisticated machine tools. A more flexible model and machinability testing technique are, therefore, sought. This study presents and verifies new models for turning, facing, and milling operations. Different cutting conditions were utilized to assess the machinability of MMCs reinforced with silicon carbide or alumina particles. Experimental data show that tool wear does not depend on the order of different cutting speeds since abrasion is the main wear mechanism. Correlation between data for turning, milling, and facing is presented. It is more economical to rank machinability using data for facing and then to convert the data for turning and milling, if required. Subsurface damages such as work-hardened and cracked matrix alloy, and fractured and delaminated particles are discussed.

  3. Machine Learning Toolkit for Extreme Scale

    SciTech Connect

    2014-03-31

    Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are considered in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets

  4. Machine Learning Toolkit for Extreme Scale

    Energy Science and Technology Software Center (ESTSC)

    2014-03-31

    Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination ofmore » samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are considered in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

  5. Using Simple Machines to Leverage Learning

    ERIC Educational Resources Information Center

    Dotger, Sharon

    2008-01-01

    What would your students say if you told them they could lift you off the ground using a block and a board? Using a simple machine, they'll find out they can, and they'll learn about work, energy, and motion in the process! In addition, this integrated lesson gives students the opportunity to investigate variables while practicing measurement…

  6. Vitrification: Machines learn to recognize glasses

    NASA Astrophysics Data System (ADS)

    Ceriotti, Michele; Vitelli, Vincenzo

    2016-05-01

    The dynamics of a viscous liquid undergo a dramatic slowdown when it is cooled to form a solid glass. Recognizing the structural changes across such a transition remains a major challenge. Machine-learning methods, similar to those Facebook uses to recognize groups of friends, have now been applied to this problem.

  7. Influence of Tool Balancing in High Speed Machining

    NASA Astrophysics Data System (ADS)

    Bašovská, Klaudia; Peterka, Jozef

    2014-12-01

    The high speed machining (HSM) is now considered as one of the key manufacturing technologies for higher throughput and productivity. HSM used higher rotational speed of the spindle (40,000 min-1 and higher). With increasing high speed spindle rotations raises a number of dynamic forces. Even a small mass unbalance in the spindle and tooling generates tool vibration. Tool vibration shortens tool life and lowers the quality of the machined surface. It is necessary to minimize this vibration by balancing tool and tool holder. The balancing process improves the mass distribution of a cutting tool and its holder, allowing the combination of the two to rotate with the minimum amount of unbalanced centrifugal forces. Machining with balanced tool will provide better surface quality, accuracy and less tool and machine wear. In this study is focused on unbalance cutting tools, definitions, balancing techniques, sources, effects, processes and machineries. The aim of this article was to examine the relationship between unbalance and tool holders used in high speed metalworking machine tools

  8. AstroML: Python-powered Machine Learning for Astronomy

    NASA Astrophysics Data System (ADS)

    Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

    2014-01-01

    As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

  9. Study of on-machine error identification and compensation methods for micro machine tools

    NASA Astrophysics Data System (ADS)

    Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

    2016-08-01

    Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

  10. Machine learning in soil classification.

    PubMed

    Bhattacharya, B; Solomatine, D P

    2006-03-01

    In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori information that might be available. In this paper, an approach for automating this classification procedure is presented. Firstly, a segmentation algorithm is developed and applied to segment the measured signals. Secondly, the salient features of these segments are extracted using boundary energy method. Based on the measured data and extracted features to assign classes to the segments classifiers are built; they employ Decision Trees, ANN and Support Vector Machines. The methodology was tested in classifying sub-surface soil using measured data from Cone Penetration Testing and satisfactory results were obtained. PMID:16530382

  11. Volumetric Verification of Multiaxis Machine Tool Using Laser Tracker

    PubMed Central

    Aguilar, Juan José

    2014-01-01

    This paper aims to present a method of volumetric verification in machine tools with linear and rotary axes using a laser tracker. Beyond a method for a particular machine, it presents a methodology that can be used in any machine type. Along this paper, the schema and kinematic model of a machine with three axes of movement, two linear and one rotational axes, including the measurement system and the nominal rotation matrix of the rotational axis are presented. Using this, the machine tool volumetric error is obtained and nonlinear optimization techniques are employed to improve the accuracy of the machine tool. The verification provides a mathematical, not physical, compensation, in less time than other methods of verification by means of the indirect measurement of geometric errors of the machine from the linear and rotary axes. This paper presents an extensive study about the appropriateness and drawbacks of the regression function employed depending on the types of movement of the axes of any machine. In the same way, strengths and weaknesses of measurement methods and optimization techniques depending on the space available to place the measurement system are presented. These studies provide the most appropriate strategies to verify each machine tool taking into consideration its configuration and its available work space. PMID:25202744

  12. Haptics-Augmented Simple-Machine Educational Tools.

    ERIC Educational Resources Information Center

    Williams, Robert L., II; Chen, Meng-Yun; Seaton, Jeffrey M.

    2003-01-01

    Describes a unique project using commercial haptic interfaces to augment the teaching of simple machines in elementary school. Suggests that the use of haptics in virtual simple-machine simulations has the potential for deeper, more engaging learning. (Contains 13 references.) (Author/YDS)

  13. Tool simplifies machining of pipe ends for precision welding

    NASA Technical Reports Server (NTRS)

    Matus, S. T.

    1969-01-01

    Single tool prepares a pipe end for precision welding by simultaneously performing internal machining, end facing, and bevel cutting to specification standards. The machining operation requires only one milling adjustment, can be performed quickly, and produces the high quality pipe-end configurations required to ensure precision-welded joints.

  14. Job Grading Standard for Machine Tool Operator, WG-3431.

    ERIC Educational Resources Information Center

    Civil Service Commission, Washington, DC. Bureau of Policies and Standards.

    The standard covers nonsupervisory work involved in the set up, adjustment, and operation of conventional machine tools to perform machining operations in the manufacture and repair of castings, forgings, or parts from raw stock made of various metals, metal alloys, and other materials. A general description of the job at both the WG-8 and WG-9…

  15. 27. View within machine room showing water tank, tool chest ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    27. View within machine room showing water tank, tool chest and oil/grease cans used for maintenance. (Nov. 25, 1988) - University Heights Bridge, Spanning Harlem River at 207th Street & West Harlem Road, New York County, NY

  16. Identifying hosts of families of viruses: a machine learning approach.

    PubMed

    Raj, Anil; Dewar, Michael; Palacios, Gustavo; Rabadan, Raul; Wiggins, Christopher H

    2011-01-01

    Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome. PMID:22174744

  17. Machine Learning Assessments of Soil Drying

    NASA Astrophysics Data System (ADS)

    Coopersmith, E. J.; Minsker, B. S.; Wenzel, C.; Gilmore, B. J.

    2011-12-01

    Agricultural activities require the use of heavy equipment and vehicles on unpaved farmlands. When soil conditions are wet, equipment can cause substantial damage, leaving deep ruts. In extreme cases, implements can sink and become mired, causing considerable delays and expense to extricate the equipment. Farm managers, who are often located remotely, cannot assess sites before allocating equipment, causing considerable difficulty in reliably assessing conditions of countless sites with any reliability and frequency. For example, farmers often trace serpentine paths of over one hundred miles each day to assess the overall status of various tracts of land spanning thirty, forty, or fifty miles in each direction. One means of assessing the moisture content of a field lies in the strategic positioning of remotely-monitored in situ sensors. Unfortunately, land owners are often reluctant to place sensors across their properties due to the significant monetary cost and complexity. This work aspires to overcome these limitations by modeling the process of wetting and drying statistically - remotely assessing field readiness using only information that is publically accessible. Such data includes Nexrad radar and state climate network sensors, as well as Twitter-based reports of field conditions for validation. Three algorithms, classification trees, k-nearest-neighbors, and boosted perceptrons are deployed to deliver statistical field readiness assessments of an agricultural site located in Urbana, IL. Two of the three algorithms performed with 92-94% accuracy, with the majority of misclassifications falling within the calculated margins of error. This demonstrates the feasibility of using a machine learning framework with only public data, knowledge of system memory from previous conditions, and statistical tools to assess "readiness" without the need for real-time, on-site physical observation. Future efforts will produce a workflow assimilating Nexrad, climate network

  18. Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

    PubMed

    Howard, Rebecca; Rattray, Magnus; Prosperi, Mattia; Custovic, Adnan

    2015-07-01

    Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies. PMID:26143394

  19. Reducing tool wear when machining austenitic stainless steels

    SciTech Connect

    Magee, J.H.; Kosa, T.

    1998-07-01

    Austenitic stainless steels are considered more difficult to machine than carbon steels due to their high work hardening rate, large spread between yield and ultimate tensile strength, high toughness and ductility, and low thermal conductivity. These characteristics can result in a built-up edge or excessive tool wear during machining, especially when the cutting speed is too high. The practical solution is to lower the cutting speed until tool life reaches an acceptable level. However, lower machining speed negatively impacts productivity. Thus, in order to overcome tool wear at relatively high machining speeds for these alloys, on-going research is being performed to improve cutting fluids, develop more wear-resistant tools, and to modify stainless steels to make them less likely to cause tool wear. This paper discusses compositional modifications to the two most commonly machined austenitic stainless steels (Type 303 and 304) which reduced their susceptibility to tool wear, and allowed these grades to be machined at higher cutting speeds.

