A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

Science.gov (United States)

Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

2005-04-01

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

Diffusion of knowledge and skills through labour markets: Evidence from the furniture cluster in Metro Cebu (the Philippines)

NARCIS (Netherlands)

Beerepoot, N.

2008-01-01

A skilled and flexible labour force is often given recognition as one of the key features of industrial clusters of similar enterprises. In clusters of small enterprises, knowledge and skills are not embedded in firms, but in the local labour force and the movements of a skilled and flexible labour

Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

Energy Technology Data Exchange (ETDEWEB)

Hajian, Amir; Alvarez, Marcelo A.; Bond, J. Richard, E-mail: ahajian@cita.utoronto.ca, E-mail: malvarez@cita.utoronto.ca, E-mail: bond@cita.utoronto.ca [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada)

2015-01-01

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features.

Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

International Nuclear Information System (INIS)

Hajian, Amir; Alvarez, Marcelo A.; Bond, J. Richard

2015-01-01

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

Science.gov (United States)

Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D

2017-01-01

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, Pdepression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 11: Computer-Aided Manufacturing & Advanced CNC, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

Science.gov (United States)

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Skill clusters of ability to manage everyday technology among people with and without cognitive impairment, dementia and acquired brain injury.

Science.gov (United States)

Malinowsky, Camilla; Fallahpour, Mandana; Lund, Maria Larsson; Nygård, Louise; Kottorp, Anders

2018-03-01

In order to develop supporting interventions for people demonstrating problems ET use, a detailed level of description of strengths and deficits is needed. To explore clusters of specific performance skill required when using ET, and to evaluate if and in what way such clusters are associated with age, gender, diagnosis, and types of ETs managed. A secondary analysis of 661 data records from 203 heterogeneous samples of participants using the Management of Everyday Technology Assessment (META) was used. Ward's method and a hierarchical tree cluster analysis were used to determine and define the skill clusters. Four distinct clusters of performance skill item profiles were found, across the 661 data records. These were then, based on each individuals' cluster profiles in managing ET, categorized into two groups. The two groups were associated with, diagnosis and type of ETs managed. The findings support a more dyadic person-ET approach in evaluation of ET management. The information from the skill clusters can be used to develop targeted intervention guides for occupational therapy and healthcare.

Driver style and driver skills – clustering drivers differing in their potential danger in traffic

DEFF Research Database (Denmark)

Martinussen, Laila Marianne; Møller, Mette; Prato, Carlo Giacomo

The Driver Behavior Questionnaire (DBQ) and the Driver Skill Inventory (DSI) are two of the most frequently used measures of driving style and driving skill. The motivation behind the present study was to test drivers’ insight into their own driving ability based on a combined use of the DBQ......, annual mileage and accident involvement. 3908 drivers aged 18–84 participated in the survey. The results suggested that the drivers have good insight into their own driving ability, as the driving skill level mirrored the frequency of aberrant driving behaviors. K-means cluster analysis revealed four...... distinct clusters that differed in the frequency of aberrant driving behavior and driving skills, as well as individual characteristics and driving related factors such as annual mileage, accident frequency and number of tickets and fines. Thus, two sub-groups were identified as more unsafe than the two...

Investigating Solution Convergence in a Global Ocean Model Using a 2048-Processor Cluster of Distributed Shared Memory Machines

Directory of Open Access Journals (Sweden)

Chris Hill

2007-01-01

Full Text Available Up to 1920 processors of a cluster of distributed shared memory machines at the NASA Ames Research Center are being used to simulate ocean circulation globally at horizontal resolutions of 1/4, 1/8, and 1/16-degree with the Massachusetts Institute of Technology General Circulation Model, a finite volume code that can scale to large numbers of processors. The study aims to understand physical processes responsible for skill improvements as resolution is increased and to gain insight into what resolution is sufficient for particular purposes. This paper focuses on the computational aspects of reaching the technical objective of efficiently performing these global eddy-resolving ocean simulations. At 1/16-degree resolution the model grid contains 1.2 billion cells. At this resolution it is possible to simulate approximately one month of ocean dynamics in about 17 hours of wallclock time with a model timestep of two minutes on a cluster of four 512-way NUMA Altix systems. The Altix systems' large main memory and I/O subsystems allow computation and disk storage of rich sets of diagnostics during each integration, supporting the scientific objective to develop a better understanding of global ocean circulation model solution convergence as model resolution is increased.

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster

Directory of Open Access Journals (Sweden)

Xia-an Bi

2018-02-01

Full Text Available Autism spectrum disorder (ASD is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC. Resting-state functional magnetic resonance imaging (fMRI data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG (orbital and opercula part, hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.

Development of Estimating Equation of Machine Operational Skill by Utilizing Eye Movement Measurement and Analysis of Stress and Fatigue

Directory of Open Access Journals (Sweden)

Satoshi Suzuki

2013-01-01

Full Text Available For an establishment of a skill evaluation method for human support systems, development of an estimating equation of the machine operational skill is presented. Factors of the eye movement such as frequency, velocity, and moving distance of saccade were computed using the developed eye gaze measurement system, and the eye movement features were determined from these factors. The estimating equation was derived through an outlier test (to eliminate nonstandard data and a principal component analysis (to find dominant components. Using a cooperative carrying task (cc-task simulator, the eye movement and operational data of the machine operators were recorded, and effectiveness of the derived estimating equation was investigated. As a result, it was confirmed that the estimating equation was effective strongly against actual simple skill levels (r=0.56–0.84. In addition, effects of internal condition such as fatigue and stress on the estimating equation were analyzed. Using heart rate (HR and coefficient of variation of R-R interval (Cvrri. Correlation analysis between these biosignal indexes and the estimating equation of operational skill found that the equation reflected effects of stress and fatigue, although the equation could estimate the skill level adequately.

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

Science.gov (United States)

Oluwadare, Oluwatosin; Cheng, Jianlin

2017-11-14

With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts. The identification of the TADs for a genome is useful for studying gene regulation, genomic interaction, and genome function. Here, we formulate the TAD identification problem as an unsupervised machine learning (clustering) problem, and develop a new TAD identification method called ClusterTAD. We introduce a novel method to represent chromosomal contacts as features to be used by the clustering algorithm. Our results show that ClusterTAD can accurately predict the TADs on a simulated Hi-C data. Our method is also largely complementary and consistent with existing methods on the real Hi-C datasets of two mouse cells. The validation with the chromatin immunoprecipitation (ChIP) sequencing (ChIP-Seq) data shows that the domain boundaries identified by ClusterTAD have a high enrichment of CTCF binding sites, promoter-related marks, and enhancer-related histone modifications. As ClusterTAD is based on a proven clustering approach, it opens a new avenue to apply a large array of clustering methods developed in the machine learning field to the TAD identification problem. The source code, the results, and the TADs generated for the simulated and real Hi-C datasets are available here: https://github.com/BDM-Lab/ClusterTAD .

Minimalist's linux cluster

International Nuclear Information System (INIS)

Choi, Chang-Yeong; Kim, Jeong-Hyun; Kim, Seyong

2004-01-01

Using barebone PC components and NIC's, we construct a linux cluster which has 2-dimensional mesh structure. This cluster has smaller footprint, is less expensive, and use less power compared to conventional linux cluster. Here, we report our experience in building such a machine and discuss our current lattice project on the machine

Hadoop cluster deployment

CERN Document Server

Zburivsky, Danil

2013-01-01

This book is a step-by-step tutorial filled with practical examples which will show you how to build and manage a Hadoop cluster along with its intricacies.This book is ideal for database administrators, data engineers, and system administrators, and it will act as an invaluable reference if you are planning to use the Hadoop platform in your organization. It is expected that you have basic Linux skills since all the examples in this book use this operating system. It is also useful if you have access to test hardware or virtual machines to be able to follow the examples in the book.

Towards Machine Learning of Motor Skills

Science.gov (United States)

Peters, Jan; Schaal, Stefan; Schölkopf, Bernhard

Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks that a robot should fulfill. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.

The effect of a physical activity intervention on preschoolers' fundamental motor skills - A cluster RCT.

Science.gov (United States)

Wasenius, Niko S; Grattan, Kimberly P; Harvey, Alysha L J; Naylor, Patti-Jean; Goldfield, Gary S; Adamo, Kristi B

2018-07-01

To assess the effect of a physical activity intervention delivered in the childcare centres (CC), with or without a parent-driven home physical activity component, on children's fundamental motor skills (FMS). Six-month 3-arm cluster randomized controlled trial. Preschoolers were recruited from 18 licensed CC. CC were randomly assigned to a typical curriculum comparison group (COM), childcare intervention alone (CC), or childcare intervention with parental component (CC+HOME). FMS was measured with the Test of Gross Motor Development-2. Linear mixed models were performed at the level of the individual while accounting for clustering. Raw locomotor skills score increased significantly in the CC group (mean difference=2.5 units, 95% Confidence Intervals, CI, 1.0-4.1, p0.05) between group differences were observed in the raw object control skills, sum of raw scores, or gross motor quotient. No significant sex differences were found in any of the measured outcomes. A physical activity intervention delivered in childcare with or without parents' involvement was effective in increasing locomotor skills in preschoolers. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Machine Shop Lathes.

Science.gov (United States)

Dunn, James

This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

Mixed-Initiative Clustering

Science.gov (United States)

Huang, Yifen

2010-01-01

Mixed-initiative clustering is a task where a user and a machine work collaboratively to analyze a large set of documents. We hypothesize that a user and a machine can both learn better clustering models through enriched communication and interactive learning from each other. The first contribution or this thesis is providing a framework of…

Apparel Manufacturing (Course Outline), Industrial Single Needle Machines and Machine Practice: 9377.02.

Science.gov (United States)

Dade County Public Schools, Miami, FL.

This course includes a study of the industrial single needle machine, its principal parts, general care, threading, and basic skills in machine practice. Instructional materials include films, illustration, information sheets, and other materials. (CK)

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

Science.gov (United States)

Guo, Lei; Abbosh, Amin

2018-05-01

For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed. It is based on microwave tomography, k-means clustering, and a support vector machine (SVM) method. The dielectric profile of the brain is first calculated using the Born iterative method, whereas the amplitude of the dielectric profile is then taken as the input to k-means clustering. The cluster is selected as the feature vector for constructing and testing the SVM. A database of MRI-derived realistic head phantoms at different signal-to-noise ratios is used in the classification procedure. The performance of the proposed framework is evaluated using the receiver operating characteristic (ROC) curve. The results based on a two-dimensional framework show that 88% classification accuracy, with a sensitivity of 91% and a specificity of 87%, can be achieved. Bioelectromagnetics. 39:312-324, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

Directory of Open Access Journals (Sweden)

Xite Wang

2017-01-01

Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

White matter microstructure changes induced by motor skill learning utilizing a body machine interface.

Science.gov (United States)

Wang, Xue; Casadio, Maura; Weber, Kenneth A; Mussa-Ivaldi, Ferdinando A; Parrish, Todd B

2014-03-01

The purpose of this study is to identify white matter microstructure changes following bilateral upper extremity motor skill training to increase our understanding of learning-induced structural plasticity and enhance clinical strategies in physical rehabilitation. Eleven healthy subjects performed two visuo-spatial motor training tasks over 9 sessions (2-3 sessions per week). Subjects controlled a cursor with bilateral simultaneous movements of the shoulders and upper arms using a body machine interface. Before the start and within 2days of the completion of training, whole brain diffusion tensor MR imaging data were acquired. Motor training increased fractional anisotropy (FA) values in the posterior and anterior limbs of the internal capsule, the corona radiata, and the body of the corpus callosum by 4.19% on average indicating white matter microstructure changes induced by activity-dependent modulation of axon number, axon diameter, or myelin thickness. These changes may underlie the functional reorganization associated with motor skill learning. Copyright © 2013 Elsevier Inc. All rights reserved.

Scientific Cluster Deployment and Recovery - Using puppet to simplify cluster management

Science.gov (United States)

Hendrix, Val; Benjamin, Doug; Yao, Yushu

2012-12-01

Deployment, maintenance and recovery of a scientific cluster, which has complex, specialized services, can be a time consuming task requiring the assistance of Linux system administrators, network engineers as well as domain experts. Universities and small institutions that have a part-time FTE with limited time for and knowledge of the administration of such clusters can be strained by such maintenance tasks. This current work is the result of an effort to maintain a data analysis cluster (DAC) with minimal effort by a local system administrator. The realized benefit is the scientist, who is the local system administrator, is able to focus on the data analysis instead of the intricacies of managing a cluster. Our work provides a cluster deployment and recovery process (CDRP) based on the puppet configuration engine allowing a part-time FTE to easily deploy and recover entire clusters with minimal effort. Puppet is a configuration management system (CMS) used widely in computing centers for the automatic management of resources. Domain experts use Puppet's declarative language to define reusable modules for service configuration and deployment. Our CDRP has three actors: domain experts, a cluster designer and a cluster manager. The domain experts first write the puppet modules for the cluster services. A cluster designer would then define a cluster. This includes the creation of cluster roles, mapping the services to those roles and determining the relationships between the services. Finally, a cluster manager would acquire the resources (machines, networking), enter the cluster input parameters (hostnames, IP addresses) and automatically generate deployment scripts used by puppet to configure it to act as a designated role. In the event of a machine failure, the originally generated deployment scripts along with puppet can be used to easily reconfigure a new machine. The cluster definition produced in our CDRP is an integral part of automating cluster deployment

Improving Early Adolescent Girls' Motor Skill: A Cluster Randomized Controlled Trial.

Science.gov (United States)

Lander, Natalie; Morgan, Philip J; Salmon, J O; Barnett, Lisa M

2017-12-01

Physical activity (PA) levels decline substantially during adolescence and are consistently lower in girls. Competency in a range of fundamental movement skills (FMSs) may serve as a protective factor for the decline in PA typically observed in adolescent girls; yet, girls' mastery in FMS is low. Although interventions can improve FMS, there is a lack of interventions targeting girls, and very few are conducted in high schools. In addition, interventions are usually conducted by researchers, not teachers, and thus have little chance of being embedded into curricula. This study aimed to evaluate the effectiveness of a school-based intervention, delivered by teachers, in improving adolescent girls' FMS. Four all-girls Australian secondary schools were recruited and randomized into intervention or control groups. In total, 190 year 7 girls (103 control/87 intervention; mean age, 12.4 ± 0.3 yr) completed baseline and posttest measures at 12 wk. Six FMS (i.e., catch, throw, kick, jump, leap, and dodge) were measured using the Victorian FMS Assessment instrument. Mixed models with posttest skill (i.e., locomotor, object control, and total skill) as the outcome, adjusting for baseline skill, intervention and control status, and relevant covariates, as well as accounting for clustering at school and class level, were used to assess the intervention impact. There were significant intervention effects, and large effect sizes (Cohen d) noted in locomotor (P = 0.04, t = 5.15, d = 1.6), object control (P < 0.001, t = 11.06, d = 0.83), and total skill (P = 0.02, t = 7.22, d = 1.36). Teachers adequately trained in authentic assessment and student-centered instruction can significantly improve the FMS competency of early adolescent girls. Therefore, comprehensive teacher training should be viewed as an integral component of future school-based interventions.

Machine Shop Grinding Machines.

Science.gov (United States)

Dunn, James

This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

Scientific Cluster Deployment and Recovery – Using puppet to simplify cluster management

International Nuclear Information System (INIS)

Hendrix, Val; Yao Yushu; Benjamin, Doug

2012-01-01

Deployment, maintenance and recovery of a scientific cluster, which has complex, specialized services, can be a time consuming task requiring the assistance of Linux system administrators, network engineers as well as domain experts. Universities and small institutions that have a part-time FTE with limited time for and knowledge of the administration of such clusters can be strained by such maintenance tasks. This current work is the result of an effort to maintain a data analysis cluster (DAC) with minimal effort by a local system administrator. The realized benefit is the scientist, who is the local system administrator, is able to focus on the data analysis instead of the intricacies of managing a cluster. Our work provides a cluster deployment and recovery process (CDRP) based on the puppet configuration engine allowing a part-time FTE to easily deploy and recover entire clusters with minimal effort. Puppet is a configuration management system (CMS) used widely in computing centers for the automatic management of resources. Domain experts use Puppet's declarative language to define reusable modules for service configuration and deployment. Our CDRP has three actors: domain experts, a cluster designer and a cluster manager. The domain experts first write the puppet modules for the cluster services. A cluster designer would then define a cluster. This includes the creation of cluster roles, mapping the services to those roles and determining the relationships between the services. Finally, a cluster manager would acquire the resources (machines, networking), enter the cluster input parameters (hostnames, IP addresses) and automatically generate deployment scripts used by puppet to configure it to act as a designated role. In the event of a machine failure, the originally generated deployment scripts along with puppet can be used to easily reconfigure a new machine. The cluster definition produced in our CDRP is an integral part of automating cluster deployment

Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine

Directory of Open Access Journals (Sweden)

Hualou Liang

2008-04-01

Full Text Available We propose an empirical mode decomposition (EMD- based method to extract features from the multichannel recordings of local field potential (LFP, collected from the middle temporal (MT visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs with time scales dependent on the data. Second, we adopt unsupervised K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI.

Dynamically allocated virtual clustering management system

Science.gov (United States)

Marcus, Kelvin; Cannata, Jess

2013-05-01

The U.S Army Research Laboratory (ARL) has built a "Wireless Emulation Lab" to support research in wireless mobile networks. In our current experimentation environment, our researchers need the capability to run clusters of heterogeneous nodes to model emulated wireless tactical networks where each node could contain a different operating system, application set, and physical hardware. To complicate matters, most experiments require the researcher to have root privileges. Our previous solution of using a single shared cluster of statically deployed virtual machines did not sufficiently separate each user's experiment due to undesirable network crosstalk, thus only one experiment could be run at a time. In addition, the cluster did not make efficient use of our servers and physical networks. To address these concerns, we created the Dynamically Allocated Virtual Clustering management system (DAVC). This system leverages existing open-source software to create private clusters of nodes that are either virtual or physical machines. These clusters can be utilized for software development, experimentation, and integration with existing hardware and software. The system uses the Grid Engine job scheduler to efficiently allocate virtual machines to idle systems and networks. The system deploys stateless nodes via network booting. The system uses 802.1Q Virtual LANs (VLANs) to prevent experimentation crosstalk and to allow for complex, private networks eliminating the need to map each virtual machine to a specific switch port. The system monitors the health of the clusters and the underlying physical servers and it maintains cluster usage statistics for historical trends. Users can start private clusters of heterogeneous nodes with root privileges for the duration of the experiment. Users also control when to shutdown their clusters.

Ellipsoid clustering machine: a front line to aid in disease diagnosis - DOI: 10.3395/reciis.v1i2.Sup.101en

Directory of Open Access Journals (Sweden)

Paulo Costa Carvalho

2007-12-01

Full Text Available This study presents a new machine learning strategy to address the disease diagnosis classification problem that comprises an unknown number of disease classes. This is exemplified by a software called Ellipsoid Clustering Machine (ECM that identifies conserved regions in mass spectrometry proteomic profiles obtained from control subjects and uses these to estimate classification boundaries based on sample variance. The software can also be used for visual inspection of data reproducibility. ECM was evaluated using mass spectrometry protein profiles obtained from serum of Hodgkin’s disease patients (HD and control subjects. According to the leave-one-out cross validation, ECM completely separated both groups based only on the information derived from four selected mass spectral peaks. Classification details and a 3D graphical model showing the separation between the control subject cluster and HD patients is also presented. The software is available on the project website together with online interactive models of the dataset and an animation demonstrating the method.

Conceptual clustering and its relation to numerical taxonomy

International Nuclear Information System (INIS)

Fisher, D.; Langley, P.

1986-01-01

Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statistical methods generally connoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. The authors explore this difference within a limited context, devoting the bulk of our chapter to the explication of conceptual clustering, an extension to the statistically based methods of numerical taxonomy. In conceptual clustering the formation of object cluster is dependent on the quality of 'higher level' characterization, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. They conclude with a discussion of how alternative concept representations might enhance the effectiveness of future conceptual clustering systems

Introduction to AC machine design

CERN Document Server

Lipo, Thomas A

2018-01-01

AC electrical machine design is a key skill set for developing competitive electric motors and generators for applications in industry, aerospace, and defense. This book presents a thorough treatment of AC machine design, starting from basic electromagnetic principles and continuing through the various design aspects of an induction machine. Introduction to AC Machine Design includes one chapter each on the design of permanent magnet machines, synchronous machines, and thermal design. It also offers a basic treatment of the use of finite elements to compute the magnetic field within a machine without interfering with the initial comprehension of the core subject matter. Based on the author's notes, as well as after years of classroom instruction, Introduction to AC Machine Design: * Brings to light more advanced principles of machine design--not just the basic principles of AC and DC machine behavior * Introduces electrical machine design to neophytes while also being a resource for experienced designers * ...

Diskless Linux Cluster How-To

National Research Council Canada - National Science Library

Shumaker, Justin L

2005-01-01

Diskless linux clustering is not yet a turn-key solution. The process of configuring a cluster of diskless linux machines requires many modifications to the stock linux operating system before they can boot cleanly...

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

Science.gov (United States)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Identifying student stuck states in programmingassignments using machine learning

OpenAIRE

Lindell, Johan

2014-01-01

Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of...

Virtual Machine Scheduling in Dedicated Computing Clusters

CERN Document Server

Boettger, Stefan; Zicari, V Roberto

2014-01-08

Time-critical applications process a continuous stream of input data and have to meet specific timing constraints. A common approach to ensure that such an application satisfies its constraints is over-provisioning: The application is deployed in a dedicated cluster environment with enough processing power to achieve the target performance for every specified data input rate. This approach comes with a drawback: At times of decreased data input rates, the cluster resources are not fully utilized. A typical use case is the HLT-Chain application that processes physics data at runtime of the ALICE experiment at CERN. From a perspective of cost and efficiency it is desirable to exploit temporarily unused cluster resources. Existing approaches aim for that goal by running additional applications. These approaches, however, a) lack in flexibility to dynamically grant the time-critical application the resources it needs, b) are insufficient for isolating the time-critical application from harmful side-effects i...

A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine

Directory of Open Access Journals (Sweden)

Zhi-tao Wang

2015-01-01

Full Text Available As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM clustering algorithm and support vector machine (SVM classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT.

Data clustering algorithms and applications

CERN Document Server

Aggarwal, Charu C

2013-01-01

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea

Cluster computing for lattice QCD simulations

International Nuclear Information System (INIS)

Coddington, P.D.; Williams, A.G.

2000-01-01

Full text: Simulations of lattice quantum chromodynamics (QCD) require enormous amounts of compute power. In the past, this has usually involved sharing time on large, expensive machines at supercomputing centres. Over the past few years, clusters of networked computers have become very popular as a low-cost alternative to traditional supercomputers. The dramatic improvements in performance (and more importantly, the ratio of price/performance) of commodity PCs, workstations, and networks have made clusters of off-the-shelf computers an attractive option for low-cost, high-performance computing. A major advantage of clusters is that since they can have any number of processors, they can be purchased using any sized budget, allowing research groups to install a cluster for their own dedicated use, and to scale up to more processors if additional funds become available. Clusters are now being built for high-energy physics simulations. Wuppertal has recently installed ALiCE, a cluster of 128 Alpha workstations running Linux, with a peak performance of 158 G flops. The Jefferson Laboratory in the US has a 16 node Alpha cluster and plans to upgrade to a 256 processor machine. In Australia, several large clusters have recently been installed. Swinburne University of Technology has a cluster of 64 Compaq Alpha workstations used for astrophysics simulations. Early this year our DHPC group constructed a cluster of 116 dual Pentium PCs (i.e. 232 processors) connected by a Fast Ethernet network, which is used by chemists at Adelaide University and Flinders University to run computational chemistry codes. The Australian National University has recently installed a similar PC cluster with 192 processors. The Centre for the Subatomic Structure of Matter (CSSM) undertakes large-scale high-energy physics calculations, mainly lattice QCD simulations. The choice of the computer and network hardware for a cluster depends on the particular applications to be run on the machine. Our

Abrasives and Grinding Machines; Machine Shop Work--Advanced: 9557.02.

Science.gov (United States)

Dade County Public Schools, Miami, FL.

The course outline has been prepared as a guide to assist the instructor in systematically planning and presenting a variety of meaningful lessons to facilitate the necessary training for the machine shop student. The material contained in the outline is designed to enable the student to learn the manipulative skills and related knowledge…

Integrated analysis of CFD data with K-means clustering algorithm and extreme learning machine for localized HVAC control

International Nuclear Information System (INIS)

Zhou, Hongming; Soh, Yeng Chai; Wu, Xiaoying

2015-01-01

Maintaining a desired comfort level while minimizing the total energy consumed is an interesting optimization problem in Heating, ventilating and air conditioning (HVAC) system control. This paper proposes a localized control strategy that uses Computational Fluid Dynamics (CFD) simulation results and K-means clustering algorithm to optimally partition an air-conditioned room into different zones. The temperature and air velocity results from CFD simulation are combined in two ways: 1) based on the relationship indicated in predicted mean vote (PMV) formula; 2) based on the relationship extracted from ASHRAE RP-884 database using extreme learning machine (ELM). Localized control can then be effected in which each of the zones can be treated individually and an optimal control strategy can be developed based on the partitioning result. - Highlights: • The paper provides a visual guideline for thermal comfort analysis. • CFD, K-means, PMV and ELM are used to analyze thermal conditions within a room. • Localized control strategy could be developed based on our clustering results

Fundamental movement skills and motivational factors influencing engagement in physical activity.

Science.gov (United States)

Kalaja, Sami; Jaakkola, Timo; Liukkonen, Jarmo; Watt, Anthony

2010-08-01

To assess whether subgroups based on children's fundamental movement skills, perceived competence, and self-determined motivation toward physical education vary with current self-reported physical activity, a sample of 316 Finnish Grade 7 students completed fundamental movement skills measures and self-report questionnaires assessing perceived competence, self-determined motivation toward physical education, and current physical activity. Cluster analysis indicated a three-cluster structure: "Low motivation/low skills profile," "High skills/low motivation profile," and "High skills/high motivation profile." Analysis of variance indicated that students in the third cluster engaged in significantly more physical activity than students of clusters one and two. These results provide support for previous claims regarding the importance of the relationship of fundamental movement skills with continuing engagement in physical activity. High fundamental movement skills, however, may represent only one element in maintaining adolescents' engagement in physical activity.

Learning scikit-learn machine learning in Python

CERN Document Server

Garreta, Raúl

2013-01-01

The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

Learning Activity Packets for Grinding Machines. Unit I--Grinding Machines.

Science.gov (United States)

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

This learning activity packet (LAP) is one of three that accompany the curriculum guide on grinding machines. It outlines the study activities and performance tasks for the first unit of this curriculum guide. Its purpose is to aid the student in attaining a working knowledge of this area of training and in achieving a skilled or moderately…

The Effects of Skill Training on Social Workers' Professional Competences in Norway: Results of a Cluster-Randomised Study

Science.gov (United States)

Malmberg-Heimonen, Ira; Natland, Sidsel; Tøge, Anne Grete; Hansen, Helle Cathrine

2016-01-01

Using a cluster-randomised design, this study analyses the effects of a government-administered skill training programme for social workers in Norway. The training programme aims to improve social workers' professional competences by enhancing and systematising follow-up work directed towards longer-term unemployed clients in the following areas: encountering the user, system-oriented efforts and administrative work. The main tools and techniques of the programme are based on motivational interviewing and appreciative inquiry. The data comprise responses to baseline and eighteen-month follow-up questionnaires administered to all social workers (n = 99) in eighteen participating Labour and Welfare offices randomised into experimental and control groups. The findings indicate that the skill training programme positively affected the social workers' evaluations of their professional competences and quality of work supervision received. The acquisition and mastering of combinations of specific tools and techniques, a comprehensive supervision structure and the opportunity to adapt the learned skills to local conditions were important in explaining the results. PMID:27559232

Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors.

Science.gov (United States)

Rivera-Borroto, Oscar Miguel; Marrero-Ponce, Yovani; García-de la Vega, José Manuel; Grau-Ábalo, Ricardo del Corazón

2011-12-27

Cluster algorithms play an important role in diversity related tasks of modern chemoinformatics, with the widest applications being in pharmaceutical industry drug discovery programs. The performance of these grouping strategies depends on various factors such as molecular representation, mathematical method, algorithmical technique, and statistical distribution of data. For this reason, introduction and comparison of new methods are necessary in order to find the model that best fits the problem at hand. Earlier comparative studies report on Ward's algorithm using fingerprints for molecular description as generally superior in this field. However, problems still remain, i.e., other types of numerical descriptions have been little exploited, current descriptors selection strategy is trial and error-driven, and no previous comparative studies considering a broader domain of the combinatorial methods in grouping chemoinformatic data sets have been conducted. In this work, a comparison between combinatorial methods is performed,with five of them being novel in cheminformatics. The experiments are carried out using eight data sets that are well established and validated in the medical chemistry literature. Each drug data set was represented by real molecular descriptors selected by machine learning techniques, which are consistent with the neighborhood principle. Statistical analysis of the results demonstrates that pharmacological activities of the eight data sets can be modeled with a few of families with 2D and 3D molecular descriptors, avoiding classification problems associated with the presence of nonrelevant features. Three out of five of the proposed cluster algorithms show superior performance over most classical algorithms and are similar (or slightly superior in the most optimistic sense) to Ward's algorithm. The usefulness of these algorithms is also assessed in a comparative experiment to potent QSAR and machine learning classifiers, where they perform

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Science.gov (United States)

Polyak, Stephen T.; von Davier, Alina A.; Peterschmidt, Kurt

2017-01-01

This paper describes a psychometrically-based approach to the measurement of collaborative problem solving skills, by mining and classifying behavioral data both in real-time and in post-game analyses. The data were collected from a sample of middle school children who interacted with a game-like, online simulation of collaborative problem solving tasks. In this simulation, a user is required to collaborate with a virtual agent to solve a series of tasks within a first-person maze environment. The tasks were developed following the psychometric principles of Evidence Centered Design (ECD) and are aligned with the Holistic Framework developed by ACT. The analyses presented in this paper are an application of an emerging discipline called computational psychometrics which is growing out of traditional psychometrics and incorporates techniques from educational data mining, machine learning and other computer/cognitive science fields. In the real-time analysis, our aim was to start with limited knowledge of skill mastery, and then demonstrate a form of continuous Bayesian evidence tracing that updates sub-skill level probabilities as new conversation flow event evidence is presented. This is performed using Bayes' rule and conversation item conditional probability tables. The items are polytomous and each response option has been tagged with a skill at a performance level. In our post-game analysis, our goal was to discover unique gameplay profiles by performing a cluster analysis of user's sub-skill performance scores based on their patterns of selected dialog responses. PMID:29238314

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Directory of Open Access Journals (Sweden)

Stephen T. Polyak

2017-11-01

Full Text Available This paper describes a psychometrically-based approach to the measurement of collaborative problem solving skills, by mining and classifying behavioral data both in real-time and in post-game analyses. The data were collected from a sample of middle school children who interacted with a game-like, online simulation of collaborative problem solving tasks. In this simulation, a user is required to collaborate with a virtual agent to solve a series of tasks within a first-person maze environment. The tasks were developed following the psychometric principles of Evidence Centered Design (ECD and are aligned with the Holistic Framework developed by ACT. The analyses presented in this paper are an application of an emerging discipline called computational psychometrics which is growing out of traditional psychometrics and incorporates techniques from educational data mining, machine learning and other computer/cognitive science fields. In the real-time analysis, our aim was to start with limited knowledge of skill mastery, and then demonstrate a form of continuous Bayesian evidence tracing that updates sub-skill level probabilities as new conversation flow event evidence is presented. This is performed using Bayes' rule and conversation item conditional probability tables. The items are polytomous and each response option has been tagged with a skill at a performance level. In our post-game analysis, our goal was to discover unique gameplay profiles by performing a cluster analysis of user's sub-skill performance scores based on their patterns of selected dialog responses.

