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.

Machine-z: Rapid Machine-Learned Redshift Indicator for Swift Gamma-Ray Bursts

Science.gov (United States)

Ukwatta, T. N.; Wozniak, P. R.; Gehrels, N.

2016-01-01

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce 'machine-z', a redshift prediction algorithm and a 'high-z' classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve approximately 100 per cent recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

International Nuclear Information System (INIS)

Sharma Mradul; Koul Maharaj Krishna; Mitra Abhas; Nayak Jitadeepa; Bose Smarajit

2014-01-01

A detailed case study of γ-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers, Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for γ-hadron segregation. (research papers)

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

Random and Systematic Errors Share in Total Error of Probes for CNC Machine Tools

Directory of Open Access Journals (Sweden)

Adam Wozniak

2018-03-01

Full Text Available Probes for CNC machine tools, as every measurement device, have accuracy limited by random errors and by systematic errors. Random errors of these probes are described by a parameter called unidirectional repeatability. Manufacturers of probes for CNC machine tools usually specify only this parameter, while parameters describing systematic errors of the probes, such as pre-travel variation or triggering radius variation, are used rarely. Systematic errors of the probes, linked to the differences in pre-travel values for different measurement directions, can be corrected or compensated, but it is not a widely used procedure. In this paper, the share of systematic errors and random errors in total error of exemplary probes are determined. In the case of simple, kinematic probes, systematic errors are much greater than random errors, so compensation would significantly reduce the probing error. Moreover, it shows that in the case of kinematic probes commonly specified unidirectional repeatability is significantly better than 2D performance. However, in the case of more precise strain-gauge probe systematic errors are of the same order as random errors, which means that errors correction or compensation, in this case, would not yield any significant benefits.

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...

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

Science.gov (United States)

Mascaro, Joseph; Asner, Gregory P; Knapp, David E; Kennedy-Bowdoin, Ty; Martin, Roberta E; Anderson, Christopher; Higgins, Mark; Chadwick, K Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

Directory of Open Access Journals (Sweden)

Joseph Mascaro

Full Text Available Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus. The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag", which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

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…

rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

Directory of Open Access Journals (Sweden)

Miron B. Kursa

2014-11-01

Full Text Available Random ferns is a very simple yet powerful classification method originally introduced for specific computer vision tasks. In this paper, I show that this algorithm may be considered as a constrained decision tree ensemble and use this interpretation to introduce a series of modifications which enable the use of random ferns in general machine learning problems. Moreover, I extend the method with an internal error approximation and an attribute importance measure based on corresponding features of the random forest algorithm. I also present the R package rFerns containing an efficient implementation of this modified version of random ferns.

Banc d'essai des machines de pression positive continue ...

African Journals Online (AJOL)

Le syndrome d'apnées du sommeil est une pathologie fréquente qui se manifeste par l'obstruction (de plus de 10 secondes) des voies aériennes supérieures chez l'homme pendant le sommeil. Pour le traiter, le patient dort avec un masque nasal raccordé à une machine auto-pilotée qui délivre une pression positive ...

The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.

Science.gov (United States)

Kurczab, Rafał; Bojarski, Andrzej J

2017-01-01

The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated with training predictive models can influence the final performance and, consequently, the number of hits found. Thus, we performed a systematic study of the simultaneous influence of the proportion of negatives to positives in the testing set, the size of screening databases and the type of molecular representations on the effectiveness of classification. The results obtained for eight protein targets, five machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest), two types of molecular fingerprints (MACCS and CDK FP) and eight screening databases with different numbers of molecules confirmed our previous findings that increases in the ratio of negative to positive training instances greatly influenced most of the investigated parameters of the ML methods in simulated virtual screening experiments. However, the performance of screening was shown to also be highly dependent on the molecular library dimension. Generally, with the increasing size of the screened database, the optimal training ratio also increased, and this ratio can be rationalized using the proposed cost-effectiveness threshold approach. To increase the performance of machine learning-based virtual screening, the training set should be constructed in a way that considers the size of the screening database.

Classification of Phishing Email Using Random Forest Machine Learning Technique

OpenAIRE

Akinyelu, Andronicus A.; Adewumi, Aderemi O.

2013-01-01

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learnin...

Position error compensation via a variable reluctance sensor applied to a Hybrid Vehicle Electric machine.

Science.gov (United States)

Bucak, Ihsan Ömür

2010-01-01

In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion.

Position Error Compensation via a Variable Reluctance Sensor Applied to a Hybrid Vehicle Electric Machine

Directory of Open Access Journals (Sweden)

İhsan Ömür Bucak

2010-03-01

Full Text Available In the automotive industry, electromagnetic variable reluctance (VR sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion.

OPTIMIZING THE PLACEMENT OF A WORK-PIECE AT A MULTI-POSITION ROTARY TABLE OF TRANSFER MACHINE WITH VERTICAL MULTI-SPINDLE HEAD

Directory of Open Access Journals (Sweden)

N. N. Guschinski

2015-01-01

Full Text Available The problem of minimizing the weight of transfer machine with a multi-position rotary table by placing of a work-piece at the table for processing of homogeneous batch of work-pieces is considered. To solve this problem the mathematical model and heuristic particle swarm optimization algorithm are proposed. The results of numerical experiments for two real problems of this type are given. The experiments revealed that the particle swarm optimization algorithm is more effective for the solution of the problem compared to the methods of random search and LP-search.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

Science.gov (United States)

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

International Nuclear Information System (INIS)

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.