  20. Survey of Machine Learning Methods for Database Security

    NASA Astrophysics Data System (ADS)

    Kamra, Ashish; Ber, Elisa

    Application of machine learning techniques to database security is an emerging area of research. In this chapter, we present a survey of various approaches that use machine learning/data mining techniques to enhance the traditional security mechanisms of databases. There are two key database security areas in which these techniques have found applications, namely, detection of SQL Injection attacks and anomaly detection for defending against insider threats. Apart from the research prototypes and tools, various third-party commercial products are also available that provide database activity monitoring solutions by profiling database users and applications. We present a survey of such products. We end the chapter with a primer on mechanisms for responding to database anomalies.

  1. Application of machine learning to structural molecular biology.

    PubMed

    Sternberg, M J; King, R D; Lewis, R A; Muggleton, S

    1994-06-29

    A technique of machine learning, inductive logic programming implemented in the program GOLEM, has been applied to three problems in structural molecular biology. These problems are: the prediction of protein secondary structure; the identification of rules governing the arrangement of beta-sheets strands in the tertiary folding of proteins; and the modelling of a quantitative structure activity relationship (QSAR) of a series of drugs. For secondary structure prediction and the QSAR, GOLEM yielded predictions comparable with contemporary approaches including neural networks. Rules for beta-strand arrangement are derived and it is planned to contrast their accuracy with those obtained by human inspection. In all three studies GOLEM discovered rules that provided insight into the stereochemistry of the system. We conclude machine learning used together with human intervention will provide a powerful tool to discover patterns in biological sequences and structures. PMID:7800706

  2. Machine Learning and Geometric Technique for SLAM

    NASA Astrophysics Data System (ADS)

    Bernal-Marin, Miguel; Bayro-Corrochano, Eduardo

    This paper describes a new approach for building 3D geometric maps using a laser rangefinder, a stereo camera system and a mathematical system the Conformal Geometric Algebra. The use of a known visual landmarks in the map helps to carry out a good localization of the robot. A machine learning technique is used for recognition of objects in the environment. These landmarks are found using the Viola and Jones algorithm and are represented with their position in the 3D virtual map.

  3. Mississippi Curriculum Framework for Machine Tool Operation/Machine Shop (Program CIP: 48.0503--Machine Shop Assistant). Secondary Programs.

    ERIC Educational Resources Information Center

    Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

    This document, which reflects Mississippi's statutory requirement that instructional programs be based on core curricula and performance-based assessment, contains outlines of the instructional units required in local instructional management plans and daily lesson plans for machine tool operation/machine shop I and II. Presented first are a…

  4. Prototype-based models in machine learning.

    PubMed

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. PMID:26800334

  5. Toward a metrology for precision-machine-tool control systems

    SciTech Connect

    Pomernacki, C.L.; McCue, H.K.; Newton, L.E.

    1982-07-20

    The difficulty of determining the source of an error in the performance of the control system of a computer numerically controlled (CNC) precision machine tool is discussed and recommendations are made for error isolation using the Machine Control System Meterology Tree. These recommendations refer to types of tests for specific errors and to a possible architecture for a CNC performance tester. It is concluded that there is a need for both a control system metrology and for establishing standards of performance and testing methods for precision machine tool control systems. (LCL)

  6. Scaling up: Distributed machine learning with cooperation

    SciTech Connect

    Provost, F.J.; Hennessy, D.N.

    1996-12-31

    Machine-learning methods are becoming increasingly popular for automated data analysis. However, standard methods do not scale up to massive scientific and business data sets without expensive hardware. This paper investigates a practical alternative for scaling up: the use of distributed processing to take advantage of the often dormant PCs and workstations available on local networks. Each workstation runs a common rule-learning program on a subset of the data. We first show that for commonly used rule-evaluation criteria, a simple form of cooperation can guarantee that a rule will look good to the set of cooperating learners if and only if it would look good to a single learner operating with the entire data set. We then show how such a system can further capitalize on different perspectives by sharing learned knowledge for significant reduction in search effort. We demonstrate the power of the method by learning from a massive data set taken from the domain of cellular fraud detection. Finally, we provide an overview of other methods for scaling up machine learning.

  7. Dimension Reduction With Extreme Learning Machine.

    PubMed

    Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

    2016-08-01

    Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error. PMID:27214902

  8. Machine Shop I. Learning Activity Packets (LAPs). Section B--Basic and Related Technology.

    ERIC Educational Resources Information Center

    Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This document contains eight learning activity packets (LAPs) for the "basic and related technology" instructional area of a Machine Shop I course. The eight LAPs cover the following topics: basic mathematics, blueprints, rules, micrometer measuring tools, Vernier measuring tools, dial indicators, gaging and inspection tools, and materials and…

  9. Lathe tool bit and holder for machining fiberglass materials

    NASA Technical Reports Server (NTRS)

    Winn, L. E. (Inventor)

    1972-01-01

    A lathe tool and holder combination for machining resin impregnated fiberglass cloth laminates is described. The tool holder and tool bit combination is designed to accommodate a conventional carbide-tipped, round shank router bit as the cutting medium, and provides an infinite number of cutting angles in order to produce a true and smooth surface in the fiberglass material workpiece with every pass of the tool bit. The technique utilizes damaged router bits which ordinarily would be discarded.

  10. Diamond tool machining of materials which react with diamond

    DOEpatents

    Lundin, Ralph L.; Stewart, Delbert D.; Evans, Christopher J.

    1992-01-01

    Apparatus for the diamond machining of materials which detrimentally react with diamond cutting tools in which the cutting tool and the workpiece are chilled to very low temperatures. This chilling halts or retards the chemical reaction between the workpiece and the diamond cutting tool so that wear rates of the diamond tool on previously detrimental materials are comparable with the diamond turning of materials which do not react with diamond.

  11. Diamond tool machining of materials which react with diamond

    DOEpatents

    Lundin, R.L.; Stewart, D.D.; Evans, C.J.

    1992-04-14

    An apparatus is described for the diamond machining of materials which detrimentally react with diamond cutting tools in which the cutting tool and the workpiece are chilled to very low temperatures. This chilling halts or retards the chemical reaction between the workpiece and the diamond cutting tool so that wear rates of the diamond tool on previously detrimental materials are comparable with the diamond turning of materials which do not react with diamond. 1 figs.

  12. Machine learning: how to get more out of HEP data and the Higgs Boson Machine Learning Challenge

    NASA Astrophysics Data System (ADS)

    Wolter, Marcin

    2015-09-01

    Multivariate techniques using machine learning algorithms have become an integral part in many High Energy Physics (HEP) data analyses. The article shows the gain in physics reach of the physics experiments due to the adaptation of machine learning techniques. Rapid development in the field of machine learning in the last years is a challenge for the HEP community. The open competition for machine learning experts "Higgs Boson Machine Learning Challenge" shows, that the modern techniques developed outside HEP can significantly improve the analysis of data from HEP experiments and improve the sensitivity of searches for new particles and processes.

  13. Wearable Learning Tools.

    ERIC Educational Resources Information Center

    Bowskill, Jerry; Dyer, Nick

    1999-01-01

    Describes wearable computers, or information and communication technology devices that are designed to be mobile. Discusses how such technologies can enhance computer-mediated communications, focusing on collaborative working for learning. Describes an experimental system, MetaPark, which explores communications, data retrieval and recording, and…

  14. Scalable Machine Learning for Massive Astronomical Datasets

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.; Astronomy Data Centre, Canadian

    2014-01-01

    We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

  15. Scalable Machine Learning for Massive Astronomical Datasets

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.; Gray, A.

    2014-04-01

    We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex

  16. Finding new perovskite halides via machine learning

    DOE PAGESBeta

    Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

    2016-04-26

    Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.« less

  17. Finding New Perovskite Halides via Machine learning

    NASA Astrophysics Data System (ADS)

    Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

    2016-04-01

    Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

  18. NUMERICAL CONTROL OF MACHINE TOOLS, AN INSTRUCTOR'S GUIDE.

    ERIC Educational Resources Information Center

    California State Dept. of Education, Sacramento. Bureau of Industrial Education.

    IN A SUMMER WORKSHOP, JUNIOR COLLEGE INSTRUCTORS AND INDUSTRIAL SUPERVISORS DEVELOPED THIS GUIDE FOR TEACHER USE IN A 3-SEMESTER-HOUR COURSE AT THE JUNIOR COLLEGE LEVEL. THE COURSE OBJECTIVES ARE TO (1) UPGRADE JOURNEYMEN IN MACHINE TOOL OPERATION, MAINTENANCE, AND TOOLING, AND (2) ACQUAINT MANUFACTURING, SUPERVISORY, PLANNING, AND MAINTENANCE…

  19. Hard turning micro-machine tool

    DOEpatents

    DeVor, Richard E; Adair, Kurt; Kapoor, Shiv G

    2013-10-22

    A micro-scale apparatus for supporting a tool for hard turning comprises a base, a pivot coupled to the base, an actuator coupled to the base, and at least one member coupled to the actuator at one end and rotatably coupled to the pivot at another end. A tool mount is disposed on the at least one member. The at least one member defines a first lever arm between the pivot and the tool mount, and a second lever arm between the pivot and the actuator. The first lever arm has a length that is less than a length of the second lever arm. The actuator moves the tool mount along an arc.