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills.

Science.gov (United States)

Polyak, Stephen T; von Davier, Alina A; Peterschmidt, Kurt

2017-01-01

This paper describes a psychometrically-based approach to the measurement of collaborative problem solving skills, by mining and classifying behavioral data both in real-time and in post-game analyses. The data were collected from a sample of middle school children who interacted with a game-like, online simulation of collaborative problem solving tasks. In this simulation, a user is required to collaborate with a virtual agent to solve a series of tasks within a first-person maze environment. The tasks were developed following the psychometric principles of Evidence Centered Design (ECD) and are aligned with the Holistic Framework developed by ACT. The analyses presented in this paper are an application of an emerging discipline called computational psychometrics which is growing out of traditional psychometrics and incorporates techniques from educational data mining, machine learning and other computer/cognitive science fields. In the real-time analysis, our aim was to start with limited knowledge of skill mastery, and then demonstrate a form of continuous Bayesian evidence tracing that updates sub-skill level probabilities as new conversation flow event evidence is presented. This is performed using Bayes' rule and conversation item conditional probability tables. The items are polytomous and each response option has been tagged with a skill at a performance level. In our post-game analysis, our goal was to discover unique gameplay profiles by performing a cluster analysis of user's sub-skill performance scores based on their patterns of selected dialog responses.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children

International Nuclear Information System (INIS)

Stingone, Jeanette A.; Pandey, Om P.; Claudio, Luz; Pandey, Gaurav

2017-01-01

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was −1.19 points (95% CI −1.94, −0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be

Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

Science.gov (United States)

Muraoka, Masae; Okuda, Hiroshi

With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

Basic machines and how they work

CERN Document Server

Education, Naval

1997-01-01

Only elementary math skills are needed to follow this manual, which covers many machines and their components, including hydrostatics and hydraulics, internal combustion engines, trains, and more. 204 black-and-white illustrations.

Transliteration normalization for Information Extraction and Machine Translation

Directory of Open Access Journals (Sweden)

Yuval Marton

2014-12-01

Full Text Available Foreign name transliterations typically include multiple spelling variants. These variants cause data sparseness and inconsistency problems, increase the Out-of-Vocabulary (OOV rate, and present challenges for Machine Translation, Information Extraction and other natural language processing (NLP tasks. This work aims to identify and cluster name spelling variants using a Statistical Machine Translation method: word alignment. The variants are identified by being aligned to the same “pivot” name in another language (the source-language in Machine Translation settings. Based on word-to-word translation and transliteration probabilities, as well as the string edit distance metric, names with similar spellings in the target language are clustered and then normalized to a canonical form. With this approach, tens of thousands of high-precision name transliteration spelling variants are extracted from sentence-aligned bilingual corpora in Arabic and English (in both languages. When these normalized name spelling variants are applied to Information Extraction tasks, improvements over strong baseline systems are observed. When applied to Machine Translation tasks, a large improvement potential is shown.

Machine learning for evolution strategies

CERN Document Server

Kramer, Oliver

2016-01-01

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

Reducing child conduct problems and promoting social skills in a middle-income country: cluster randomised controlled trial.

Science.gov (United States)

Baker-Henningham, Helen; Scott, Stephen; Jones, Kelvyn; Walker, Susan

2012-08-01

There is an urgent need for effective, affordable interventions to prevent child mental health problems in low- and middle-income countries. To determine the effects of a universal pre-school-based intervention on child conduct problems and social skills at school and at home. In a cluster randomised design, 24 community pre-schools in inner-city areas of Kingston, Jamaica, were randomly assigned to receive the Incredible Years Teacher Training intervention (n = 12) or to a control group (n = 12). Three children from each class with the highest levels of teacher-reported conduct problems were selected for evaluation, giving 225 children aged 3-6 years. The primary outcome was observed child behaviour at school. Secondary outcomes were child behaviour by parent and teacher report, child attendance and parents' attitude to school. The study is registered as ISRCTN35476268. Children in intervention schools showed significantly reduced conduct problems (effect size (ES) = 0.42) and increased friendship skills (ES = 0.74) through observation, significant reductions to teacher-reported (ES = 0.47) and parent-reported (ES = 0.22) behaviour difficulties and increases in teacher-reported social skills (ES = 0.59) and child attendance (ES = 0.30). Benefits to parents' attitude to school were not significant. A low-cost, school-based intervention in a middle-income country substantially reduces child conduct problems and increases child social skills at home and at school.

Exploiting the ALICE HLT for PROOF by scheduling of Virtual Machines

International Nuclear Information System (INIS)

Meoni, Marco; Boettger, Stefan; Zelnicek, Pierre; Kebschull, Udo; Lindenstruth, Volker

2011-01-01

The HLT (High-Level Trigger) group of the ALICE experiment at the LHC has prepared a virtual Parallel ROOT Facility (PROOF) enabled cluster (HAF - HLT Analysis Facility) for fast physics analysis, detector calibration and reconstruction of data samples. The HLT-Cluster currently consists of 2860 CPU cores and 175TB of storage. Its purpose is the online filtering of the relevant part of data produced by the particle detector. However, data taking is not running continuously and exploiting unused cluster resources for other applications is highly desirable and improves the usage-cost ratio of the HLT cluster. As such, unused computing resources are dedicated to a PROOF-enabled virtual cluster available to the entire collaboration. This setup is especially aimed at the prototyping phase of analyses that need a high number of development iterations and a short response time, e.g. tuning of analysis cuts, calibration and alignment. HAF machines are enabled and disabled upon user request to start or complete analysis tasks. This is achieved by a virtual machine scheduling framework which dynamically assigns and migrates virtual machines running PROOF workers to unused physical resources. Using this approach we extend the HLT usage scheme to running both online and offline computing, thereby optimizing the resource usage.

Cluster processing business level monitor

International Nuclear Information System (INIS)

Muniz, Francisco J.

2017-01-01

This article describes a Cluster Processing Monitor. Several applications with this functionality can be freely found doing a search in the Google machine. However, those applications may offer more features that are needed on the Processing Monitor being proposed. Therefore, making the monitor output evaluation difficult to be understood by the user, at-a-glance. In addition, such monitors may add unnecessary processing cost to the Cluster. For these reasons, a completely new Cluster Processing Monitor module was designed and implemented. In the CDTN, Clusters are broadly used, mainly, in deterministic methods (CFD) and non-deterministic methods (Monte Carlo). (author)

Cluster processing business level monitor

Energy Technology Data Exchange (ETDEWEB)

Muniz, Francisco J., E-mail: muniz@cdtn.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

2017-07-01

This article describes a Cluster Processing Monitor. Several applications with this functionality can be freely found doing a search in the Google machine. However, those applications may offer more features that are needed on the Processing Monitor being proposed. Therefore, making the monitor output evaluation difficult to be understood by the user, at-a-glance. In addition, such monitors may add unnecessary processing cost to the Cluster. For these reasons, a completely new Cluster Processing Monitor module was designed and implemented. In the CDTN, Clusters are broadly used, mainly, in deterministic methods (CFD) and non-deterministic methods (Monte Carlo). (author)

TensorFlow: A system for large-scale machine learning

OpenAIRE

Abadi, Martín; Barham, Paul; Chen, Jianmin; Chen, Zhifeng; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Irving, Geoffrey; Isard, Michael; Kudlur, Manjunath; Levenberg, Josh; Monga, Rajat; Moore, Sherry; Murray, Derek G.

2016-01-01

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexib...

SETTING OF TASK OF OPTIMIZATION OF THE ACTIVITY OF A MACHINE-BUILDING CLUSTER COMPANY

Directory of Open Access Journals (Sweden)

A. V. Romanenko

2014-01-01

Full Text Available The work is dedicated to the development of methodological approaches to the management of machine-building enterprise on the basis of cost reduction, optimization of the portfolio of orders and capacity utilization in the process of operational management. Evaluation of economic efficiency of such economic entities of the real sector of the economy is determined, including the timing of orders, which depend on the issues of building a production facility, maintenance of fixed assets and maintain them at a given level. Formulated key components of economic-mathematical model of industrial activity and is defined as the optimization criterion. As proposed formula accumulating profits due to production capacity and technology to produce products current direct variable costs, the amount of property tax and expenses appearing as a result of manifestations of variance when performing replacement of production tasks for a single period of time. The main component of the optimization of the production activity of the enterprise on the basis of this criterion is the vector of direct variable costs. It depends on the number of types of products in the current portfolio of orders, production schedules production, the normative time for the release of a particular product available Fund time efficient production positions, the current valuation for certain groups of technological operations and the current priority of operations for the degree of readiness performed internal orders. Modeling of industrial activity based on the proposed provisions would allow the enterprises of machine-building cluster, active innovation, improve the efficient use of available production resources by optimizing current operations at the high uncertainty of the magnitude of the demand planning and carrying out maintenance and routine repairs.

Mediating effects of resistance training skill competency on health-related fitness and physical activity: the ATLAS cluster randomised controlled trial.

Science.gov (United States)

Smith, Jordan J; Morgan, Philip J; Plotnikoff, Ronald C; Stodden, David F; Lubans, David R

2016-01-01

The purpose of this study was to examine the mediating effect of resistance training skill competency on percentage of body fat, muscular fitness and physical activity among a sample of adolescent boys participating in a school-based obesity prevention intervention. Participants were 361 adolescent boys taking part in the Active Teen Leaders Avoiding Screen-time (ATLAS) cluster randomised controlled trial: a school-based program targeting the health behaviours of economically disadvantaged adolescent males considered "at-risk" of obesity. Body fat percentage (bioelectrical impedance), muscular fitness (hand grip dynamometry and push-ups), physical activity (accelerometry) and resistance training skill competency were assessed at baseline and post-intervention (i.e., 8 months). Three separate multi-level mediation models were analysed to investigate the potential mediating effects of resistance training skill competency on each of the study outcomes using a product-of-coefficients test. Analyses followed the intention-to-treat principle. The intervention had a significant impact on the resistance training skill competency of the boys, and improvements in skill competency significantly mediated the effect of the intervention on percentage of body fat and the combined muscular fitness score. No significant mediated effects were found for physical activity. Improving resistance training skill competency may be an effective strategy for achieving improvements in body composition and muscular fitness in adolescent boys.

A cluster randomized implementation trial to measure the effectiveness of an intervention package aiming to increase the utilization of skilled birth attendants by women for childbirth: study protocol.

Science.gov (United States)

Bhandari, Gajananda P; Subedi, Narayan; Thapa, Janak; Choulagai, Bishnu; Maskey, Mahesh K; Onta, Sharad R

2014-03-19

Nepal is on track to achieve MDG 5 but there is a huge sub-national disparity with existing high maternal mortality in western and hilly regions. The national priority is to reduce this disparity to achieve the goal at sub-national level. Evidences from developing countries show that increasing utilization of skilled attendant at birth is an important indicator for reducing maternal death. Further, there is a very low utilization during childbirth in western and hilly regions of Nepal which clearly depicts the barriers in utilization of skilled birth attendants. So, there is a need to overcome the identified barriers to increase the utilization thereby decreasing the maternal mortality. The hypothesis of this study is that through a package of interventions the utilization of skilled birth attendants will be increased and hence improve maternal health in Nepal. This study involves a cluster randomized controlled trial involving approximately 5000 pregnant women in 36 clusters. The 18 intervention clusters will receive the following interventions: i) mobilization of family support for pregnant women to reach the health facility, ii) availability of emergency funds for institutional childbirth, iii) availability of transport options to reach a health facility for childbirth, iv) training to health workers on communication skills, v) security provisions for SBAs to reach services 24/24 through community mobilization; 18 control clusters will not receive the intervention package. The final evaluation of the intervention is planned to be completed by October 2014. Primary study output of this study is utilization of SBA services. Secondary study outputs measure the uptake of antenatal care, post natal checkup for mother and baby, availability of transportation for childbirth, operation of emergency fund, improved reception of women at health services, and improved physical security of SBAs. The intervention package is designed to increase the utilization of skilled

Machine Dictation and Transcription.

Science.gov (United States)

Harvey, Evelyn; And Others

This instructional package contains both an instructor's manual and a student's manual for a course in machine dictation and transcription. The instructor's manual contains an overview with tips on teaching the course, letters for dictation, and a key to the letters. The student's manual contains an overview of the course and of the skills needed…

Simulation and Community-Based Instruction of Vending Machines with Time Delay.

Science.gov (United States)

Browder, Diane M.; And Others

1988-01-01

The study evaluated the use of simulated instruction on vending machine use as an adjunct to community-based instruction with two moderately retarded children. Results showed concurrent acquisition of the vending machine skills across trained and untrained sites. (Author/DB)

Introduction to Machine Learning: Class Notes 67577

OpenAIRE

Shashua, Amnon

2009-01-01

Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

High-performance dynamic quantum clustering on graphics processors

Energy Technology Data Exchange (ETDEWEB)

Wittek, Peter, E-mail: peterwittek@acm.org [Swedish School of Library and Information Science, University of Boras, Boras (Sweden)

2013-01-15

Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schroedinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning.

Reducing child conduct problems and promoting social skills in a middle-income country: cluster randomised controlled trial†

Science.gov (United States)

Baker-Henningham, Helen; Scott, Stephen; Jones, Kelvyn; Walker, Susan

2012-01-01

Background There is an urgent need for effective, affordable interventions to prevent child mental health problems in low- and middle-income countries. Aims To determine the effects of a universal pre-school-based intervention on child conduct problems and social skills at school and at home. Method In a cluster randomised design, 24 community pre-schools in inner-city areas of Kingston, Jamaica, were randomly assigned to receive the Incredible Years Teacher Training intervention (n = 12) or to a control group (n = 12). Three children from each class with the highest levels of teacher-reported conduct problems were selected for evaluation, giving 225 children aged 3–6 years. The primary outcome was observed child behaviour at school. Secondary outcomes were child behaviour by parent and teacher report, child attendance and parents’ attitude to school. The study is registered as ISRCTN35476268. Results Children in intervention schools showed significantly reduced conduct problems (effect size (ES) = 0.42) and increased friendship skills (ES = 0.74) through observation, significant reductions to teacher-reported (ES = 0.47) and parent-reported (ES = 0.22) behaviour difficulties and increases in teacher-reported social skills (ES = 0.59) and child attendance (ES = 0.30). Benefits to parents’ attitude to school were not significant. Conclusions A low-cost, school-based intervention in a middle-income country substantially reduces child conduct problems and increases child social skills at home and at school. PMID:22500015

Acquisition, Maintenance and Generalization of Vending Machine Purchasing Skills by Moderately Handicapped Students.

Science.gov (United States)

Nietupski, John; And Others

1984-01-01

Four elementary age moderately disabled students were taught to use a picture-prompt prosthetic to make vending machine purchases. All students reached criterion on the vending machine use task, demonstrated partial generalization to untrained machines, and three Ss exhibited maintenance as much as six weeks beyond the termination of instruction.…

Machine Learning wins the Higgs Challenge

CERN Multimedia

Abha Eli Phoboo

2014-01-01

The winner of the four-month-long Higgs Machine Learning Challenge, launched on 12 May, is Gábor Melis from Hungary, followed closely by Tim Salimans from the Netherlands and Pierre Courtiol from France. The challenge explored the potential of advanced machine learning methods to improve the significance of the Higgs discovery.   Winners of the Higgs Machine Learning Challenge: Gábor Melis and Tim Salimans (top row), Tianqi Chen and Tong He (bottom row). Participants in the Higgs Machine Learning Challenge were tasked with developing an algorithm to improve the detection of Higgs boson signal events decaying into two tau particles in a sample of simulated ATLAS data* that contains few signal and a majority of non-Higgs boson “background” events. No knowledge of particle physics was required for the challenge but skills in machine learning - the training of computers to recognise patterns in data – were essential. The Challenge, hosted by Ka...

Development of a small-scale computer cluster

Science.gov (United States)

Wilhelm, Jay; Smith, Justin T.; Smith, James E.

2008-04-01

An increase in demand for computing power in academia has necessitated the need for high performance machines. Computing power of a single processor has been steadily increasing, but lags behind the demand for fast simulations. Since a single processor has hard limits to its performance, a cluster of computers can have the ability to multiply the performance of a single computer with the proper software. Cluster computing has therefore become a much sought after technology. Typical desktop computers could be used for cluster computing, but are not intended for constant full speed operation and take up more space than rack mount servers. Specialty computers that are designed to be used in clusters meet high availability and space requirements, but can be costly. A market segment exists where custom built desktop computers can be arranged in a rack mount situation, gaining the space saving of traditional rack mount computers while remaining cost effective. To explore these possibilities, an experiment was performed to develop a computing cluster using desktop components for the purpose of decreasing computation time of advanced simulations. This study indicates that small-scale cluster can be built from off-the-shelf components which multiplies the performance of a single desktop machine, while minimizing occupied space and still remaining cost effective.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

Internet2-based 3D PET image reconstruction using a PC cluster

International Nuclear Information System (INIS)

Shattuck, D.W.; Rapela, J.; Asma, E.; Leahy, R.M.; Chatzioannou, A.; Qi, J.

2002-01-01

We describe an approach to fast iterative reconstruction from fully three-dimensional (3D) PET data using a network of PentiumIII PCs configured as a Beowulf cluster. To facilitate the use of this system, we have developed a browser-based interface using Java. The system compresses PET data on the user's machine, sends these data over a network, and instructs the PC cluster to reconstruct the image. The cluster implements a parallelized version of our preconditioned conjugate gradient method for fully 3D MAP image reconstruction. We report on the speed-up factors using the Beowulf approach and the impacts of communication latencies in the local cluster network and the network connection between the user's machine and our PC cluster. (author)

Using Machine Learning Techniques in the Analysis of Oceanographic Data

Science.gov (United States)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

OpenAIRE

Thiruvenkadam, T; Karthikeyani, V

2014-01-01

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

High-performance dynamic quantum clustering on graphics processors

International Nuclear Information System (INIS)

Wittek, Peter

2013-01-01

Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schrödinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning.

Minimal improvement of nurses' motivational interviewing skills in routine diabetes care one year after training: a cluster randomized trial.

Science.gov (United States)

Jansink, Renate; Braspenning, Jozé; Laurant, Miranda; Keizer, Ellen; Elwyn, Glyn; Weijden, Trudy van der; Grol, Richard

2013-03-28

The effectiveness of nurse-led motivational interviewing (MI) in routine diabetes care in general practice is inconclusive. Knowledge about the extent to which nurses apply MI skills and the factors that affect the usage can help to understand the black box of this intervention. The current study compared MI skills of trained versus non-trained general practice nurses in diabetes consultations. The nurses participated in a cluster randomized trial in which a comprehensive program (including MI training) was tested on improving clinical parameters, lifestyle, patients' readiness to change lifestyle, and quality of life. Fifty-eight general practices were randomly assigned to usual care (35 nurses) or the intervention (30 nurses). The ratings of applying 24 MI skills (primary outcome) were based on five consultation recordings per nurse at baseline and 14 months later. Two judges evaluated independently the MI skills and the consultation characteristics time, amount of nurse communication, amount of lifestyle discussion and patients' readiness to change. The effect of the training on the MI skills was analysed with a multilevel linear regression by comparing baseline and the one-year follow-up between the interventions with usual care group. The overall effect of the consultation characteristics on the MI skills was studied in a multilevel regression analyses. At one year follow up, it was demonstrated that the nurses improved on 2 of the 24 MI skills, namely, "inviting the patient to talk about behaviour change" (mean difference=0.39, p=0.009), and "assessing patient's confidence in changing their lifestyle" (mean difference=0.28, p=0.037). Consultation time and the amount of lifestyle discussion as well as the patients' readiness to change health behaviour was associated positively with applying MI skills. The maintenance of the MI skills one year after the training program was minimal. The question is whether the success of MI to change unhealthy behaviour must be

Weighing the Dark and Light in Cosmology with Machine Learning

Science.gov (United States)

Trac, Hy

2017-09-01

Galaxy clusters contain large amounts of cold dark matter, hot ionized gas, and tens to hundreds of visible galaxies. They are the largest gravitationally bound systems in the Universe and make excellent laboratories for studying cosmology and astrophysics. Historically, Fritz Zwicky postulated the existence of dark matter when he inferred the total mass of the nearby Coma Cluster from the motions of its galaxies and found it to be much larger than the visible mass. Nowadays, the abundance of clusters as a function of mass and time can be used to study structure formation and constrain cosmological parameters. Dynamical measurements of the motions of galaxies can be used to probe the entire mass distribution, but standard analyses yield unwanted high mass errors. First, we show that modern machine learning algorithms can improve mass measurements by more than a factor of two compared to using standard scaling relations. Support Distribution Machines are used to train and test on the entire distribution of galaxy velocities to maximally use available information. Second, we discuss how Deep Learning can be used to train on multi-wavelength images of galaxies and clusters and to predict the underlying total matter distribution. By applying machine learning to observations and simulations, we can map out the dark and light in the Universe. DOE DE-SC0011114, NSF RI-1563887.

Machine Learning for Security

CERN Multimedia

CERN. Geneva

2015-01-01

Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...

Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering

Directory of Open Access Journals (Sweden)

Zhanjiang Li

2018-01-01

Full Text Available The micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information by clustering variables through the principle of minimum sum of deviation squares. This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis. The test results show that, first, the constructed final micro enterprises’ credit indicator system is in line with the 5C model; second, the validity test based on the ROC (Receiver Operating Characteristic curve reveals that each of the screened credit evaluation indicators is valid.

Mobile phones as a health communication tool to improve skilled attendance at delivery in Zanzibar: a cluster-randomised controlled trial.

Science.gov (United States)

Lund, S; Hemed, M; Nielsen, B B; Said, A; Said, K; Makungu, M H; Rasch, V

2012-09-01

To examine the association between a mobile phone intervention and skilled delivery attendance in a resource-limited setting. Pragmatic cluster-randomised controlled trial with primary healthcare facilities as the unit of randomisation. Primary healthcare facilities in Zanzibar. Two thousand, five hundred and fifty pregnant women (1311 interventions and 1239 controls) who attended antenatal care at one of the selected primary healthcare facilities were included at their first antenatal care visit and followed until 42 days after delivery. All pregnant women were eligible for study participation. Twenty-four primary healthcare facilities in six districts in Zanzibar were allocated by simple randomisation to either mobile phone intervention (n = 12) or standard care (n = 12). The intervention consisted of a short messaging service (SMS) and mobile phone voucher component. Skilled delivery attendance. The mobile phone intervention was associated with an increase in skilled delivery attendance: 60% of the women in the intervention group versus 47% in the control group delivered with skilled attendance. The intervention produced a significant increase in skilled delivery attendance amongst urban women (odds ratio, 5.73; 95% confidence interval, 1.51-21.81), but did not reach rural women. The mobile phone intervention significantly increased skilled delivery attendance amongst women of urban residence. Mobile phone solutions may contribute to the saving of lives of women and their newborns and the achievement of Millennium Development Goals 4 and 5, and should be considered by maternal and child health policy makers in developing countries. © 2012 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2012 RCOG.

Automatic management software for large-scale cluster system

International Nuclear Information System (INIS)

Weng Yunjian; Chinese Academy of Sciences, Beijing; Sun Gongxing

2007-01-01

At present, the large-scale cluster system faces to the difficult management. For example the manager has large work load. It needs to cost much time on the management and the maintenance of large-scale cluster system. The nodes in large-scale cluster system are very easy to be chaotic. Thousands of nodes are put in big rooms so that some managers are very easy to make the confusion with machines. How do effectively carry on accurate management under the large-scale cluster system? The article introduces ELFms in the large-scale cluster system. Furthermore, it is proposed to realize the large-scale cluster system automatic management. (authors)

Minimal improvement of nurses’ motivational interviewing skills in routine diabetes care one year after training: a cluster randomized trial

Science.gov (United States)

2013-01-01

Background The effectiveness of nurse-led motivational interviewing (MI) in routine diabetes care in general practice is inconclusive. Knowledge about the extent to which nurses apply MI skills and the factors that affect the usage can help to understand the black box of this intervention. The current study compared MI skills of trained versus non-trained general practice nurses in diabetes consultations. The nurses participated in a cluster randomized trial in which a comprehensive program (including MI training) was tested on improving clinical parameters, lifestyle, patients’ readiness to change lifestyle, and quality of life. Methods Fifty-eight general practices were randomly assigned to usual care (35 nurses) or the intervention (30 nurses). The ratings of applying 24 MI skills (primary outcome) were based on five consultation recordings per nurse at baseline and 14 months later. Two judges evaluated independently the MI skills and the consultation characteristics time, amount of nurse communication, amount of lifestyle discussion and patients’ readiness to change. The effect of the training on the MI skills was analysed with a multilevel linear regression by comparing baseline and the one-year follow-up between the interventions with usual care group. The overall effect of the consultation characteristics on the MI skills was studied in a multilevel regression analyses. Results At one year follow up, it was demonstrated that the nurses improved on 2 of the 24 MI skills, namely, “inviting the patient to talk about behaviour change” (mean difference=0.39, p=0.009), and “assessing patient’s confidence in changing their lifestyle” (mean difference=0.28, p=0.037). Consultation time and the amount of lifestyle discussion as well as the patients’ readiness to change health behaviour was associated positively with applying MI skills. Conclusions The maintenance of the MI skills one year after the training program was minimal. The question is whether

Modern machine learning techniques and their applications in cartoon animation research

CERN Document Server

Yu, Jun

2013-01-01

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

Semi-Supervised Clustering for High-Dimensional and Sparse Features

Science.gov (United States)

Yan, Su

2010-01-01

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

Voting-based consensus clustering for combining multiple clusterings of chemical structures

Directory of Open Access Journals (Sweden)

Saeed Faisal

2012-12-01

Full Text Available Abstract Background Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results The cumulative voting-based aggregation algorithm (CVAA, cluster-based similarity partitioning algorithm (CSPA and hyper-graph partitioning algorithm (HGPA were examined. The F-measure and Quality Partition Index method (QPI were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA was the method of choice among consensus clustering methods.

Rise of the machines: the effects of labor-saving innovations on jobs and wages

OpenAIRE

Andy Feng; Georg Graetz

2015-01-01

Job polarization the rise in employment shares of high and low skill jobs at the expense of middle skill jobs occurred in the US not just recently, but also in the late nineteenth and early twentieth centuries. We argue that in each case polarization resulted from increased automation, and provide a theoretical explanation. In our model, firms deciding whether to employ machines or workers in a given task weigh the cost of using machines, which is increasing in the complexity (in an engineeri...

The Parental Environment Cluster Model of Child Neglect: An Integrative Conceptual Model.

Science.gov (United States)

Burke, Judith; Chandy, Joseph; Dannerbeck, Anne; Watt, J. Wilson

1998-01-01

Presents Parental Environment Cluster model of child neglect which identifies three clusters of factors involved in parents' neglectful behavior: (1) parenting skills and functions; (2) development and use of positive social support; and (3) resource availability and management skills. Model offers a focal theory for research, structure for…

Investigation on IMCP based clustering in LTE-M communication for smart metering applications

Directory of Open Access Journals (Sweden)

Kartik Vishal Deshpande

2017-06-01

Full Text Available Machine to Machine (M2M is foreseen as an emerging technology for smart metering applications where devices communicate seamlessly for information transfer. The M2M communication makes use of long term evolution (LTE as its backbone network and it results in long-term evolution for machine type communication (LTE-M network. As huge number of M2M devices is to be handled by single eNB (evolved Node B, clustering is exploited for efficient processing of the network. This paper investigates the proposed Improved M2M Clustering Process (IMCP based clustering technique and it is compared with two well-known clustering algorithms, namely, Low Energy Adaptive Clustering Hierarchical (LEACH and Energy Aware Multihop Multipath Hierarchical (EAMMH techniques. Further, the IMCP algorithm is analyzed with two-tier and three-tier M2M systems for various mobility conditions. The proposed IMCP algorithm improves the last node death by 63.15% and 51.61% as compared to LEACH and EAMMH, respectively. Further, the average energy of each node in IMCP is increased by 89.85% and 81.15%, as compared to LEACH and EAMMH, respectively.

The Rise of the Machines: Automation, Horizontal Innovation and Income Inequality

OpenAIRE

Morten Olsen; David Hemous

2014-01-01

We construct an endogenous growth model of directed technical change with automation (the introduction of machines which replace low-skill labor and complement high-skill labor) and horizontal innovation (the introduction of new products, which increases demand for both types of labor). For general processes of technical change, we demonstrate that although low-skill wages can drop during periods of increasing automation intensity, the asymptotic growth rate is weakly positive --- though lowe...

Machine performance and its effects on experiments in JT-60U

International Nuclear Information System (INIS)

Kondo, I.

1995-01-01

The operational results of JT-60U were reviewed in light of the strategy made at the design stage. The operational plan for better confinement shifted from that of low q to high poloidal beta plasma configuration with higher q value according to the revealed machine properties. Some technical and operational skills helped bring about the recent results out of the machine. (orig.)

Clustering methods for the optimization of atomic cluster structure

Science.gov (United States)

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

2018-04-01

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis

International Nuclear Information System (INIS)

Heo, Min Suk; Kavitha, Muthu Subash; Asano, Akira; Taguchi, Akira

2013-01-01

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Information extraction from dynamic PS-InSAR time series using machine learning

Science.gov (United States)

van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

2017-12-01

Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

Automatic microseismic event picking via unsupervised machine learning

Science.gov (United States)

Chen, Yangkang

2018-01-01

Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Machine throughput improvement achieved using innovative control technique

International Nuclear Information System (INIS)

Sharma, V.; Acharya, S.; Mittal, K.C.