2015-01-01

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

A modern automatic Carriage-Trolley Position Control System for Dhruva fuelling machine

International Nuclear Information System (INIS)

Agrawal, Ankit; Hari Balakrishna; Narvekar, J.P.; Sanadhya, Vivek

2014-01-01

A fully automatic absolute encoder based position control system has been designed developed implemented and commissioned for the Dhruva Fuelling Machine A (FM/A). This supports both the coarse and fine positioning modes. Provision for fully manual positioning as a standby system has been retained. This system replaces the ageing peg counting based incremental positioner used briefly during the early period after the Dhruva FM/A was commissioned. The older system suffered from peg detection skipping problems; hence it was not being used. Only full manual positioning was being carried out. This paper describes the automatic Carriage Trolley Position Control System (CTPCS). (author)

Adaptation response of Arabidopsis thaliana to random positioning

Science.gov (United States)

Kittang, A.-I.; Winge, P.; van Loon, J. J. W. A.; Bones, A. M.; Iversen, T.-H.

2013-10-01

Arabidopsis thaliana seedlings were exposed on a Random Positioning Machine (RPM) under light conditions for 16 h and the samples were analysed using microarray techniques as part of a preparation for a space experiment on the International Space Station (ISS). The results demonstrated a moderate to low regulation of 55 genes (genes). Genes encoding proteins associated with the chaperone system (e.g. heat shock proteins, HSPs) and enzymes in the flavonoid biosynthesis were induced. Most of the repressed genes were associated with light and sugar responses. Significant up-regulation of selected HSP genes was found by quantitative Real-Time PCR in 1 week old plants after the RPM exposure both in light and darkness. Higher quantity of DPBA (diphenylboric acid 2-amino-ethyl ester) staining was observed in the whole root and in the root elongation zone of the seedlings exposed on the RPM by use of fluorescent microscopy, indicating higher flavonoid content. The regulated genes and an increase of flavonoids are related to several stresses, but increased occurrence of HSPs and flavonoids are also representative for normal growth (e.g. gravitropism). The response could be a direct stress response or an integrated response of the two signal pathways of light and gravity resulting in an overall light response.

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.

High Frequency Voltage Injection Methods and Observer Design for Initial Position Detection of Permanent Magnet Synchronous Machines

DEFF Research Database (Denmark)

Jin, Xinhai; Ni, Ronggang; Chen, Wei

2018-01-01

The information of the initial rotor position is essential for smooth start up and robust control of Permanent Magnet Synchronous Machines (PMSMs). RoTating Voltage Injection (RTVI) methods in the stationary reference frame have been commonly adopted to detect the initial rotor position at stands......The information of the initial rotor position is essential for smooth start up and robust control of Permanent Magnet Synchronous Machines (PMSMs). RoTating Voltage Injection (RTVI) methods in the stationary reference frame have been commonly adopted to detect the initial rotor position...

Random Access for Machine-Type Communication based on Bloom Filtering

DEFF Research Database (Denmark)

Pratas, Nuno; Stefanovic, Cedomir; Madueño, Germán Corrales

2016-01-01

utilizes the system resources more efficiently and achieves similar or lower latency of connection establishment in case of synchronous arrivals, compared to the variant of the LTE-A access protocol that is optimized for MTC traffic. A dividend of the proposed method is that allows the base station (BS......We present a random access method inspired on Bloom filters that is suited for Machine-Type Communications (MTC). Each accessing device sends a signature during the contention process. A signature is constructed using the Bloom filtering method and contains information on the device identity...... and the connection establishment cause. We instantiate the proposed method over the current LTE-A access protocol. However, the method is applicable to a more general class of random access protocols that use preambles or other reservation sequences, as expected to be the case in 5G systems. We show that our method...

The influence of negative training set size on machine learning-based virtual screening.

Science.gov (United States)

Kurczab, Rafał; Smusz, Sabina; Bojarski, Andrzej J

2014-01-01

The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

Science.gov (United States)

Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

2011-01-01

Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting

Science.gov (United States)

Yu, Pao-Shan; Yang, Tao-Chang; Chen, Szu-Yin; Kuo, Chen-Min; Tseng, Hung-Wei

2017-09-01

This study aims to compare two machine learning techniques, random forests (RF) and support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time radar-derived rainfall forecasting models use the present grid-based radar-derived rainfall as the output variable and use antecedent grid-based radar-derived rainfall, grid position (longitude and latitude) and elevation as the input variables to forecast 1- to 3-h ahead rainfalls for all grids in a catchment. Grid-based radar-derived rainfalls of six typhoon events during 2012-2015 in three reservoir catchments of Taiwan are collected for model training and verifying. Two kinds of forecasting models are constructed and compared, which are single-mode forecasting model (SMFM) and multiple-mode forecasting model (MMFM) based on RF and SVM. The SMFM uses the same model for 1- to 3-h ahead rainfall forecasting; the MMFM uses three different models for 1- to 3-h ahead forecasting. According to forecasting performances, it reveals that the SMFMs give better performances than MMFMs and both SVM-based and RF-based SMFMs show satisfactory performances for 1-h ahead forecasting. However, for 2- and 3-h ahead forecasting, it is found that the RF-based SMFM underestimates the observed radar-derived rainfalls in most cases and the SVM-based SMFM can give better performances than RF-based SMFM.