  20. Discriminative clustering via extreme learning machine.

    PubMed

    Huang, Gao; Liu, Tianchi; Yang, Yan; Lin, Zhiping; Song, Shiji; Wu, Cheng

    2015-10-01

    Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discriminative clustering approaches based on Extreme Learning Machine (ELM). The first algorithm iteratively trains weighted ELM (W-ELM) classifier to gradually maximize the data discrimination. The second and third methods are both built on Fisher's Linear Discriminant Analysis (LDA); but one approach adopts alternative optimization, while the other leverages kernel k-means. We show that the proposed algorithms can be easily implemented, and yield competitive clustering accuracy on real world data sets compared to state-of-the-art clustering methods. PMID:26143036

  1. Machine learning methods for predictive proteomics.

    PubMed

    Barla, Annalisa; Jurman, Giuseppe; Riccadonna, Samantha; Merler, Stefano; Chierici, Marco; Furlanello, Cesare

    2008-03-01

    The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies. PMID:18310105

  2. Entanglement-Based Machine Learning on a Quantum Computer

    NASA Astrophysics Data System (ADS)

    Cai, X.-D.; Wu, D.; Su, Z.-E.; Chen, M.-C.; Wang, X.-L.; Li, Li; Liu, N.-L.; Lu, C.-Y.; Pan, J.-W.

    2015-03-01

    Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

  3. Entanglement-based machine learning on a quantum computer.

    PubMed

    Cai, X-D; Wu, D; Su, Z-E; Chen, M-C; Wang, X-L; Li, Li; Liu, N-L; Lu, C-Y; Pan, J-W

    2015-03-20

    Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning. PMID:25839250

  4. Process Damping and Cutting Tool Geometry in Machining

    NASA Astrophysics Data System (ADS)

    Taylor, C. M.; Sims, N. D.; Turner, S.

    2011-12-01

    Regenerative vibration, or chatter, limits the performance of machining processes. Consequences of chatter include tool wear and poor machined surface finish. Process damping by tool-workpiece contact can reduce chatter effects and improve productivity. Process damping occurs when the flank (also known as the relief face) of the cutting tool makes contact with waves on the workpiece surface, created by chatter motion. Tool edge features can act to increase the damping effect. This paper examines how a tool's edge condition combines with the relief angle to affect process damping. An analytical model of cutting with chatter leads to a two-section curve describing how process damped vibration amplitude changes with surface speed for radiussed tools. The tool edge dominates the process damping effect at the lowest surface speeds, with the flank dominating at higher speeds. A similar curve is then proposed regarding tools with worn edges. Experimental data supports the notion of the two-section curve. A rule of thumb is proposed which could be useful to machine operators, regarding tool wear and process damping. The question is addressed, should a tool of a given geometry, used for a given application, be considered as sharp, radiussed or worn regarding process damping.

  5. Machine learning: An artificial intelligence approach. Vol. II

    SciTech Connect

    Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M.

    1986-01-01

    This book reflects the expansion of machine learning research through presentation of recent advances in the field. The book provides an account of current research directions. Major topics covered include the following: learning concepts and rules from examples; cognitive aspects of learning; learning by analogy; learning by observation and discovery; and an exploration of general aspects of learning.

  6. Extreme Learning Machine for Multilayer Perceptron.

    PubMed

    Tang, Jiexiong; Deng, Chenwei; Huang, Guang-Bin

    2016-04-01

    Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme. PMID:25966483

  7. Applying Machine Learning to Star Cluster Classification

    NASA Astrophysics Data System (ADS)

    Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

    2016-01-01

    Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

  8. 25. VIEW OF THE MACHINE TOOL LAYOUT IN ROOMS 244 ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    25. VIEW OF THE MACHINE TOOL LAYOUT IN ROOMS 244 AND 296. MACHINES WERE USED FOR STAINLESS STEEL FABRICATION (THE J-LINE). THE ORIGINAL DRAWING HAS BEEN ARCHIVED ON MICROFILM. THE DRAWING WAS REPRODUCED AT THE BEST QUALITY POSSIBLE. LETTERS AND NUMBERS IN THE CIRCLES INDICATE FOOTER AND/OR COLUMN LOCATIONS. - Rocky Flats Plant, General Manufacturing, Support, Records-Central Computing, Southern portion of Plant, Golden, Jefferson County, CO

  9. Smarter Instruments, Smarter Archives: Machine Learning for Tactical Science

    NASA Astrophysics Data System (ADS)

    Thompson, D. R.; Kiran, R.; Allwood, A.; Altinok, A.; Estlin, T.; Flannery, D.

    2014-12-01

    There has been a growing interest by Earth and Planetary Sciences in machine learning, visualization and cyberinfrastructure to interpret ever-increasing volumes of instrument data. Such tools are commonly used to analyze archival datasets, but they can also play a valuable real-time role during missions. Here we discuss ways that machine learning can benefit tactical science decisions during Earth and Planetary Exploration. Machine learning's potential begins at the instrument itself. Smart instruments endowed with pattern recognition can immediately recognize science features of interest. This allows robotic explorers to optimize their limited communications bandwidth, triaging science products and prioritizing the most relevant data. Smart instruments can also target their data collection on the fly, using principles of experimental design to reduce redundancy and generally improve sampling efficiency for time-limited operations. Moreover, smart instruments can respond immediately to transient or unexpected phenomena. Examples include detections of cometary plumes, terrestrial floods, or volcanism. We show recent examples of smart instruments from 2014 tests including: aircraft and spacecraft remote sensing instruments that recognize cloud contamination, field tests of a "smart camera" for robotic surface geology, and adaptive data collection by X-Ray fluorescence spectrometers. Machine learning can also assist human operators when tactical decision making is required. Terrestrial scenarios include airborne remote sensing, where the decision to re-fly a transect must be made immediately. Planetary scenarios include deep space encounters or planetary surface exploration, where the number of command cycles is limited and operators make rapid daily decisions about where next to collect measurements. Visualization and modeling can reveal trends, clusters, and outliers in new data. This can help operators recognize instrument artifacts or spot anomalies in real time

  10. Machine-learning-assisted materials discovery using failed experiments.

    PubMed

    Raccuglia, Paul; Elbert, Katherine C; Adler, Philip D F; Falk, Casey; Wenny, Malia B; Mollo, Aurelio; Zeller, Matthias; Friedler, Sorelle A; Schrier, Joshua; Norquist, Alexander J

    2016-05-01

    Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions

  11. Machine-learning-assisted materials discovery using failed experiments

    NASA Astrophysics Data System (ADS)

    Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.; Falk, Casey; Wenny, Malia B.; Mollo, Aurelio; Zeller, Matthias; Friedler, Sorelle A.; Schrier, Joshua; Norquist, Alexander J.

    2016-05-01

    Inorganic–organic hybrid materials such as organically templated metal oxides, metal–organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure–property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions—failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully

  12. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    ERIC Educational Resources Information Center

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  13. Modeling quantum physics with machine learning

    NASA Astrophysics Data System (ADS)

    Lopez-Bezanilla, Alejandro; Arsenault, Louis-Francois; Millis, Andrew; Littlewood, Peter; von Lilienfeld, Anatole

    2014-03-01

    Machine Learning (ML) is a systematic way of inferring new results from sparse information. It directly allows for the resolution of computationally expensive sets of equations by making sense of accumulated knowledge and it is therefore an attractive method for providing computationally inexpensive 'solvers' for some of the important systems of condensed matter physics. In this talk a non-linear regression statistical model is introduced to demonstrate the utility of ML methods in solving quantum physics related problem, and is applied to the calculation of electronic transport in 1D channels. DOE contract number DE-AC02-06CH11357.

  14. Multivariate Mapping of Environmental Data Using Extreme Learning Machines

    NASA Astrophysics Data System (ADS)

    Leuenberger, Michael; Kanevski, Mikhail

    2014-05-01

    In most real cases environmental data are multivariate, highly variable at several spatio-temporal scales, and are generated by nonlinear and complex phenomena. Mapping - spatial predictions of such data, is a challenging problem. Machine learning algorithms, being universal nonlinear tools, have demonstrated their efficiency in modelling of environmental spatial and space-time data (Kanevski et al. 2009). Recently, a new approach in machine learning - Extreme Learning Machine (ELM), has gained a great popularity. ELM is a fast and powerful approach being a part of the machine learning algorithm category. Developed by G.-B. Huang et al. (2006), it follows the structure of a multilayer perceptron (MLP) with one single-hidden layer feedforward neural networks (SLFNs). The learning step of classical artificial neural networks, like MLP, deals with the optimization of weights and biases by using gradient-based learning algorithm (e.g. back-propagation algorithm). Opposed to this optimization phase, which can fall into local minima, ELM generates randomly the weights between the input layer and the hidden layer and also the biases in the hidden layer. By this initialization, it optimizes just the weight vector between the hidden layer and the output layer in a single way. The main advantage of this algorithm is the speed of the learning step. In a theoretical context and by growing the number of hidden nodes, the algorithm can learn any set of training data with zero error. To avoid overfitting, cross-validation method or "true validation" (by randomly splitting data into training, validation and testing subsets) are recommended in order to find an optimal number of neurons. With its universal property and solid theoretical basis, ELM is a good machine learning algorithm which can push the field forward. The present research deals with an extension of ELM to multivariate output modelling and application of ELM to the real data case study - pollution of the sediments in

  15. Method for producing hard-surfaced tools and machine components

    DOEpatents

    McHargue, Carl J.