2012-01-01

In any type of fully or semi automatic machine the control systems plays an important role. The control system on the one hand has to consider the human psychology, intelligence requirement for an operator, and attention needed from him. On the other hand the complexity of the control has also to be understood well before designing a control system that can be handled comfortably and safely by the operator. As far as the user experience/comfort is concerned the design of control system GUI is vital. Considering these two aspects related to the user of the machine it is evident that the control system design is very important because it is has to accommodate the human behaviour and skill sets required/available as well as the capability of the machine under the control of the control system. An intelligently designed control system can enhance the productivity of the machine. (author)

RBMK full scope simulator gets virtual refuelling machine

International Nuclear Information System (INIS)

Khoudiakov, M.; Slonimsky, V.; Mitrofanov, S.

2006-01-01

The paper describes a continuation of efforts of an international Russian-Norwegian joint team to drastically increase operational safety during the refuelling process of an RBMK-type reactor by implementing a training simulator based on an innovative Virtual Reality (VR) approach. During the preceding stage of the project a display-based simulator was extended with VR models of the real Refueling Machine (RM) and its environment in order to improve both the learning process and operation's effectiveness. The simulator's challenge is to support the performance (operational activity) of RM operational staff firstly and to take major part in developing basic knowledge and skills as well as to keep skilled staff in close touch with the complex machinery of the Refueling Machine. At the given 2nd stage the functional scope of the VR-simulator was greatly enhanced - firstly, by connecting to the RBMK-unit full-scope simulator, and, secondly, by a training program and simulator model upgrade. (author)

PENGEMBANGAN EMPLOYABILITY SKILLS SISWA SMK DITINJAU DARI IMPLEMENTASI PENDEKATAN SAINTIFIK

Directory of Open Access Journals (Sweden)

Sunardi Sunardi

2016-07-01

Full Text Available The industry now needs a workforce that has the technical skills and employability skills. Completion of the CMS so that students have a technical skill and employability skills based on a scientific approach to implementation that is one indicator of the quality of learning. This research aims to know the contribution of the scientific approach towards implementation of employability skills the students of SMK Package Engineering Machining in South Sulawesi. Research using quantitative non experimental design approach is the type of survey that is ex-post facto. Pupulasi research is a grade XII Package Engineering Machining on SMK in South Sulawesi as much as 503 students with samples of 221. Data collection techniques used are the now. Research data were analyzed with descriptive analysis, comfirmatory factor analysis (CFA, regression analysis. The data analysis was done with the help of SPSS software version 4.5 for Windows and version of LISREL 9.10 Windows Application. Based on the results of the study it can be concluded that the implementation of the scientific approach contributes to employability skills students of SMK Package Engineering Machining in South Sulawesi. Therefore it can be said that the implementation of the scientific approach as a system of learning can develop employability skills graduates SMK. Industri saat ini membutuhkan tenaga kerja yang memiliki keterampilan teknis dan employability skill. Penyiapan siswa SMK agar memiliki keterampilan teknis dan employability skills berpangkal pada implementasi pendekatan saintifik yang merupakan salah satu indikator kualitas pembelajaran. Penelitian ini bertujuan untuk mengetahui kontribusi implementasi pendekatan saintifik terhadap employability skills siswa SMK Paket Keahlian Teknik Pemesinan di Sulawesi Selatan. Penelitian menggunakan pendekatan kuantitatif rancangan non eksperimen jenis survey yang bersifat ex-post facto. Pupulasi penelitian adalah siswa kelas XII Paket

Development of large size NC trepanning and horning machine

International Nuclear Information System (INIS)

Wada, Yoshiei; Aono, Fumiaki; Siga, Toshihiko; Sudo, Eiichi; Takasa, Seiju; Fukuyama, Masaaki; Sibukawa, Koichi; Nakagawa, Hirokatu

2010-01-01

Due to the recent increase in world energy demand, construction of considerable number of nuclear and fossil power plant has been proceeded and is further planned. High generating capacity plant requires large forged components such as monoblock turbine rotor shafts and the dimensions of them tend to increase. Some of these components have center bore for material test, NDE and other use. In order to cope with the increase in production of these large forgings with center bores, a new trepanning machine, which exclusively bore a deep hole, was developed in JSW taking account of many accumulated experiences and know-how of experts. The machine is the world largest 400t trepanning and horning machine with numerical control and has many advantage in safety, the machining precision, machining efficiency, operability, labor-saving, and energy saving. Furthermore, transfer of the technical skill became easy through concentrated monitoring system based on numerically analysed experts' know-how. (author)

Discussion of CoSA: Clustering of Sparse Approximations

Energy Technology Data Exchange (ETDEWEB)

Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-07

The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

An Interesting Review on Soft Skills and Dental Practice

OpenAIRE

Dalaya, Maya; Ishaquddin, Syed; Ghadage, Mahesh; Hatte, Geeta

2015-01-01

In today’s world of education, we concentrate on teaching activities and academic knowledge. We are taught to improve our clinical skills. Soft skills refer to the cluster of personality traits, social graces, and personal habits, facility with language, friendliness and personal habits that mark people to varying degrees. Soft Skills are interpersonal, psychological, self-promoted and non-technical qualities for every practitioner and academician, whereas hard skills are new tools or equipme...

Efficacy of an internet-based learning module and small-group debriefing on trainees' attitudes and communication skills toward patients with substance use disorders: results of a cluster randomized controlled trial.

Science.gov (United States)

Lanken, Paul N; Novack, Dennis H; Daetwyler, Christof; Gallop, Robert; Landis, J Richard; Lapin, Jennifer; Subramaniam, Geetha A; Schindler, Barbara A

2015-03-01

To examine whether an Internet-based learning module and small-group debriefing can improve medical trainees' attitudes and communication skills toward patients with substance use disorders (SUDs). In 2011-2012, 129 internal and family medicine residents and 370 medical students at two medical schools participated in a cluster randomized controlled trial, which assessed the effect of adding a two-part intervention to the SUDs curricula. The intervention included a self-directed, media-rich Internet-based learning module and a small-group, faculty-led debriefing. Primary study outcomes were changes in self-assessed attitudes in the intervention group (I-group) compared with those in the control group (C-group) (i.e., a difference of differences). For residents, the authors used real-time, Web-based interviews of standardized patients to assess changes in communication skills. Statistical analyses, conducted separately for residents and students, included hierarchical linear modeling, adjusted for site, participant type, cluster, and individual scores at baseline. The authors found no significant differences between the I- and C-groups in attitudes for residents or students at baseline. Compared with those in the C-group, residents, but not students, in the I-group had more positive attitudes toward treatment efficacy and self-efficacy at follow-up (Pcommunication skills toward patients with SUDs among residents. Enhanced attitudes and skills may result in improved care for these patients.

A new hybrid imperialist competitive algorithm on data clustering

Indian Academy of Sciences (India)

Modified imperialist competitive algorithm; simulated annealing; ... Clustering is one of the unsupervised learning branches where a set of patterns, usually vectors ..... machine classification is based on design, operation, and/or purpose.

Effectiveness of Podcasts as Laboratory Instructional Support: Learner Perceptions of Machine Shop and Welding Students

Science.gov (United States)

Lauritzen, Louis Dee

2014-01-01

Machine shop students face the daunting task of learning the operation of complex three-dimensional machine tools, and welding students must develop specific motor skills in addition to understanding the complexity of material types and characteristics. The use of consumer technology by the Millennial generation of vocational students, the…

Prediction Model of Machining Failure Trend Based on Large Data Analysis

Science.gov (United States)

Li, Jirong

2017-12-01

The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.

Asnuntuck Community College's Machine Technology Certificate and Degree Programs.

Science.gov (United States)

Irlen, Harvey S.; Gulluni, Frank D.

2002-01-01

States that although manufacturing remains a viable sector in Connecticut, it is experiencing skills shortages in the workforce. Describes the machine technology program's purpose, the development of the Asnuntuck Community College's (Connecticut) partnership with private sector manufacturers, the curriculum, the outcomes, and benefits of…

Factored Translation with Unsupervised Word Clusters

DEFF Research Database (Denmark)

Rishøj, Christian; Søgaard, Anders

2011-01-01

Unsupervised word clustering algorithms — which form word clusters based on a measure of distributional similarity — have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word...... clusters as factors in statistical machine translation. Although some of the language pairs in this work clearly benefit from the factor augmentation, there is no consistent improvement in translation accuracy across the board. For all language pairs, the word clusters clearly improve translation for some...... proportion of the sentences in the test set, but has a weak or even detrimental effect on the rest. It is shown that if one could determine whether or not to use a factor when translating a given sentence, rather substantial improvements in precision could be achieved for all of the language pairs evaluated...

Machine learning and computer vision approaches for phenotypic profiling.

Science.gov (United States)

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

Dynamic Resource Allocation and Access Class Barring Scheme for Delay-Sensitive Devices in Machine to Machine (M2M) Communications.

Science.gov (United States)

Li, Ning; Cao, Chao; Wang, Cong

2017-06-15

Supporting simultaneous access of machine-type devices is a critical challenge in machine-to-machine (M2M) communications. In this paper, we propose an optimal scheme to dynamically adjust the Access Class Barring (ACB) factor and the number of random access channel (RACH) resources for clustered machine-to-machine (M2M) communications, in which Delay-Sensitive (DS) devices coexist with Delay-Tolerant (DT) ones. In M2M communications, since delay-sensitive devices share random access resources with delay-tolerant devices, reducing the resources consumed by delay-sensitive devices means that there will be more resources available to delay-tolerant ones. Our goal is to optimize the random access scheme, which can not only satisfy the requirements of delay-sensitive devices, but also take the communication quality of delay-tolerant ones into consideration. We discuss this problem from the perspective of delay-sensitive services by adjusting the resource allocation and ACB scheme for these devices dynamically. Simulation results show that our proposed scheme realizes good performance in satisfying the delay-sensitive services as well as increasing the utilization rate of the random access resources allocated to them.

Illinois Occupational Skill Standards: HVAC/R Technician Cluster.

Science.gov (United States)

Illinois Occupational Skill Standards and Credentialing Council, Carbondale.

This document, which is intended to serve as a guide for work force preparation program providers, details the Illinois occupational skill standards for programs preparing students for employment in jobs in the heating, ventilation, air conditioning, and refrigeration (HVAC/R) industry. Agency partners involved in this project include: the…

Cluman: Advanced cluster management for the large-scale infrastructures

International Nuclear Information System (INIS)

Babik, Marian; Fedorko, Ivan; Rodrigues, David

2011-01-01

The recent uptake of multi-core computing has produced a rapid growth of virtualisation and cloud computing services. With the increased use of the many-core processors this trend will likely accelerate and computing centres will be faced with the management of the tens of thousands of the virtual machines. Furthermore, these machines will likely be geographically distributed and need to be allocated on demand. In order to cope with such complexity we have designed and developed an advanced cluster management system that can execute administrative tasks targeting thousands of machines as well as provide an interactive high-density visualisation of the fabrics. The job management subsystem can perform complex tasks while following their progress and output and report aggregated information back to the system administrators. The visualisation subsystem can display tree maps of the infrastructure elements with data and monitoring information, thus providing a very detailed overview of the large clusters at a glance. The initial experience with development and testing of the system will be presented as well as an evaluation of its performance.

Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

Science.gov (United States)

Mahmoud, E.; Shoukry, A.; Takey, A.

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values

Computers and the Future of Skill Demand. Educational Research and Innovation Series

Science.gov (United States)

Elliott, Stuart W.

2017-01-01

Computer scientists are working on reproducing all human skills using artificial intelligence, machine learning and robotics. Unsurprisingly then, many people worry that these advances will dramatically change work skills in the years ahead and perhaps leave many workers unemployable. This report develops a new approach to understanding these…

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

Science.gov (United States)

Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

Effect of mobile application-based versus DVD-based CPR training on students’ practical CPR skills and willingness to act: a cluster randomised study

Science.gov (United States)

Nord, Anette; Svensson, Leif; Hult, Håkan; Kreitz-Sandberg, Susanne; Nilsson, Lennart

2016-01-01

Objectives The aim was to compare students’ practical cardiopulmonary resuscitation (CPR) skills and willingness to perform bystander CPR, after a 30 min mobile application (app)-based versus a 50 min DVD-based training. Settings Seventh grade students in two Swedish municipalities. Design A cluster randomised trial. The classes were randomised to receive app-based or DVD-based training. Willingness to act and practical CPR skills were assessed, directly after training and at 6 months, by using a questionnaire and a PC Skill Reporting System. Data on CPR skills were registered in a modified version of the Cardiff test, where scores were given in 12 different categories, adding up to a total score of 12–48 points. Training and measurements were performed from December 2013 to October 2014. Participants 63 classes or 1232 seventh grade students (13-year-old) were included in the study. Primary and secondary outcome measures Primary end point was the total score of the modified Cardiff test. The individual variables of the test and self-reported willingness to make a life-saving intervention were secondary end points. Results The DVD-based group was superior to the app-based group in CPR skills; a total score of 36 (33–38) vs 33 (30–36) directly after training (pCPR skill components. Both groups improved compression depth from baseline to follow-up. If a friend suffered cardiac arrest, 78% (DVD) versus 75% (app) would do compressions and ventilations, whereas only 31% (DVD) versus 32% (app) would perform standard CPR if the victim was a stranger. Conclusions At 6 months follow-up, the 50 min DVD-based group showed superior CPR skills compared with the 30 min app-based group. The groups did not differ in regard to willingness to make a life-saving effort. PMID:27130166

Training and generalization of laundry skills: a multiple probe evaluation with handicapped persons.

Science.gov (United States)

Thompson, T J; Braam, S J; Fugua, R W

1982-01-01

An instructional procedure composed of a graded sequence of prompts and token reinforcement was used to train a complex chain of behaviors which included sorting, washing, and drying clothes. A multiple probe design with sequential instruction across seven major components of the laundering routine was used to demonstrate experimental control. Students were taught to launder clothing using machines located in their school and generalization was assessed later on machines located in the public laundromat. A comparison of students' laundry skills with those of normal peers indicated similar levels of proficiency. Follow-up probes demonstrated maintenance of laundry skills over a 10-month period.

Clustervision: Visual Supervision of Unsupervised Clustering.

Science.gov (United States)

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

Science.gov (United States)

Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

2018-01-11

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

Optimal design method to minimize users' thinking mapping load in human-machine interactions.

Science.gov (United States)

Huang, Yanqun; Li, Xu; Zhang, Jie

2015-01-01

The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today's mental work mastered society. Improving the usability of a product and minimizing user's thinking mapping and interpreting load in human-machine interactions. An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users' intentions and affordance of product interface states. By analyzing the users' thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. Considering multiple factors to minimize users' thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.

An interesting review on soft skills and dental practice.

Science.gov (United States)

Dalaya, Maya; Ishaquddin, Syed; Ghadage, Mahesh; Hatte, Geeta

2015-03-01

In today's world of education, we concentrate on teaching activities and academic knowledge. We are taught to improve our clinical skills. Soft skills refer to the cluster of personality traits, social graces, and personal habits, facility with language, friendliness and personal habits that mark people to varying degrees. Soft Skills are interpersonal, psychological, self-promoted and non-technical qualities for every practitioner and academician, whereas hard skills are new tools or equipment and professional knowledge. Hence, more and more clinicians now days consider soft skills as important job criteria. An increase in service industry and competitive practices emphasizes the need for soft skills. Soft Skills are very important and useful in personal and professional life.

Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations

Directory of Open Access Journals (Sweden)

Yingfeng Zhang

2014-01-01

Full Text Available This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS and precision forging stage (PFS of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.

A review of machine learning in obesity.

Science.gov (United States)

DeGregory, K W; Kuiper, P; DeSilvio, T; Pleuss, J D; Miller, R; Roginski, J W; Fisher, C B; Harness, D; Viswanath, S; Heymsfield, S B; Dungan, I; Thomas, D M

2018-05-01

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity. © 2018 World Obesity Federation.

Dialectical behavior therapy skills use and emotion dysregulation in personality disorders and psychopathy: a community self-report study.

Science.gov (United States)

Neacsiu, Andrada D; Tkachuck, Mathew A

2016-01-01

Emotion dysregulation is a critical transdiagnostic mental health problem that needs to be further examined in personality disorders (PDs). The current study examined dialectical behavior therapy (DBT) skills use, emotion dysregulation, and dysfunctional coping among adults who endorsed symptoms of cluster B PDs and psychopathy. We hypothesized that skills taught in DBT and emotion dysregulation are useful for adults with PDs other than borderline personality disorder (BPD). Using a self-report questionnaire, we examined these constructs in three groups of community adults: those who reported symptoms consistent with borderline personality disorder (BPD; N = 29), those who reported symptoms consistent with any other cluster B PD (N = 22), and those with no reported cluster B PD symptoms (N = 77) as measured by the Personality Diagnostic Questionnaire-4 + . Both PD groups reported higher emotion dysregulation and dysfunctional coping when compared to the no PD group. Only the BPD group had significantly lower DBT skills use. DBT skills use was found to be a significant predictor of cluster B psychopathology but only before accounting for emotion dysregulation. When added to the regression model, emotion dysregulation was found to be a significant predictor of cluster B psychopathology but DBT skills use no longer had a significant effect. Across all groups, DBT skills use deficits and maladaptive coping, but not emotion dysregulation, predicted different facets of psychopathy. Emotion dysregulation and use of maladaptive coping are problems in cluster B PDs, outside of BPD, but not in psychopathy. Inability to use DBT skills may be unique to BPD. Because this study relied exclusively on self-report, this data is preliminary and warrants further investigation.

Relationship between Procedural Tactical Knowledge and Specific Motor Skills in Young Soccer Players

Directory of Open Access Journals (Sweden)

Rodrigo Aquino

2016-11-01

Full Text Available The purpose of this study was to investigate the association between offensive tactical knowledge and the soccer-specific motor skills performance. Fifteen participants were submitted to two evaluation tests, one to assess their technical and tactical analysis. The motor skills performance was measured through four tests of technical soccer skills: ball control, shooting, passing and dribbling. The tactical performance was based on a tactical assessment system called FUT-SAT (Analyses of Procedural Tactical Knowledge in Soccer. Afterwards, technical and tactical evaluation scores were ranked with and without the use of the cluster method. A positive, weak correlation was perceived in both analyses (rho = 0.39, not significant p = 0.14 (with cluster analysis; and rho = 0.35; not significant p = 0.20 (without cluster analysis. We can conclude that there was a weak association between the technical and the offensive tactical knowledge. This shows the need to reflect on the use of such tests to assess technical skills in team sports since they do not take into account the variability and unpredictability of game actions and disregard the inherent needs to assess such skill performance in the game.

Android Malware Classification Using K-Means Clustering Algorithm

Science.gov (United States)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Testing the ghost with the machine

International Nuclear Information System (INIS)

De Zubicaray, G.

2002-01-01

Since its introduction during the 1990s, functional magnetic resonance imaging (fMRI) has been used to investigate brain activity occurring during a bewildering variety of sensory, motor and cognitive tasks. That is, a machine is being used to test 'the ghost in the machine' - the human mind. The use of imaging techniques to investigate these issues has even led to the emergence of a new scientific field called cognitive neuroscience. Currently, there are only a few groups in Australia actively publishing fMRI studies in the international literature, and the majority of these laboratories are clustered on the east coast. My own research with fMRI has focused on areas such as language and memory, with a special interest in how we solve competitive processes in our thinking

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

OpenAIRE

Qing Ye; Hao Pan; Changhua Liu

2015-01-01

A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA)...

Performance of machine learning methods for ligand-based virtual screening.

Science.gov (United States)

Plewczynski, Dariusz; Spieser, Stéphane A H; Koch, Uwe

2009-05-01

Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an overview of publications that have evaluated different machine learning methods, such as support vector machines, decision trees, ensemble methods such as boosting, bagging and random forests, clustering methods, neuronal networks, naïve Bayesian, data fusion methods and others.

Quantum annealing for combinatorial clustering

Science.gov (United States)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

Practical skills of the future innovator

Science.gov (United States)

Kaurov, Vitaliy

2015-03-01

Physics graduates face and often are disoriented by the complex and turbulent world of startups, incubators, emergent technologies, big data, social network engineering, and so on. In order to build the curricula that foster the skills necessary to navigate this world, we will look at the experiences at the Wolfram Science Summer School that gathers annually international students for already more than a decade. We will look at the examples of projects and see the development of such skills as innovative thinking, data mining, machine learning, cloud technologies, device connectivity and the Internet of things, network analytics, geo-information systems, formalized computable knowledge, and the adjacent applied research skills from graph theory to image processing and beyond. This should give solid ideas to educators who will build standard curricula adapted for innovation and entrepreneurship education.

Short-term effects of the "Together at School" intervention program on children's socio-emotional skills: a cluster randomized controlled trial.

Science.gov (United States)

Kiviruusu, Olli; Björklund, Katja; Koskinen, Hanna-Leena; Liski, Antti; Lindblom, Jallu; Kuoppamäki, Heini; Alasuvanto, Paula; Ojala, Tiina; Samposalo, Hanna; Harmes, Nina; Hemminki, Elina; Punamäki, Raija-Leena; Sund, Reijo; Santalahti, Päivi

2016-05-26

Together at School is a universal intervention program designed to promote socio-emotional skills among primary-school children. It is based on a whole school approach, and implemented in school classes by teachers. The aim of the present study is to examine the short-term effects of the intervention program in improving socio-emotional skills and reducing psychological problems among boys and girls. We also examine whether these effects depend on grade level (Grades 1 to 3) and intervention dosage. This cluster randomized controlled trial design included 79 Finnish primary schools (40 intervention and 39 control) with 3 704 children. The outcome measures were the Strengths and Difficulties Questionnaire (SDQ) and the Multisource Assessment of Social Competence Scale (MASCS) with teachers as raters. The intervention dosage was indicated by the frequencies six central tools were used by the teachers. The data was collected at baseline and 6 months later. Intervention effects were analyzed using multilevel modeling. When analyzed across all grades no intervention effect was observed in improving children's socio-emotional skills or in reducing their psychological problems at 6-month follow-up. Among third (compared to first) graders the intervention decreased psychological problems. Stratified analyses by gender showed that this effect was significant only among boys and that among them the intervention also improved third graders' cooperation skills. Among girls the intervention effects were not moderated by grade. Implementing the intervention with intended intensity (i.e. a high enough dosage) had a significant positive effect on cooperation skills. When analyzed separately among genders, this effect was significant only in girls. These first, short-term results of the Together at School intervention program did not show any main effects on children's socio-emotional skills or psychological problems. This lack of effects may be due to the relatively short follow

PROMOTING GROSS MOTOR SKILLS IN TODDLERS: THE ACTIVE BEGINNINGS PILOT CLUSTER RANDOMIZED TRIAL.

Science.gov (United States)

Veldman, Sanne L C; Okely, Anthony D; Jones, Rachel A

2015-12-01

This study examined the feasibility, acceptability, and potential efficacy of a gross motor skill program for toddlers. An 8-wk. skills program in which children practiced three skills was implemented for 10 min. daily in two randomly designated childcare centers. Two other centers served as the control group. Recruitment and retention rates were collected for feasibility. Data on professional development, children's participation, program duration, and appropriateness of the lessons were collected for acceptability, and the Test of Gross Motor Development-2 and Get Skilled, Get Active (total of 28 points) were used to look at the potential efficacy. The participants were 60 toddlers (M age=2.5 yr., SD=0.4; n=29 boys), and the retention rate was 95%. Overall participation was 76%, and educators rated 98% of the lessons as appropriate. Compared with the control group, the intervention group showed significantly greater improvements in motor skills (pmotor skills among toddlers.

Machine learning, computer vision, and probabilistic models in jet physics

CERN Multimedia

CERN. Geneva; NACHMAN, Ben

2015-01-01

In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

Archetypal Analysis for Machine Learning

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

2010-01-01

Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - ...... for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering....

Clusters of PCS for high-speed computation for modelling of the climate

International Nuclear Information System (INIS)

Pabon C, Jose Daniel; Eslava R, Jesus Antonio; Montoya G, Gerardo de Jesus

2001-01-01

In order to create high speed computing capability, the Program of Post grade in Meteorology of the Department of Geosciences, National University of Colombia installed a cluster of 8 PCs for parallel processing. This high-speed processing machine was tested with the Climate Community Model (CCM3). In this paper, the results related to the performance of this machine are presented

Stereodivergent synthesis with a programmable molecular machine

Science.gov (United States)

Kassem, Salma; Lee, Alan T. L.; Leigh, David A.; Marcos, Vanesa; Palmer, Leoni I.; Pisano, Simone

2017-09-01

It has been convincingly argued that molecular machines that manipulate individual atoms, or highly reactive clusters of atoms, with Ångström precision are unlikely to be realized. However, biological molecular machines routinely position rather less reactive substrates in order to direct chemical reaction sequences, from sequence-specific synthesis by the ribosome to polyketide synthases, where tethered molecules are passed from active site to active site in multi-enzyme complexes. Artificial molecular machines have been developed for tasks that include sequence-specific oligomer synthesis and the switching of product chirality, a photo-responsive host molecule has been described that is able to mechanically twist a bound molecular guest, and molecular fragments have been selectively transported in either direction between sites on a molecular platform through a ratchet mechanism. Here we detail an artificial molecular machine that moves a substrate between different activating sites to achieve different product outcomes from chemical synthesis. This molecular robot can be programmed to stereoselectively produce, in a sequential one-pot operation, an excess of any one of four possible diastereoisomers from the addition of a thiol and an alkene to an α,β-unsaturated aldehyde in a tandem reaction process. The stereodivergent synthesis includes diastereoisomers that cannot be selectively synthesized through conventional iminium-enamine organocatalysis. We anticipate that future generations of programmable molecular machines may have significant roles in chemical synthesis and molecular manufacturing.

Evidence for Multiple Rhythmic Skills

Science.gov (United States)

Tierney, Adam; Kraus, Nina

2015-01-01

Rhythms, or patterns in time, play a vital role in both speech and music. Proficiency in a number of rhythm skills has been linked to language ability, suggesting that certain rhythmic processes in music and language rely on overlapping resources. However, a lack of understanding about how rhythm skills relate to each other has impeded progress in understanding how language relies on rhythm processing. In particular, it is unknown whether all rhythm skills are linked together, forming a single broad rhythmic competence, or whether there are multiple dissociable rhythm skills. We hypothesized that beat tapping and rhythm memory/sequencing form two separate clusters of rhythm skills. This hypothesis was tested with a battery of two beat tapping and two rhythm memory tests. Here we show that tapping to a metronome and the ability to adjust to a changing tempo while tapping to a metronome are related skills. The ability to remember rhythms and to drum along to repeating rhythmic sequences are also related. However, we found no relationship between beat tapping skills and rhythm memory skills. Thus, beat tapping and rhythm memory are dissociable rhythmic aptitudes. This discovery may inform future research disambiguating how distinct rhythm competencies track with specific language functions. PMID:26376489

Evidence for Multiple Rhythmic Skills.

Directory of Open Access Journals (Sweden)

Adam Tierney

Full Text Available Rhythms, or patterns in time, play a vital role in both speech and music. Proficiency in a number of rhythm skills has been linked to language ability, suggesting that certain rhythmic processes in music and language rely on overlapping resources. However, a lack of understanding about how rhythm skills relate to each other has impeded progress in understanding how language relies on rhythm processing. In particular, it is unknown whether all rhythm skills are linked together, forming a single broad rhythmic competence, or whether there are multiple dissociable rhythm skills. We hypothesized that beat tapping and rhythm memory/sequencing form two separate clusters of rhythm skills. This hypothesis was tested with a battery of two beat tapping and two rhythm memory tests. Here we show that tapping to a metronome and the ability to adjust to a changing tempo while tapping to a metronome are related skills. The ability to remember rhythms and to drum along to repeating rhythmic sequences are also related. However, we found no relationship between beat tapping skills and rhythm memory skills. Thus, beat tapping and rhythm memory are dissociable rhythmic aptitudes. This discovery may inform future research disambiguating how distinct rhythm competencies track with specific language functions.

An AK-LDMeans algorithm based on image clustering

Science.gov (United States)

Chen, Huimin; Li, Xingwei; Zhang, Yongbin; Chen, Nan

2018-03-01

Clustering is an effective analytical technique for handling unmarked data for value mining. Its ultimate goal is to mark unclassified data quickly and correctly. We use the roadmap for the current image processing as the experimental background. In this paper, we propose an AK-LDMeans algorithm to automatically lock the K value by designing the Kcost fold line, and then use the long-distance high-density method to select the clustering centers to further replace the traditional initial clustering center selection method, which further improves the efficiency and accuracy of the traditional K-Means Algorithm. And the experimental results are compared with the current clustering algorithm and the results are obtained. The algorithm can provide effective reference value in the fields of image processing, machine vision and data mining.

Single Molecule Cluster Analysis Identifies Signature Dynamic Conformations along the Splicing Pathway

Science.gov (United States)

Blanco, Mario R.; Martin, Joshua S.; Kahlscheuer, Matthew L.; Krishnan, Ramya; Abelson, John; Laederach, Alain; Walter, Nils G.

2016-01-01

The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines. PMID:26414013

Understanding dental CAD/CAM for restorations--dental milling machines from a mechanical engineering viewpoint. Part B: labside milling machines.

Science.gov (United States)

Lebon, Nicolas; Tapie, Laurent; Duret, Francois; Attal, Jean-Pierre

2016-01-01

Nowadays, dental numerical controlled (NC) milling machines are available for dental laboratories (labside solution) and dental production centers. This article provides a mechanical engineering approach to NC milling machines to help dental technicians understand the involvement of technology in digital dentistry practice. The technical and economic criteria are described for four labside and two production center dental NC milling machines available on the market. The technical criteria are focused on the capacities of the embedded technologies of milling machines to mill prosthetic materials and various restoration shapes. The economic criteria are focused on investment cost and interoperability with third-party software. The clinical relevance of the technology is discussed through the accuracy and integrity of the restoration. It can be asserted that dental production center milling machines offer a wider range of materials and types of restoration shapes than labside solutions, while labside solutions offer a wider range than chairside solutions. The accuracy and integrity of restorations may be improved as a function of the embedded technologies provided. However, the more complex the technical solutions available, the more skilled the user must be. Investment cost and interoperability with third-party software increase according to the quality of the embedded technologies implemented. Each private dental practice may decide which fabrication option to use depending on the scope of the practice.

Behavioral Modeling for Mental Health using Machine Learning Algorithms.

Science.gov (United States)

Srividya, M; Mohanavalli, S; Bhalaji, N

2018-04-03

Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

Effectiveness of music education for the improvement of reading skills and academic achievement in young poor readers: a pragmatic cluster-randomized, controlled clinical trial.