MOUNTABILITY PARTS OF MACHINE WITH ROTATING SURFACE, FITTED WITH POSITIVE CLEARANCE

Directory of Open Access Journals (Sweden)

Zbigniew BUDNIAK

2014-06-01

Full Text Available In this paper demonstrates the conditions of automatic assembly the parts of machines with rotating surfaces, fitted with positive clearance. Determination of the general condition of asseblability allowed for designation of the acceptable relative displacement and torsion axle, combined parts on the mounting position. The designation of depending allowed for assess the technological capacity of the installation equipment. On the basis of this mathematical model was developed a computer program that allows to determine the effect of geometric, strength and dynamic parameters of the assembly process. The examples of results of numerical calculations are shown in the graphs

Friction-resilient position control for machine tools—Adaptive and sliding-mode methods compared

DEFF Research Database (Denmark)

Papageorgiou, Dimitrios; Blanke, Mogens; Niemann, Hans Henrik

2018-01-01

Robust trajectory tracking and increasing demand for high-accuracy tool positioning have motivated research in advanced control design for machine tools. State-of-the-art industry solutions employ cascades of Proportional (P) and Proportional-Integral (PI) controllers for closed-loop servo contro...

Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

OpenAIRE

Fu Yu; Mu Jiong; Duan Xu Liang

2016-01-01

By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research...

Random non-proportional fatigue tests with planar tri-axial fatigue testing machine

Directory of Open Access Journals (Sweden)

T. Inoue

2016-10-01

Full Text Available Complex stresses, which occur on the mechanical surfaces of transport machinery in service, bring a drastic degradation in fatigue life. However, it is hard to reproduce such complex stress states for evaluating the fatigue life with conventional multiaxial fatigue machines. We have developed a fatigue testing machine that enables reproduction of such complex stresses. The testing machine can reproduce arbitrary in-plane stress states by applying three independent loads to the test specimen using actuators which apply loads in the 0, 45, and 90 degree directions. The reproduction was tested with complex stress data obtained from the actual operation of transport machinery. As a result, it was found that the reproduced stress corresponded to the measured stress with an error range of less than 10 %. Then, we made a comparison between measured fatigue lives under random non-proportional loading conditions and predicted fatigue lives. It was found that predicted fatigue lives with cr, stress on critical plane, were over a factor of 10 against measured fatigue lives. On the other hand, predicted fatigue lives with ma, stress in consideration of a non-proportional level evaluated by using amplitude and direction of principal stress, were within a factor of 3 against measured fatigue lives

Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

Directory of Open Access Journals (Sweden)

Shahid Ikramullah Butt

2017-01-01

Full Text Available Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

An integrated production inventory model of deteriorating items subject to random machine breakdown with a stochastic repair time

Directory of Open Access Journals (Sweden)

Huynh Trung Luong

2016-11-01

Full Text Available In a continuous manufacturing environment where production and consumption occur simultaneously, one of the biggest challenges is the efficient management of production and inventory system. In order to manage the integrated production inventory system economically it is necessary to identify the optimal production time and the optimal production reorder point that either maximize the profit or minimize the cost. In addition, during production the process has to go through some natural phenomena like random breakdown of machine, deterioration of product over time, uncertainty in repair time that eventually create the possibility of shortage. In this situation, efficient management of inventory & production is crucial. This paper addresses the situation where a perishable (deteriorated product is manufactured and consumed simultaneously, the demand of this product is stable over the time, machine that produce the product also face random failure and the time to repair this machine is also uncertain. In order to describe this scenario more appropriately, the continuously reviewed Economic Production Quantity (EPQ model is considered in this research work. The main goal is to identify the optimal production uptime and the production reorder point that ultimately minimize the expected value of total cost consisting of machine setup, deterioration, inventory holding, shortage and corrective maintenance cost.

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors.

Science.gov (United States)

Zemp, Roland; Tanadini, Matteo; Plüss, Stefan; Schnüriger, Karin; Singh, Navrag B; Taylor, William R; Lorenzetti, Silvio

2016-01-01

Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

Machine-learning techniques for family demography: an application of random forests to the analysis of divorce determinants in Germany

OpenAIRE

Arpino, Bruno; Le Moglie, Marco; Mencarini, Letizia

2018-01-01

Demographers often analyze the determinants of life-course events with parametric regression-type approaches. Here, we present a class of nonparametric approaches, broadly defined as machine learning (ML) techniques, and discuss advantages and disadvantages of a popular type known as random forest. We argue that random forests can be useful either as a substitute, or a complement, to more standard parametric regression modeling. Our discussion of random forests is intuitive and...

Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

Directory of Open Access Journals (Sweden)

Fu Yu

2016-01-01

Full Text Available By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research thinking concerning extreme learning machine into the economics classification area so as to fulfill the purpose of computerizing the speedy but effective evaluation of massive financial statements of listed companies pertain to different classes

Simulated Microgravity Regulates Gene Transcript Profiles of 2T3 Preosteoblasts: Comparison of the Random Positioning Machine and the Rotating Wall Vessel Bioreactor

Science.gov (United States)

Patel, Mamta J.; Liu, Wenbin; Sykes, Michelle C.; Ward, Nancy E.; Risin, Semyon A.; Risin, Diana; Hanjoong, Jo