    1985-01-01

    In one aspect, the invention comprises a method for producing tools and machine components having superhard crystalline-ceramic work surfaces. Broadly, the method comprises two steps: A tool or machine component having a ceramic near-surface region is mounted in ion-implantation apparatus. The region then is implanted with metal ions to form, in the region, a metastable alloy of the ions and said ceramic. The region containing the alloy is characterized by a significant increase in hardness properties, such as microhardness, fracture-toughness, and/or scratch-resistance. The resulting improved article has good thermal stability at temperatures characteristic of typical tool and machine-component uses. The method is relatively simple and reproducible.

  16. Method for producing hard-surfaced tools and machine components

    DOEpatents

    McHargue, C.J.

    1981-10-21

    In one aspect, the invention comprises a method for producing tools and machine components having superhard crystalline-ceramic work surfaces. Broadly, the method comprises two steps: a tool or machine component having a ceramic near-surface region is mounted in ion-implantation apparatus. The region then is implanted with metal ions to form, in the region, a metastable alloy of the ions and said ceramic. The region containing the alloy is characterized by a significant increase in hardness properties, such as microhardness, fracture-toughness, and/or scratch-resistance. The resulting improved article has good thermal stability at temperatures characteristic of typical tool and machine-component uses. The method is relatively simple and reproducible.

  17. Patient-centered yes/no prognosis using learning machines

    PubMed Central

    König, I.R.; Malley, J.D.; Pajevic, S.; Weimar, C.; Diener, H-C.

    2009-01-01

    In the last 15 years several machine learning approaches have been developed for classification and regression. In an intuitive manner we introduce the main ideas of classification and regression trees, support vector machines, bagging, boosting and random forests. We discuss differences in the use of machine learning in the biomedical community and the computer sciences. We propose methods for comparing machines on a sound statistical basis. Data from the German Stroke Study Collaboration is used for illustration. We compare the results from learning machines to those obtained by a published logistic regression and discuss similarities and differences. PMID:19216340

  18. Weka-A Machine Learning Workbench for Data Mining

    NASA Astrophysics Data System (ADS)

    Frank, Eibe; Hall, Mark; Holmes, Geoffrey; Kirkby, Richard; Pfahringer, Bernhard; Witten, Ian H.; Trigg, Len

    The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License.

  19. Effective and efficient optics inspection approach using machine learning algorithms

    SciTech Connect

    Abdulla, G; Kegelmeyer, L; Liao, Z; Carr, W

    2010-11-02

    The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

  20. Effective and efficient optics inspection approach using machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Abdulla, Ghaleb M.; Kegelmeyer, Laura Mascio; Liao, Zhi M.; Carr, Wren

    2010-11-01

    The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is "truthed" or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called "Avatar Tools" is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

  1. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

    PubMed

    Subbulakshmi, C V; Deepa, S N

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers. PMID:26491713

  2. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    PubMed Central

    Subbulakshmi, C. V.; Deepa, S. N.

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers. PMID:26491713

  3. Modelling of Tool Wear and Residual Stress during Machining of AISI H13 Tool Steel

    NASA Astrophysics Data System (ADS)

    Outeiro, José C.; Umbrello, Domenico; Pina, José C.; Rizzuti, Stefania

    2007-05-01

    Residual stresses can enhance or impair the ability of a component to withstand loading conditions in service (fatigue, creep, stress corrosion cracking, etc.), depending on their nature: compressive or tensile, respectively. This poses enormous problems in structural assembly as this affects the structural integrity of the whole part. In addition, tool wear issues are of critical importance in manufacturing since these affect component quality, tool life and machining cost. Therefore, prediction and control of both tool wear and the residual stresses in machining are absolutely necessary. In this work, a two-dimensional Finite Element model using an implicit Lagrangian formulation with an automatic remeshing was applied to simulate the orthogonal cutting process of AISI H13 tool steel. To validate such model the predicted and experimentally measured chip geometry, cutting forces, temperatures, tool wear and residual stresses on the machined affected layers were compared. The proposed FE model allowed us to investigate the influence of tool geometry, cutting regime parameters and tool wear on residual stress distribution in the machined surface and subsurface of AISI H13 tool steel. The obtained results permit to conclude that in order to reduce the magnitude of surface residual stresses, the cutting speed should be increased, the uncut chip thickness (or feed) should be reduced and machining with honed tools having large cutting edge radii produce better results than chamfered tools. Moreover, increasing tool wear increases the magnitude of surface residual stresses.

  4. Modelling of Tool Wear and Residual Stress during Machining of AISI H13 Tool Steel

    SciTech Connect

    Outeiro, Jose C.; Pina, Jose C.; Umbrello, Domenico; Rizzuti, Stefania

    2007-05-17

    Residual stresses can enhance or impair the ability of a component to withstand loading conditions in service (fatigue, creep, stress corrosion cracking, etc.), depending on their nature: compressive or tensile, respectively. This poses enormous problems in structural assembly as this affects the structural integrity of the whole part. In addition, tool wear issues are of critical importance in manufacturing since these affect component quality, tool life and machining cost. Therefore, prediction and control of both tool wear and the residual stresses in machining are absolutely necessary. In this work, a two-dimensional Finite Element model using an implicit Lagrangian formulation with an automatic remeshing was applied to simulate the orthogonal cutting process of AISI H13 tool steel. To validate such model the predicted and experimentally measured chip geometry, cutting forces, temperatures, tool wear and residual stresses on the machined affected layers were compared. The proposed FE model allowed us to investigate the influence of tool geometry, cutting regime parameters and tool wear on residual stress distribution in the machined surface and subsurface of AISI H13 tool steel. The obtained results permit to conclude that in order to reduce the magnitude of surface residual stresses, the cutting speed should be increased, the uncut chip thickness (or feed) should be reduced and machining with honed tools having large cutting edge radii produce better results than chamfered tools. Moreover, increasing tool wear increases the magnitude of surface residual stresses.

  5. Evaluation as a Learning Tool

    ERIC Educational Resources Information Center

    Feinstein, Osvaldo Nestor

    2012-01-01

    Evaluation of programs or projects is often perceived as a threat. This is to a great extent related to the anticipated use of evaluation for accountability, which is often prioritized at the expense of using evaluation as a learning tool. Frequently it is argued that there is a trade-off between these two evaluation functions. An alternative…

  6. Classifying Structures in the ISM with Machine Learning Techniques

    NASA Astrophysics Data System (ADS)

    Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

    2011-01-01

    The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

  7. Geological applications of machine learning on hyperspectral remote sensing data

    NASA Astrophysics Data System (ADS)

    Tse, C. H.; Li, Yi-liang; Lam, Edmund Y.

    2015-02-01

    The CRISM imaging spectrometer orbiting Mars has been producing a vast amount of data in the visible to infrared wavelengths in the form of hyperspectral data cubes. These data, compared with those obtained from previous remote sensing techniques, yield an unprecedented level of detailed spectral resolution in additional to an ever increasing level of spatial information. A major challenge brought about by the data is the burden of processing and interpreting these datasets and extract the relevant information from it. This research aims at approaching the challenge by exploring machine learning methods especially unsupervised learning to achieve cluster density estimation and classification, and ultimately devising an efficient means leading to identification of minerals. A set of software tools have been constructed by Python to access and experiment with CRISM hyperspectral cubes selected from two specific Mars locations. A machine learning pipeline is proposed and unsupervised learning methods were implemented onto pre-processed datasets. The resulting data clusters are compared with the published ASTER spectral library and browse data products from the Planetary Data System (PDS). The result demonstrated that this approach is capable of processing the huge amount of hyperspectral data and potentially providing guidance to scientists for more detailed studies.

  8. Measure Transformer Semantics for Bayesian Machine Learning

    NASA Astrophysics Data System (ADS)

    Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; van Gael, Jurgen

    The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define combinators for measure transformers, based on theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that has a straightforward semantics via factor graphs, data structures that enable many efficient inference algorithms. We use an existing inference engine for efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models.

  9. Mining the Kepler Data using Machine Learning

    NASA Astrophysics Data System (ADS)

    Walkowicz, Lucianne; Howe, A. R.; Nayar, R.; Turner, E. L.; Scargle, J.; Meadows, V.; Zee, A.

    2014-01-01

    Kepler's high cadence and incredible precision has provided an unprecedented view into stars and their planetary companions, revealing both expected and novel phenomena and systems. Due to the large number of Kepler lightcurves, the discovery of novel phenomena in particular has often been serendipitous in the course of searching for known forms of variability (for example, the discovery of the doubly pulsating elliptical binary KOI-54, originally identified by the transiting planet search pipeline). In this talk, we discuss progress on mining the Kepler data through both supervised and unsupervised machine learning, intended to both systematically search the Kepler lightcurves for rare or anomalous variability, and to create a variability catalog for community use. Mining the dataset in this way also allows for a quantitative identification of anomalous variability, and so may also be used as a signal-agnostic form of optical SETI. As the Kepler data are exceptionally rich, they provide an interesting counterpoint to machine learning efforts typically performed on sparser and/or noisier survey data, and will inform similar characterization carried out on future survey datasets.