Science.gov (United States)

Cogo-Moreira, Hugo; Brandão de Ávila, Clara Regina; Ploubidis, George B; Mari, Jair de Jesus

2013-01-01

Difficulties in word-level reading skills are prevalent in Brazilian schools and may deter children from gaining the knowledge obtained through reading and academic achievement. Music education has emerged as a potential method to improve reading skills because due to a common neurobiological substratum. To evaluate the effectiveness of music education for the improvement of reading skills and academic achievement among children (eight to 10 years of age) with reading difficulties. 235 children with reading difficulties in 10 schools participated in a five-month, randomized clinical trial in cluster (RCT) in an impoverished zone within the city of São Paulo to test the effects of music education intervention while assessing reading skills and academic achievement during the school year. Five schools were chosen randomly to incorporate music classes (n = 114), and five served as controls (n = 121). Two different methods of analysis were used to evaluate the effectiveness of the intervention: The standard method was intention-to-treat (ITT), and the other was the Complier Average Causal Effect (CACE) estimation method, which took compliance status into account. The ITT analyses were not very promising; only one marginal effect existed for the rate of correct real words read per minute. Indeed, considering ITT, improvements were observed in the secondary outcomes (slope of Portuguese = 0.21 [pmusic lessons as public policy.

A New Type of Tea Baking Machine Based on Pro/E Design

Science.gov (United States)

Lin, Xin-Ying; Wang, Wei

2017-11-01

In this paper, the production process of wulong tea was discussed, mainly the effect of baking on the quality of tea. The suitable baking temperature of different tea was introduced. Based on Pro/E, a new type of baking machine suitable for wulong tea baking was designed. The working principle, mechanical structure and constant temperature timing intelligent control system of baking machine were expounded. Finally, the characteristics and innovation of new baking machine were discussed.The mechanical structure of this baking machine is more simple and reasonable, and can use the heat of the inlet and outlet, more energy saving and environmental protection. The temperature control part adopts fuzzy PID control, which can improve the accuracy and response speed of temperature control and reduce the dependence of baking operation on skilled experience.

Assessing Progress in Mastery of Counseling Communication Skills

NARCIS (Netherlands)

A.J. Kuntze (Jeroen)

2009-01-01

textabstractDuring the last century the attention paid in higher education to the development of professional skills has progressively increased. In the first half of the last century the term ‘skill’ mainly referred to motor or technical actions, for instance driving a car or operating a machine

Server consolidation for heterogeneous computer clusters using Colored Petri Nets and CPN Tools

Directory of Open Access Journals (Sweden)

Issam Al-Azzoni

2015-10-01

Full Text Available In this paper, we present a new approach to server consolidation in heterogeneous computer clusters using Colored Petri Nets (CPNs. Server consolidation aims to reduce energy costs and improve resource utilization by reducing the number of servers necessary to run the existing virtual machines in the cluster. It exploits the emerging technology of live migration which allows migrating virtual machines between servers without stopping their provided services. Server consolidation approaches attempt to find migration plans that aim to minimize the necessary size of the cluster. Our approach finds plans which not only minimize the overall number of used servers, but also minimize the total data migration overhead. The latter objective is not taken into consideration by other approaches and heuristics. We explore the use of CPN Tools in analyzing the state spaces of the CPNs. Since the state space of the CPN model can grow exponentially with the size of the cluster, we examine different techniques to generate and analyze the state space in order to find good plans to server consolidation within acceptable time and computing power.

Metastable decay of photoionized niobium clusters: Evaporation vs fission fragmentation

International Nuclear Information System (INIS)

Cole, S.K.; Liu, K.; Riley, S.J.

1986-01-01

The metastable decay of photoionized niobium clusters (Nb/sub n/ + ) has been observed in a newly constructed cluster beam machine. The decay manifests itself in the time-of-flight (TOF) mass spectrum as an asymmetric broadening of daughter ion peaks. Pulsed ion extraction has been used to measure the decay rate constants and to establish the mechanism of the fragmentation, evaporation and/or fission of the photoionized clusters. It is found that within the experimental time window evaporation dominates for the smaller clusters (n 6 sec -1 . The average kinetic energy release is also determined and is found to be on the order of 5 MeV. 8 refs., 3 figs., 1 tab

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Risk assessment of atmospheric emissions using machine learning

OpenAIRE

Cervone, G.; Franzese, P.; Ezber, Y.; Boybeyi, Z.

2008-01-01

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions.

First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find compl...

Promoting the purchase of low-calorie foods from school vending machines: A cluster-randomized controlled study

NARCIS (Netherlands)

Kocken, P.L.; Eeuwijk, J.; Kesten, N.M.C. van; Dusseldorp, E.; Buijs, G.; Bassa-Dafesh, Z.; Snel, J.

2012-01-01

BACKGROUND: Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. METHODS: A school-based randomized controlled trial was conducted in 13 experimental

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

Science.gov (United States)

Hennig, Holger; Rees, Paul; Blasi, Thomas; Kamentsky, Lee; Hung, Jane; Dao, David; Carpenter, Anne E; Filby, Andrew

2017-01-01

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery

Be STARS IN THE OPEN CLUSTER NGC 6830

International Nuclear Information System (INIS)

Yu, Po-Chieh; Lin, Chien-Cheng; Lin, Hsing-Wen; Lee, Chien-De; Ngeow, Chow-Choong; Ip, Wing-Huen; Chen, Wen-Ping; Chang, Chan-Kao; Huang, Li-Ching; Cheng, Yu-Chi; Ritter, Andreas; Konidaris, Nick; Chen, Hui-Chen; Malkan, Matthew A.; Laher, Russ; Surace, Jason; Edelson, Rick; Quimby, Robert; Ben-Ami, Sagi; Ofek, Eran O.

2016-01-01

We report the discovery of two new Be stars, and re-identify one known Be star in the open cluster NGC 6830. Eleven H α emitters were discovered using the H α imaging photometry of the Palomar Transient Factory Survey. Stellar membership of the candidates was verified with photometric and kinematic information using 2MASS data and proper motions. The spectroscopic confirmation was carried out by using the Shane 3 m telescope at the Lick observatory. Based on their spectral types, three H α emitters were confirmed as Be stars with H α equivalent widths greater than −10 Å. Two objects were also observed by the new spectrograph spectral energy distribution-machine (SED-machine) on the Palomar 60-inch Telescope. The SED-machine results show strong H α emission lines, which are consistent with the results of the Lick observations. The high efficiency of the SED-machine can provide rapid observations for Be stars in a comprehensive survey in the future.

Be STARS IN THE OPEN CLUSTER NGC 6830

Energy Technology Data Exchange (ETDEWEB)

Yu, Po-Chieh; Lin, Chien-Cheng; Lin, Hsing-Wen; Lee, Chien-De; Ngeow, Chow-Choong; Ip, Wing-Huen; Chen, Wen-Ping; Chang, Chan-Kao; Huang, Li-Ching; Cheng, Yu-Chi; Ritter, Andreas [Graduate Institute of Astronomy, National Central University, 300 Jhongda Road, Jhongli 32001, Taiwan (China); Konidaris, Nick [Cahill Center for Astronomy and Astrophysics, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125 (United States); Chen, Hui-Chen [Department of Natural Sciences and Sustainable Development, Ministry of Science and Technology, 106, Sec. 2, Heping E. Road, Taipei 10622, Taiwan (China); Malkan, Matthew A. [Department of Physics and Astronomy, University of California, Los Angeles, CA 90024 (United States); Laher, Russ; Surace, Jason [Spitzer Science Center, California Institute of Technology, M/S 314-6, Pasadena, CA 91125 (United States); Edelson, Rick [Department of Astronomy, University of Maryland, College Park, MD 20742 (United States); Quimby, Robert [Kavli-Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwanoha 5-1-5, Kashiwa-shi, Chiba (Japan); Ben-Ami, Sagi; Ofek, Eran O. [Department of Particle Physics and Astrophysics, The Weizmann Institute of Science, Rehovot 76100 (Israel); and others

2016-05-01

We report the discovery of two new Be stars, and re-identify one known Be star in the open cluster NGC 6830. Eleven H α emitters were discovered using the H α imaging photometry of the Palomar Transient Factory Survey. Stellar membership of the candidates was verified with photometric and kinematic information using 2MASS data and proper motions. The spectroscopic confirmation was carried out by using the Shane 3 m telescope at the Lick observatory. Based on their spectral types, three H α emitters were confirmed as Be stars with H α equivalent widths greater than −10 Å. Two objects were also observed by the new spectrograph spectral energy distribution-machine (SED-machine) on the Palomar 60-inch Telescope. The SED-machine results show strong H α emission lines, which are consistent with the results of the Lick observations. The high efficiency of the SED-machine can provide rapid observations for Be stars in a comprehensive survey in the future.

Mutation Clusters from Cancer Exome.

Science.gov (United States)

Kakushadze, Zura; Yu, Willie

2017-08-15

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development.

REFRAMING THE SKILLED WORKER: THE PSYCHOLOGICAL AND INSTITUTIONAL ORIGINS OF THE GERMAN SKILLS MACHINE

Directory of Open Access Journals (Sweden)

David Meskill

2005-01-01

Full Text Available In 1926, shortly after the German economy had emerged from the fog of post-World War I hyperinflation, the principle employers’ groups, the National Association of German Industry and the Association of German Employers’ Organizations, founded a Working Committee on Vocational Training. The establishment of this body represented a decisive turning point in the emergence of the highly skilled modern German work force. By standardizing vocational definitions, training schemes, and national qualifying exams, the Committee and its successors helped German apprentices and employers overcome previous disincentives to investing in worker training.

A Cluster- Based Secure Active Network Environment

Institute of Scientific and Technical Information of China (English)

CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

2005-01-01

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

Directory of Open Access Journals (Sweden)

Yeo‐Chan Yoon

2017-08-01

Full Text Available In this paper, we propose a fine‐grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high‐performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K‐means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Promoting the purchase of low-calorie foods from school vending machines: a cluster-randomized controlled study.

Science.gov (United States)

Kocken, Paul L; Eeuwijk, Jennifer; Van Kesteren, Nicole M C; Dusseldorp, Elise; Buijs, Goof; Bassa-Dafesh, Zeina; Snel, Jeltje

2012-03-01

Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. A school-based randomized controlled trial was conducted in 13 experimental schools and 15 control schools. Three strategies were tested within each experimental school: increasing the availability of lower-calorie products in vending machines, labeling products, and reducing the price of lower-calorie products. The experimental schools introduced the strategies in 3 consecutive phases, with phase 3 incorporating all 3 strategies. The control schools remained the same. The sales volumes from the vending machines were registered. Products were grouped into (1) extra foods containing empty calories, for example, candies and potato chips, (2) nutrient-rich basic foods, and (3) beverages. They were also divided into favorable, moderately unfavorable, and unfavorable products. Total sales volumes for experimental and control schools did not differ significantly for the extra and beverage products. Proportionally, the higher availability of lower-calorie extra products in the experimental schools led to higher sales of moderately unfavorable extra products than in the control schools, and to higher sales of favorable extra products in experimental schools where students have to stay during breaks. Together, availability, labeling, and price reduction raised the proportional sales of favorable beverages. Results indicate that when the availability of lower-calorie foods is increased and is also combined with labeling and reduced prices, students make healthier choices without buying more or fewer products from school vending machines. Changes to school vending machines help to create a healthy school environment. © 2012, American School Health Association.

Ethical, environmental and social issues for machine vision in manufacturing industry

Science.gov (United States)

Batchelor, Bruce G.; Whelan, Paul F.

1995-10-01

Some of the ethical, environmental and social issues relating to the design and use of machine vision systems in manufacturing industry are highlighted. The authors' aim is to emphasize some of the more important issues, and raise general awareness of the need to consider the potential advantages and hazards of machine vision technology. However, in a short article like this, it is impossible to cover the subject comprehensively. This paper should therefore be seen as a discussion document, which it is hoped will provoke more detailed consideration of these very important issues. It follows from an article presented at last year's workshop. Five major topics are discussed: (1) The impact of machine vision systems on the environment; (2) The implications of machine vision for product and factory safety, the health and well-being of employees; (3) The importance of intellectual integrity in a field requiring a careful balance of advanced ideas and technologies; (4) Commercial and managerial integrity; and (5) The impact of machine visions technology on employment prospects, particularly for people with low skill levels.

Some children with autism have latent social skills that can be tested

Czech Academy of Sciences Publication Activity Database

Hrdlička, M.; Urbánek, Tomáš; Vacová, M.; Beranová, Š.; Dudová, I.

2017-01-01

Roč. 13, March (2017), s. 827-833 ISSN 1178-2021 Institutional support: RVO:68081740 Keywords : autism * latent social skills * Autism Diagnostic Interview – Revised * Autism Diagnostic Observation Schedule * prediction Subject RIV: AN - Psychology OBOR OECD: Psychology (including human - machine relations) Impact factor: 2.198, year: 2016 https://www.dovepress.com/some-children-with- autism -have-latent-social-skills-that-can-be-tested-peer-reviewed-article-NDT

Some children with autism have latent social skills that can be tested

Czech Academy of Sciences Publication Activity Database

Hrdlička, M.; Urbánek, Tomáš; Vacová, M.; Beranová, Š.; Dudová, I.

2017-01-01

Roč. 13, March (2017), s. 827-833 ISSN 1178-2021 Institutional support: RVO:68081740 Keywords : autism * latent social skills * Autism Diagnostic Interview – Revised * Autism Diagnostic Observation Schedule * prediction Subject RIV: AN - Psychology OBOR OECD: Psychology (including human - machine relations) Impact factor: 2.198, year: 2016 https://www.dovepress.com/some-children-with-autism-have-latent- social -skills-that-can-be-tested-peer-reviewed-article-NDT

Automated robot-assisted surgical skill evaluation: Predictive analytics approach.

Science.gov (United States)

Fard, Mahtab J; Ameri, Sattar; Darin Ellis, R; Chinnam, Ratna B; Pandya, Abhilash K; Klein, Michael D

2018-02-01

Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise. Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied. The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task. This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features. Copyright © 2017 John Wiley & Sons, Ltd.

Energy-Based Metrics for Arthroscopic Skills Assessment.

Science.gov (United States)

Poursartip, Behnaz; LeBel, Marie-Eve; McCracken, Laura C; Escoto, Abelardo; Patel, Rajni V; Naish, Michael D; Trejos, Ana Luisa

2017-08-05

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.

Promoting the Purchase of Low-Calorie Foods from School Vending Machines: A Cluster-Randomized Controlled Study

Science.gov (United States)

Kocken, Paul L.; Eeuwijk, Jennifer; van Kesteren, Nicole M.C.; Dusseldorp, Elise; Buijs, Goof; Bassa-Dafesh, Zeina; Snel, Jeltje

2012-01-01

Background: Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. Methods: A school-based randomized controlled trial was conducted in 13 experimental schools and 15 control schools. Three strategies…

Advances in Machine Learning and Data Mining for Astronomy

Science.gov (United States)

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.

Energy landscapes for a machine learning application to series data

Energy Technology Data Exchange (ETDEWEB)

Ballard, Andrew J.; Stevenson, Jacob D.; Das, Ritankar; Wales, David J., E-mail: dw34@cam.ac.uk [University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW (United Kingdom)

2016-03-28

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.

Energy landscapes for a machine learning application to series data

International Nuclear Information System (INIS)

Ballard, Andrew J.; Stevenson, Jacob D.; Das, Ritankar; Wales, David J.

2016-01-01

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.

Learning Activity Packets for Grinding Machines. Unit II--Surface Grinding.

Science.gov (United States)

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

This learning activity packet (LAP) is one of three that accompany the curriculum guide on grinding machines. It outlines the study activities and performance tasks for the second unit of this curriculum guide. Its purpose is to aid the student in attaining a working knowledge of this area of training and in achieving a skilled or moderately…

Learning Activity Packets for Grinding Machines. Unit III--Cylindrical Grinding.

Science.gov (United States)

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

This learning activity packet (LAP) is one of three that accompany the curriculum guide on grinding machines. It outlines the study activities and performance tasks for the third unit of this curriculum guide. Its purpose is to aid the student in attaining a working knowledge of this area of training and in achieving a skilled or moderately…

Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Science.gov (United States)

2015-03-26

clustering is an algorithm that has been used in data mining applications such as machine learning applications , pattern recognition, hyper-spectral imagery...42 3.7.2 Application of K-means Clustering . . . . . . . . . . . . . . . . . 42 3.8 Experiment Design...Tomographic Imaging WLAN Wireless Local Area Networks WSN Wireless Sensor Network xx ESTIMATING SINGLE AND MULTIPLE TARGET LOCATIONS USING K-MEANS CLUSTERING

Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches

Science.gov (United States)

Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Thomas

To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.

[Classification of gamblers from self-help groups using cluster analysis].

Science.gov (United States)

Meyer, G

1991-01-01

In an empirically based classification by cluster analysis of 437 gamblers from self-help groups five distinct homogeneous subgroups were determined on the basis of such characteristics as frequency of gambling of various kinds, function of gambling and sensation during gambling, symptoms of pathological gambling as well as personality characteristics. These can be characterized as: Pathological slot-machine gamblers with 1) an emotionally instable, depressive-aggressive personality structure and 2) an emotionally instable, depressive personality structure; 3) pathological gamblers on German-style slot-machines and 4) pathological gamblers on classical games of chance--both without conspicuous personality, and 5) gamblers on German-style slot-machines under a subjective strain. On the whole, the distinctions are due to psychological variables, the social data hardly differ. A comparison of the subgroups on the basis of variables regarding the course and result of treatment shows that the pathological gamblers with a conspicuous personality structure more often failed to reach, the goal of abstinence set by "Gamblers Anonymous" and instead report about an improvement of their gambling behaviour. On the other hand, the gamblers on German-style slot-machines who were under a subjective strain more often found it easier to stop gambling completely. The results of the cluster analysis are compared with clinical diagnostic classifications of gamblers who received out-patient or in-patient treatment as well as with empirical classifications of addicts, and first hypotheses of a differential therapy indication are being discussed.

2nd Machine Learning School for High Energy Physics

CERN Document Server

2016-01-01

The Second Machine Learning summer school organized by Yandex School of Data Analysis and Laboratory of Methods for Big Data Analysis of National Research University Higher School of Economics will be held in Lund, Sweden from 20 to 26 June 2016. It is hosted by Lund University. The school is intended to cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including “Multivariate Analysis”, “Neural Networks”, “Classification/Clusterization techniques”. In more generic terms, these techniques belong to the field of “Machine Learning”, which is an area that is based on research performed in Statistics and has received a lot of attention from the Data Science community. There are plenty of essential problems in High energy Physics that can be solved using Machine Learning methods. These vary from online data filtering and reconstruction to offline data analysis. Students of the school w...

DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

OpenAIRE

Qiao Wei; Li Ying; Wu Zhong-Hai

2017-01-01

Deep neural networks (DNNs) have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling ...

From Curve Fitting to Machine Learning

CERN Document Server

Zielesny, Achim

2011-01-01

The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clus

Weighted voting-based consensus clustering for chemical structure databases

Science.gov (United States)

Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

2014-06-01

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

Classifying bent radio galaxies from a mixture of point-like/extended images with Machine Learning.

Science.gov (United States)

Bastien, David; Oozeer, Nadeem; Somanah, Radhakrishna

2017-05-01

The hypothesis that bent radio sources are supposed to be found in rich, massive galaxy clusters and the avalibility of huge amount of data from radio surveys have fueled our motivation to use Machine Learning (ML) to identify bent radio sources and as such use them as tracers for galaxy clusters. The shapelet analysis allowed us to decompose radio images into 256 features that could be fed into the ML algorithm. Additionally, ideas from the field of neuro-psychology helped us to consider training the machine to identify bent galaxies at different orientations. From our analysis, we found that the Random Forest algorithm was the most effective with an accuracy rate of 92% for a classification of point and extended sources as well as an accuracy of 80% for bent and unbent classification.

Impact of Skill-Based Approaches in Reducing Stigma in Primary Care Physicians: Results from a Double-Blind, Parallel-Cluster, Randomized Controlled Trial.

Science.gov (United States)

Beaulieu, Tara; Patten, Scott; Knaak, Stephanie; Weinerman, Rivian; Campbell, Helen; Lauria-Horner, Bianca

2017-05-01

Most interventions to reduce stigma in health professionals emphasize education and social contact-based strategies. We sought to evaluate a novel skill-based approach: the British Columbia Adult Mental Health Practice Support Program. We sought to determine the program's impact on primary care providers' stigma and their perceived confidence and comfort in providing care for mentally ill patients. We hypothesized that enhanced skills and increased comfort and confidence on the part of practitioners would lead to diminished social distance and stigmatization. Subsequently, we explored the program's impact on clinical outcomes and health care costs. These outcomes are reported separately, with reference to this article. In a double-blind, cluster randomized controlled trial, 111 primary care physicians were assigned to intervention or control groups. A validated stigma assessment tool, the Opening Minds Scale for Health Care Providers (OMS-HC), was administered to both groups before and after training. Confidence and comfort were assessed using scales constructed from ad hoc items. In the primary analysis, no significant differences in stigma were found. However, a subscale assessing social distance showed significant improvement in the intervention group after adjustment for a variable (practice size) that was unequally distributed in the randomization. Significant increases in confidence and comfort in managing mental illness were observed among intervention group physicians. A positive correlation was found between increased levels of confidence/comfort and improvements in overall stigma, especially in men. This study provides some preliminary evidence of a positive impact on health care professionals' stigma through a skill-building approach to management of mild to moderate depression and anxiety in primary care. The intervention can be used as a primary vehicle for enhancing comfort and skills in health care providers and, ultimately, reducing an important

Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

Science.gov (United States)

Iyigun, Cem; Ben-Israel, Adi

Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Numerical calculation of the conductivity of percolation clusters and the use of special purpose computers

International Nuclear Information System (INIS)

Herrmann, H.J.

1989-01-01

Electrical conductivity diffusion or phonons, have an anomalous behaviour on percolation clusters at the percolation threshold due to the fractality of these clusters. The results that have been found numerically for this anomalous behaviour are reviewed. A special purpose computer built for this purpose is described and the evaluation of the data from this machine is discussed

Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement

Science.gov (United States)

Matsuda, Noboru; Furukawa, Tadanobu; Bier, Norman; Faloutsos, Christos

2015-01-01

How can we automatically determine which skills must be mastered for the successful completion of an online course? Large-scale online courses (e.g., MOOCs) often contain a broad range of contents frequently intended to be a semester's worth of materials; this breadth often makes it difficult to articulate an accurate set of skills and knowledge…

Strategies and Principles of Distributed Machine Learning on Big Data

Directory of Open Access Journals (Sweden)

Eric P. Xing

2016-06-01

Full Text Available The rise of big data has led to new demands for machine learning (ML systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

Science.gov (United States)

Liu, Jianjun; Kan, Jianquan

2018-04-01

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Comparison of various clustered interaction regions with regard to chromatic and dynamic behavior

International Nuclear Information System (INIS)

Leemann, B.; Wrulich, A.

1986-05-01

Clustered interaction regions for the SSC may be preferable from the viewpoint of costs and operation. In going from distributed to clustered IR's the superperiodicity of the machine is reduced and therefore the number of resonances induced by chromaticity correcting sextupoles is increased. This break in symmetry may cause a reduction in dynamic stability. The chromatic and dynamic behavior of the bare lattice is investigated for various cluster configurations. That means only chromaticity correcting sextupoles have been included and no magnetic imperfection errors have been considered. Then, the dynamic apertures of lattices with various IR clustering schemes are compared when random magnetic imperfections are included

Profiles of Emergent Literacy Skills among Preschool Children Who Are at Risk for Academic Difficulties

Science.gov (United States)

Cabell, Sonia Q.; Justice, Laura M.; Konold, Timothy R.; McGinty, Anita S.

2011-01-01

The purpose of this study was to explore patterns of within-group variability in the emergent literacy skills of preschoolers who are at risk for academic difficulties. We used the person-centered approach of cluster analysis to identify profiles of emergent literacy skills, taking into account both oral language and code-related skills.…

Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days

Directory of Open Access Journals (Sweden)

Qiu Jing

2017-08-01

Full Text Available Traditional fuzzy c-means (FCM clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM algorithms.

The OGCleaner: filtering false-positive homology clusters.

Science.gov (United States)

Fujimoto, M Stanley; Suvorov, Anton; Jensen, Nicholas O; Clement, Mark J; Snell, Quinn; Bybee, Seth M

2017-01-01

Detecting homologous sequences in organisms is an essential step in protein structure and function prediction, gene annotation and phylogenetic tree construction. Heuristic methods are often employed for quality control of putative homology clusters. These heuristics, however, usually only apply to pairwise sequence comparison and do not examine clusters as a whole. We present the Orthology Group Cleaner (the OGCleaner), a tool designed for filtering putative orthology groups as homology or non-homology clusters by considering all sequences in a cluster. The OGCleaner relies on high-quality orthologous groups identified in OrthoDB to train machine learning algorithms that are able to distinguish between true-positive and false-positive homology groups. This package aims to improve the quality of phylogenetic tree construction especially in instances of lower-quality transcriptome assemblies. https://github.com/byucsl/ogcleaner CONTACT: sfujimoto@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

Directory of Open Access Journals (Sweden)

Cooper James B

2010-03-01

Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.

Knowledge intensive entrepreneurship from firm exit in a high-tech cluster: the case of the wireless communications cluster in Aalborg, Denmark

DEFF Research Database (Denmark)

Østergaard, Christian Richter; Park, Eun Kyung

2012-01-01

This chapter addresses how the existence of a cluster of firms with a specific knowledge base in a region affects future knowledge intensive entrepreneurship (KIE) in that region. Focusing on spinoff activities, the case of the wireless communication cluster in North Jutland in Denmark demonstrates...... how entrepreneurs develop knowledge, skills, routines, social capital and networks while working in an industry and then go on to use these resources to create new business in the same or related industries in the same approximate location....

Automated detection of microcalcification clusters in mammograms

Science.gov (United States)

Karale, Vikrant A.; Mukhopadhyay, Sudipta; Singh, Tulika; Khandelwal, Niranjan; Sadhu, Anup

2017-03-01

Mammography is the most efficient modality for detection of breast cancer at early stage. Microcalcifications are tiny bright spots in mammograms and can often get missed by the radiologist during diagnosis. The presence of microcalcification clusters in mammograms can act as an early sign of breast cancer. This paper presents a completely automated computer-aided detection (CAD) system for detection of microcalcification clusters in mammograms. Unsharp masking is used as a preprocessing step which enhances the contrast between microcalcifications and the background. The preprocessed image is thresholded and various shape and intensity based features are extracted. Support vector machine (SVM) classifier is used to reduce the false positives while preserving the true microcalcification clusters. The proposed technique is applied on two different databases i.e DDSM and private database. The proposed technique shows good sensitivity with moderate false positives (FPs) per image on both databases.

Machine Learning with Squared-Loss Mutual Information

Directory of Open Access Journals (Sweden)

Masashi Sugiyama

2012-12-01

Full Text Available Mutual information (MI is useful for detecting statistical independence between random variables, and it has been successfully applied to solving various machine learning problems. Recently, an alternative to MI called squared-loss MI (SMI was introduced. While ordinary MI is the Kullback–Leibler divergence from the joint distribution to the product of the marginal distributions, SMI is its Pearson divergence variant. Because both the divergences belong to the ƒ-divergence family, they share similar theoretical properties. However, a notable advantage of SMI is that it can be approximated from data in a computationally more efficient and numerically more stable way than ordinary MI. In this article, we review recent development in SMI approximation based on direct density-ratio estimation and SMI-based machine learning techniques such as independence testing, dimensionality reduction, canonical dependency analysis, independent component analysis, object matching, clustering, and causal inference.

Using Complementary Learning Clusters in Studying Literature to Enhance Students' Medical Humanities Literacy, Critical Thinking, and English Proficiency.

Science.gov (United States)

Liao, Hung-Chang; Wang, Ya-Huei

2016-04-01

This study examined whether students studying literature in complementary learning clusters would show more improvement in medical humanities literacy, critical thinking skills, and English proficiency compared to those in conventional learning clusters. Ninety-three students participated in the study (M age = 18.2 years, SD = 0.4; 36 men, 57 women). A quasi-experimental design was used over 16 weeks, with the control group (n = 47) working in conventional learning clusters and the experimental group (n = 46) working in complementary learning clusters. Complementary learning clusters were those in which individuals had complementary strengths enabling them to learn from and offer assistance to other cluster members, hypothetically facilitating the learning process. Measures included the Medical Humanities Literacy Scale, Critical Thinking Disposition Assessment, English proficiency tests, and Analytic Critical Thinking Scoring Rubric. The results showed that complementary learning clusters have the potential to improve students' medical humanities literacy, critical thinking skills, and English proficiency. © The Author(s) 2016.

From virtual clustering analysis to self-consistent clustering analysis: a mathematical study

Science.gov (United States)

Tang, Shaoqiang; Zhang, Lei; Liu, Wing Kam

2018-03-01

In this paper, we propose a new homogenization algorithm, virtual clustering analysis (VCA), as well as provide a mathematical framework for the recently proposed self-consistent clustering analysis (SCA) (Liu et al. in Comput Methods Appl Mech Eng 306:319-341, 2016). In the mathematical theory, we clarify the key assumptions and ideas of VCA and SCA, and derive the continuous and discrete Lippmann-Schwinger equations. Based on a key postulation of "once response similarly, always response similarly", clustering is performed in an offline stage by machine learning techniques (k-means and SOM), and facilitates substantial reduction of computational complexity in an online predictive stage. The clear mathematical setup allows for the first time a convergence study of clustering refinement in one space dimension. Convergence is proved rigorously, and found to be of second order from numerical investigations. Furthermore, we propose to suitably enlarge the domain in VCA, such that the boundary terms may be neglected in the Lippmann-Schwinger equation, by virtue of the Saint-Venant's principle. In contrast, they were not obtained in the original SCA paper, and we discover these terms may well be responsible for the numerical dependency on the choice of reference material property. Since VCA enhances the accuracy by overcoming the modeling error, and reduce the numerical cost by avoiding an outer loop iteration for attaining the material property consistency in SCA, its efficiency is expected even higher than the recently proposed SCA algorithm.

Proceedings of the meeting for coordinating precision machining of optics research and requirements

International Nuclear Information System (INIS)

Saito, T.T.