2007-01-01

Microgravity of spaceflight induces bone loss due in part to decreased bone formation by osteoblasts. We have previously examined the microgravity-induced changes in gene expression profiles in 2T3 preosteoblasts using the Random Positioning Machine (RPM) to simulate microgravity conditions. Here, we hypothesized that exposure of preosteoblasts to an independent microgravity simulator, the Rotating Wall Vessel (RWV), induces similar changes in differentiation and gene transcript profiles, resulting in a more confined list of gravi-sensitive genes that may play a role in bone formation. In comparison to static 1g controls, exposure of 2T3 cells to RWV for 3 days inhibited alkaline phosphatase activity, a marker of differentiation, and downregulated 61 genes and upregulated 45 genes by more than two-fold as shown by microarray analysis. The microarray results were confirmed with real time PCR for downregulated genes osteomodulin, bone morphogenic protein 4 (BMP4), runx2, and parathyroid hormone receptor 1. Western blot analysis validated the expression of three downregulated genes, BMP4, peroxiredoxin IV, and osteoglycin, and one upregulated gene peroxiredoxin I. Comparison of the microarrays from the RPM and the RWV studies identified 14 gravi-sensitive genes that changed in the same direction in both systems. Further comparison of our results to a published database showing gene transcript profiles of mechanically loaded mouse tibiae revealed 16 genes upregulated by the loading that were shown to be downregulated by RWV and RPM. These mechanosensitive genes identified by the comparative studies may provide novel insights into understanding the mechanisms regulating bone formation and potential targets of countermeasure against decreased bone formation both in astronauts and in general patients with musculoskeletal disorders.

Nuclear reactor machine refuelling system

International Nuclear Information System (INIS)

Cashen, W.S.; Erwin, D.

1977-01-01

Part of an on-line fuelling machine for a CANDU pressure-tube reactor is described. The present invention provides a refuelling machine wherein the fuelling components, including the fuel carrier and the closure adapter, are positively positioned and retained within the machine magazine or positively secured to the machine charge tube head, and cannot be accidentally disengaged as in former practice. The positive positioning devices include an arcuate keeper plate. Simplified hooked fingers are used. (NDH)

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

Directory of Open Access Journals (Sweden)

Roland Zemp

2016-01-01

Full Text Available Occupational musculoskeletal disorders, particularly chronic low back pain (LBP, are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user’s sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest. Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples. Sixteen force sensor values and the backrest angle were used as the explanatory variables (features for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

Impact of implant–abutment connection and positioning of the machined collar/microgap on crestal bone level changes: a systematic review

Science.gov (United States)

Schwarz, Frank; Hegewald, Andrea; Becker, Jürgen

2014-01-01

Objectives To address the following focused question: What is the impact of implant–abutment configuration and the positioning of the machined collar/microgap on crestal bone level changes? Material and methods Electronic databases of the PubMed and the Web of Knowledge were searched for animal and human studies reporting on histological/radiological crestal bone level changes (CBL) at nonsubmerged one-/two-piece implants (placed in healed ridges) exhibiting different abutment configurations, positioning of the machined collar/microgap (between 1992 and November 2012: n = 318 titles). Quality assessment of selected full-text articles was performed according to the ARRIVE and CONSORT statement guidelines. Results A total of 13 publications (risk of bias: high) were eligible for the review. The weighted mean difference (WMD) (95% CI) between machined collars placed either above or below the bone crest amounted to 0.835 mm favoring an epicrestal positioning of the rough/smooth border (P abutment configurations. Due to a high heterogeneity, a meta-analysis was not feasible. Conclusions While the positioning of the machined neck and microgap may limit crestal bone level changes at nonsubmerged implants, the impact of the implant–abutment connection lacks documentation. PMID:23782338

A randomized controlled trial of positioning treatments in infants with positional head shape deformities.

Science.gov (United States)

Hutchison, B Lynne; Stewart, Alistair W; De Chalain, Tristan B; Mitchell, Edwin A

2010-10-01

Randomized controlled trials of treatment for deformational plagiocephaly and brachycephaly have been lacking in the literature. Infants (n = 126) presenting to a plagiocephaly clinic were randomized to either positioning strategies or to positioning plus the use of a Safe T Sleep™ positioning wrap. Head shape was measured using a digital photographic technique, and neck function was assessed. They were followed up at home 3, 6 and 12 months later. There was no difference in head shape outcomes for the two treatment groups after 12 months of follow-up, with 42% of infants having head shapes in the normal range by that time. Eighty per cent of children showed good improvement. Those that had poor improvement were more likely to have both plagiocephaly and brachycephaly and to have presented later to clinic. Most infants improved over the 12-month study period, although the use of a sleep positioning wrap did not increase the rate of improvement. © 2010 The Author(s)/Journal Compilation © 2010 Foundation Acta Paediatrica.

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

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.

A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

Science.gov (United States)

Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

2017-08-01

Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

A randomized, controlled intervention of machine guarding and related safety programs in small metal-fabrication businesses.

Science.gov (United States)

Parker, David L; Brosseau, Lisa M; Samant, Yogindra; Xi, Min; Pan, Wei; Haugan, David

2009-01-01

Metal fabrication employs an estimated 3.1 million workers in the United States. The absence of machine guarding and related programs such as lockout/tagout may result in serious injury or death. The purpose of this study was to improve machine-related safety in small metal-fabrication businesses. We used a randomized trial with two groups: management only and management-employee. We evaluated businesses for the adequacy of machine guarding (machine scorecard) and related safety programs (safety audit). We provided all businesses with a report outlining deficiencies and prioritizing their remediation. In addition, the management-employee group received four one-hour interactive training sessions from a peer educator. We evaluated 40 metal-fabrication businesses at baseline and 37 (93%) one year later. Of the three nonparticipants, two had gone out of business. More than 40% of devices required for adequate guarding were missing or inadequate, and 35% of required safety programs and practices were absent at baseline. Both measures improved significantly during the course of the intervention. No significant differences in changes occurred between the two intervention groups. Machine-guarding practices and programs improved by up to 13% and safety audit scores by up to 23%. Businesses that added safety committees or those that started with the lowest baseline measures showed the greatest improvements. Simple and easy-to-use assessment tools allowed businesses to significantly improve their safety practices, and safety committees facilitated this process.

Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening.

Science.gov (United States)

Heikamp, Kathrin; Bajorath, Jürgen

2013-07-22

The choice of negative training data for machine learning is a little explored issue in chemoinformatics. In this study, the influence of alternative sets of negative training data and different background databases on support vector machine (SVM) modeling and virtual screening has been investigated. Target-directed SVM models have been derived on the basis of differently composed training sets containing confirmed inactive molecules or randomly selected database compounds as negative training instances. These models were then applied to search background databases consisting of biological screening data or randomly assembled compounds for available hits. Negative training data were found to systematically influence compound recall in virtual screening. In addition, different background databases had a strong influence on the search results. Our findings also indicated that typical benchmark settings lead to an overestimation of SVM-based virtual screening performance compared to search conditions that are more relevant for practical applications.

High-speed ultrafast laser machining with tertiary beam positioning (Conference Presentation)

Science.gov (United States)

Yang, Chuan; Zhang, Haibin

2017-03-01

For an industrial laser application, high process throughput and low average cost of ownership are critical to commercial success. Benefiting from high peak power, nonlinear absorption and small-achievable spot size, ultrafast lasers offer advantages of minimal heat affected zone, great taper and sidewall quality, and small via capability that exceeds the limits of their predecessors in via drilling for electronic packaging. In the past decade, ultrafast lasers have both grown in power and reduced in cost. For example, recently, disk and fiber technology have both shown stable operation in the 50W to 200W range, mostly at high repetition rate (beyond 500 kHz) that helps avoid detrimental nonlinear effects. However, to effectively and efficiently scale the throughput with the fast-growing power capability of the ultrafast lasers while keeping the beneficial laser-material interactions is very challenging, mainly because of the bottleneck imposed by the inertia-related acceleration limit and servo gain bandwidth when only stages and galvanometers are being used. On the other side, inertia-free scanning solutions like acoustic optics and electronic optical deflectors have small scan field, and therefore not suitable for large-panel processing. Our recent system developments combine stages, galvanometers, and AODs into a coordinated tertiary architecture for high bandwidth and meanwhile large field beam positioning. Synchronized three-level movements allow extremely fast local speed and continuous motion over the whole stage travel range. We present the via drilling results from such ultrafast system with up to 3MHz pulse to pulse random access, enabling high quality low cost ultrafast machining with emerging high average power laser sources.

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.

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.

Resistance Training using Low Cost Elastic Tubing is Equally Effective to Conventional Weight Machines in Middle-Aged to Older Healthy Adults: A Quasi-Randomized Controlled Clinical Trial.

Science.gov (United States)

Lima, Fabiano F; Camillo, Carlos A; Gobbo, Luis A; Trevisan, Iara B; Nascimento, Wesley B B M; Silva, Bruna S A; Lima, Manoel C S; Ramos, Dionei; Ramos, Ercy M C

2018-03-01

The objectives of the study were to compare the effects of resistance training using either a low cost and portable elastic tubing or conventional weight machines on muscle force, functional exercise capacity, and health-related quality of life (HRQOL) in middle-aged to older healthy adults. In this clinical trial twenty-nine middle-aged to older healthy adults were randomly assigned to one of the three groups a priori defined: resistance training with elastic tubing (ETG; n = 10), conventional resistance training (weight machines) (CTG; n = 9) and control group (CG, n = 10). Both ETG and CTG followed a 12-week resistance training (3x/week - upper and lower limbs). Muscle force, functional exercise capacity and HRQOL were evaluated at baseline, 6 and 12 weeks. CG underwent the three evaluations with no formal intervention or activity counseling provided. ETG and CTG increased similarly and significantly muscle force (Δ16-44% in ETG and Δ25-46% in CTG, p tubing (a low cost and portable tool) and conventional resistance training using weight machines promoted similar positive effects on peripheral muscle force and functional exercise capacity in middle-aged to older healthy adults.

Exponential Inequalities for Positively Associated Random Variables and Applications

Directory of Open Access Journals (Sweden)

Yang Shanchao

2008-01-01

Full Text Available Abstract We establish some exponential inequalities for positively associated random variables without the boundedness assumption. These inequalities improve the corresponding results obtained by Oliveira (2005. By one of the inequalities, we obtain the convergence rate for the case of geometrically decreasing covariances, which closes to the optimal achievable convergence rate for independent random variables under the Hartman-Wintner law of the iterated logarithm and improves the convergence rate derived by Oliveira (2005 for the above case.

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts.

Science.gov (United States)

Fernandes, Henrique; Zhang, Hai; Figueiredo, Alisson; Malheiros, Fernando; Ignacio, Luis Henrique; Sfarra, Stefano; Ibarra-Castanedo, Clemente; Guimaraes, Gilmar; Maldague, Xavier

2018-01-19

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

Self-organization through random input by biological and machine systems - the pre-cognition sub-system

International Nuclear Information System (INIS)

Tahir Shah, K.