  10. A Fast Reduced Kernel Extreme Learning Machine.

    PubMed

    Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

    2016-04-01

    In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. PMID:26829605

  11. Galaxy morphology - An unsupervised machine learning approach

    NASA Astrophysics Data System (ADS)

    Schutter, A.; Shamir, L.

    2015-09-01

    Structural properties poses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being analyzed. The source code of the method is publicly available, and the protocol of the experiment is included in the paper so that the experiment can be replicated, and the method can be used to analyze user-defined datasets of galaxy images.

  12. Knowledge discovery via machine learning for neurodegenerative disease researchers.

    PubMed

    Ozyurt, I Burak; Brown, Gregory G

    2009-01-01

    Ever-increasing size of the biomedical literature makes more precise information retrieval and tapping into implicit knowledge in scientific literature a necessity. In this chapter, first, three new variants of the expectation-maximization (EM) method for semisupervised document classification (Machine Learning 39:103-134, 2000) are introduced to refine biomedical literature meta-searches. The retrieval performance of a multi-mixture per class EM variant with Agglomerative Information Bottleneck clustering (Slonim and Tishby (1999) Agglomerative information bottleneck. In Proceedings of NIPS-12) using Davies-Bouldin cluster validity index (IEEE Transactions on Pattern Analysis and Machine Intelligence 1:224-227, 1979), rivaled the state-of-the-art transductive support vector machines (TSVM) (Joachims (1999) Transductive inference for text classification using support vector machines. In Proceedings of the International Conference on Machine Learning (ICML)). Moreover, the multi-mixture per class EM variant refined search results more quickly with more than one order of magnitude improvement in execution time compared with TSVM. A second tool, CRFNER, uses conditional random fields (Lafferty et al. (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML-2001) to recognize 15 types of named entities from schizophrenia abstracts outperforming ABNER (Settles (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In Proceedings of COLING 2004 International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA)) in biological named entity recognition and reaching F(1) performance of 82.5% on the second set of named entities. PMID:19623491

  13. Photometric Supernova Classification with Machine Learning

    NASA Astrophysics Data System (ADS)

    Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

    2016-08-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

  14. Online Sequential Extreme Learning Machine With Kernels.

    PubMed

    Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio

    2015-09-01

    The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets. PMID:25561597

  15. Machine Learning Approaches: From Theory to Application in Schizophrenia

    PubMed Central

    Veronese, Elisa; Castellani, Umberto; Peruzzo, Denis; Bellani, Marcella; Brambilla, Paolo

    2013-01-01

    In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice. PMID:24489603

  16. Behavior of some sealing arrangements for machine tool spindles

    SciTech Connect

    Philpott, M.L.; Colton, M.W.; Cusano, C.

    1995-09-01

    A test stand has been built and instrumented to simulate conditions in the spindle cavity of production machine tools, such as high-speed transfer machines, machining centers, milling machines, etc. The purpose of the simulation is to better understand causes of premature support rolling element bearing failures due to grease degradation and corrosion, from the ingress of coolant vapor. Performance characteristics based on coolant vapor in the test chamber, as measured by relative humidity, chamber temperature and chamber pressure relative to the lab atmosphere were obtained for a radial double-lip seal, labyrinth seal, viscoseal/face seal combination and a mechanical face seal. For the operating conditions considered, the best performance was obtained from the viscoseal/face combination followed by the labyrinth seal. 14 refs., 15 figs.

  17. Machine Shop I. Learning Activity Packets (LAPs). Section D--Power Saws and Drilling Machines.

    ERIC Educational Resources Information Center

    Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This document contains two learning activity packets (LAPs) for the "power saws and drilling machines" instructional area of a Machine Shop I course. The two LAPs cover the following topics: power saws and drill press. Each LAP contains a cover sheet that describes its purpose, an introduction, and the tasks included in the LAP; learning steps…

  18. Learning Activity Packets for Milling Machines. Unit I--Introduction to Milling Machines.

    ERIC Educational Resources Information Center

    Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

    This learning activity packet (LAP) outlines the study activities and performance tasks covered in a related curriculum guide on milling machines. The course of study in this LAP is intended to help students learn to identify parts and attachments of vertical and horizontal milling machines, identify work-holding devices, state safety rules, and…

  19. Educational Resources for the Machine Tool Industry. Executive Summary.

    ERIC Educational Resources Information Center

    Texas State Technical Coll. System, Waco.

    This document describes the MASTER (Machine Tool Advanced Skills Educational Resources) program, a geographic partnership of seven of the nation's best 2-year technical and community colleges located in seven states. The project developed and disseminated a national training model for manufacturing processes and new technologies within the…

  20. Machine Tool Advanced Skills Technology Program (MAST). Overview and Methodology.

    ERIC Educational Resources Information Center

    Texas State Technical Coll., Waco.

    The Machine Tool Advanced Skills Technology Program (MAST) is a geographical partnership of six of the nation's best two-year colleges located in the six states that have about one-third of the density of metals-related industries in the United States. The purpose of the MAST grant is to develop and implement a national training model to overcome…

  1. Portable power tool machines weld joints in field

    NASA Technical Reports Server (NTRS)

    Spier, R. A.

    1966-01-01

    Portable routing machine for cutting precise weld joints required by nonstandard pipe sections used in the field for transfer of cryogenic fluids. This tool is adaptable for various sizes of pipes and has a selection of router bits for different joint configurations.

  2. Tool nos. 277 and 2201, details for bending machine, Johnson ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    Tool nos. 277 and 2201, details for bending machine, Johnson Company, Johnstown, Pa. Scale 3 inches - 1 ft, Feb 13th 1893, drawing number 15098. (Photograph of drawing held at the Johnstown Corporation General Office, Johnstown, Pennsylvania) - Johnson Steel Street Rail Company, 525 Central Avenue, Johnstown, Cambria County, PA

  3. Developing an Intelligent Diagnosis and Assessment E-Learning Tool for Introductory Programming

    ERIC Educational Resources Information Center

    Huang, Chenn-Jung; Chen, Chun-Hua; Luo, Yun-Cheng; Chen, Hong-Xin; Chuang, Yi-Ta

    2008-01-01

    Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning…

  4. A Real-Time Tool Positioning Sensor for Machine-Tools

    PubMed Central

    Ruiz, Antonio Ramon Jimenez; Rosas, Jorge Guevara; Granja, Fernando Seco; Honorato, Jose Carlos Prieto; Taboada, Jose Juan Esteve; Serrano, Vicente Mico; Jimenez, Teresa Molina

    2009-01-01

    In machining, natural oscillations, and elastic, gravitational or temperature deformations, are still a problem to guarantee the quality of fabricated parts. In this paper we present an optical measurement system designed to track and localize in 3D a reference retro-reflector close to the machine-tool's drill. The complete system and its components are described in detail. Several tests, some static (including impacts and rotations) and others dynamic (by executing linear and circular trajectories), were performed on two different machine tools. It has been integrated, for the first time, a laser tracking system into the position control loop of a machine-tool. Results indicate that oscillations and deformations close to the tool can be estimated with micrometric resolution and a bandwidth from 0 to more than 100 Hz. Therefore this sensor opens the possibility for on-line compensation of oscillations and deformations. PMID:22408472

  5. Laboratory directed research and development final report: Intelligent tools for on-machine acceptance of precision machined components

    SciTech Connect

    Christensen, N.G.; Harwell, L.D.; Hazelton, A.

    1997-02-01

    On-Machine Acceptance (OMA) is an agile manufacturing concept being developed for machine tools at SNL. The concept behind OMA is the integration of product design, fabrication, and qualification processes by using the machining center as a fabrication and inspection tool. This report documents the final results of a Laboratory Directed Research and Development effort to qualify OMA.

  6. Trends in extreme learning machines: a review.

    PubMed

    Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou

    2015-01-01

    Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives. PMID:25462632

  7. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    ERIC Educational Resources Information Center

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  8. Machine learning and genome annotation: a match meant to be?

    PubMed Central

    2013-01-01

    By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE. PMID:23731483

  9. Large-Scale Machine Learning for Classification and Search

    ERIC Educational Resources Information Center

    Liu, Wei

    2012-01-01

    With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest…

  10. Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

    ERIC Educational Resources Information Center

    Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

    2015-01-01

    Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead…

  11. Vision-based on-machine measurement for CNC machine tool

    NASA Astrophysics Data System (ADS)

    Xia, Ruixue; Han, Jiang; Lu, Rongsheng; Xia, Lian

    2015-02-01

    A vision-based on-machine measurement system (OMM) was developed to improve manufacturing effectiveness. It was based on a visual probe to enable the CNC machine tool itself to act as a coordinate measuring machine (CMM) to inspect a workpiece. The proposed OMM system was composed of a visual probe and two software modules: computer-aided inspection planning (CAIP) module and measurement data processing (MDP) module. The auto-focus function of the visual probe was realized by using astigmatic method. The CAIP module was developed based on a CAD development platform with Open CASCADE as its kernel. The MDP module includes some algorithms for determination of inspection parameters, for example, the chamfered hole was measured through focus variation. The entire system was consequently verified on a CNC milling machine.