1975-12-01

The meeting for ''Coordinating Precision Machining of Optics Research and Requirements'' on September 18, 1975, was sponsored by the Air Force Weapons Laboratory at Kirtland AFB, NM. These proceedings contain an introduction to the meeting including a brief description of the participants and the objectives. The developments and capabilities of Union Carbide Y-12 plant are described in detail. A short summary of the new Moore no. 5 machine at Bendix, Kansas City, Mo. is included as well as a description of using light scattering for roughness characterization at Rockwell International, Rocky Flats, Colorado. The executive summary of the meeting mentions some of the discussions that also followed. Important conclusions of the meeting were that a 5 y lead time is required to obtain a machine and acquire the necessary skills for precision machining, and that demands for diamond turning optics will be increasing

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

Science.gov (United States)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

Science.gov (United States)

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

Risk assessment of atmospheric emissions using machine learning

Directory of Open Access Journals (Sweden)

G. Cervone

2008-09-01

Full Text Available Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions.

First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination.

The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere.

Optimization of line configuration and balancing for flexible machining lines

Science.gov (United States)

Liu, Xuemei; Li, Aiping; Chen, Zurui

2016-05-01

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Assessing treatment fidelity and contamination in a cluster randomised controlled trial of motivational interviewing and cognitive behavioural therapy skills in type 2 diabetes.

Science.gov (United States)

Magill, Nicholas; Graves, Helen; de Zoysa, Nicole; Winkley, Kirsty; Amiel, Stephanie; Shuttlewood, Emma; Landau, Sabine; Ismail, Khalida

2018-05-10

Competencies in psychological techniques delivered by primary care nurses to support diabetes self-management were compared between the intervention and control arms of a cluster randomised controlled trial as part of a process evaluation. The trial was pragmatic and designed to assess effectiveness. This article addresses the question of whether the care that was delivered in the intervention and control trial arms represented high fidelity treatment and attention control, respectively. Twenty-three primary care nurses were either trained in motivational interviewing (MI) and cognitive behavioural therapy (CBT) skills or delivered attention control. Nurses' skills in these treatments were evaluated soon after training (treatment arm) and treatment fidelity was assessed after treatment delivery for sessions midway through regimen (both arms) using the Motivational Interviewing Treatment Integrity (MITI) domains and Behaviour Change Counselling Index (BECCI) based on consultations with 151 participants (45% of those who entered the study). The MITI Global Spirit subscale measured demonstration of MI principles: evocation, collaboration, autonomy/support. After training, median MITI MI-Adherence was 86.2% (IQR 76.9-100%) and mean MITI Empathy was 4.09 (SD 1.04). During delivery of treatment, in the intervention arm mean MITI Spirit was 4.03 (SD 1.05), mean Empathy was 4.23 (SD 0.89), and median Percentage Complex Reflections was 53.8% (IQR 40.0-71.4%). In the attention control arm mean Empathy was 3.40 (SD 0.98) and median Percentage Complex Reflections was 55.6% (IQR 41.9-71.4%). After MI and CBT skills training, detailed assessment showed that nurses had basic competencies in some psychological techniques. There appeared to be some delivery of elements of psychological treatment by nurses in the control arm. This model of training and delivery of MI and CBT skills integrated into routine nursing care to support diabetes self-management in primary care was not

A support vector machine approach for detection of microcalcifications.

Science.gov (United States)

El-Naqa, Issam; Yang, Yongyi; Wernick, Miles N; Galatsanos, Nikolas P; Nishikawa, Robert M

2002-12-01

In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Support Vector Machine and Application in Seizure Prediction

KAUST Repository

Qiu, Simeng

2018-04-01

Nowadays, Machine learning (ML) has been utilized in various kinds of area which across the range from engineering field to business area. In this paper, we first present several kernel machine learning methods of solving classification, regression and clustering problems. These have good performance but also have some limitations. We present examples to each method and analyze the advantages and disadvantages for solving different scenarios. Then we focus on one of the most popular classification methods, Support Vectors Machine (SVM). In addition, we introduce the basic theory, advantages and scenarios of using Support Vector Machine (SVM) deal with classification problems. We also explain a convenient approach of tacking SVM problems which are called Sequential Minimal Optimization (SMO). Moreover, one class SVM can be understood in a different way which is called Support Vector Data Description (SVDD). This is a famous non-linear model problem compared with SVM problems, SVDD can be solved by utilizing Gaussian RBF kernel function combined with SMO. At last, we compared the difference and performance of SVM-SMO implementation and SVM-SVDD implementation. About the application part, we utilized SVM method to handle seizure forecasting in canine epilepsy, after comparing the results from different methods such as random forest, extremely randomized tree, and SVM to classify preictal (pre-seizure) and interictal (interval-seizure) binary data. We draw the conclusion that SVM has the best performance.

Clustering analysis of malware behavior using Self Organizing Map

DEFF Research Database (Denmark)

Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup

2016-01-01

For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...

A study of several CAD methods for classification of clustered microcalcifications

Science.gov (United States)

Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.; Jiang, Yulei

2005-04-01

In this paper we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs), aimed to assisting radiologists for more accurate diagnosis of breast cancer in a computer-aided diagnosis (CADx) scheme. The methods we consider include: support vector machine (SVM), kernel Fisher discriminant (KFD), and committee machines (ensemble averaging and AdaBoost), most of which have been developed recently in statistical learning theory. We formulate differentiation of malignant from benign MCs as a supervised learning problem, and apply these learning methods to develop the classification algorithms. As input, these methods use image features automatically extracted from clustered MCs. We test these methods using a database of 697 clinical mammograms from 386 cases, which include a wide spectrum of difficult-to-classify cases. We use receiver operating characteristic (ROC) analysis to evaluate and compare the classification performance by the different methods. In addition, we also investigate how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD) yield the best performance, significantly outperforming a well-established CADx approach based on neural network learning.

"Together at school"--a school-based intervention program to promote socio-emotional skills and mental health in children: study protocol for a cluster randomized controlled trial.

Science.gov (United States)

Björklund, Katja; Liski, Antti; Samposalo, Hanna; Lindblom, Jallu; Hella, Juho; Huhtinen, Heini; Ojala, Tiina; Alasuvanto, Paula; Koskinen, Hanna-Leena; Kiviruusu, Olli; Hemminki, Elina; Punamäki, Raija-Leena; Sund, Reijo; Solantaus, Tytti; Santalahti, Päivi

2014-10-07

Schools provide a natural context to promote children's mental health. However, there is a need for more evidence-based, high quality school intervention programs combined with an accurate evaluation of their general effectiveness and effectiveness of specific intervention methods. The aim of this paper is to present a study protocol of a cluster randomized controlled trial evaluating the "Together at School" intervention program. The intervention program is designed to promote social-emotional skills and mental health by utilizing whole-school approach and focuses on classroom curriculum, work environment of school staff, and parent-teacher collaboration methods. The evaluation study examines the effects of the intervention on children's socio-emotional skills and mental health in a cluster randomized controlled trial design with 1) an intervention group and 2) an active control group. Altogether 79 primary school participated at baseline. A multi-informant setting involves the children themselves, their parents, and teachers. The primary outcomes are measured using parent and teacher ratings of children's socio-emotional skills and psychological problems measured by the Strengths and Difficulties Questionnaire and the Multisource Assessment of Social Competence Scale. Secondary outcomes for the children include emotional understanding, altruistic behavior, and executive functions (e.g. working memory, planning, and inhibition). Secondary outcomes for the teachers include ratings of e.g. school environment, teaching style and well-being. Secondary outcomes for both teachers and parents include e.g. emotional self-efficacy, child rearing practices, and teacher-parent collaboration. The data was collected at baseline (autumn 2013), 6 months after baseline, and will be collected also 18 months after baseline from the same participants. This study protocol outlines a trial which aims to add to the current state of intervention programs by presenting and studying a

Spike sorting based upon machine learning algorithms (SOMA).

Science.gov (United States)

Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

2007-02-15

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Are employment-interview skills a correlate of subtypes of schizophrenia?

Science.gov (United States)

Charisiou, J; Jackson, H J; Boyle, G J; Burgess, P; Minas, I H; Joshua, S D

1989-12-01

46 inpatients with a DSM-III diagnosis of schizophrenia were assessed in the week prior to discharge from hospital on measures of positive and negative symptoms and on 12 measures of employment interview skills (i.e., eye contact, facial gestures, body posture, verbal content, voice volume, length of speech, motivation, self-confidence, ability to communicate, manifest adjustment, manifest intelligence, over-all interview skill), and a global measure of employability. A cluster analysis based on the total positive and negative symptom scores produced two groups. The group with the lower mean negative symptom score exhibited better employment-interview skills and higher ratings on employability.

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography.

Science.gov (United States)

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, Pavlos; Loupas, Thanasis; Hazle, John D; Kagadis, George C

2017-09-01

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

LogDet Rank Minimization with Application to Subspace Clustering

Directory of Open Access Journals (Sweden)

Zhao Kang

2015-01-01

Full Text Available Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.

Efficacy of the Social Skills Improvement System Classwide Intervention Program (SSIS-CIP) Primary Version

Science.gov (United States)

DiPerna, James Clyde; Lei, Puiwa; Bellinger, Jillian; Cheng, Weiyi

2015-01-01

A multisite cluster randomized trial was conducted to examine the effects of the Social Skills Improvement System Classwide Intervention Program (SSIS-CIP; Elliott & Gresham, 2007) on students' classroom social behavior. The final sample included 432 students across 38 second grade classrooms. Social skills and problem behaviors were measured…

Development of Methods of Preparing Materials for Teaching Machines: Professional Paper 29-68.

Science.gov (United States)

Skinner, B. F.; Zook, Lola M., Ed.

In the preparation of 12-inch disc teaching machine materials for elementary college courses, a preliminary analysis of subject matter and required skills precedes sequential framing. The programer must assess the beginning level of student competence and frame questions to supply new material until the proper response stands alone. Statements for…

Comparison of Clustering Algorithms for the Identification of Topics on Twitter

Directory of Open Access Journals (Sweden)

Marjori N. M. Klinczak

2016-05-01

Full Text Available Topic Identification in Social Networks has become an important task when dealing with event detection, particularly when global communities are affected. In order to attack this problem, text processing techniques and machine learning algorithms have been extensively used. In this paper we compare four clustering algorithms – k-means, k-medoids, DBSCAN and NMF (Non-negative Matrix Factorization – in order to detect topics related to textual messages obtained from Twitter. The algorithms were applied to a database initially composed by tweets having hashtags related to the recent Nepal earthquake as initial context. Obtained results suggest that the NMF clustering algorithm presents superior results, providing simpler clusters that are also easier to interpret.

PMLB: a large benchmark suite for machine learning evaluation and comparison.

Science.gov (United States)

Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H

2017-01-01

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Integrating PROOF Analysis in Cloud and Batch Clusters

International Nuclear Information System (INIS)

Rodríguez-Marrero, Ana Y; Fernández-del-Castillo, Enol; López García, Álvaro; Marco de Lucas, Jesús; Matorras Weinig, Francisco; González Caballero, Isidro; Cuesta Noriega, Alberto

2012-01-01

High Energy Physics (HEP) analysis are becoming more complex and demanding due to the large amount of data collected by the current experiments. The Parallel ROOT Facility (PROOF) provides researchers with an interactive tool to speed up the analysis of huge volumes of data by exploiting parallel processing on both multicore machines and computing clusters. The typical PROOF deployment scenario is a permanent set of cores configured to run the PROOF daemons. However, this approach is incapable of adapting to the dynamic nature of interactive usage. Several initiatives seek to improve the use of computing resources by integrating PROOF with a batch system, such as Proof on Demand (PoD) or PROOF Cluster. These solutions are currently in production at Universidad de Oviedo and IFCA and are positively evaluated by users. Although they are able to adapt to the computing needs of users, they must comply with the specific configuration, OS and software installed at the batch nodes. Furthermore, they share the machines with other workloads, which may cause disruptions in the interactive service for users. These limitations make PROOF a typical use-case for cloud computing. In this work we take profit from Cloud Infrastructure at IFCA in order to provide a dynamic PROOF environment where users can control the software configuration of the machines. The Proof Analysis Framework (PAF) facilitates the development of new analysis and offers a transparent access to PROOF resources. Several performance measurements are presented for the different scenarios (PoD, SGE and Cloud), showing a speed improvement closely correlated with the number of cores used.

Usage of OpenStack Virtual Machine and MATLAB HPC Add-on leads to faster turnaround

KAUST Repository

Van Waveren, Matthijs

2017-03-16

We need to run hundreds of MATLAB® simulations while changing the parameters between each simulation. These simulations need to be run sequentially, and the parameters are defined manually from one simulation to the next. This makes this type of workload unsuitable for a shared cluster. For this reason we are using a cluster running in an OpenStack® Virtual Machine and are using the MATLAB HPC Add-on for submitting jobs to the cluster. As a result we are now able to have a turnaround time for the simulations of the order of a few hours, instead of the 24 hours needed on a local workstation.

Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

Science.gov (United States)

Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

2016-10-05

An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

Dynamic integration of remote cloud resources into local computing clusters

Energy Technology Data Exchange (ETDEWEB)

Fleig, Georg; Erli, Guenther; Giffels, Manuel; Hauth, Thomas; Quast, Guenter; Schnepf, Matthias [Institut fuer Experimentelle Kernphysik, Karlsruher Institut fuer Technologie (Germany)

2016-07-01

In modern high-energy physics (HEP) experiments enormous amounts of data are analyzed and simulated. Traditionally dedicated HEP computing centers are built or extended to meet this steadily increasing demand for computing resources. Nowadays it is more reasonable and more flexible to utilize computing power at remote data centers providing regular cloud services to users as they can be operated in a more efficient manner. This approach uses virtualization and allows the HEP community to run virtual machines containing a dedicated operating system and transparent access to the required software stack on almost any cloud site. The dynamic management of virtual machines depending on the demand for computing power is essential for cost efficient operation and sharing of resources with other communities. For this purpose the EKP developed the on-demand cloud manager ROCED for dynamic instantiation and integration of virtualized worker nodes into the institute's computing cluster. This contribution will report on the concept of our cloud manager and the implementation utilizing a remote OpenStack cloud site and a shared HPC center (bwForCluster located in Freiburg).

Recursive Cluster Elimination (RCE for classification and feature selection from gene expression data

Directory of Open Access Journals (Sweden)

Showe Louise C

2007-05-01

Full Text Available Abstract Background Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE rather than recursive feature elimination (RFE. We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE. Results We have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs, a supervised machine learning classification method, to identify and score (rank those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA with recursive feature elimination (SVM-RFE and PDA-RFE are used to remove genes based on their individual discriminant weights. Conclusion SVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together

Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints

Directory of Open Access Journals (Sweden)

Diego Peluffo-Ordóñez

2017-08-01

Full Text Available To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM. For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.

Construction Cluster Volume I [Wood Structural Framing].

Science.gov (United States)

Pennsylvania State Dept. of Justice, Harrisburg. Bureau of Correction.

The document is the first of a series, to be integrated with a G.E.D. program, containing instructional materials at the basic skills level for the construction cluster. It focuses on wood structural framing and contains 20 units: (1) occupational information; (2) blueprint reading; (3) using leveling instruments and laying out building lines; (4)…

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

Science.gov (United States)

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

A Numerical Comparison of Rule Ensemble Methods and Support Vector Machines

Energy Technology Data Exchange (ETDEWEB)

Meza, Juan C.; Woods, Mark

2009-12-18

Machine or statistical learning is a growing field that encompasses many scientific problems including estimating parameters from data, identifying risk factors in health studies, image recognition, and finding clusters within datasets, to name just a few examples. Statistical learning can be described as 'learning from data' , with the goal of making a prediction of some outcome of interest. This prediction is usually made on the basis of a computer model that is built using data where the outcomes and a set of features have been previously matched. The computer model is called a learner, hence the name machine learning. In this paper, we present two such algorithms, a support vector machine method and a rule ensemble method. We compared their predictive power on three supernova type 1a data sets provided by the Nearby Supernova Factory and found that while both methods give accuracies of approximately 95%, the rule ensemble method gives much lower false negative rates.

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

Approximate multi-state reliability expressions using a new machine learning technique

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Muselli, Marco

2005-01-01

The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

Launching large computing applications on a disk-less cluster

International Nuclear Information System (INIS)

Schwemmer, Rainer; Caicedo Carvajal, Juan Manuel; Neufeld, Niko

2011-01-01

The LHCb Event Filter Farm system is based on a cluster of the order of 1.500 disk-less Linux nodes. Each node runs one instance of the filtering application per core. The amount of cores in our current production environment is 8 per machine for the old cluster and 12 per machine on extension of the cluster. Each instance has to load about 1.000 shared libraries, weighting 200 MB from several directory locations from a central repository. The repository is currently hosted on a SAN and exported via NFS. The libraries are all available in the local file system cache on every node. Loading a library still causes a huge number of requests to the server though, because the loader will try to probe every available path. Measurements show there are between 100.000-200.000 calls per application instance start up. Multiplied by the numbers of cores in the farm, this translates into a veritable DDoS attack on the servers, which lasts several minutes. Since the application is being restarted frequently, a better solution had to be found.scp Rolling out the software to the nodes is out of the question, because they have no disks and the software in it's entirety is too large to put into a ram disk. To solve this problem we developed a FUSE based file systems which acts as a permanent, controllable cache that keeps the essential files that are necessary in stock.

Contemporary machine learning: techniques for practitioners in the physical sciences

Science.gov (United States)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Factors affecting utilization of skilled maternal care in Northwest Ethiopia: a multilevel analysis.

Science.gov (United States)

Worku, Abebaw Gebeyehu; Yalew, Alemayehu Worku; Afework, Mesganaw Fantahun

2013-04-15

The evaluation of all potential sources of low skilled maternal care utilization is crucial for Ethiopia. Previous studies have largely disregarded the contribution of different levels. This study was planned to assess the effect of individual, communal, and health facility characteristics in the utilization of antenatal, delivery, and postnatal care by a skilled provider. A linked facility and population-based survey was conducted over three months (January - March 2012) in twelve "kebeles" of North Gondar Zone, Amhara Region. A total of 1668 women who had births in the year preceding the survey were selected for analysis. Using a multilevel modelling, we examined the effect of cluster variation and a number of individual, communal (kebele), and facility-related variables for skilled maternal care utilization. About 32.3%, 13.8% and 6.3% of the women had the chance to get skilled providers for their antenatal, delivery and postnatal care, respectively. A significant heterogeneity was observed among clusters for each indicator of skilled maternal care utilization. At the individual level, variables related to awareness and perceptions were found to be much more relevant for skilled maternal service utilization. Preference for skilled providers and previous experience of antenatal care were consistently strong predictors of all indicators of skilled maternal health care utilizations. Birth order, maternal education, and awareness about health facilities to get skilled professionals were consistently strong predictors of skilled antenatal and delivery care use. Communal factors were relevant for both delivery and postnatal care, whereas the characteristics of a health facility were more relevant for use of skilled delivery care than other maternity services. Factors operating at individual and "kebele" levels play a significant role in determining utilization of skilled maternal health services. Interventions to create better community awareness and perception about

Machine learning algorithms for mode-of-action classification in toxicity assessment.

Science.gov (United States)

Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

2016-01-01

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Ukufundisa izicuku zeziqhakancu emagameni (Teaching click clusters in words

Directory of Open Access Journals (Sweden)

Gxowa-Dlayedwa, Ntombizodwa Cynthia

2015-12-01

Full Text Available Some teachers find it uninteresting and difficult to teach isiXhosa phonemes and syllables to grade one to three learners. This has a negative impact as the literacy results are low because learners’ reading and writing skills are poor. The linguistics terms featuring in the title, namely; consonants, vowels and syllables as found in words facilitate reading, and thus improve literacy standards in every language. IsiXhosa is one of the eleven official languages in South Africa. Phonemes include clicks and/or click cluster and vowels. On the other hand, there are people who are interested in learning to speak isiXhosa, but the difficulties encountered during the pronunciation of clicks discourage many of them. This study believes that the knowledge of phonemes and syllables will boost the literacy standard in isiXhosa. Therefore, the purposes of this study are to show that clicks and click clusters are found in major word categories which are in life circles. Secondly, if words are divided into segments, it becomes easy to produce them in print and reading skills. Thirdly, reading is possible in every language, and most importantly, skills are transferable. The current study therefore, argues that the knowledge of phonemes and syllables facilitates reading and creative writing skills. The data used in this study were taken from a novel written by Sidlayi (2009. Few examples have been given by the researchers themselves with an objective to clarify some ideas.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Slot Machines: Pursuing Responsible Gaming Practices for Virtual Reels and Near Misses

Science.gov (United States)

Harrigan, Kevin A.

2009-01-01

Since 1983, slot machines in North America have used a computer and virtual reels to determine the odds. Since at least 1988, a technique called clustering has been used to create a high number of near misses, failures that are close to wins. The result is that what the player sees does not represent the underlying probabilities and randomness,…

REGIONAL DEVELOPMENT BASED ON CLUSTER IN LIVESTOCK DEVELOPMENT. CLUSTER IN LIVESTOCK SECTOR IN THE KYRGYZ REPUBLIC

Directory of Open Access Journals (Sweden)

Meerim SYDYKOVA

2014-11-01

Full Text Available In most developing countries, where agriculture is the main economical source, clusters have been found as a booster to develop their economy. The Asian countries are now starting to implement agro-food clusters into the mainstream of changes in agriculture, farming and food industry. The long-term growth of meat production in the Kyrgyz Republic during the last decade, as well as the fact that agriculture has become one of the prioritized sectors of the economy, proved the importance of livestock sector in the economy of the Kyrgyz Republic. The research question is “Does the Kyrgyz Republic has strong economic opportunities and prerequisites in agriculture in order to implement an effective agro cluster in the livestock sector?” Paper focuses on describing the prerequisites of the Kyrgyz Republic in agriculture to implement livestock cluster. The main objective of the paper is to analyse the livestock sector of the Kyrgyz Republic and observe the capacity of this sector to implement agro-cluster. The study focuses on investigating livestock sector and a complex S.W.O.T. The analysis was carried out based on local and regional database and official studies. The results of research demonstrate the importance of livestock cluster for national economy. It can be concluded that cluster implementation could provide to its all members with benefits if they could build strong collaborative relationship in order to facilitate the access to the labour market and implicitly, the access to exchange of good practices. Their ability of potential cluster members to act as a convergence pole is critical for acquiring practical skills necessary for the future development of the livestock sector.

Mechanical considerations and design skills.

Energy Technology Data Exchange (ETDEWEB)

Alvis, Robert L.

2008-03-01

The purpose of the report is to provide experienced-based insights into design processes that will benefit designers beginning their employment at Sandia National Laboratories or those assuming new design responsibilities. The main purpose of this document is to provide engineers with the practical aspects of system design. The material discussed here may not be new to some readers, but some of it was to me. Transforming an idea to a design to solve a problem is a skill, and skills are similar to history lessons. We gain these skills from experience, and many of us have not been fortunate enough to grow in an environment that provided the skills that we now need. I was fortunate to grow up on a farm where we had to learn how to maintain and operate several different kinds of engines and machines. If you are like me, my formal experience is partially based upon the two universities from which I graduated, where few practical applications of the technologies were taught. What was taught was mainly theoretical, and few instructors had practical experience to offer the students. I understand this, as students have their hands full just to learn the theoretical. The practical part was mainly left up to 'on the job experience'. However, I believe it is better to learn the practical applications early and apply them quickly 'on the job'. System design engineers need to know several technical things, both in and out of their field of expertise. An engineer is not expected to know everything, but he should know when to ask an expert for assistance. This 'expert' can be in any field, whether it is in analyses, drafting, machining, material properties, testing, etc. The best expert is a person who has practical experience in the area of needed information, and consulting with that individual can be the best and quickest way for one to learn. If the information provided here can improve your design skills and save one design from having a problem

Towards the compression of parton densities through machine learning algorithms

CERN Document Server

Carrazza, Stefano

2016-01-01

One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.

Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes

Science.gov (United States)

Satymbekov, M. N.; Pak, I. T.; Naizabayeva, L.; Nurzhanov, Ch. A.

2017-12-01

In this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.

Clustering of near clusters versus cluster compactness

International Nuclear Information System (INIS)

Yu Gao; Yipeng Jing

1989-01-01

The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)

Level of Skill Argued Students on Physics Material

Science.gov (United States)

Viyanti, V.; Cari, C.; Sunarno, W.; Prasetyo, Z. K.

2017-09-01

This study aims to analyze the prior knowledge of students to map the level of skills to argue floating and sinking material. Prior knowledge is the process of concept formation in cognitive processes spontaneously or based on student experience. The study population is high school students of class XI. The sample selection using cluster random sampling, obtained the number of sampel as many as 50 student. The research used descriptive survey method. The data were obtained through a multiple choice test both grounded and interviewed. The data analyzed refers to: alignment the concept and the activity of developing the skill of the argument. The result obtained by the average level of skill argue in terms of the prior knowladge of on “Level 2”. The data show that students have difficulty expressing simple arguments consisting of only one statement. This indicates a lack of student experience in cultivating argumentative skills in their learning. The skill level mapping argued in this study to be a reference for researchers to provide feedback measures to obtain positive change in cognitive conflict argued.

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

Man-machine interface versus full automation

International Nuclear Information System (INIS)

Hatton, V.

1984-01-01

As accelerators grow in size and complexity of operation there is an increasing economical as well as an operational incentive for the controls and operations teams to use computers to help the man-machine interface. At first the computer network replaced the traditional controls racks filled with knobs, buttons and digital displays of voltages and potentiometer readings. The computer system provided the operator with the extension of his hands and eyes. It was quickly found that much more could be achieved. Where previously it was necessary for the human operator to decide the order of the actions to be executed by the computer as a result of a visual indication of malfunctioning of the accelerator, now the operation is becoming more and more under the direct control of the computer system. Expert knowledge is programmed into the system to help the non-specialist make decision and to safeguard the equipment. Machine physics concepts have been incorporated and critical machine parameters can be optimised easily by the physicists or operators without any detailed knowledge of the intervening medium or of the equipment being controlled. As confidence grows and reliability improves, more and more automation can be added. How far can this process of automation replace the skilled operator. Can the accelerators of tomorrow be run like the ever increasing robotic assembly plants of today. How is the role of the operator changing in this new environment

THE EFFECT OF INQUIRY TRAINING MODEL USE THE MEDIA PHET AGAINST SCIENCE PROCESS SKILLS AND LOGICAL THINKING SKILLS STUDENTS

Directory of Open Access Journals (Sweden)

Fajrul Wahdi Ginting

2015-12-01

Full Text Available The Purpose of The study: science process skills and logical thinking ability of students who use inquiry learning model training using PhET media; science process skills and logical thinking ability of students who use conventional learning model; and the difference science process skills and logical thinking ability of students to use learning model Inquiry Training using PhET media and conventional learning models. This research is a quasi experimental. Sample selection is done by cluster random sampling are two classes of classes VIII-E and class VIII-B, where the class VIII-E is taught by inquiry training model using media PhET and VIII-B with conventional learning model. The instrument used consisted of tests science process skills such as essay tests and tests of the ability to think logically in the form of multiple-choice tests. The data were analyzed using t test. The results showed that physics science process skills use Inquiry Training models using PhET media is different and showed better results compared with conventional learning model, and logical thinking skills students use Inquiry Training model using PhET media is different and show better results compared with conventional learning, and there is a difference between the ability to think logically and science process skills of students who use Inquiry Training model using PhET media and conventional learning models.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Science.gov (United States)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Cluster-cluster clustering

International Nuclear Information System (INIS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches.

Science.gov (United States)

Oliynyk, Anton O; Mar, Arthur

2018-01-16

Intermetallic compounds are bestowed by diverse compositions, complex structures, and useful properties for many materials applications. How metallic elements react to form these compounds and what structures they adopt remain challenging questions that defy predictability. Traditional approaches offer some rational strategies to prepare specific classes of intermetallics, such as targeting members within a modular homologous series, manipulating building blocks to assemble new structures, and filling interstitial sites to create stuffed variants. Because these strategies rely on precedent, they cannot foresee surprising results, by definition. Exploratory synthesis, whether through systematic phase diagram investigations or serendipity, is still essential for expanding our knowledge base. Eventually, the relationships may become too complex for the pattern recognition skills to be reliably or practically performed by humans. Complementing these traditional approaches, new machine-learning approaches may be a viable alternative for materials discovery, not only among intermetallics but also more generally to other chemical compounds. In this Account, we survey our own efforts to discover new intermetallic compounds, encompassing gallides, germanides, phosphides, arsenides, and others. We apply various machine-learning methods (such as support vector machine and random forest algorithms) to confront two significant questions in solid state chemistry. First, what crystal structures are adopted by a compound given an arbitrary composition? Initial efforts have focused on binary equiatomic phases AB, ternary equiatomic phases ABC, and full Heusler phases AB 2 C. Our analysis emphasizes the use of real experimental data and places special value on confirming predictions through experiment. Chemical descriptors are carefully chosen through a rigorous procedure called cluster resolution feature selection. Predictions for crystal structures are quantified by evaluating

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

Science.gov (United States)

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/.

The identification of credit card encoders by hierarchical cluster analysis of the jitters of magnetic stripes.

Science.gov (United States)

Leung, S C; Fung, W K; Wong, K H

1999-01-01

The relative bit density variation graphs of 207 specimen credit cards processed by 12 encoding machines were examined first visually, and then classified by means of hierarchical cluster analysis. Twenty-nine credit cards being treated as 'questioned' samples were tested by way of cluster analysis against 'controls' derived from known encoders. It was found that hierarchical cluster analysis provided a high accuracy of identification with all 29 'questioned' samples classified correctly. On the other hand, although visual comparison of jitter graphs was less discriminating, it was nevertheless capable of giving a reasonably accurate result.

T & I--Machine Shop. Kit No. 83. Instructor's Manual [and] Student Learning Activity Guide.

Science.gov (United States)

White, Jim

An instructor's manual and student activity guide on the machine shop are provided in this set of prevocational education materials which focuses on the vocational area of trade and industry. (This set of materials is one of ninety-two prevocational education sets arranged around a cluster of seven vocational offerings: agriculture, home…

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

Science.gov (United States)

Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong

2012-01-01

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181

Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

DEFF Research Database (Denmark)

Yoo, C.; Gernaey, Krist

2008-01-01

importance in the projection (VIP) information of the DPLS method. The power of the gene selection method and the proposed supervised hierarchical clustering method is illustrated on a three microarray data sets of leukemia, breast, and colon cancer. Supervised machine learning algorithms thus enable...

Skilled delivery service utilization and its association with the establishment of Women's Health Development Army in Yeky district, South West Ethiopia: a multilevel analysis.