1981-04-01

We give an axiomatic prescription for self-organization in the brain and in intelligent machines through random input of data. This self-organization leads to the formation of pre-cognition long term memory (LTM) subsystem. By using the notions of p-equivalent and its negation instead of true and false in the predicate calculus and pre-cognition LTM, a method is proposed for pattern recognition which can also be utilized for studying relations between the genetic code and the observed properties of respective species. (author)

Randomized Trial of Asprin as Adjuvant Therapy for Node-Positive Breast Cancer

Science.gov (United States)

2017-10-01

AWARD NUMBER: W81XWH-15-1-0268 TITLE: Randomized Trial of Asprin as Adjuvant Therapy for Node-Positive Breast Cancer PRINCIPAL INVESTIGATOR...Eric Winer CONTRACTING ORGANIZATION: Dana-Farber Cancer Institute Boston, MA 02215 REPORT DATE: OCTOBER 2017 TYPE OF REPORT: ANNUAL PREPARED FOR...CONTRACT NUMBER Randomized Trial of Asprin as Adjuvant Therapy for Node- Positive Breast Cancer 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts

Science.gov (United States)

Maldague, Xavier

2018-01-01

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed. PMID:29351240

Positive random fields for modeling material stiffness and compliance

DEFF Research Database (Denmark)

Hasofer, Abraham Michael; Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob

1998-01-01

Positive random fields with known marginal properties and known correlation function are not numerous in the literature. The most prominent example is the log\\-normal field for which the complete distribution is known and for which the reciprocal field is also lognormal. It is of interest to supp...

Maintenance strategies to reduce downtime due to machine positional errors

OpenAIRE

Shagluf, Abubaker; Longstaff, A.P.; Fletcher, S.

2014-01-01

Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014 Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competentely and similiarly identify the machines performance. Total productive maintenance (TPM) is a maintenance progr...

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Science.gov (United States)

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

Machine Learning Techniques for Prediction of Early Childhood Obesity.

Science.gov (United States)

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

Science.gov (United States)

Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

2016-05-18

Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Science.gov (United States)

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Prone position as prevention of lung injury in comatose patients: a prospective, randomized, controlled study.

Science.gov (United States)

Beuret, Pascal; Carton, Marie-Jose; Nourdine, Karim; Kaaki, Mahmoud; Tramoni, Gerard; Ducreux, Jean-Claude

2002-05-01

Comatose patients frequently exhibit pulmonary function worsening, especially in cases of pulmonary infection. It appears to have a deleterious effect on neurologic outcome. We therefore conducted a randomized trial to determine whether daily prone positioning would prevent lung worsening in these patients. Prospective, randomized, controlled study. Sixteen-bed intensive care unit. Fifty-one patients who required invasive mechanical ventilation because of coma with Glascow coma scores of 9 or less. In the prone position (PP) group: prone positioning for 4 h once daily until the patients could get up to sit in an armchair; in the supine position (SP) group: supine positioning. The primary end point was the incidence of lung worsening defined by an increase in the Lung Injury Score of at least 1 point since the time of randomization. The secondary end point was the incidence of ventilator-associated pneumonia (VAP). A total of 25 patients were randomly assigned to the PP group and 26 patients to the SP group. The characteristics of the patients from the two groups were similar at randomization. The incidence of lung worsening was lower in the PP group (12%) than in the SP group (50%) ( p=0.003). The incidence of VAP was 20% in the PP group and 38.4% in the SP group ( p=0.14). There was no serious complication attributable to prone positioning, however, there was a significant increase of intracranial pressure in the PP. In a selected population of comatose ventilated patients, daily prone positioning reduced the incidence of lung worsening.

Randomized Prediction Games for Adversarial Machine Learning.

Science.gov (United States)

Rota Bulo, Samuel; Biggio, Battista; Pillai, Ignazio; Pelillo, Marcello; Roli, Fabio

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different

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).

comparative study of moore and mealy machine models adaptation

African Journals Online (AJOL)

user

automata model was developed for ABS manufacturing process using Moore and Mealy Finite State Machines. Simulation ... The simulation results showed that the Mealy Machine is faster than the Moore ..... random numbers from MATLAB.

Coded Random Access

DEFF Research Database (Denmark)

Paolini, Enrico; Stefanovic, Cedomir; Liva, Gianluigi

2015-01-01

The rise of machine-to-machine communications has rekindled the interest in random access protocols as a support for a massive number of uncoordinatedly transmitting devices. The legacy ALOHA approach is developed under a collision model, where slots containing collided packets are considered as ...

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

Science.gov (United States)

Marques, Yuri Bento; de Paiva Oliveira, Alcione; Ribeiro Vasconcelos, Ana Tereza; Cerqueira, Fabio Ribeiro

2016-12-15

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

Modeling Music Emotion Judgments Using Machine Learning Methods

Directory of Open Access Journals (Sweden)

Naresh N. Vempala

2018-01-01

Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

Sensorless Control of Permanent Magnet Synchronous Machines

DEFF Research Database (Denmark)

Matzen, Torben N.

Permanent magnet machines, with either surface mounted or embedded magnets on the rotor, are becoming more common due to the key advantages of higher energy conversion efficiency and higher torque density compared to the classical induction machine. Besides energy efficiency the permanent magnet...... the synchronous machine requires knowledge of the rotor shaft position due to the synchronous and undamped nature of the machine. The rotor position may be measured using a mechanical sensor, but the sensor reduces reliability and adds cost to the system and for this reason sensorless control methods started...... are dependent on the phase currents and rotor position. Based on the flux linkages the differential inductances are determined and used to establish the inductance saliency in terms of ratio and orientation. The orientation and its dependence on the current and rotor position are used to analyse the behaviour...

Optimizing block-based maintenance under random machine usage

NARCIS (Netherlands)

de Jonge, Bram; Jakobsons, Edgars

Existing studies on maintenance optimization generally assume that machines are either used continuously, or that times until failure do not depend on the actual usage. In practice, however, these assumptions are often not realistic. In this paper, we consider block-based maintenance optimization

Quantum tunneling recombination in a system of randomly distributed trapped electrons and positive ions.