  12. Machine Learning for Dynamical Mean Field Theory

    NASA Astrophysics Data System (ADS)

    Arsenault, Louis-Francois; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole; Littlewood, P. B.; Millis, Andy

    2014-03-01

    Machine Learning (ML), an approach that infers new results from accumulated knowledge, is in use for a variety of tasks ranging from face and voice recognition to internet searching and has recently been gaining increasing importance in chemistry and physics. In this talk, we investigate the possibility of using ML to solve the equations of dynamical mean field theory which otherwise requires the (numerically very expensive) solution of a quantum impurity model. Our ML scheme requires the relation between two functions: the hybridization function describing the bare (local) electronic structure of a material and the self-energy describing the many body physics. We discuss the parameterization of the two functions for the exact diagonalization solver and present examples, beginning with the Anderson Impurity model with a fixed bath density of states, demonstrating the advantages and the pitfalls of the method. DOE contract DE-AC02-06CH11357.

  13. On machine learning classification of otoneurological data.

    PubMed

    Juhola, Martti

    2008-01-01

    A dataset including cases of six otoneurological diseases was analysed using machine learning methods to investigate the classification problem of these diseases and to compare the effectiveness of different methods for this data. Linear discriminant analysis was the best method and next multilayer perceptron neural networks provided that the data was input into a network in the form of principal components. Nearest neighbour searching, k-means clustering and Kohonen neural networks achieved almost as good results as the former, but decision trees slightly worse. Thus, these methods fared well, but Naïve Bayes rule could not be used since some data matrices were singular. Otoneurological cases subject to the six diseases given can be reliably distinguished. PMID:18487733

  14. Application of Machine Learning to the Prediction of Vegetation Health

    NASA Astrophysics Data System (ADS)

    Burchfield, Emily; Nay, John J.; Gilligan, Jonathan

    2016-06-01

    This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

  15. Tracking medical genetic literature through machine learning.

    PubMed

    Bornstein, Aaron T; McLoughlin, Matthew H; Aguilar, Jesus; Wong, Wendy S W; Solomon, Benjamin D

    2016-08-01

    There has been remarkable progress in identifying the causes of genetic conditions as well as understanding how changes in specific genes cause disease. Though difficult (and often superficial) to parse, an interesting tension involves emphasis on basic research aimed to dissect normal and abnormal biology versus more clearly clinical and therapeutic investigations. To examine one facet of this question and to better understand progress in Mendelian-related research, we developed an algorithm that classifies medical literature into three categories (Basic, Clinical, and Management) and conducted a retrospective analysis. We built a supervised machine learning classification model using the Azure Machine Learning (ML) Platform and analyzed the literature (1970-2014) from NCBI's Entrez Gene2Pubmed Database (http://www.ncbi.nlm.nih.gov/gene) using genes from the NHGRI's Clinical Genomics Database (http://research.nhgri.nih.gov/CGD/). We applied our model to 376,738 articles: 288,639 (76.6%) were classified as Basic, 54,178 (14.4%) as Clinical, and 24,569 (6.5%) as Management. The average classification accuracy was 92.2%. The rate of Clinical publication was significantly higher than Basic or Management. The rate of publication of article types differed significantly when divided into key eras: Human Genome Project (HGP) planning phase (1984-1990); HGP launch (1990) to publication (2001); following HGP completion to the "Next Generation" advent (2009); the era following 2009. In conclusion, in addition to the findings regarding the pace and focus of genetic progress, our algorithm produced a database that can be used in a variety of contexts including automating the identification of management-related literature. PMID:27268407

  16. Machine Learning in the Big Data Era: Are We There Yet?

    SciTech Connect

    Sukumar, Sreenivas Rangan

    2014-01-01

    In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

  17. Mississippi Curriculum Framework for Machine Tool Operation/Machine Shop and Tool and Die Making Technology Cluster (Program CIP: 48.0507--Tool and Die Maker/Technologist) (Program CIP: 48.0503--Machine Shop Assistant). Postsecondary Programs.

    ERIC Educational Resources Information Center

    Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

    This document, which is intended for use by community and junior colleges throughout Mississippi, contains curriculum frameworks for the course sequences in the machine tool operation/machine tool and tool and die making technology programs cluster. Presented in the introductory section are a framework of courses and programs, description of the…

  18. Machine Tool User Cylindrical Die Rolling Performance Support System

    SciTech Connect

    Bohley, M.C.; Grothe, V.D.

    1998-08-06

    This project was initiated to provide the machine tool industry and the DOE a method for evaluating educating potential users about various aspects of the cylindrical die rolling process including: characteristics of the cylindrical die rolling processes, major productivity and material savings benefits, advantages for use in the fastener industry, production capabilities based on part parameters, and production capabilities based on machine specifications. AlliedSignal Federal Manufacturing and Technologies (ASFM and T) utilized data provided by Kinefac Corporation to develop an interactive performance support system. AlliedSignal developed one complete branch of the program and Kinefac will develop the remaining two branches. Macromedia Authorware version 3.5 and Microsoft Access version 7.0 were selected for development tools. These software tools maximize continued program development ease and program management with future machine technology advancements. Using this authoring tool and the external database resulted in development of a product that has many potential uses within the manufacturing industry. Source code for the product can be used as a template for other applications is reusable and can provide potential solutions to non-manufacturing needs. The final product will be released on CD-ROM.

  19. Calibration of rotary joints in multi-axis machine tools

    NASA Astrophysics Data System (ADS)

    Khan, Abdul Wahid; Liu, Fei; Chen, Wuyi

    2009-05-01

    A novel technique is developed and implemented for error quantification in a rotary joint of a multi-axis machine tool by using a calibrated double ball bar (DBB) system as a working standard. This technique greatly simplified the measurement setup requirement and accelerated the calibration of rotary joints. In addition it is highly economical by reducing the complex optics and eliminating the usage of various tooling, instrumentation and accessories. This methodology is capable of measuring the five degree of freedom (DOF) errors out of 6DOF of a rotary joint by using the calibrated DBB system and a point locating fixture. The methodology is implemented on rotary joints of a five axis CNC machine tools. Equation solvers and error modeling technique are implemented and validity of the methodology and authenticity of the results obtained are tested through simulation in UG and Matlab software. The methodology is found extremely feasible pragmatic, quite simple, efficient and easy to use for error characterization of rotary joints of multi axis machine tools.

  20. Quantum learning and universal quantum matching machine

    NASA Astrophysics Data System (ADS)

    Sasaki, Masahide; Carlini, Alberto

    2002-08-01

    Suppose that three kinds of quantum systems are given in some unknown states |f>⊗N, |g1>⊗K, and |g2>⊗K, and we want to decide which template state |g1> or |g2>, each representing the feature of the pattern class C1 or C2, respectively, is closest to the input feature state |f>. This is an extension of the pattern matching problem into the quantum domain. Assuming that these states are known a priori to belong to a certain parametric family of pure qubit systems, we derive two kinds of matching strategies. The first one is a semiclassical strategy that is obtained by the natural extension of conventional matching strategies and consists of a two-stage procedure: identification (estimation) of the unknown template states to design the classifier (learning process to train the classifier) and classification of the input system into the appropriate pattern class based on the estimated results. The other is a fully quantum strategy without any intermediate measurement, which we might call as the universal quantum matching machine. We present the Bayes optimal solutions for both strategies in the case of K=1, showing that there certainly exists a fully quantum matching procedure that is strictly superior to the straightforward semiclassical extension of the conventional matching strategy based on the learning process.

  1. Machine learning for real time remote detection

    NASA Astrophysics Data System (ADS)

    Labbé, Benjamin; Fournier, Jérôme; Henaff, Gilles; Bascle, Bénédicte; Canu, Stéphane

    2010-10-01

    Infrared systems are key to providing enhanced capability to military forces such as automatic control of threats and prevention from air, naval and ground attacks. Key requirements for such a system to produce operational benefits are real-time processing as well as high efficiency in terms of detection and false alarm rate. These are serious issues since the system must deal with a large number of objects and categories to be recognized (small vehicles, armored vehicles, planes, buildings, etc.). Statistical learning based algorithms are promising candidates to meet these requirements when using selected discriminant features and real-time implementation. This paper proposes a new decision architecture benefiting from recent advances in machine learning by using an effective method for level set estimation. While building decision function, the proposed approach performs variable selection based on a discriminative criterion. Moreover, the use of level set makes it possible to manage rejection of unknown or ambiguous objects thus preserving the false alarm rate. Experimental evidences reported on real world infrared images demonstrate the validity of our approach.

  2. Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

    PubMed

    Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

    2016-06-21

    Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. PMID:27049046

  3. Automatic programming of binary morphological machines by PAC learning

    NASA Astrophysics Data System (ADS)

    Barrera, Junior; Tomita, Nina S.; Correa da Silva, Flavio S.; Terada, Routo

    1995-08-01

    Binary image analysis problems can be solved by set operators implemented as programs for a binary morphological machine (BMM). This is a very general and powerful approach to solve this type of problem. However, the design of these programs is not a task manageable by nonexperts on mathematical morphology. In order to overcome this difficulty we have worked on tools that help users describe their goals at higher levels of abstraction and to translate them into BMM programs. Some of these tools are based on the representation of the goals of the user as a collection of input-output pairs of images and the estimation of the target operator from these data. PAC learning is a well suited methodology for this task, since in this theory 'concepts' are represented as Boolean functions that are equivalent to set operators. In order to apply this technique in practice we must have efficient learning algorithms. In this paper we introduce two PAC learning algorithms, both are based on the minimal representation of Boolean functions, which has a straightforward translation to the canonical decomposition of set operators. The first algorithm is based on the classical Quine-McCluskey algorithm for the simplification of Boolean functions, and the second one is based on a new idea for the construction of Boolean functions: the incremental splitting of intervals. We also present a comparative complexity analysis of the two algorithms. Finally, we give some application examples.