Science.gov (United States)

Negero, Melese Girmaye; Mitike, Yifru Berhan; Worku, Abebaw Gebeyehu; Abota, Tafesse Lamaro

2018-01-30

Because of the unacceptably high maternal and perinatal morbidity and mortality, the government of Ethiopia has established health extension program with a community-based network involving health extension workers (HEWs) and a community level women organization which is known as "Women's Health Development Army" (WHDA). Currently, the HEWs and WHDA network is the approach preferred by the government to register pregnant women and encourage them to link in the healthcare system. However, its association with skilled delivery service utilization is not well known. A community-based cross-sectional study was conducted from January to February 2015. Within 380 clusters of WHDA, a total of 748 reproductive-age women who gave birth in 1 year preceding the study, were included using multistage sampling technique. The data were entered into EPI info version 7 statistical software and exported to STATA version 11 for analysis. Multilevel analysis technique was applied to check for an association of selected variables with a utilization of skilled delivery service. About 45% of women have received skilled delivery care. A significant heterogeneity was observed between "Women's Health Development Teams (clusters)" for skilled delivery care service utilization which explains about 62% of the total variation. Individual-level predictors including urban residence [AOR (95% CI) 35.10 (4.62, 266.52)], previous exposure of complications [AOR (95% CI) 3.81 (1.60, 9.08)], at least four ANC visits [AOR (95% CI) 7.44 (1.48, 37.42)] and preference of skilled personnel [AOR (95% CI) 8.11 (2.61, 25.15)] were significantly associated with skilled delivery service use. Among cluster level variables, the distance of clusters within 2 km radius from the nearest health facility was significantly associated [AOR (95% CI) 6.03 (1.92, 18.93)] with skilled delivery service utilization. In this study, significant variation among clusters of WHDA was observed. Both individual and cluster level

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Science.gov (United States)

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

Machine learning based switching model for electricity load forecasting

International Nuclear Information System (INIS)

Fan Shu; Chen Luonan; Lee, Weijen

2008-01-01

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

International Nuclear Information System (INIS)

Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

2005-01-01

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

Machine assisted histogram classification

Science.gov (United States)

Benyó, B.; Gaspar, C.; Somogyi, P.

2010-04-01

LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.

Machine assisted histogram classification

Energy Technology Data Exchange (ETDEWEB)

Benyo, B; Somogyi, P [BME-IIT, H-1117 Budapest, Magyar tudosok koerutja 2. (Hungary); Gaspar, C, E-mail: Peter.Somogyi@cern.c [CERN-PH, CH-1211 Geneve 23 (Switzerland)

2010-04-01

LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.

Comparison of four statistical and machine learning methods for crash severity prediction.

Science.gov (United States)

Iranitalab, Amirfarrokh; Khattak, Aemal

2017-11-01

Crash severity prediction models enable different agencies to predict the severity of a reported crash with unknown severity or the severity of crashes that may be expected to occur sometime in the future. This paper had three main objectives: comparison of the performance of four statistical and machine learning methods including Multinomial Logit (MNL), Nearest Neighbor Classification (NNC), Support Vector Machines (SVM) and Random Forests (RF), in predicting traffic crash severity; developing a crash costs-based approach for comparison of crash severity prediction methods; and investigating the effects of data clustering methods comprising K-means Clustering (KC) and Latent Class Clustering (LCC), on the performance of crash severity prediction models. The 2012-2015 reported crash data from Nebraska, United States was obtained and two-vehicle crashes were extracted as the analysis data. The dataset was split into training/estimation (2012-2014) and validation (2015) subsets. The four prediction methods were trained/estimated using the training/estimation dataset and the correct prediction rates for each crash severity level, overall correct prediction rate and a proposed crash costs-based accuracy measure were obtained for the validation dataset. The correct prediction rates and the proposed approach showed NNC had the best prediction performance in overall and in more severe crashes. RF and SVM had the next two sufficient performances and MNL was the weakest method. Data clustering did not affect the prediction results of SVM, but KC improved the prediction performance of MNL, NNC and RF, while LCC caused improvement in MNL and RF but weakened the performance of NNC. Overall correct prediction rate had almost the exact opposite results compared to the proposed approach, showing that neglecting the crash costs can lead to misjudgment in choosing the right prediction method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatio-temporal outbreaks of campylobacteriosis and the role of fresh-milk vending machines in the Czech Republic: A methodological study.

Science.gov (United States)

Marek, Lukáš; Pászto, Vít

2017-11-08

Inspired by local outbreaks of campylobacteriosis in the Czech Republic in 2010 linked to the debate about alleged health risks of the raw milk consumption, a detailed study was carried out. Firstly, scanning was utilised to identify spatio-temporal clusters of the disease from 2008 to 2012. Then a spatial method (geographical profiling originally developed for criminology) served as assessment in selecting fresh-milk vending machines that could have contributed to some of the local campylobacteriosis outbreaks. Even though an area of increased relative risk of the disease was identified in the affected city of České Budějovice during January and February 2010, geoprofiling did not identify any vending machines in the area as the potential source. However, possible sources in some nearby cities were suggested. Overall, 14 high-rate clusters including the localisation of 9% of the vending machines installed in the Czech Republic were found in the period 2008-2012. Although the vending machines are subject to strict hygiene standards and regular testing, a potential link between a small number of them and the spatial distribution of campylobacteriosis has been detected in the Czech Republic. This should be taken into account in public health research of the disease.

Spatio-temporal outbreaks of campylobacteriosis and the role of fresh-milk vending machines in the Czech Republic: A methodological study

Directory of Open Access Journals (Sweden)

Lukáš Marek

2017-11-01

Full Text Available Inspired by local outbreaks of campylobacteriosis in the Czech Republic in 2010 linked to the debate about alleged health risks of the raw milk consumption, a detailed study was carried out. Firstly, scanning was utilised to identify spatio-temporal clusters of the disease from 2008 to 2012. Then a spatial method (geographical profiling originally developed for criminology served as assessment in selecting fresh-milk vending machines that could have contributed to some of the local campylobacteriosis outbreaks. Even though an area of increased relative risk of the disease was identified in the affected city of České Budějovice during January and February 2010, geoprofiling did not identify any vending machines in the area as the potential source. However, possible sources in some nearby cities were suggested. Overall, 14 high-rate clusters including the localisation of 9% of the vending machines installed in the Czech Republic were found in the period 2008-2012. Although the vending machines are subject to strict hygiene standards and regular testing, a potential link between a small number of them and the spatial distribution of campylobacteriosis has been detected in the Czech Republic. This should be taken into account in public health research of the disease.

Machine learning methods for clinical forms analysis in mental health.

Science.gov (United States)

Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

2013-01-01

In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Directory of Open Access Journals (Sweden)

Y. Zhang

2018-01-01

Full Text Available Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS values of up to 0.5 and 33 %, respectively. The general skill (after bias correction of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Activities of IAEA related to human interface in man-machine system

International Nuclear Information System (INIS)

Nishiwaki, Yasushi

1988-01-01

The present paper outlines some activities of IAEA related to human interface in man-machine systems. It has been recognized for quite some time that in large and complex man-machine interactive systems human errors can contribute substantially to failures of these systems, and that the improvement in the human interface in man-machine systems is essential for the safety of the plant. Many important surveys have been made in some member countries. These studies and operational experience have shown that it is possible to substantially reduce this adverse impact of human errors in nuclear power plant operations by the application of human factors technology. This technology. This technology includes: (1) selection of people with the requisite skills and knowledge and providing them with job-relevant training, (2) maintenance of the necessary job qualifications for each person in the plant, (3) design of man-machine interfaces which are fully compatible with the capabilities and limitations of the people in the system, and (4) design of job operations, including written materials, to facilitate required quality of human performance. A review is made of education/training, operator support systems, human error data collection, analysis of safety significant events and future activities. (Nogami, K.)

Using Machine Learning for Sentiment and Social Influence Analysis in Text

OpenAIRE

Kolog, Emmanuel Awuni; Montero, Calkin Suero; Toivonen, Tapani

2017-01-01

Students’ academic achievement is largely driven by their social phenomena, which is shaped by social influence and opinion dynamics. In this paper, we employed a machine learning technique to detect social influence and sentiment in text-based students’ life stories. The life stories were first pre-processed and clustered using k-means with euclidean distance. After that, we identified domestic, peer and school staff as the main influences on students’ academic development. The various influ...

Massively parallel Monte Carlo. Experiences running nuclear simulations on a large condor cluster

International Nuclear Information System (INIS)

Tickner, James; O'Dwyer, Joel; Roach, Greg; Uher, Josef; Hitchen, Greg

2010-01-01

The trivially-parallel nature of Monte Carlo (MC) simulations make them ideally suited for running on a distributed, heterogeneous computing environment. We report on the setup and operation of a large, cycle-harvesting Condor computer cluster, used to run MC simulations of nuclear instruments ('jobs') on approximately 4,500 desktop PCs. Successful operation must balance the competing goals of maximizing the availability of machines for running jobs whilst minimizing the impact on users' PC performance. This requires classification of jobs according to anticipated run-time and priority and careful optimization of the parameters used to control job allocation to host machines. To maximize use of a large Condor cluster, we have created a powerful suite of tools to handle job submission and analysis, as the manual creation, submission and evaluation of large numbers (hundred to thousands) of jobs would be too arduous. We describe some of the key aspects of this suite, which has been interfaced to the well-known MCNP and EGSnrc nuclear codes and our in-house PHOTON optical MC code. We report on our practical experiences of operating our Condor cluster and present examples of several large-scale instrument design problems that have been solved using this tool. (author)

Blocked inverted indices for exact clustering of large chemical spaces.

Science.gov (United States)

Thiel, Philipp; Sach-Peltason, Lisa; Ottmann, Christian; Kohlbacher, Oliver

2014-09-22

The calculation of pairwise compound similarities based on fingerprints is one of the fundamental tasks in chemoinformatics. Methods for efficient calculation of compound similarities are of the utmost importance for various applications like similarity searching or library clustering. With the increasing size of public compound databases, exact clustering of these databases is desirable, but often computationally prohibitively expensive. We present an optimized inverted index algorithm for the calculation of all pairwise similarities on 2D fingerprints of a given data set. In contrast to other algorithms, it neither requires GPU computing nor yields a stochastic approximation of the clustering. The algorithm has been designed to work well with multicore architectures and shows excellent parallel speedup. As an application example of this algorithm, we implemented a deterministic clustering application, which has been designed to decompose virtual libraries comprising tens of millions of compounds in a short time on current hardware. Our results show that our implementation achieves more than 400 million Tanimoto similarity calculations per second on a common desktop CPU. Deterministic clustering of the available chemical space thus can be done on modern multicore machines within a few days.

Identifying clinical course patterns in SMS data using cluster analysis

DEFF Research Database (Denmark)

Kent, Peter; Kongsted, Alice

2012-01-01

ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important...... showed that clinical course patterns can be identified by cluster analysis using all SMS time points as cluster variables. This method is simple, intuitive and does not require a high level of statistical skill. However, there are alternative ways of managing SMS data and many different methods...

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

Methodology for creating dedicated machine and algorithm on sunflower counting

Science.gov (United States)

Muracciole, Vincent; Plainchault, Patrick; Mannino, Maria-Rosaria; Bertrand, Dominique; Vigouroux, Bertrand

2007-09-01

In order to sell grain lots in European countries, seed industries need a government certification. This certification requests purity testing, seed counting in order to quantify specified seed species and other impurities in lots, and germination testing. These analyses are carried out within the framework of international trade according to the methods of the International Seed Testing Association. Presently these different analyses are still achieved manually by skilled operators. Previous works have already shown that seeds can be characterized by around 110 visual features (morphology, colour, texture), and thus have presented several identification algorithms. Until now, most of the works in this domain are computer based. The approach presented in this article is based on the design of dedicated electronic vision machine aimed to identify and sort seeds. This machine is composed of a FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor) and a PC bearing the GUI (Human Machine Interface) of the system. Its operation relies on the stroboscopic image acquisition of a seed falling in front of a camera. A first machine was designed according to this approach, in order to simulate all the vision chain (image acquisition, feature extraction, identification) under the Matlab environment. In order to perform this task into dedicated hardware, all these algorithms were developed without the use of the Matlab toolbox. The objective of this article is to present a design methodology for a special purpose identification algorithm based on distance between groups into dedicated hardware machine for seed counting.

ALICE HLT Cluster operation during ALICE Run 2

Science.gov (United States)

Lehrbach, J.; Krzewicki, M.; Rohr, D.; Engel, H.; Gomez Ramirez, A.; Lindenstruth, V.; Berzano, D.; ALICE Collaboration

2017-10-01

ALICE (A Large Ion Collider Experiment) is one of the four major detectors located at the LHC at CERN, focusing on the study of heavy-ion collisions. The ALICE High Level Trigger (HLT) is a compute cluster which reconstructs the events and compresses the data in real-time. The data compression by the HLT is a vital part of data taking especially during the heavy-ion runs in order to be able to store the data which implies that reliability of the whole cluster is an important matter. To guarantee a consistent state among all compute nodes of the HLT cluster we have automatized the operation as much as possible. For automatic deployment of the nodes we use Foreman with locally mirrored repositories and for configuration management of the nodes we use Puppet. Important parameters like temperatures, network traffic, CPU load etc. of the nodes are monitored with Zabbix. During periods without beam the HLT cluster is used for tests and as one of the WLCG Grid sites to compute offline jobs in order to maximize the usage of our cluster. To prevent interference with normal HLT operations we separate the virtual machines running the Grid jobs from the normal HLT operation via virtual networks (VLANs). In this paper we give an overview of the ALICE HLT operation in 2016.

The Enhancement of Consistency of Interpretation Skills on the Newton’s Laws Concept

Directory of Open Access Journals (Sweden)

Yudi Kurniawan

2018-03-01

Full Text Available Conceptual understanding is the most important thing that students should have rather than they had reaches achievement. The interpretation skill is one of conceptual understanding aspects. The aim of this paper is to know the consistency of students’ interpreting skills and all at once to get the levels of increasing of students’ interpretations skill. These variables learned by Interactive Lecture Demonstrations (ILD common sense. The method of this research is pre-experimental research with one group pretest-posttest design. The sample has taken by cluster random sampling. The result had shown that 16 % of all student that are have perfect consistency of interpretation skill and there are increasing of interpretation skill on 84 % from unknown to be understand (this skill. This finding could be used by the future researcher to study in the other areas of conceptual understanding aspects

The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

Science.gov (United States)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, Anwar P. P. Abdul; Razali Abdullah, Mohamad; Amirul Abdullah, Muhammad; Hasnun Arif Hassan, Mohd; Khalil, Zubair

2018-04-01

The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined.

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

LHCb: Machine assisted histogram classification

CERN Multimedia

Somogyi, P; Gaspar, C

2009-01-01

LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty components can be either done visually using instruments such as the LHCb Histogram Presenter, or by automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, a graph-theoretic based clustering tool, combined with machine learning algorithms is proposed and demonstrated by processing histograms representing 2D event hitmaps. The concept is proven by detecting ion feedback events in the LHCb RICH subdetector.

Effectiveness of music education for the improvement of reading skills and academic achievement in young poor readers: a pragmatic cluster-randomized, controlled clinical trial.

Directory of Open Access Journals (Sweden)

Hugo Cogo-Moreira

Full Text Available Difficulties in word-level reading skills are prevalent in Brazilian schools and may deter children from gaining the knowledge obtained through reading and academic achievement. Music education has emerged as a potential method to improve reading skills because due to a common neurobiological substratum.To evaluate the effectiveness of music education for the improvement of reading skills and academic achievement among children (eight to 10 years of age with reading difficulties.235 children with reading difficulties in 10 schools participated in a five-month, randomized clinical trial in cluster (RCT in an impoverished zone within the city of São Paulo to test the effects of music education intervention while assessing reading skills and academic achievement during the school year. Five schools were chosen randomly to incorporate music classes (n = 114, and five served as controls (n = 121. Two different methods of analysis were used to evaluate the effectiveness of the intervention: The standard method was intention-to-treat (ITT, and the other was the Complier Average Causal Effect (CACE estimation method, which took compliance status into account.The ITT analyses were not very promising; only one marginal effect existed for the rate of correct real words read per minute. Indeed, considering ITT, improvements were observed in the secondary outcomes (slope of Portuguese = 0.21 [p<0.001] and slope of math = 0.25 [p<0.001]. As for CACE estimation (i.e., complier children versus non-complier children, more promising effects were observed in terms of the rate of correct words read per minute [β = 13.98, p<0.001] and phonological awareness [β = 19.72, p<0.001] as well as secondary outcomes (academic achievement in Portuguese [β = 0.77, p<0.0001] and math [β = 0.49, p<0.001] throughout the school year.The results may be seen as promising, but they are not, in themselves, enough to make music lessons as public

Interpreting Measures of Fundamental Movement Skills and Their Relationship with Health-Related Physical Activity and Self-Concept

Science.gov (United States)

Jarvis, Stuart; Williams, Morgan; Rainer, Paul; Jones, Eleri Sian; Saunders, John; Mullen, Richard

2018-01-01

The aims of this study were to determine proficiency levels of fundamental movement skills using cluster analysis in a cohort of U.K. primary school children; and to further examine the relationships between fundamental movement skills proficiency and other key aspects of health-related physical activity behavior. Participants were 553 primary…

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

Science.gov (United States)

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Bionic machines and systems

Energy Technology Data Exchange (ETDEWEB)

Halme, A.; Paanajaervi, J. (eds.)

2004-07-01

Introduction Biological systems form a versatile and complex entirety on our planet. One evolutionary branch of primates, called humans, has created an extraordinary skill, called technology, by the aid of which it nowadays dominate life on the planet. Humans use technology for producing and harvesting food, healthcare and reproduction, increasing their capability to commute and communicate, defending their territory etc., and to develop more technology. As a result of this, humans have become much technology dependent, so that they have been forced to form a specialized class of humans, called engineers, who take care of the knowledge of technology developing it further and transferring it to later generations. Until now, technology has been relatively independent from biology, although some of its branches, e.g. biotechnology and biomedical engineering, have traditionally been in close contact with it. There exist, however, an increasing interest to expand the interface between technology and biology either by directly utilizing biological processes or materials by combining them with 'dead' technology, or by mimicking in technological solutions the biological innovations created by evolution. The latter theme is in focus of this report, which has been written as the proceeding of the post-graduate seminar 'Bionic Machines and Systems' held at HUT Automation Technology Laboratory in autumn 2003. The underlaying idea of the seminar was to analyze biological species by considering them as 'robotic machines' having various functional subsystems, such as for energy, motion and motion control, perception, navigation, mapping and localization. We were also interested about intelligent capabilities, such as learning and communication, and social structures like swarming behavior and its mechanisms. The word 'bionic machine' comes from the book which was among the initial material when starting our mission to the fascinating world

The effectiveness of birth plans in increasing use of skilled care at delivery and postnatal care in rural Tanzania: a cluster randomised trial.

Science.gov (United States)

Magoma, Moke; Requejo, Jennifer; Campbell, Oona; Cousens, Simon; Merialdi, Mario; Filippi, Veronique

2013-04-01

To determine the effectiveness of birth plans in increasing use of skilled care at delivery and in the postnatal period among antenatal care (ANC) attendees in a rural district with low occupancy of health units for delivery but high antenatal care uptake in northern Tanzania. Cluster randomised trial in Ngorongoro district, Arusha region, involving 16 health units (8 per arm). Nine hundred and five pregnant women at 24 weeks of gestation and above (404 in the intervention arm) were recruited and followed up to at least 1 month postpartum. Skilled delivery care uptake was 16.8% higher in the intervention units than in the control [95% CI 2.6-31.0; P = 0.02]. Postnatal care utilisation in the first month of delivery was higher (difference in proportions: 30.0% [95% CI 1.3-47.7; P < 0.01]) and also initiated earlier (mean duration 6.6 ± 1.7 days vs. 20.9 ± 4.4 days, P < 0.01) in the intervention than in the control arm. Women's and providers' reports of care satisfaction (received or provided) did not differ greatly between the two arms of the study (difference in proportion: 12.1% [95% CI -6.3-30.5] P = 0.17 and 6.9% [95% CI -3.2-17.1] P = 0.15, respectively). Implementation of birth plans during ANC can increase the uptake of skilled delivery and post delivery care in the study district without negatively affecting women's and providers' satisfaction with available ANC services. Birth plans should be considered along with the range of other recommended interventions as a strategy to improve the uptake of maternal health services. © 2013 Blackwell Publishing Ltd.

Use of machine learning methods to classify Universities based on the income structure

Science.gov (United States)

Terlyga, Alexandra; Balk, Igor

2017-10-01

In this paper we discuss use of machine learning methods such as self organizing maps, k-means and Ward’s clustering to perform classification of universities based on their income. This classification will allow us to quantitate classification of universities as teaching, research, entrepreneur, etc. which is important tool for government, corporations and general public alike in setting expectation and selecting universities to achieve different goals.

Seminal Quality Prediction Using Clustering-Based Decision Forests

Directory of Open Access Journals (Sweden)

Hong Wang

2014-08-01

Full Text Available Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.

Unsupervised active learning based on hierarchical graph-theoretic clustering.

Science.gov (United States)

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

Hands-On Defibrillation Skills of Pediatric Acute Care Providers During a Simulated Ventricular Fibrillation Cardiac Arrest Scenario.

Science.gov (United States)

Bhalala, Utpal S; Balakumar, Niveditha; Zamora, Maria; Appachi, Elumalai

2018-01-01

Introduction: Timely defibrillation in ventricular fibrillation cardiac arrest (VFCA) is associated with good outcome. While defibrillation skills of pediatric providers have been reported to be poor, the factors related to poor hands-on defibrillation skills of pediatric providers are largely unknown. The aim of our study was to evaluate delay in individual steps of the defibrillation and human and non-human factors associated with poor hands-on defibrillation skills among pediatric acute care providers during a simulated VFCA scenario. Methods: We conducted a prospective observational study of video evaluation of hands-on defibrillation skills of pediatric providers in a simulated VFCA in our children's hospital. Each provider was asked to use pads followed by paddles to provide 2 J/kg shock to an infant mannequin in VFCA. The hands-on skills were evaluated for struggle with any step of defibrillation, defined a priori as >10 s delay with particular step. The data was analyzed using chi-square test with significant p -value 10 s delay) with each of connecting the pads/paddles to the device, using pads/paddles on the mannequin and using buttons on the machine was 34 (50%), 26 (38%), and 31 (46%), respectively. Conclusions: The defibrillation skills of providers in a tertiary care children's hospital are poor. Both human and machine-related factors are associated with delay in defibrillation. Prior use of the study defibrillator is associated with a significantly shorter time-to-first shock as compared to prior use of any other defibrillator or no prior use of any defibrillator.

Ensemble Clustering using Semidefinite Programming with Applications.

Science.gov (United States)

Singh, Vikas; Mukherjee, Lopamudra; Peng, Jiming; Xu, Jinhui

2010-05-01

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

Simulations of Quantum Turing Machines by Quantum Multi-Stack Machines

OpenAIRE

Qiu, Daowen

2005-01-01

As was well known, in classical computation, Turing machines, circuits, multi-stack machines, and multi-counter machines are equivalent, that is, they can simulate each other in polynomial time. In quantum computation, Yao [11] first proved that for any quantum Turing machines $M$, there exists quantum Boolean circuit $(n,t)$-simulating $M$, where $n$ denotes the length of input strings, and $t$ is the number of move steps before machine stopping. However, the simulations of quantum Turing ma...

Study on text mining algorithm for ultrasound examination of chronic liver diseases based on spectral clustering

Science.gov (United States)

Chang, Bingguo; Chen, Xiaofei

2018-05-01

Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.

Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data.

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Manal Helal

Full Text Available BACKGROUND: The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. METHODS: A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. RESULTS: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52% corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra

Critical thinking skills profile of high school students in learning chemistry

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Budi Utami

2017-08-01

Full Text Available Critical thinking skill is the priority in the goals of education. In this case, the critical thinking has the higher process, such as analyzing, synthesizing, evaluating, drawing conclusion and reflecting which enables the individual to make the reasonable assessment both in the classroom and in the daily life.  This research is aimed to determine the students’ critical thinking skill in learning Chemistry at senior high school. This research used descriptive method in which the instruments were developed based on the indicators of critical thinking skill. The population of this research was 100 students of tenth, eleventh and twelfth grade from senior high schools in Surakarta which was chosen using cluster random sampling technique. The result of the research shows that the students of tenth, eleventh and twelfth grade have adequate critical thinking skills.

Machine musicianship

Science.gov (United States)

Rowe, Robert

2002-05-01

The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

Machine tool structures

CERN Document Server

Koenigsberger, F

1970-01-01

Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

PGHPF – An Optimizing High Performance Fortran Compiler for Distributed Memory Machines

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Zeki Bozkus

1997-01-01

Full Text Available High Performance Fortran (HPF is the first widely supported, efficient, and portable parallel programming language for shared and distributed memory systems. HPF is realized through a set of directive-based extensions to Fortran 90. It enables application developers and Fortran end-users to write compact, portable, and efficient software that will compile and execute on workstations, shared memory servers, clusters, traditional supercomputers, or massively parallel processors. This article describes a production-quality HPF compiler for a set of parallel machines. Compilation techniques such as data and computation distribution, communication generation, run-time support, and optimization issues are elaborated as the basis for an HPF compiler implementation on distributed memory machines. The performance of this compiler on benchmark programs demonstrates that high efficiency can be achieved executing HPF code on parallel architectures.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

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Ashley I. Heinson

2017-02-01

Full Text Available Reverse vaccinology (RV is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML techniques to distinguish bacterial protective antigens (BPAs from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM classifier that could discriminate BPAs (n = 200 from non-BPAs (n = 200 with an area under the curve (AUC of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

KAUST Repository

Heinson, Ashley

2017-02-01

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

KAUST Repository

Heinson, Ashley; Gunawardana, Yawwani; Moesker, Bastiaan; Hume, Carmen; Vataga, Elena; Hall, Yper; Stylianou, Elena; McShane, Helen; Williams, Ann; Niranjan, Mahesan; Woelk, Christopher

2017-01-01

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

Scheduling a maintenance activity under skills constraints to minimize total weighted tardiness and late tasks

Directory of Open Access Journals (Sweden)

Djalal Hedjazi

2015-04-01

Full Text Available Skill management is a key factor in improving effectiveness of industrial companies, notably their maintenance services. The problem considered in this paper concerns scheduling of maintenance tasks under resource (maintenance teams constraints. This problem is generally known as unrelated parallel machine scheduling. We consider the problem with a both objectives of minimizing total weighted tardiness (TWT and number of tardiness tasks. Our interest is focused particularly on solving this problem under skill constraints, which each resource has a skill level. So, we propose a new efficient heuristic to obtain an approximate solution for this NP-hard problem and demonstrate his effectiveness through computational experiments. This heuristic is designed for implementation in a static maintenance scheduling problem (with unequal release dates, processing times and resource skills, while minimizing objective functions aforementioned.

School Physical Activity Programming and Gross Motor Skills in Children.

Science.gov (United States)

Burns, Ryan D; Fu, You; Hannon, James C; Brusseau, Timothy A

2017-09-01

We examined the effect of a comprehensive school physical activity program (CSPAP) on gross motor skills in children. Participants were 959 children (1st-6th grade; Mean age = 9.1 ± 1.5 years; 406 girls, 553 boys) recruited from 5 low-income schools receiving a year-long CSPAP intervention. Data were collected at the beginning of the school year and at a 36-week follow-up. Gross motor skills were assessed using the Test for Gross Motor Development (3rd ed.) (TGMD-3) instrument. Multi-level mixed effects models were employed to examine the effect of CSPAP on TGMD-3 scores, testing age and sex as effect modifiers and adjusting for clustering of observations within the data structure. There were statistically significant coefficients for time (β = 8.1, 95% CI [3.9, 12.3], p skills and ball skills sub-test scores. Children showed improved gross motor skill scores at the end of the 36-week CSPAP that were modified by age, as younger children displayed greater improvements in TGMD-3 scores compared to older children.

Electricity of machine tool

International Nuclear Information System (INIS)

Gijeon media editorial department

1977-10-01

This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

Machine medical ethics

CERN Document Server

Pontier, Matthijs

2015-01-01

The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

Analysis and design of machine learning techniques evolutionary solutions for regression, prediction, and control problems

CERN Document Server

Stalph, Patrick

2014-01-01

Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain – at least to some extent. Therefore three suitable machine learning algorithms are selected – algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the...

Humanizing machines: Anthropomorphization of slot machines increases gambling.

Science.gov (United States)

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

Science.gov (United States)

Florios, Kostas; Kontogiannis, Ioannis; Park, Sung-Hong; Guerra, Jordan A.; Benvenuto, Federico; Bloomfield, D. Shaun; Georgoulis, Manolis K.

2018-02-01

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {>} M1 and {>} C1 within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02), and Heidke skill score HSS=0.49(0.01) for {>} M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01), and HSS=0.59(0.01) for {>} C1 flare prediction with probability threshold 35%.

Arabic web pages clustering and annotation using semantic class features

Directory of Open Access Journals (Sweden)

Hanan M. Alghamdi

2014-12-01

Full Text Available To effectively manage the great amount of data on Arabic web pages and to enable the classification of relevant information are very important research problems. Studies on sentiment text mining have been very limited in the Arabic language because they need to involve deep semantic processing. Therefore, in this paper, we aim to retrieve machine-understandable data with the help of a Web content mining technique to detect covert knowledge within these data. We propose an approach to achieve clustering with semantic similarities. This approach comprises integrating k-means document clustering with semantic feature extraction and document vectorization to group Arabic web pages according to semantic similarities and then show the semantic annotation. The document vectorization helps to transform text documents into a semantic class probability distribution or semantic class density. To reach semantic similarities, the approach extracts the semantic class features and integrates them into the similarity weighting schema. The quality of the clustering result has evaluated the use of the purity and the mean intra-cluster distance (MICD evaluation measures. We have evaluated the proposed approach on a set of common Arabic news web pages. We have acquired favorable clustering results that are effective in minimizing the MICD, expanding the purity and lowering the runtime.

From curve fitting to machine learning an illustrative guide to scientific data analysis and computational intelligence

CERN Document Server

Zielesny, Achim

2016-01-01

This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with M...

Code-expanded radio access protocol for machine-to-machine communications

DEFF Research Database (Denmark)

Thomsen, Henning; Kiilerich Pratas, Nuno; Stefanovic, Cedomir

2013-01-01

The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated b...... subframes and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the massive support of Machine-Type Communication users that is beyond the reach of current systems.......The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated...... by the random access method employed in LTE, which significantly increases the amount of contention resources without increasing the system resources, such as contention subframes and preambles. This is accomplished by a logical, rather than physical, extension of the access method in which the available system...

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification.