Science.gov (United States)

Pagonis, Vasilis; Kulp, Christopher; Chaney, Charity-Grace; Tachiya, M

2017-09-13

During the past 10 years, quantum tunneling has been established as one of the dominant mechanisms for recombination in random distributions of electrons and positive ions, and in many dosimetric materials. Specifically quantum tunneling has been shown to be closely associated with two important effects in luminescence materials, namely long term afterglow luminescence and anomalous fading. Two of the common assumptions of quantum tunneling models based on random distributions of electrons and positive ions are: (a) An electron tunnels from a donor to the nearest acceptor, and (b) the concentration of electrons is much lower than that of positive ions at all times during the tunneling process. This paper presents theoretical studies for arbitrary relative concentrations of electrons and positive ions in the solid. Two new differential equations are derived which describe the loss of charge in the solid by tunneling, and they are solved analytically. The analytical solution compares well with the results of Monte Carlo simulations carried out in a random distribution of electrons and positive ions. Possible experimental implications of the model are discussed for tunneling phenomena in long term afterglow signals, and also for anomalous fading studies in feldspars and apatite samples.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

Science.gov (United States)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

Science.gov (United States)

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

2015-01-01

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

Random non-proportional fatigue tests with planar tri-axial fatigue testing machine

OpenAIRE

Inoue, T.; Nagao, R.; Takeda, N.

2016-01-01

Complex stresses, which occur on the mechanical surfaces of transport machinery in service, bring a drastic degradation in fatigue life. However, it is hard to reproduce such complex stress states for evaluating the fatigue life with conventional multiaxial fatigue machines. We have developed a fatigue testing machine that enables reproduction of such complex stresses. The testing machine can reproduce arbitrary in-plane stress states by applying three independent loads to the test specimen u...

Operating System For Numerically Controlled Milling Machine

Science.gov (United States)

Ray, R. B.

1992-01-01

OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

Joint optimization of production scheduling and machine group preventive maintenance

International Nuclear Information System (INIS)

Xiao, Lei; Song, Sanling; Chen, Xiaohui; Coit, David W.

2016-01-01

Joint optimization models were developed combining group preventive maintenance of a series system and production scheduling. In this paper, we propose a joint optimization model to minimize the total cost including production cost, preventive maintenance cost, minimal repair cost for unexpected failures and tardiness cost. The total cost depends on both the production process and the machine maintenance plan associated with reliability. For the problems addressed in this research, any machine unavailability leads to system downtime. Therefore, it is important to optimize the preventive maintenance of machines because their performance impacts the collective production processing associated with all machines. Too lengthy preventive maintenance intervals may be associated with low scheduled machine maintenance cost, but may incur expensive costs for unplanned failure due to low machine reliability. Alternatively, too frequent preventive maintenance activities may achieve the desired high reliability machines, but unacceptably high scheduled maintenance cost. Additionally, product scheduling plans affect tardiness and maintenance cost. Two results are obtained when solving the problem; the optimal group preventive maintenance interval for machines, and the assignment of each job, including the corresponding start time and completion time. To solve this non-deterministic polynomial-time problem, random keys genetic algorithms are used, and a numerical example is solved to illustrate the proposed model. - Highlights: • Group preventive maintenance (PM) planning and production scheduling are jointed. • Maintenance interval and assignment of jobs are decided by minimizing total cost. • Relationships among system age, PM, job processing time are quantified. • Random keys genetic algorithms (GA) are used to solve the NP-hard problem. • Random keys GA and Particle Swarm Optimization (PSO) are compared.

ANALYSIS OF PARAMETERS AFFECTING THE QUALITY OF A CUTTING MACHINE

Directory of Open Access Journals (Sweden)

Iveta Onderová

2014-02-01

Full Text Available The quality of cutting machines is affected by several factors that can be directly or indirectly influenced by manufacturers, technicians and users of machine tools. The most critical qualitative evaluation parameters of machine tools include accuracy and stability. Investigations of accuracy and repeatable positioning accuracy were essential for the research presented in this paper. The aim was to develop and experimentally verify the design of a methodology for cutting centers aimed at achieving the desired working precision. Before working on the topic described here, it was necessary to make several scientific analyses, which are summarized in this paper. We can build on the initial working hypothesis that by improving the technological parameters (e.g. by increasing the working speed of the machine, or by improving the precision of the positioning the quality of the cutting machine will also be improved. For the purposes of our study, several investigated parameters were set affecting positioning accuracy, such as rigidity, positioning speed, etc. First, the stiffness of the portal structure of the cutting machine was analyzed. FEM analysis was used to investigate several alternative structures of the cutting machine, and also an innovative solution for beam mounting. The second step was to integrate two types of drives into the design of the cutting machine. The first drive is a classic rack and pinion drive for cutting machines. To increase (improve the working speed of the machine, linear motors were designed as an alternative drive. The portal of the cutting machine was designed for a working speed of 260mmin−1 and acceleration of 25 m. s−2. The third step was based on the results of the analysis. In collaboration with Microstep, an experimental cutting machine in a portal version was produced using linear synchronous motors driving the portal on both sides, and with direct linear metering of its position. In the fourth step, an

Precision mechatronics based on high-precision measuring and positioning systems and machines

Science.gov (United States)

Jäger, Gerd; Manske, Eberhard; Hausotte, Tino; Mastylo, Rostyslav; Dorozhovets, Natalja; Hofmann, Norbert

2007-06-01

Precision mechatronics is defined in the paper as the science and engineering of a new generation of high precision systems and machines. Nanomeasuring and nanopositioning engineering represents important fields of precision mechatronics. The nanometrology is described as the today's limit of the precision engineering. The problem, how to design nanopositioning machines with uncertainties as small as possible will be discussed. The integration of several optical and tactile nanoprobes makes the 3D-nanopositioning machine suitable for various tasks, such as long range scanning probe microscopy, mask and wafer inspection, nanotribology, nanoindentation, free form surface measurement as well as measurement of microoptics, precision molds, microgears, ring gauges and small holes.