  4. Analysis of Pollution Patterns Using Unsupervised Machine Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Kanevski, M.; Timonin, V.; Pozdnoukhov, A.; Maignan, M.

    2009-04-01

    The research presents an application of Machine Learning Algorithms, mainly unsupervised learning techniques like self-organising Kohonen maps (SOM), to study spatial patterns of multivariate environmental spatial data. SOM are well-known neural networks widely used for high-dimensional data analysis, modelling (clustering and classification), and visualization. Self-organising maps belong to the unsupervised machine learning algorithms providing solutions to clustering, classification or density modelling problems using unlabeled data. SOM are efficiently used for the dimensionality reduction and for the visualisation of high-dimensional data (projection into a two-dimensional space). Unlabeled data are points/vectors in a high-dimensional feature space that have some attributes (or coordinates) but have no target values, neither continuous (as in a regression problem) nor discrete labels (as in the case of classification problem). The main task of SOM is to "group" or to "range" in some manner these input vectors and to try to catch regularities (to find patterns) in data by preserving topological structure and by using some well defined similarity measures. A generic methodology presented in this study consists of detailed spatial exploratory data analysis using statistical and geostatistical tools, analysis and modelling of spatial (cross)-correlations anisotropic structures, and application of SOM as a nonlinear modelling and visualisation tool. The case study considers multivariate data of sediments contamination by heavy metals (eight spatially distributes pollutants) in Geneva Lake. The most important modelling task is formulated as a problem of revealing structures or coherent clusters in this multivariate data set that would shed some light on the underlying phenomena of the contamination. Three major clusters, clearly spatially separated, were detected and explained by using the SOM technique.

  5. Optimizing transition states via kernel-based machine learning.

    PubMed

    Pozun, Zachary D; Hansen, Katja; Sheppard, Daniel; Rupp, Matthias; Müller, Klaus-Robert; Henkelman, Graeme

    2012-05-01

    We present a method for optimizing transition state theory dividing surfaces with support vector machines. The resulting dividing surfaces require no a priori information or intuition about reaction mechanisms. To generate optimal dividing surfaces, we apply a cycle of machine-learning and refinement of the surface by molecular dynamics sampling. We demonstrate that the machine-learned surfaces contain the relevant low-energy saddle points. The mechanisms of reactions may be extracted from the machine-learned surfaces in order to identify unexpected chemically relevant processes. Furthermore, we show that the machine-learned surfaces significantly increase the transmission coefficient for an adatom exchange involving many coupled degrees of freedom on a (100) surface when compared to a distance-based dividing surface. PMID:22583204

  6. Applying machine learning classification techniques to automate sky object cataloguing

    NASA Astrophysics Data System (ADS)

    Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

    1993-08-01

    We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is

  7. Geological Mapping Using Machine Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Harvey, A. S.; Fotopoulos, G.

    2016-06-01

    Remotely sensed spectral imagery, geophysical (magnetic and gravity), and geodetic (elevation) data are useful in a variety of Earth science applications such as environmental monitoring and mineral exploration. Using these data with Machine Learning Algorithms (MLA), which are widely used in image analysis and statistical pattern recognition applications, may enhance preliminary geological mapping and interpretation. This approach contributes towards a rapid and objective means of geological mapping in contrast to conventional field expedition techniques. In this study, four supervised MLAs (naïve Bayes, k-nearest neighbour, random forest, and support vector machines) are compared in order to assess their performance for correctly identifying geological rocktypes in an area with complete ground validation information. Geological maps of the Sudbury region are used for calibration and validation. Percent of correct classifications was used as indicators of performance. Results show that random forest is the best approach. As expected, MLA performance improves with more calibration clusters, i.e. a more uniform distribution of calibration data over the study region. Performance is generally low, though geological trends that correspond to a ground validation map are visualized. Low performance may be the result of poor spectral images of bare rock which can be covered by vegetation or water. The distribution of calibration clusters and MLA input parameters affect the performance of the MLAs. Generally, performance improves with more uniform sampling, though this increases required computational effort and time. With the achievable performance levels in this study, the technique is useful in identifying regions of interest and identifying general rocktype trends. In particular, phase I geological site investigations will benefit from this approach and lead to the selection of sites for advanced surveys.

  8. DREAM: diabetic retinopathy analysis using machine learning.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2014-09-01

    This paper presents a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. Classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM), and AdaBoost are analyzed for classifying retinopathy lesions from nonlesions. GMM and kNN classifiers are found to be the best classifiers for bright and red lesion classification, respectively. A main contribution of this paper is the reduction in the number of features used for lesion classification by feature ranking using Adaboost where 30 top features are selected out of 78. A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. This lesion classification problem deals with unbalanced datasets and SVM or combination classifiers derived from SVM using the Dempster-Shafer theory are found to incur more classification error than the GMM and kNN classifiers due to the data imbalance. The DR severity grading system is tested on 1200 images from the publicly available MESSIDOR dataset. The DREAM system achieves 100% sensitivity, 53.16% specificity, and 0.904 AUC, compared to the best reported 96% sensitivity, 51% specificity, and 0.875 AUC, for classifying images as with or without DR. The feature reduction further reduces the average computation time for DR severity per image from 59.54 to 3.46 s. PMID:25192577

  9. Error compensation for thermally induced errors on a machine tool

    SciTech Connect

    Krulewich, D.A.

    1996-11-08

    Heat flow from internal and external sources and the environment create machine deformations, resulting in positioning errors between the tool and workpiece. There is no industrially accepted method for thermal error compensation. A simple model has been selected that linearly relates discrete temperature measurements to the deflection. The biggest problem is how to locate the temperature sensors and to determine the number of required temperature sensors. This research develops a method to determine the number and location of temperature measurements.

  10. Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

    NASA Astrophysics Data System (ADS)

    Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

    2016-01-01

    Galaxy clusters are a rich source of information for examining fundamental astrophysical processes and cosmological parameters, however, employing clusters as cosmological probes requires accurate mass measurements derived from cluster observables. We study dynamical mass measurements of galaxy clusters contaminated by interlopers, and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create a mock catalog from Multidark's publicly-available N-body MDPL1 simulation where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power law scaling relation to infer cluster mass from galaxy line of sight (LOS) velocity dispersion. The presence of interlopers in the catalog produces a wide, flat fractional mass error distribution, with width = 2.13. We employ the Support Distribution Machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (width = 0.67). Remarkably, SDM applied to contaminated clusters is better able to recover masses than even a scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

  11. Prediction Of Abrasive And Diffusive Tool Wear Mechanisms In Machining

    NASA Astrophysics Data System (ADS)

    Rizzuti, S.; Umbrello, D.

    2011-01-01

    Tool wear prediction is regarded as very important task in order to maximize tool performance, minimize cutting costs and improve the quality of workpiece in cutting. In this research work, an experimental campaign was carried out at the varying of cutting conditions with the aim to measure both crater and flank tool wear, during machining of an AISI 1045 with an uncoated carbide tool P40. Parallel a FEM-based analysis was developed in order to study the tool wear mechanisms, taking also into account the influence of the cutting conditions and the temperature reached on the tool surfaces. The results show that, when the temperature of the tool rake surface is lower than the activation temperature of the diffusive phenomenon, the wear rate can be estimated applying an abrasive model. In contrast, in the tool area where the temperature is higher than the diffusive activation temperature, the wear rate can be evaluated applying a diffusive model. Finally, for a temperature ranges within the above cited values an adopted abrasive-diffusive wear model furnished the possibility to correctly evaluate the tool wear phenomena.

  12. Decorating Cutting as New Approach to Machine Tool System Dynamics

    NASA Astrophysics Data System (ADS)

    Murcinkova, Zuzana; Vasilko, Karol

    2014-12-01

    The paper presents so called decorating cutting focused on turning. It uses self-excited vibrations that are typical for turning and other types of cutting operations. The decorating turning do not utilize setting of unstable technological conditions of cutting process but it actively use the acting of cutting force on machine tool without generation of unwanted chatter vibrations. The special tool fixture was developed to utilize self-excited vibrations invoked by periodical changeability of cutting force by cutting process itself. Thus the typical texture of surface appears. The various macro/micro-textures of surfaces can be applied either for decorating purpose or for better holding of oil film.