Science.gov (United States)

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

Directory of Open Access Journals (Sweden)

Qing Ye

2015-01-01

Full Text Available A novel semisupervised extreme learning machine (ELM with clustering discrimination manifold regularization (CDMR framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE. The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

Performance study of a cluster calculation; parallelization and application under geant4

International Nuclear Information System (INIS)

Trabelsi, Abir

2007-01-01

This work concretizes the final studies project for engineering computer sciences, it is archived within the national center of nuclear sciences and technology. The project consists in studying the performance of a set of machines in order to determine the best architecture to assemble them in a cluster. As well as the parallelism and the parallel implementation of GEANT4, as a tool of simulation. The realisation of this project consists on : 1) programming with C++ and executing the two benchmarks P MV and PMM on each station; 2) Interpreting this result in order to show the best architecture of the cluster; 3) parallelism with TOP-C the two benchmarks; 4) Executing the two Top-C versions on the cluster; 5) Generalizing this results; 6)parallelism et executing the parallel version of GEANT4. (Author). 14 refs

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

Energy Technology Data Exchange (ETDEWEB)

Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)

2016-06-15

Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong

Defect detection and classification of machined surfaces under multiple illuminant directions

Science.gov (United States)

Liao, Yi; Weng, Xin; Swonger, C. W.; Ni, Jun

2010-08-01

Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

Directory of Open Access Journals (Sweden)

Alessandro Manzi

2017-05-01

Full Text Available Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM, trained with Sequential Minimal Optimization (SMO. The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60 and the Telecommunication Systems Team (TST Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

Science.gov (United States)

Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

2017-05-11

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

Machine learning topological states

Science.gov (United States)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Simple machines

CERN Document Server

Graybill, George

2007-01-01

Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

Peac – A set of tools to quickly enable Proof on a cluster

International Nuclear Information System (INIS)

Ganis, G; Vala, M

2012-01-01

With advent of the analysis phase of Lhcdata-processing, interest in Proof technology has considerably increased. While setting up a simple Proof cluster for basic usage is reasonably straightforward, exploiting the several new functionalities added in recent times may be complicated. Peac, standing for Proof Enabled Analysis Cluster, is a set of tools aiming to facilitate the setup and management of a Proof cluster. Peac is based on the experience made by setting up Proof for the Alice analysis facilities. It allows to easily build and configure Root and the additional software needed on the cluster, and may serve as distributor of binaries via Xrootd. Peac uses Proof-On-Demand (PoD) for resource management (start, stop or daemons). Finally, Peac sets-up and configures dataset management (using the Afdsmgrd daemon), as well as cluster monitoring (machine status and Proof query summaries) using MonAlisa. In this respect, a MonAlisa page has been dedicated to Peac users, so that a cluster managed by Peac can be automatically monitored. In this paper we present and describe the status and main components of Peac and show details about its usage.

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

1998-03-19

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

Superconducting rotating machines

International Nuclear Information System (INIS)

Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

1975-01-01

The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

Software architecture for time-constrained machine vision applications

Science.gov (United States)

Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

2013-01-01

Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

Nonlinear machine learning in soft materials engineering and design

Science.gov (United States)

Ferguson, Andrew

The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable ''digital colloids'' as a novel high-density information storage substrate; probing hierarchically self-assembling onjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. We acknowledge support for this work from a National Science Foundation CAREER Award (Grant No. DMR-1350008) and the Donors of the American Chemical Society Petroleum Research Fund (ACS PRF #54240-DNI6).

Topic modeling for cluster analysis of large biological and medical datasets.

Science.gov (United States)

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

Distributed Joint Cluster Formation and Resource Allocation Scheme for Cooperative Data Collection in Virtual MIMO-Based M2M Networks

Directory of Open Access Journals (Sweden)

Xi Luan

2015-01-01

Full Text Available An efficient data collection scheme plays an important role for the real-time intelligent monitoring in many machine-to-machine (M2M networks. In this paper, a distributed joint cluster formation and resource allocation scheme for data collection in cluster-based M2M networks is proposed. Specifically, in order to utilize the advantages of cooperation, we first propose a hierarchical transmission model which contains two communication phases. In the first phase, the intracluster information sharing is carried out by all the nodes within the same cluster. Then these nodes transmit the total information to the BS cooperatively with virtual-MIMO (VMIMO protocol in the second phase. To grasp the properties and advantages of this cooperative transmission strategy, the theoretical analysis results are provided. The key issue in this system is to form the clusters and allocate resources efficiently. Since the optimization problem on this issue is an NP-hard problem, a feasible joint scheme for the cluster formation and resource allocation is proposed in this paper, which is carried out via coalition formation game with a distributed algorithm. This scheme can reduce the complexity while keeping an attractive performance. Simulation results show the properties of the proposed scheme and its advantages when comparing with the noncooperative scheme for the data collection in a practical scenario.

Sustainable machining

CERN Document Server

2017-01-01

This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

Detecting false positive sequence homology: a machine learning approach.

Science.gov (United States)

Fujimoto, M Stanley; Suvorov, Anton; Jensen, Nicholas O; Clement, Mark J; Bybee, Seth M

2016-02-24

Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. There are many existing heuristic tools, most commonly based on bidirectional BLAST searches that are used to identify homologous genes and combine them into two fundamentally distinct classes: orthologs and paralogs. Due to only using heuristic filtering based on significance score cutoffs and having no cluster post-processing tools available, these methods can often produce multiple clusters constituting unrelated (non-homologous) sequences. Therefore sequencing data extracted from incomplete genome/transcriptome assemblies originated from low coverage sequencing or produced by de novo processes without a reference genome are susceptible to high false positive rates of homology detection. In this paper we develop biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes. We demonstrate that our machine learning method trained on both known homology clusters obtained from OrthoDB and randomly generated sequence alignments (non-homologs), successfully determines apparent false positives inferred by heuristic algorithms especially among proteomes recovered from low-coverage RNA-seq data. Almost ~42 % and ~25 % of predicted putative homologies by InParanoid and HaMStR respectively were classified as false positives on experimental data set. Our process increases the quality of output from other clustering algorithms by providing a novel post-processing method that is both fast and efficient at removing low quality clusters of putative homologous genes recovered by heuristic-based approaches.

The Application of Clustering Techniques to Citation Data. Research Reports Series B No. 6.

Science.gov (United States)

Arms, William Y.; Arms, Caroline

This report describes research carried out as part of the Design of Information Systems in the Social Sciences (DISISS) project. Cluster analysis techniques were applied to a machine readable file of bibliographic data in the form of cited journal titles in order to identify groupings which could be used to structure bibliographic files. Practical…

Effects of a Web-based course on nursing skills and knowledge learning.

Science.gov (United States)

Lu, Der-Fa; Lin, Zu-Chun; Li, Yun-Ju

2009-02-01

The purpose of the study was to assess the effectiveness of supplementing traditional classroom teaching with Web-based learning design when teaching intramuscular injection nursing skills. Four clusters of nursing students at a junior college in eastern Taiwan were randomly assigned to experimental and control groups. A total of 147 students (80 in the experimental group, 67 in the control group) completed the study. All participants received the same classroom lectures and skill demonstration. The experimental group interacted using a Web-based course and were able to view the content on demand. The students and instructor interacted via a chatroom, the bulletin board, and e-mail. Participants in the experimental group had significantly higher scores on both intramuscular injection knowledge and skill learning. A Web-based design can be an effective supplementing learning tool for teaching nursing knowledge and skills.

Machine-learning-assisted materials discovery using failed experiments

Science.gov (United States)

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

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

Directory of Open Access Journals (Sweden)

Ming Cheng

2015-09-01

Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Asynchronized synchronous machines

CERN Document Server

Botvinnik, M M

1964-01-01

Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

Evaluating distance-based clustering for user (browse and click) sessions in a domain-specific collection

DEFF Research Database (Denmark)

Steinhauer, Jeremy; Delcambre, Lois M.L.; Lykke, Marianne

2014-01-01

to the question that they are answering. Since a large class of machine learning algorithms use a distance measure at the core, we evaluate the suitability of common machine learning distance measures to distinguish sessions of users searching for the answer to same or different questions. We found that two......We seek to improve information retrieval in a domain-specific collection by clustering user sessions from a click log and then classifying later user sessions in real time. As a preliminary step, we explore the main assumption of this approach: whether user sessions in such a site are related...

How to cluster in parallel with neural networks

Science.gov (United States)

Kamgar-Parsi, Behzad; Gualtieri, J. A.; Devaney, Judy E.; Kamgar-Parsi, Behrooz

1988-01-01

Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.

Tropical cyclone prediction skills - MJO and ENSO dependence in S2S data sets

Science.gov (United States)

Lee, C. Y.; Camargo, S.; Vitart, F.; Sobel, A. H.; Tippett, M.

2017-12-01

sample sizes, and the S2S TC predictions are relatively more skillful. Various skill scores will be applied to genesis and ACE, with subsets of data binned based on ENSO and MJO status. We will also look at MJO and ENSO's impact on TC tracks through cluster analysis, and analyze model skill in each cluster.

The Interaction of Procedural Skill, Conceptual Understanding and Working Memory in Early Mathematics Achievement

Directory of Open Access Journals (Sweden)

Camilla Gilmore

2017-12-01

Full Text Available Large individual differences in children’s mathematics achievement are observed from the start of schooling. Previous research has identified three cognitive skills that are independent predictors of mathematics achievement: procedural skill, conceptual understanding and working memory. However, most studies have only tested independent effects of these factors and failed to consider moderating effects. We explored the procedural skill, conceptual understanding and working memory capacity of 75 children aged 5 to 6 years as well as their overall mathematical achievement. We found that, not only were all three skills independently associated with mathematics achievement, but there was also a significant interaction between them. We found that levels of conceptual understanding and working memory moderated the relationship between procedural skill and mathematics achievement such that there was a greater benefit of good procedural skill when associated with good conceptual understanding and working memory. Cluster analysis also revealed that children with equivalent levels of overall mathematical achievement had differing strengths and weaknesses across these skills. This highlights the importance of considering children’s skill profile, rather than simply their overall achievement.

The effectiveness of three sets of school-based instructional materials and community training on the acquisition and generalization of community laundry skills by students with severe handicaps.

Science.gov (United States)

Morrow, S A; Bates, P E

1987-01-01

This study examined the effectiveness of three sets of school-based instructional materials and community training on acquisition and generalization of a community laundry skill by nine students with severe handicaps. School-based instruction involved artificial materials (pictures), simulated materials (cardboard replica of a community washing machine), and natural materials (modified home model washing machine). Generalization assessments were conducted at two different community laundromats, on two machines represented fully by the school-based instructional materials and two machines not represented fully by these materials. After three phases of school-based instruction, the students were provided ten community training trials in one laundromat setting and a final assessment was conducted in both the trained and untrained community settings. A multiple probe design across students was used to evaluate the effectiveness of the three types of school instruction and community training. After systematic training, most of the students increased their laundry performance with all three sets of school-based materials; however, generalization of these acquired skills was limited in the two community settings. Direct training in one of the community settings resulted in more efficient acquisition of the laundry skills and enhanced generalization to the untrained laundromat setting for most of the students. Results of this study are discussed in regard to the issue of school versus community-based instruction and recommendations are made for future research in this area.

Hessian regularization based non-negative matrix factorization for gene expression data clustering.

Science.gov (United States)

Liu, Xiao; Shi, Jun; Wang, Congzhi

2015-01-01

Since a key step in the analysis of gene expression data is to detect groups of genes that have similar expression patterns, clustering technique is then commonly used to analyze gene expression data. Data representation plays an important role in clustering analysis. The non-negative matrix factorization (NMF) is a widely used data representation method with great success in machine learning. Although the traditional manifold regularization method, Laplacian regularization (LR), can improve the performance of NMF, LR still suffers from the problem of its weak extrapolating power. Hessian regularization (HR) is a newly developed manifold regularization method, whose natural properties make it more extrapolating, especially for small sample data. In this work, we propose the HR-based NMF (HR-NMF) algorithm, and then apply it to represent gene expression data for further clustering task. The clustering experiments are conducted on five commonly used gene datasets, and the results indicate that the proposed HR-NMF outperforms LR-based NMM and original NMF, which suggests the potential application of HR-NMF for gene expression data.

Machining of Machine Elements Made of Polymer Composite Materials

Science.gov (United States)

Baurova, N. I.; Makarov, K. A.

2017-12-01

The machining of the machine elements that are made of polymer composite materials (PCMs) or are repaired using them is considered. Turning, milling, and drilling are shown to be most widely used among all methods of cutting PCMs. Cutting conditions for the machining of PCMs are presented. The factors that most strongly affect the roughness parameters and the accuracy of cutting PCMs are considered.

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Zio, Enrico

2007-01-01

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

Directory of Open Access Journals (Sweden)

Ravichandran C. Gopalakrishnan

2014-01-01

Full Text Available Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy

DEFF Research Database (Denmark)

Mattsson, Andreas Holm; Kringelum, Jens Vindahl; Garde, C.

2016-01-01

Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical....... Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor-ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding...

Parallel implementation of D-Phylo algorithm for maximum likelihood clusters.

Science.gov (United States)

Malik, Shamita; Sharma, Dolly; Khatri, Sunil Kumar

2017-03-01

This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of k -means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8  ×  800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.

Support vector machine learning-based fMRI data group analysis.

Science.gov (United States)

Wang, Ze; Childress, Anna R; Wang, Jiongjiong; Detre, John A

2007-07-15

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

The application of k-Nearest Neighbour in the identification of high potential archers based on relative psychological coping skills variables

Science.gov (United States)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, Anwar P. P. Abdul; Razali Abdullah, Mohamad; Muaz Alim, Muhammad; Nasir, Ahmad Fakhri Ab

2018-04-01

The present study aims at classifying and predicting high and low potential archers from a collection of psychological coping skills variables trained on different k-Nearest Neighbour (k-NN) kernels. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed k-NN models, i.e. fine, medium, coarse, cosine, cubic and weighted kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the cosine k-NN model exhibited good accuracy and precision throughout the exercise with an accuracy of 94% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the rest of the models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Impact of Clusters on Innovation, Knowledge and Competitiveness in the Romanian Economy

Directory of Open Access Journals (Sweden)

Cristina VLĂSCEANU

2014-06-01

Full Text Available The concept of clusters has become extremely popular in the recent years, as more and more practitioners and theorists refer to it.From my point of view, clusters are the ideal structure for some companies to build their skills, knowledge and know-how together. To the same extent, this form of organization enables the creation of common structures / facilities, which allows the smaller firms or the ones that lack experience to learn from the ones with more experience and to benefit from their more advanced capabilities, and thus to grow together. Moreover, the participation, collaboration and cooperation of universities, institutes, research centers and other organizations/entities inside a cluster undoubtedly leads to the growth of the economic development of the region, from which the cluster is part of. In the case of Romania, the eight development regions could greatly benefit from the formation and implementation of clusters. With the shift to a knowledge-based economy and society, the difference, made by clusters and creative networks that promote innovation, is now clear. Thus, the purpose of this paper is to outline the benefits that can come from the development of clusters, and to highlight the impact of clusters on innovation, knowledge and competitiveness in the Romanian Economy.

Effect of a 6-Week Active Play Intervention on Fundamental Movement Skill Competence of Preschool Children.

Science.gov (United States)

Foulkes, J D; Knowles, Z; Fairclough, S J; Stratton, G; O'Dwyer, M; Ridgers, N D; Foweather, L

2017-04-01

This study examined the effectiveness of an active play intervention on fundamental movement skills of 3- to 5-year-old children from deprived communities. In a cluster randomized controlled trial design, six preschools received a resource pack and a 6-week local authority program involving staff training with help implementing 60-minute weekly sessions and postprogram support. Six comparison preschools received a resource pack only. Twelve skills were assessed at baseline, postintervention, and at a 6-month follow-up using the Children's Activity and Movement in Preschool Study Motor Skills Protocol. One hundred and sixty-two children (Mean age = 4.64 ± 0.58 years; 53.1% boys) were included in the final analyses. There were no significant differences between groups for total fundamental movement skill, object-control skill or locomotor skill scores, indicating a need for program modification to facilitate greater skill improvements.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

Science.gov (United States)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Machinability of nickel based alloys using electrical discharge machining process

Science.gov (United States)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

Performance Analysis of Entropy Methods on K Means in Clustering Process

Science.gov (United States)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

The achievements of the Z-machine; Les exploits de la Z-machine

Energy Technology Data Exchange (ETDEWEB)

Larousserie, D

2008-03-15

The ZR-machine that represents the latest generation of Z-pinch machines has recently begun preliminary testing before its full commissioning in Albuquerque (Usa). During its test the machine has well operated with electrical currents whose intensities of 26 million Ampere are already 2 times as high as the intensity of the operating current of the previous Z-machine. In 2006 the Z-machine reached temperatures of 2 billions Kelvin while 100 million Kelvin would be sufficient to ignite thermonuclear fusion. In fact the concept of Z-pinch machines was imagined in the fifties but the technological breakthrough that has allowed this recent success and the reborn of Z-machine, was the replacement of gas by an array of metal wires through which the electrical current flows and vaporizes it creating an imploding plasma. It is not well understood why Z-pinch machines generate far more radiation than theoretically expected. (A.C.)

Quantum machine learning.

Science.gov (United States)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Machine protection systems

CERN Document Server

Macpherson, A L

2010-01-01

A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

Dairy Herd Mastitis Program in Argentina: Farm Clusters and Effects on Bulk Milk Somatic Cell Counts

Directory of Open Access Journals (Sweden)

C Vissio1*, SA Dieser2, CG Raspanti2, JA Giraudo1, CI Bogni2, LM Odierno2 and AJ Larriestra1

2013-01-01

Full Text Available This research has been conducted to characterize dairy farm clusters according to mastitis control program practiced among small and medium dairy producer from Argentina, and also to evaluate the effect of such farm cluster patterns on bulk milk somatic cell count (BMSCC. Two samples of 51 (cross-sectional and 38 (longitudinal herds were selected to identify farm clusters and study the influence of management on monthly BMSCC, respectively. The cross-sectional sample involved the milking routine and facilities assessment of each herd visited. Hierarchical cluster analysis was used to find the most discriminating farm attributes in the cross sectional sample. Afterward, the herd cluster typologies were identified in the longitudinal sample. Herd monthly BMSCC average was evaluated during 12 months fitting a linear mixed model. Two clusters were identified, the farms in the Cluster I applied a comprehensive mastitis program in opposite to Cluster II. Post-dipping, dry cow therapy and milking machine test were routinely applied in Cluster I. In the longitudinal study, 14 out of 38 dairy herds were labeled as Cluster I and the rest were assigned to Cluster II. Significant difference in BMSCC was found between cluster I and II (60,000 cells/mL. The present study showed the relevance and potential impact of promoting mastitis control practices among small and medium sized dairy producers in Argentina.

Preliminary Test of Upgraded Conventional Milling Machine into PC Based CNC Milling Machine

International Nuclear Information System (INIS)

Abdul Hafid

2008-01-01

CNC (Computerized Numerical Control) milling machine yields a challenge to make an innovation in the field of machining. With an action job is machining quality equivalent to CNC milling machine, the conventional milling machine ability was improved to be based on PC CNC milling machine. Mechanically and instrumentally change. As a control replacing was conducted by servo drive and proximity were used. Computer programme was constructed to give instruction into milling machine. The program structure of consists GUI model and ladder diagram. Program was put on programming systems called RTX software. The result of up-grade is computer programming and CNC instruction job. The result was beginning step and it will be continued in next time. With upgrading ability milling machine becomes user can be done safe and optimal from accident risk. By improving performance of milling machine, the user will be more working optimal and safely against accident risk. (author)

Optimization of Support Vector Machine (SVM) for Object Classification

Science.gov (United States)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Experiments with the Dragon Machine

International Nuclear Information System (INIS)

Malenfant, R.E.

2005-01-01

The basic characteristics of a self-sustaining chain reaction were demonstrated with the Chicago Pile in 1943, but it was not until early 1945 that sufficient enriched material became available to experimentally verify fast-neutron cross-sections and the kinetic characteristics of a nuclear chain reaction sustained with prompt neutrons alone. However, the demands of wartime and the rapid decline in effort following the cessation of hostilities often resulted in the failure to fully document the experiments or in the loss of documentation as personnel returned to civilian pursuits. When documented, the results were often highly classified. Even when eventually declassified, the data were often not approved for public release until years later.2 Even after declassification and approval for public release, the records are sometimes difficult to find. Through a fortuitous discovery, a set of handwritten notes by ''ORF July 1945'' entitled ''Dragon - Research with a Pulsed Fission Reactor'' was found by William L. Myers in an old storage safe at Pajarito Site of the Los Alamos National Laboratory3. Of course, ORF was identified as Otto R. Frisch. The document was attached to a page in a nondescript spiral bound notebook labeled ''494 Book'' that bore the signatures of Louis Slotin and P. Morrison. The notes also reference an ''Idea LS'' that can only be Louis Slotin. The discovery of the notes led to a search of Laboratory Archives, the negative files of the photo lab, and the Report Library for additional details of the experiments with the Dragon machine that were conducted between January and July 1945. The assembly machine and the experiments were carefully conceived and skillfully executed. The analyses--without the crutch of computers--display real insight into the characteristics of the nuclear chain reaction. The information presented here provides what is believed to be a complete collection of the original documentation of the observations made with the Dragon

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

International Nuclear Information System (INIS)

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

2016-01-01

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

Multidimensional student skills with collaborative filtering

Science.gov (United States)

Bergner, Yoav; Rayyan, Saif; Seaton, Daniel; Pritchard, David E.

2013-01-01

Despite the fact that a physics course typically culminates in one final grade for the student, many instructors and researchers believe that there are multiple skills that students acquire to achieve mastery. Assessment validation and data analysis in general may thus benefit from extension to multidimensional ability. This paper introduces an approach for model determination and dimensionality analysis using collaborative filtering (CF), which is related to factor analysis and item response theory (IRT). Model selection is guided by machine learning perspectives, seeking to maximize the accuracy in predicting which students will answer which items correctly. We apply the CF to response data for the Mechanics Baseline Test and combine the results with prior analysis using unidimensional IRT.

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

Science.gov (United States)

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

Non-conventional electrical machines

CERN Document Server

Rezzoug, Abderrezak

2013-01-01

The developments of electrical machines are due to the convergence of material progress, improved calculation tools, and new feeding sources. Among the many recent machines, the authors have chosen, in this first book, to relate the progress in slow speed machines, high speed machines, and superconducting machines. The first part of the book is dedicated to materials and an overview of magnetism, mechanic, and heat transfer.

Mobile phones as a health communication tool to improve skilled attendance at delivery in Zanzibar

DEFF Research Database (Denmark)

Lund, S; Hemed, M; Nielsen, Bb

2012-01-01

Please cite this paper as: Lund S, Hemed M, Nielsen B, Said A, Said K, Makungu M, Rasch V. Mobile phones as a health communication tool to improve skilled attendance at delivery in Zanzibar: a cluster-randomised controlled trial. BJOG 2012; DOI: 10.1111/j.1471-0528.2012.03413.x. Objective......  To examine the association between a mobile phone intervention and skilled delivery attendance in a resource-limited setting. Design  Pragmatic cluster-randomised controlled trial with primary healthcare facilities as the unit of randomisation. Setting  Primary healthcare facilities in Zanzibar. Population...... for study participation. Methods  Twenty-four primary healthcare facilities in six districts in Zanzibar were allocated by simple randomisation to either mobile phone intervention (n = 12) or standard care (n = 12). The intervention consisted of a short messaging service (SMS) and mobile phone voucher...

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

Enhancing students’ mathematical problem posing skill through writing in performance tasks strategy

Science.gov (United States)

Kadir; Adelina, R.; Fatma, M.

2018-01-01

Many researchers have studied the Writing in Performance Task (WiPT) strategy in learning, but only a few paid attention on its relation to the problem-posing skill in mathematics. The problem-posing skill in mathematics covers problem reformulation, reconstruction, and imitation. The purpose of the present study was to examine the effect of WiPT strategy on students’ mathematical problem-posing skill. The research was conducted at a Public Junior Secondary School in Tangerang Selatan. It used a quasi-experimental method with randomized control group post-test. The samples were 64 students consists of 32 students of the experiment group and 32 students of the control. A cluster random sampling technique was used for sampling. The research data were obtained by testing. The research shows that the problem-posing skill of students taught by WiPT strategy is higher than students taught by a conventional strategy. The research concludes that the WiPT strategy is more effective in enhancing the students’ mathematical problem-posing skill compared to the conventional strategy.

THE EFFECT MODEL INQUIRY TRAINING MEDIA AND LOGICAL THINKING ABILITY TO STUDENT’S SCIENCE PROCESS SKILL

Directory of Open Access Journals (Sweden)

Dahrim Pohan

2017-06-01

Full Text Available The aim of the research is to analyz : student’s science process skill using inquiry training learning model is better than konvesional learning.Student’s science process skill who have logical thinking ability above average are better than under average,and the interaction between inquiry training media and logical thinking ability to increase student’s science process skill.The experiment was conducted in SMP 6 Medan as population and class VII-K and VII-J were chosen as sample through cluster random sampling.Science prosess skill used essay test and logical thinking used multiple choice as instrument.Result of the data was analyzed by using two ways ANAVA.Result show that : student’s science process skill using inquiry training learning model is better than konvesional learning,student’s science process skill who logical thinking ability above average are better than under average and the interaction between inquiry training learning model media and logical thinking ability to increase student’s science process skill.

Latent Clustering Models for Outlier Identification in Telecom Data

Directory of Open Access Journals (Sweden)

Ye Ouyang

2016-01-01

Full Text Available Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines. The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management. Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA and time-dependent Gaussian Mixture Models (time-GMM. These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information. We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.

Electrical machines & drives

CERN Document Server

Hammond, P

1985-01-01

Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

DNA-based machines.

Science.gov (United States)

Wang, Fuan; Willner, Bilha; Willner, Itamar

2014-01-01

The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.

Machine translation

Energy Technology Data Exchange (ETDEWEB)

Nagao, M

1982-04-01

Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

Science.gov (United States)

Li, Chaoshun; Zhou, Jianzhong

2014-09-01

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

Science.gov (United States)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples

Máquinas de rastrillar y modelos de utilidad - Raking machines and utility models

Directory of Open Access Journals (Sweden)

Santos López, Pascual

2007-12-01

Full Text Available With a background and forged through hard work, Vicente Martínez Piñera, tried to improve working conditions in the esparto factories of Cieza. From "menaor" a teacher, your mechanical skills led him to invent spinning, raking and manufacture wool, always introducing innovations in this difficult and tough industry. As the inventor profession does not usually give great benefits, became industrial, manufacturing and marketing its own machines and also esparto products manufactured as large hawsers for boat

Multisource Images Analysis Using Collaborative Clustering

Directory of Open Access Journals (Sweden)

Pierre Gançarski

2008-04-01

Full Text Available The development of very high-resolution (VHR satellite imagery has produced a huge amount of data. The multiplication of satellites which embed different types of sensors provides a lot of heterogeneous images. Consequently, the image analyst has often many different images available, representing the same area of the Earth surface. These images can be from different dates, produced by different sensors, or even at different resolutions. The lack of machine learning tools using all these representations in an overall process constraints to a sequential analysis of these various images. In order to use all the information available simultaneously, we propose a framework where different algorithms can use different views of the scene. Each one works on a different remotely sensed image and, thus, produces different and useful information. These algorithms work together in a collaborative way through an automatic and mutual refinement of their results, so that all the results have almost the same number of clusters, which are statistically similar. Finally, a unique result is produced, representing a consensus among the information obtained by each clustering method on its own image. The unified result and the complementarity of the single results (i.e., the agreement between the clustering methods as well as the disagreement lead to a better understanding of the scene. The experiments carried out on multispectral remote sensing images have shown that this method is efficient to extract relevant information and to improve the scene understanding.

Induction machine handbook

CERN Document Server

Boldea, Ion

2002-01-01

Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to

Chaotic Boltzmann machines

Science.gov (United States)

Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki

2013-01-01

The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. PMID:23558425

Rotating electrical machines

CERN Document Server

Le Doeuff, René

2013-01-01

In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

“Lost in translation”. Soft skills development in European countries

Directory of Open Access Journals (Sweden)

Maria Cinque

2016-05-01

Full Text Available The world of work is changing profoundly, at a time when the global economy is not creating a sufficient number of jobs. Many documents issued by the EU and various researches, carried out by companies and human resources experts, point out that the so-called “soft” skills are closely connected with employability, particularly for young people entering the labour market. At present, EU countries have different methodologies and approaches to the teaching and assessment of soft skills. Another obstacle is represented by the absence of a common language. There are different ways of naming ‘soft skills’, different definitions of them, different manners of classifying and clustering them. The article explores some classifications of soft skills and presents a collection of best practices and methods for teaching and learning them at University level, taking into account different perspectives and basing on the results of two European projects focused on this topic. The final goal is to provide an analysis aimed at the identification of the most important soft skills needed for a successful transition from University education to the labour market. The analysis includes a brief chronological excursus on relevant studies on the subject, a review of current literature on employability skills, quantitative (surveys and qualitative (focus groups researches from Europe and Third Countries, identifying the range of soft skills relevant for newly graduates. The aim of this overview is to enhance understanding of soft skills and to indicate key areas for soft skill development at University level.

Your Sewing Machine.

Science.gov (United States)

Peacock, Marion E.

The programed instruction manual is designed to aid the student in learning the parts, uses, and operation of the sewing machine. Drawings of sewing machine parts are presented, and space is provided for the student's written responses. Following an introductory section identifying sewing machine parts, the manual deals with each part and its…

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction

Directory of Open Access Journals (Sweden)

Ruijian Zhang

2017-12-01

Full Text Available Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the assessment of water quality will be far more efficient, and by generating the decision tree, the prediction will be quite accurate. The drawback of the machine learning modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time-consuming algorithm in clustering. Therefore, we applied the high performance computing (HPC System to deal with this problem. Up to now, the pilot experiments have achieved very promising preliminary results. The visualized water quality assessment and prediction obtained from this project would be published in an interactive website so that the public and the environmental managers could use the information for their decision making.

Effect of obstetric team training on team performance and medical technical skills: a randomised controlled trial

NARCIS (Netherlands)

Fransen, A.F.; Ven, van de J.; Merién, A.E.R.; Wit-Zuurendonk, de L.D.; Houterman, S.; Mol, B.W.J.; Oei, S.G.

2012-01-01

Objective To determine whether obstetric team training in a medical simulation centre improves the team performance and utilisation of appropriate medical technical skills of healthcare professionals. Design Cluster randomised controlled trial. Setting The Netherlands. Sample The obstetric

A Comparison of Forward and Concurrent Chaining Strategies in Teaching Laundromat Skills to Students with Severe Handicaps.

Science.gov (United States)

McDonnell, John; McFarland, Susan

1988-01-01

In a study which taught four high school students with severe handicaps to use a commercial washing machine and laundry soap dispenser, a concurrent chaining strategy was found more efficient than forward chaining in facilitating skill acquisition. Concurrent chaining also resulted in better maintenance at four- and eight-week follow-up…

Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems

Directory of Open Access Journals (Sweden)

Jui-Pin Hung

2016-03-01

Full Text Available This study was aimed to investigate the machining stability of a horizontal milling machine with hybrid guideway systems by finite element method. To this purpose, we first created finite element model of the milling machine with the introduction of the contact stiffness defined at the sliding and rolling interfaces, respectively. Also, the motorized built-in spindle model was created and implemented in the whole machine model. Results of finite element simulations reveal that linear guides with different preloads greatly affect the dynamic responses and machining stability of the horizontal milling machine. The critical cutting depth predicted at the vibration mode associated with the machine tool structure is about 10 mm and 25 mm in the X and Y direction, respectively, while the cutting depth predicted at the vibration mode associated with the spindle structure is about 6.0 mm. Also, the machining stability can be increased when the preload of linear roller guides of the feeding mechanism is changed from lower to higher amount.

Precision machining commercialization

International Nuclear Information System (INIS)

1978-01-01

To accelerate precision machining development so as to realize more of the potential savings within the next few years of known Department of Defense (DOD) part procurement, the Air Force Materials Laboratory (AFML) is sponsoring the Precision Machining Commercialization Project (PMC). PMC is part of the Tri-Service Precision Machine Tool Program of the DOD Manufacturing Technology Five-Year Plan. The technical resources supporting PMC are provided under sponsorship of the Department of Energy (DOE). The goal of PMC is to minimize precision machining development time and cost risk for interested vendors. PMC will do this by making available the high precision machining technology as developed in two DOE contractor facilities, the Lawrence Livermore Laboratory of the University of California and the Union Carbide Corporation, Nuclear Division, Y-12 Plant, at Oak Ridge, Tennessee

Clustering of European winter storms: A multi-model perspective

Science.gov (United States)

Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter

2016-04-01

The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak

Are there intelligent Turing machines?

OpenAIRE

Bátfai, Norbert

2015-01-01

This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quo...

Coldness production and heat revalorization: particular machines; Production de froid et revalorisation de la chaleur: machines particulieres

Energy Technology Data Exchange (ETDEWEB)

Feidt, M. [Universite Henri Poincare - Nancy-1, 54 - Nancy (France)

2003-10-01

The machines presented in this article are not the common reverse cycle machines. They use some systems based on different physical principles which have some consequences on the analysis of cycles: 1 - permanent gas machines (thermal separators, pulse gas tube, thermal-acoustic machines); 2 - phase change machines (mechanical vapor compression machines, absorption machines, ejection machines, adsorption machines); 3 - thermoelectric machines (thermoelectric effects, thermodynamic model of a thermoelectric machine). (J.S.)

National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

Science.gov (United States)

Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine‐related hazards in 221 business. Results Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. Conclusions The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. Am. J. Ind. Med. 58:1174–1183, 2015. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc. PMID:26332060

‘Machinic Trajectories’: Appropriated Devices as Post-Digital Drawing Machines

Directory of Open Access Journals (Sweden)

Andres Wanner

2014-12-01

Full Text Available This article presents a series of works called Machinic Trajectories, consisting of domestic devices appropriated as mechanical drawing machines. These are contextualized within the post-digital discourse, which integrates messy analog conditions into the digital realm. The role of eliciting and examining glitches for investigating a technology is pointed out. Glitches are defined as short-lived, unpremeditated aesthetic results of a failure; they are mostly known as digital phenomena, but I argue that the concept is equally applicable to the output of mechanical machines. Three drawing machines will be presented: The Opener, The Mixer and The Ventilator. In analyzing their drawings, emergent patterns consisting of unpremeditated visual artifacts will be identified and connected to irregularities of the specific technologies. Several other artists who work with mechanical and robotic drawing machines are introduced, to situate the presented works and reflections in a larger context of practice and to investigate how glitch concepts are applicable to such mechanical systems. 

Achieving involvement: process outcomes from a cluster randomized trial of shared decision making skill development and use of risk communication aids in general practice.

Science.gov (United States)

Elwyn, G; Edwards, A; Hood, K; Robling, M; Atwell, C; Russell, I; Wensing, M; Grol, R

2004-08-01

A consulting method known as 'shared decision making' (SDM) has been described and operationalized in terms of several 'competences'. One of these competences concerns the discussion of the risks and benefits of treatment or care options-'risk communication'. Few data exist on clinicians' ability to acquire skills and implement the competences of SDM or risk communication in consultations with patients. The aims of this study were to evaluate the effects of skill development workshops for SDM and the use of risk communication aids on the process of consultations. A cluster randomized trial with crossover was carried out with the participation of 20 recently qualified GPs in urban and rural general practices in Gwent, South Wales. A total of 747 patients with known atrial fibrillation, prostatism, menorrhagia or menopausal symptoms were invited to a consultation to review their condition or treatments. Half the consultations were randomly selected for audio-taping, of which 352 patients attended and were audio-taped successfully. After baseline, participating doctors were randomized to receive training in (i) SDM skills or (ii) the use of simple risk communication aids, using simulated patients. The alternative training was then provided for the final study phase. Patients were allocated randomly to a consultation during baseline or intervention 1 (SDM or risk communication aids) or intervention 2 phases. A randomly selected half of the consultations were audio-taped from each phase. Raters (independent, trained and blinded to study phase) assessed the audio-tapes using a validated scale to assess levels of patient involvement (OPTION: observing patient involvement), and to analyse the nature of risk information discussed. Clinicians completed questionnaires after each consultation, assessing perceived clinician-patient agreement and level of patient involvement in decisions. Multilevel modelling was carried out with the OPTION score as the dependent variable, and

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

Science.gov (United States)

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Cluster Policy in the Light of Institutional Context—A Comparative Study of Transition Countries

Directory of Open Access Journals (Sweden)

Tine Lehmann

2015-10-01

Full Text Available The business environment in transition countries is often extraordinarily challenging for companies. The transition process these countries find themselves in leads to constant changes in the institutional environment. Hence, institutional voids prevail. These institutional voids cause competitive disadvantages for small and medium enterprises. Cluster policy can address these competitive disadvantages. As cluster policy generally aims at supporting companies’ competitive advantage by spurring innovation and productivity, it can help to bridge institutional voids. This article’s research question aims at analyzing and comparing cluster policies in the institutional context of two transition countries (Serbia and Tunisia and analyzes to what extent cluster policies in these two countries are adapted to institutional voids prevailing there. The case studies offer insights into apparent difficulties of clusters in bridging formal institutional voids, as well as, notably, into the informal void of skill mismatches in the labor market. Still, for some specific voids, clusters do at least implicitly assume a bridging role. While the cluster policies examined do not explicitly target the institutional voids identified, cluster management can—in the course of time—align its service offering more closely with these voids. Bottom-up designed cluster policies can play an especially important role in such an evolution towards bridging institutional voids.

School vending machine use and fast-food restaurant use are associated with sugar-sweetened beverage intake in youth.

Science.gov (United States)

Wiecha, Jean L; Finkelstein, Daniel; Troped, Philip J; Fragala, Maren; Peterson, Karen E

2006-10-01

To examine associations between use of school vending machines and fast-food restaurants and youth intake of sugar-sweetened beverages. A cross-sectional observational study. From a group randomized obesity intervention, we analyzed baseline data from 1,474 students in 10 Massachusetts middle schools with vending machines that sold soda and/or other sweetened drinks. Daily sugar-sweetened beverage consumption (regular soda, fruit drinks, and iced tea), purchases from school vending machines, and visits to fast-food restaurants in the preceding 7 days were estimated by self-report. Chi(2) and nonparametric tests were performed on unadjusted data; multivariable models adjusted for sex, grade, body mass index, and race/ethnicity, and accounted for clustering within schools. Among 646 students who reported using school vending machines, 456 (71%) reported purchasing sugar-sweetened beverages. Overall, 977 students (66%) reported eating at a fast-food restaurant. Sugar-sweetened beverage intakes averaged 1.2 servings per day. In adjusted models, relative to no vending machine purchases, servings per day increased by 0.21 for one to three purchases per week (P=0.0057), and 0.71 with four or more purchases (Pvending machines, more report buying sugar-sweetened beverages than any other product category examined. Both school vending machine and fast-food restaurant use are associated with overall sugar-sweetened beverage intake. Reduction in added dietary sugars may be attainable by reducing use of these sources or changing product availability.

An Investigation of the Life Skills Knowledge among Female Students of Tehran City Universities

Directory of Open Access Journals (Sweden)

K. Khushabi

2008-10-01

Full Text Available Introduction & Objective: The present research designed to examine the life skills knowledge of female college students in Tehran city.Materials & Methods: The work was a descriptive study. The statistical population consist all female college students in Tehran, and the sampling was cluster mode. The primary data recruited by a researcher made inventory to the knowledge of the individuals about the life skills. The collected data analyzed with descriptive and inferential statistics. Results: Results suggested that 9.3% of the subject group had a modest awareness of life skills; 47.5% had an average awareness and 43.2% were at high level in such skills. Also the results showed that there was a significant difference between educational course of the students and level of life skill knowledge (p<0.05, but not in demographical and knowledge of life skills. Conclusion: The results points to importance of life skills education in college contexts. With regarding that nearly 9% of the students had little knowledge and as results indicated that there is a negative correlation between low awareness of life skills and mental health; and also direct correlation between low awareness of life skills and expressing high risk behaviors, it is recommended to make and prepare effective programs in this field.

The effect of science learning integrated with local potential to improve science process skills

Science.gov (United States)

Rahardini, Riris Riezqia Budy; Suryadarma, I. Gusti Putu; Wilujeng, Insih

2017-08-01

This research was aimed to know the effectiveness of science learning that integrated with local potential to improve student`s science process skill. The research was quasi experiment using non-equivalent control group design. The research involved all student of Muhammadiyah Imogiri Junior High School on grade VII as a population. The sample in this research was selected through cluster random sampling, namely VII B (experiment group) and VII C (control group). Instrument that used in this research is a nontest instrument (science process skill observation's form) adapted Desak Megawati's research (2016). The aspect of science process skills were making observation and communication. The data were using univariat (ANOVA) analyzed at 0,05 significance level and normalized gain score for science process skill increase's category. The result is science learning that integrated with local potential was effective to improve science process skills of student (Sig. 0,00). This learning can increase science process skill, shown by a normalized gain score value at 0,63 (medium category) in experiment group and 0,29 (low category) in control group.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

Science.gov (United States)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

Science.gov (United States)

Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

2015-01-01

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

Science.gov (United States)

Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan; Lee, Intae; Jang, Giljin; Yousefi, Siamak; Zangwill, Linda M; Medeiros, Felipe A; Girkin, Christopher A; Liebmann, Jeffrey M; Goldbaum, Michael H

2014-01-01

The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

Selective automation and skill transfer in medical robotics: a demonstration on surgical knot-tying.

Science.gov (United States)

Knoll, Alois; Mayer, Hermann; Staub, Christoph; Bauernschmitt, Robert

2012-12-01

Transferring non-trivial human manipulation skills to robot systems is a challenging task. There have been a number of attempts to design research systems for skill transfer, but the level of the complexity of the actual skills transferable to the robot was rather limited, and delicate operations requiring a high dexterity and long action sequences with many sub-operations were impossible to transfer. A novel approach to human-machine skill transfer for multi-arm robot systems is presented. The methodology capitalizes on the metaphor of 'scaffolded learning', which has gained widespread acceptance in psychology. The main idea is to formalize the superior knowledge of a teacher in a certain way to generate support for a trainee. In our case, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during 'learning by demonstration'. The actual skill generalization is then based on simulating fluid dynamics. The approach has been successfully evaluated in the medical domain for the delicate task of automated knot-tying for suturing with standard surgical instruments and a realistic minimally invasive robotic surgery system. Copyright © 2012 John Wiley & Sons, Ltd.

FY1995 distributed control of man-machine cooperative multi agent systems; 1995 nendo ningen kyochogata multi agent kikai system no jiritsu seigyo

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

In the near future, distributed autonomous systems will be practical in many situations, e.g., interactive production systems, hazardous environments, nursing homes, and individual houses. The agents which consist of the distributed system must not give damages to human being and should be working economically. In this project man-machine cooperative multi agent systems are studied in many kind of respects, and basic design technology, basic control technique are developed by establishing fundamental theories and by constructing experimental systems. In this project theoretical and experimental studies are conducted in the following sub-projects: (1) Distributed cooperative control in multi agent type actuation systems (2) Control of non-holonomic systems (3) Man-machine Cooperative systems (4) Robot systems learning human skills (5) Robust force control of constrained systems In each sub-project cooperative nature between machine agent systems and human being, interference between artificial multi agents and environment and new function emergence in coordination of the multi agents and the environment, robust force control against for the environments, control methods for non-holonomic systems, robot systems which can mimic and learn human skills were studied. In each sub-project, some problems were hi-lighted and solutions for the problems have been given based on construction of experimental systems. (NEDO)

Simulation of machine-maintenance training in virtual environment

International Nuclear Information System (INIS)

Yoshikawa, Hidekazu; Tezuka, Tetsuo; Kashiwa, Ken-ichiro; Ishii, Hirotake

1997-01-01

The periodical inspection of nuclear power plants needs a lot of workforces with a high degree of technical skill for the maintenance of various sorts of machines. Therefore, a new type of maintenance training system is required, where trainees can get training safely, easily and effectively. In this study we developed a training simulation system for disassembling a check valve in virtual environment (VE). The features of this system are as follows: Firstly, the trainees can execute tasks even in wrong order, and can experience the resultant conditions. In order to realize this environment, we developed a new Petri-net model for representing the objects' states in VE. This Petri-net model has several original characteristics, which make it easier to manage the change of the objects' states. Furthermore, we made a support system for constructing the Petri-net model of machine-disassembling training, because the Petri-net model is apt to become of large size. The effectiveness of this support system is shown through the system development. Secondly, this system can perform appropriate tasks to be done next in VE whenever the trainee wants even after some mistakes have been made. The effectiveness of this function has also been confirmed by experiments. (author)

Self-Improving CNC Milling Machine

OpenAIRE

Spilling, Torjus

2014-01-01

This thesis is a study of the ability of a CNC milling machine to create parts for itself, and an evaluation of whether or not the machine is able to improve itself by creating new machine parts. This will be explored by using off-the-shelf parts to build an initial machine, using 3D printing/rapid prototyping to create any special parts needed for the initial build. After an initial working machine is completed, the design of the machine parts will be adjusted so that the machine can start p...

Clustering the Orion B giant molecular cloud based on its molecular emission.

Science.gov (United States)

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also

Machine Learning.

Science.gov (United States)

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

Machining of Metal Matrix Composites

CERN Document Server

2012-01-01

Machining of Metal Matrix Composites provides the fundamentals and recent advances in the study of machining of metal matrix composites (MMCs). Each chapter is written by an international expert in this important field of research. Machining of Metal Matrix Composites gives the reader information on machining of MMCs with a special emphasis on aluminium matrix composites. Chapter 1 provides the mechanics and modelling of chip formation for traditional machining processes. Chapter 2 is dedicated to surface integrity when machining MMCs. Chapter 3 describes the machinability aspects of MMCs. Chapter 4 contains information on traditional machining processes and Chapter 5 is dedicated to the grinding of MMCs. Chapter 6 describes the dry cutting of MMCs with SiC particulate reinforcement. Finally, Chapter 7 is dedicated to computational methods and optimization in the machining of MMCs. Machining of Metal Matrix Composites can serve as a useful reference for academics, manufacturing and materials researchers, manu...

Machine technology: a survey

International Nuclear Information System (INIS)

Barbier, M.M.

1981-01-01

An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

Collaborative Clustering for Sensor Networks

Science.gov (United States)

Wagstaff. Loro :/; Green Jillian; Lane, Terran

2011-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative

Characteristics of laser assisted machining for silicon nitride ceramic according to machining parameters

International Nuclear Information System (INIS)

Kim, Jong Do; Lee, Su Jin; Suh, Jeong

2011-01-01

This paper describes the Laser Assisted Machining (LAM) that cuts and removes softened parts by locally heating the ceramic with laser. Silicon nitride ceramics can be machined with general machining tools as well, because YSiAlON, which was made up ceramics, is soften at about 1,000 .deg. C. In particular, the laser, which concentrates on highly dense energy, can locally heat materials and very effectively control the temperature of the heated part of specimen. Therefore, this paper intends to propose an efficient machining method of ceramic by deducing the machining governing factors of laser assisted machining and understanding its mechanism. While laser power is the machining factor that controls the temperature, the CBN cutting tool could cut the material more easily as the material gets deteriorated from the temperature increase by increasing the laser power, but excessive oxidation can negatively affect the quality of the material surface after machining. As the feed rate and cutting depth increase, the cutting force increases and tool lifespan decreases, but surface oxidation also decreases. In this experiment, the material can be cut to 3 mm of cutting depth. And based on the results of the experiment, the laser assisted machining mechanism is clarified

试论机修钳工与机械修理一体化%On the Integration of Machine Repairing Bench Worker and Machine Repairing

Institute of Scientific and Technical Information of China (English)

吴壮

2015-01-01

With the continuous development and progress of machine repairing in China, the demand of society for high level mechanical machine repairing bench workers has increased year by year. The machine tool repairing needs a lot of theory as the support foundation, and it also needs the sufficient curriculum practice to master the technology. All of these need the machine repairing bench workers fully combine the theory and practice in the personnel training activities. The personnel training method of combining machine repairing bench workers and machine repairing can effectively promote the theoretical study of the workers further implement the reasonable grasp of practical skills. This method can help them utilize the theory of knowledge to the specific practice, and also help them further understanding and master the theoretical knowledge in actual practice.%随着我国机修事业的不断发展与进步，使得社会对于高素质水平机修钳工的需求也逐年增多。机床的修理工作不仅需要借助大量的理论来作为基础支撑，同时也需要进行充分的课程实践来实现技术的有效掌握，这就要求其在钳工的人才培养活动中，必须实现理论与实践的充分结合。机修钳工与机械修理一体化的人才培养方法，能够有效促使钳工在理论学习的同时，也进一步的实现实践技能的合理掌握，不仅能够帮助其将所学的理论知识充分的运用到具体的实践活动中，也能进一步帮助其在实际的实践活动中来对理论知识进行更加深入的理解与掌握。

Driver style and driver skill – Clustering sub-groups of drivers differing in their potential danger in traffic

DEFF Research Database (Denmark)

Martinussen, Laila Marianne; Møller, Mette; Prato, Carlo Giacomo

The Driver Behavior Questionnaire (DBQ) and the Driver Skill Inventory (DSI) are two of the most frequently used measures of self-reported driving style and driving skill. The motivation behind the present study was to test drivers’ consistency or judgment of their own self-reported driving ability...... based on a combined use of the DBQ and the DSI. Moreover, the joint use of the two instruments was applied to identify sub-groups of drivers that differ in their potential danger in traffic (as measured by frequency of aberrant driving behaviors and level of driving skills), as well as to test whether...... the sub-groups of drivers differed in characteristics such as age, gender, annual mileage and accident involvement. 3908 drivers aged 18–84 participated in the survey. The results suggested that the drivers are consistent in their reporting of driving ability, as the self-reported driving skill level...

Enhancement of plant metabolite fingerprinting by machine learning.

Science.gov (United States)

Scott, Ian M; Vermeer, Cornelia P; Liakata, Maria; Corol, Delia I; Ward, Jane L; Lin, Wanchang; Johnson, Helen E; Whitehead, Lynne; Kular, Baldeep; Baker, John M; Walsh, Sean; Dave, Anuja; Larson, Tony R; Graham, Ian A; Wang, Trevor L; King, Ross D; Draper, John; Beale, Michael H

2010-08-01

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines

International Nuclear Information System (INIS)

Frauenhofer, Joachim; Arndt, Tabea; Grundmann, Joern

2013-01-01

The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO 2 emissions.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

International Nuclear Information System (INIS)

Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina

2012-01-01

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which

Assessing the relationship between the Driver Behavior Questionnaire and the Driver Skill Inventory: Revealing sub-groups of drivers

DEFF Research Database (Denmark)

Martinussen, Laila Marianne; Møller, Mette; Prato, Carlo Giacomo

2014-01-01

The Driver Behavior Questionnaire and the Driver Skill Inventory are two of the most frequently used measures of self-reported driving style and driving skill. The motivation behind the present study was to identify sub-groups of drivers that potentially act dangerously in traffic (as measured...... self-reported driving skills and whether the reported skill level was reflected in the reported aberrant driving behaviors. 3908 drivers aged 18–84 participated in the survey. K-means cluster analysis revealed four distinct sub-groups that differed in driving skills and frequency of aberrant driving...... by frequency of aberrant driving behaviors and level of driving skills), as well as to test whether the sub-groups differ in characteristics such as age, gender, annual mileage and accident involvement. Furthermore, the joint analysis of the two instruments was used to test drivers’ assessment of their own...

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

Science.gov (United States)

Pankratius, V.; Gowanlock, M.; Blair, D. M.

2015-12-01

Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

Integration of Openstack cloud resources in BES III computing cluster

Science.gov (United States)

Li, Haibo; Cheng, Yaodong; Huang, Qiulan; Cheng, Zhenjing; Shi, Jingyan

2017-10-01

Cloud computing provides a new technical means for data processing of high energy physics experiment. However, the resource of each queue is fixed and the usage of the resource is static in traditional job management system. In order to make it simple and transparent for physicist to use, we developed a virtual cluster system (vpmanager) to integrate IHEPCloud and different batch systems such as Torque and HTCondor. Vpmanager provides dynamic virtual machines scheduling according to the job queue. The BES III use case results show that resource efficiency is greatly improved.

VIRTUAL MACHINES IN EDUCATION – CNC MILLING MACHINE WITH SINUMERIK 840D CONTROL SYSTEM

Directory of Open Access Journals (Sweden)

Ireneusz Zagórski

2014-11-01

Full Text Available Machining process nowadays could not be conducted without its inseparable element: cutting edge and frequently numerically controlled milling machines. Milling and lathe machining centres comprise standard equipment in many companies of the machinery industry, e.g. automotive or aircraft. It is for that reason that tertiary education should account for this rising demand. This entails the introduction into the curricula the forms which enable visualisation of machining, milling process and virtual production as well as virtual machining centres simulation. Siemens Virtual Machine (Virtual Workshop sets an example of such software, whose high functionality offers a range of learning experience, such as: learning the design of machine tools, their configuration, basic operation functions as well as basics of CNC.

THE INFLUENCE OF INTERACTIVE MULTIMEDIA AUDIO TELLING MACHINE (IMATE USE AND STUDENTS’ SELF REGU-LATED LEARNING LEVEL ON ENGLISH LANGUAGE GREET-INGS APPLICATION SKILLS (PENGARUH PENGGUNAAN INTERACTIVE MULTIMEDIA AU-DIO TELLING MACHINE (iMATE DAN TINGKAT SELF REGU-LATED LEARNING SISWA TERHADAP KEMAMPUAN MENERAP-KAN GREETINGS BAHASA INGGRIS

Directory of Open Access Journals (Sweden)

Muhammad Ridwan Sutisna

2018-02-01

Full Text Available Abstract. New trends of technology and also the higher needs of English proficiency have encouraged the quality improvent of English instructions. The aim of this research is to deter-mine the effect of Interactive Multimedia Audio Telling Machine (iMATE and self regulated learning level in English language greetings application skill of vocational school students. iMATE is an Interactive instructional media used in this research. While student’s self regulated learning is divided into high and low level. This research used experimental design. This re-search was held at SMK Pasundan 3 Bandung. Findings of this research were; (1 Generally students achieved better result when using iMATE. (2 There was an interaction between use of instructional media and students’ self regulated learning level. (3 Students with high self regu-lated learning achieved much better when using iMATE. (4 Students with low self regulated learning had a better result when not using iMATE. This Findings lead to the conclusion that students’ self regulated learning level may affect the succes of instructional media use, especially in teaching English language skills. Abstrak. Perkembangan teknologi dan kebutuhan akan kemampuan Bahasa Inggris yang lebih tinggi mendorong kualitas pembelajaran Bahasa Inggris juga mengalami perkem-bangan. Tujuan penelitian ini adalah untuk mengetahui pengaruh dari penggunaan Interactive Multimedia Audio Telling Machine (iMATE dan tingkat self regulated learning terhadap ke-mampuan menerapkan greetings Bahasa Inggris siswa SMK. iMATE adalah multimedia inter-aktif pembelajaran yang digunakan dalam penelitian ini. Siswa sebagai subjek penelitian dibagi kedalam dua kelompok yaitu yang memiliki tingkat self regulated learning yang tinggi dan ren-dah. Penelitian yang dilaksanakan di SMK Pasundan 3 Bandung ini menggunakan desain ek-sperimen. Temuan dari penelitian ini adalah (1 Secara umum siswa memperoleh hasil yang lebih baik dengan

Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

Science.gov (United States)

Hsiao, Ming-Chih; Su, Ling-Huey

2018-02-01

This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.

Theory of Belief Functions for Data Analysis and Machine Learning Applications: Review and Prospects

Science.gov (United States)

Denoeux, Thierry

The Dempster-Shafer theory of belief functions provides a unified framework for handling both aleatory uncertainty, arising from statistical variability in populations, and epistemic uncertainty, arising from incompleteness of knowledge. An overview of both the fundamentals and some recent developments in this theory will first be presented. Several applications in data analysis and machine learning will then be reviewed, including learning under partial supervision, multi-label classification, ensemble clustering and the treatment of pairwise comparisons in sensory or preference analysis.

Machine-to-machine communications architectures, technology, standards, and applications

CERN Document Server

Misic, Vojislav B

2014-01-01

With the number of machine-to-machine (M2M)-enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology.Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology.Details a practical scheme for the forward error correction code designInvestigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communicationsIdentifies algorithms that will ensure functionality, performance, reliability, ...

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

Science.gov (United States)

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Nonplanar machines

International Nuclear Information System (INIS)

Ritson, D.

1989-05-01

This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

Machine learning for the automatic detection of anomalous events

Science.gov (United States)

Fisher, Wendy D.

In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

Ultraprecision machining. Cho seimitsu kako

Energy Technology Data Exchange (ETDEWEB)

Suga, T [The Univ. of Tokyo, Tokyo (Japan). Research Center for Advanced Science and Technology

1992-10-05

It is said that the image of ultraprecision improved from 0.1[mu]m to 0.01[mu]m within recent years. Ultraprecision machining is a production technology which forms what is called nanotechnology with ultraprecision measuring and ultraprecision control. Accuracy means average machined sizes close to a required value, namely the deflection errors are small; precision means the scattered errors of machined sizes agree very closely. The errors of machining are related to both of the above errors and ultraprecision means the combined errors are very small. In the present ultraprecision machining, the relative precision to the size of a machined object is said to be in the order of 10[sup -6]. The flatness of silicon wafers is usually less than 0.5[mu]m. It is the fact that the appearance of atomic scale machining is awaited as the limit of ultraprecision machining. The machining of removing and adding atomic units using scanning probe microscopes are expected to reach the limit actually. 2 refs.

Theory and practice in machining systems

CERN Document Server

Ito, Yoshimi

2017-01-01

This book describes machining technology from a wider perspective by considering it within the machining space. Machining technology is one of the metal removal activities that occur at the machining point within the machining space. The machining space consists of structural configuration entities, e.g., the main spindle, the turret head and attachments such the chuck and mandrel, and also the form-generating movement of the machine tool itself. The book describes fundamental topics, including the form-generating movement of the machine tool and the important roles of the attachments, before moving on to consider the supply of raw materials into the machining space, and the discharge of swarf from it, and then machining technology itself. Building on the latest research findings “Theory and Practice in Machining System” discusses current challenges in machining. Thus, with the inclusion of introductory and advanced topics, the book can be used as a guide and survey of machining technology for students an...

Setup Time Reduction On Solder Paste Printing Machine – A Case Study

Directory of Open Access Journals (Sweden)

Rajesh Dhake

2013-06-01

Full Text Available Lean manufacturing envisages the reduction of the seven deadly wastes referred to as MUDA. Setup time forms a major component of the equipment downtime. It leads to lower machine utilization and restricts the output and product variety. This necessitates the requirement for quick setups. Single Minute Exchange of Die philosophy (a lean manufacturing tool here after referred as “SMED” is one of the important tool which aims at quick setups driving smaller lot sizes, lower production costs, improve productivity in terms of increased output, increased utilization of machine and labor hours, make additional capacity available (often at bottleneck resources, reduce scrap and rework, and increase flexibility[3]. This paper focuses on the application of Single Minute Exchange of Die[1] and Quick Changeover Philosophy[2] for reducing setup time on Solder Past Printing Machine (bottleneck machine in a electronic speedo-cluster manufacturing company. The four step SMED philosophy was adopted to effect reduction in setup time. The initial step was gathering information about the present setup times and its proportion to the total productive time. A detailed video based time study of setup activities was done to classify them into external and internal setup activities in terms of their need (i.e. preparation, replacement or adjustment, time taken and the way these could be reduced, simplified or eliminated. The improvements effected were of three categories viz., mechanical, procedural and organizational. The paper concludes by comparing the present and proposed (implemented methods of setup procedures.

Brightest Cluster Galaxies in REXCESS Clusters

Science.gov (United States)

Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

2009-01-01

Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

Face machines

Energy Technology Data Exchange (ETDEWEB)

Hindle, D.

1999-06-01

The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

A Cluster Randomized Trial of the Social Skills Improvement System-Classwide Intervention Program (SSIS-CIP) in First Grade

Science.gov (United States)

DiPerna, James Clyde; Lei, Puiwa; Cheng, Weiyi; Hart, Susan Crandall; Bellinger, Jillian

2018-01-01

The purpose of this study was to evaluate the efficacy of a universal social skills program, the Social Skills Improvement System Classwide Intervention Program (SSIS-CIP; Elliott & Gresham, 2007), for students in first grade. Classrooms from 6 elementary schools were randomly assigned to treatment or business-as-usual control conditions.…

Tattoo machines, needles and utilities.

Science.gov (United States)

Rosenkilde, Frank

2015-01-01

Starting out as a professional tattooist back in 1977 in Copenhagen, Denmark, Frank Rosenkilde has personally experienced the remarkable development of tattoo machines, needles and utilities: all the way from home-made equipment to industrial products of substantially improved quality. Machines can be constructed like the traditional dual-coil and single-coil machines or can be e-coil, rotary and hybrid machines, with the more convenient and precise rotary machines being the recent trend. This development has resulted in disposable needles and utilities. Newer machines are more easily kept clean and protected with foil to prevent crosscontaminations and infections. The machines and the tattooists' knowledge and awareness about prevention of infection have developed hand-in-hand. For decades, Frank Rosenkilde has been collecting tattoo machines. Part of his collection is presented here, supplemented by his personal notes. © 2015 S. Karger AG, Basel.

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