  13. Classification of ROTSE Variable Stars using Machine Learning

    NASA Astrophysics Data System (ADS)

    Wozniak, P. R.; Akerlof, C.; Amrose, S.; Brumby, S.; Casperson, D.; Gisler, G.; Kehoe, R.; Lee, B.; Marshall, S.; McGowan, K. E.; McKay, T.; Perkins, S.; Priedhorsky, W.; Rykoff, E.; Smith, D. A.; Theiler, J.; Vestrand, W. T.; Wren, J.; ROTSE Collaboration

    2001-12-01

    We evaluate several Machine Learning algorithms as potential tools for automated classification of variable stars. Using the ROTSE sample of ~1800 variables from a pilot study of 5% of the whole sky, we compare the effectiveness of a supervised technique (Support Vector Machines, SVM) versus unsupervised methods (K-means and Autoclass). There are 8 types of variables in the sample: RR Lyr AB, RR Lyr C, Delta Scuti, Cepheids, detached eclipsing binaries, contact binaries, Miras and LPVs. Preliminary results suggest a very high ( ~95%) efficiency of SVM in isolating a few best defined classes against the rest of the sample, and good accuracy ( ~70-75%) for all classes considered simultaneously. This includes some degeneracies, irreducible with the information at hand. Supervised methods naturally outperform unsupervised methods, in terms of final error rate, but unsupervised methods offer many advantages for large sets of unlabeled data. Therefore, both types of methods should be considered as promising tools for mining vast variability surveys. We project that there are more than 30,000 periodic variables in the ROTSE-I data base covering the entire local sky between V=10 and 15.5 mag. This sample size is already stretching the time capabilities of human analysts.

  14. Method and apparatus for characterizing and enhancing the dynamic performance of machine tools

    DOEpatents

    Barkman, William E; Babelay, Jr., Edwin F

    2013-12-17

    Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include dynamic one axis positional accuracy of the machine tool, dynamic cross-axis stability of the machine tool, and dynamic multi-axis positional accuracy of the machine tool.

  15. Studying depression using imaging and machine learning methods

    PubMed Central

    Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.

    2015-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies. PMID:26759786

  16. Predicting increased blood pressure using machine learning.

    PubMed

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Gomes, Cristiano Mauro Assis; Soares, Telma de Jesus; Dos Reis, Luciana Araujo; Santos, Joselito

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power. PMID:24669313

  17. Machine learning optimization of cross docking accuracy.

    PubMed

    Bjerrum, Esben J

    2016-06-01

    Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening capabilities of the docking program, respectively. Benchmarks show that different docking programs can excel in individual benchmarks which suggests that the scoring function employed by the programs can be optimized for a particular task. Here the scoring function of Smina is re-optimized towards enhancing the docking power using a supervised machine learning approach and a manually curated database of ligands and cross docking receptor pairs. The optimization method does not need associated binding data for the receptor-ligand examples used in the data set and works with small train sets. The re-optimization of the weights for the scoring function results in a similar docking performance with regard to docking power towards a cross docking test set. A ligand decoy based benchmark indicates a better discrimination between poses with high and low RMSD. The reported parameters for Smina are compatible with Autodock Vina and represent ready-to-use alternative parameters for researchers who aim at pose prediction rather than affinity prediction. PMID:27179709

  18. Many-body physics via machine learning

    NASA Astrophysics Data System (ADS)

    Arsenault, Louis-Francois; von Lilienfeld, O. Anatole; Millis, Andrew J.

    We demonstrate a method for the use of machine learning (ML) to solve the equations of many-body physics, which are functional equations linking a bare to an interacting Green's function (or self-energy) offering transferable power of prediction for physical quantities for both the forward and the reverse engineering problem of materials. Functions are represented by coefficients in an orthogonal polynomial expansion and kernel ridge regression is used. The method is demonstrated using as an example a database built from Dynamical Mean Field theory (DMFT) calculations on the three dimensional Hubbard model. We discuss the extension to a database for real materials. We also discuss some new area of investigation concerning high throughput predictions for real materials by offering a perspective of how our scheme is general enough for applications to other problems involving the inversion of integral equations from the integrated knowledge such as the analytical continuation of the Green's function and the reconstruction of lattice structures from X-ray spectra. Office of Science of the U.S. Department of Energy under SubContract DOE No. 3F-3138 and FG-ER04169.

  19. Machine learning applications in proteomics research: how the past can boost the future.

    PubMed

    Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, Sven; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart

    2014-03-01

    Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis. PMID:24323524

  20. Selected aspects of microelectronics technology and applications: Numerically controlled machine tools. Technology trends series no. 2

    NASA Astrophysics Data System (ADS)

    Sigurdson, J.; Tagerud, J.

    1986-05-01

    A UNIDO publication about machine tools with automatic control discusses the following: (1) numerical control (NC) machine tool perspectives, definition of NC, flexible manufacturing systems, robots and their industrial application, research and development, and sensors; (2) experience in developing a capability in NC machine tools; (3) policy issues; (4) procedures for retrieval of relevant documentation from data bases. Diagrams, statistics, bibliography are included.

  1. Monitoring frog communities: An application of machine learning

    SciTech Connect

    Taylor, A.; Watson, G.; Grigg, G.; McCallum, H.

    1996-12-31

    Automatic recognition of animal vocalizations would be a valuable tool for a variety of biological research and environmental monitoring applications. We report the development of a software system which can recognize the vocalizations of 22 species of frogs which occur in an area of northern Australia. This software system will be used in unattended operation to monitor the effect on frog populations of the introduced Cane Toad. The system is based around classification of local peaks in the spectrogram of the audio signal using Quinlan`s machine learning system, C4.5. Unreliable identifications of peaks are aggregated together using a hierarchical structure of segments based on the typical temporal vocalization species` patterns. This produces robust system performance.

  2. Machine Translation in Foreign Language Learning: Language Learners' and Tutors' Perceptions of Its Advantages and Disadvantages

    ERIC Educational Resources Information Center

    Nino, Ana

    2009-01-01

    This paper presents a snapshot of what has been investigated in terms of the relationship between machine translation (MT) and foreign language (FL) teaching and learning. For this purpose four different roles of MT in the language class have been identified: MT as a bad model, MT as a good model, MT as a vocational training tool (especially in…

  3. Learn about Physical Science: Simple Machines. [CD-ROM].

    ERIC Educational Resources Information Center

    2000

    This CD-ROM, designed for students in grades K-2, explores the world of simple machines. It allows students to delve into the mechanical world and learn the ways in which simple machines make work easier. Animated demonstrations are provided of the lever, pulley, wheel, screw, wedge, and inclined plane. Activities include practical matching and…

  4. Learning Machine, Vietnamese Based Human-Computer Interface.

    ERIC Educational Resources Information Center

    Northwest Regional Educational Lab., Portland, OR.

    The sixth session of IT@EDU98 consisted of seven papers on the topic of the learning machine--Vietnamese based human-computer interface, and was chaired by Phan Viet Hoang (Informatics College, Singapore). "Knowledge Based Approach for English Vietnamese Machine Translation" (Hoang Kiem, Dinh Dien) presents the knowledge base approach, which…

  5. Machine learning challenges in Mars rover traverse science

    NASA Technical Reports Server (NTRS)

    Castano, R.; Judd, M.; Anderson, R. C.; Estlin, T.

    2003-01-01

    The successful implementation of machine learning in autonomous rover traverse science requires addressing challenges that range from the analytical technical realm, to the fuzzy, philosophical domain of entrenched belief systems within scientists and mission managers.

  6. A Machine Learning System for Recognizing Subclasses (Demo)

    SciTech Connect

    Vatsavai, Raju

    2012-01-01

    Thematic information extraction from remote sensing images is a complex task. In this demonstration, we present *Miner machine learning system. In particular, we demonstrate an advanced subclass recognition algorithm that is specifically designed to extract finer classes from aggregate classes.

  7. Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and promises

    PubMed Central

    Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

    2014-01-01

    Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al., 2012a; Wall et al., 2012b) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science. PMID:25294649

  8. Shedding Light on Synergistic Chemical Genetic Connections with Machine Learning.

    PubMed

    Ekins, Sean; Siqueira-Neto, Jair Lage

    2015-12-23

    Machine learning can be used to predict compounds acting synergistically, and this could greatly expand the universe of available potential treatments for diseases that are currently hidden in the dark chemical matter. PMID:27136350

  9. Experimental Choice of Suitable Cutting Tool for Machining of Plastic

    NASA Astrophysics Data System (ADS)

    Sokova, Dagmar; Cep, Robert; Cepova, Lenka; Kocifajova, Simona

    2014-12-01

    In today's competitive times overall development of the technology is moving somewhere further, including automotive industry, which went toward relieving material. One of the many materials which are applied in the automotive industry, are polymers. The aim of the article was to test three different types of cutters for machining material group N - nonferrous metals. The article was tested three different types of cutters from different vendors on electro material SKLOTEXTIT G 11 and samples size 12x100x500mm. The entire experiment was conducted in a company Slavík- Technické plasty on the machine tool SCM RECORD 220. In the conclusion are technical-evaluation, experimental results and conclusions for company.

  10. Machine learning on Parkinson's disease? Let's translate into clinical practice.

    PubMed

    Cerasa, Antonio

    2016-06-15

    Machine learning techniques represent the third-generation of clinical neuroimaging studies where the principal interest is not related to describe anatomical changes of a neurological disorder, but to evaluate if a multivariate approach may use these abnormalities to predict the correct classification of previously unseen clinical cohort. In the next few years, Machine learning will revolutionize clinical practice of Parkinson's disease, but enthusiasm should be turned down before removing some important barriers. PMID:26743974