Gram staining with an automatic machine.

Science.gov (United States)

Felek, S; Arslan, A

1999-01-01

This study was undertaken to develop a new Gram-staining machine controlled by a micro-controller and to investigate the quality of slides that were stained in the machine. The machine was designed and produced by the authors. It uses standard 220 V AC. Staining, washing, and drying periods are controlled by a timer built in the micro-controller. A software was made that contains a certain algorithm and time intervals for the staining mode. One-hundred and forty smears were prepared from Escherichia coli, Staphylococcus aureus, Neisseria sp., blood culture, trypticase soy broth, direct pus and sputum smears for comparison studies. Half of the slides in each group were stained with the machine, the other half by hand and then examined by four different microbiologists. Machine-stained slides had a higher clarity and less debris than the hand-stained slides (p stained slides, some Gram-positive organisms showed poor Gram-positive staining features (p Gram staining with the automatic machine increases the staining quality and helps to decrease the work load in a busy diagnostic laboratory.

The signature of positive selection at randomly chosen loci.

OpenAIRE

Przeworski, Molly

2002-01-01

In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequ...

Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

Directory of Open Access Journals (Sweden)

Ivana Šemanjski

2015-12-01

Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

Reactor refueling machine simulator

International Nuclear Information System (INIS)

Rohosky, T.L.; Swidwa, K.J.

1987-01-01

This patent describes in combination: a nuclear reactor; a refueling machine having a bridge, trolley and hoist each driven by a separate motor having feedback means for generating a feedback signal indicative of movement thereof. The motors are operable to position the refueling machine over the nuclear reactor for refueling the same. The refueling machine also has a removable control console including means for selectively generating separate motor signals for operating the bridge, trolley and hoist motors and for processing the feedback signals to generate an indication of the positions thereof, separate output leads connecting each of the motor signals to the respective refueling machine motor, and separate input leads for connecting each of the feedback means to the console; and a portable simulator unit comprising: a single simulator motor; a single simulator feedback signal generator connected to the simulator motor for generating a simulator feedback signal in response to operation of the simulator motor; means for selectively connecting the output leads of the console to the simulator unit in place of the refueling machine motors, and for connecting the console input leads to the simulator unit in place of the refueling machine motor feedback means; and means for driving the single simulator motor in response to any of the bridge, trolley or hoist motor signals generated by the console and means for applying the simulator feedback signal to the console input lead associated with the motor signal being generated by the control console

Sensorless Characteristics of Hybrid PM Machines at Zero and Low Speed

DEFF Research Database (Denmark)

Matzen, Torben N.; Rasmussen, Peter Omand

2009-01-01

Sensorless methods for zero and low speed operation in drives with hybrid PM machines make use of the machine saliency to determine the rotor position in an indirect fashion. When integrating the position measurement in the electrical power supply to the machine, i.e. make the machine self......-sensing, the sensorless obtained position can be affected by the actual operation conditions of the machine e.g. the stator currents. This may deteriorate the machine self-sensing suitability using injection methods. In this paper an analysis method based on accurate knowledge of the machine flux linkages is proposed...... for analysing the suitability for sensorless control at zero and low speed. The method can be used to evaluate a particular machine design so the self-sensing characteristics for sensorless control of machine can be found. The characteristics can be obtained from finite element simulation data or experimental...

Machine learning models in breast cancer survival prediction.

Science.gov (United States)

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

Efficiency of the modified Sims maternal position in the rotation of persistent occiput posterior position during labor: A randomized clinical trial.

Science.gov (United States)

Bueno-Lopez, Vanessa; Fuentelsaz-Gallego, Carmen; Casellas-Caro, Manel; Falgueras-Serrano, Ana Maria; Crespo-Berros, Silvia; Silvano-Cocinero, Ana Maria; Alcaine-Guisado, Carolina; Zamoro Fuentes, Manuela; Carreras, Elena; Terré-Rull, Carmen

2018-03-14

Fetal occiput posterior position in labor is associated with more painful and prolonged labor, and an increase in both maternal and fetal morbidity. The aim of this study is to assess whether the modified Sims position on the side of the fetal spine increases the rotation to occiput anterior position in women with epidural analgesia and a fetus in persistent occiput posterior (POP) position. This is an open, randomized controlled, clinical trial. One hundred and twenty women in labor with fetuses in POP position were included. The diagnosis was performed through digital vaginal examination and confirmed with an ultrasound scan. Women were randomized into the free position group or the modified Sims on the side of the fetal spine. The primary outcome was rotation to occiput anterior, and secondary outcomes were type of delivery, postpartum perineal condition, perinatal results, and maternal satisfaction. In pregnant women undergoing labor in the Sims position, fetuses in POP rotated to occiput anterior in 50.8% of cases, whilst in the free position group, the rotation occurred in 21.7% (P = .001). The rate of vaginal deliveries was higher in the Sims group compared with the free position group (84.7% vs 68.3%, P = .035). The modified Sims position is a maternal posture intervention efficient in POP rotation, which decreases cesarean delivery rate. It is a simple and noninvasive intervention, reproducible, and well tolerated by pregnant women. © 2018 Wiley Periodicals, Inc.

Automated system of monitoring and positioning of functional units of mining technological machines for coal-mining enterprises

Directory of Open Access Journals (Sweden)

Meshcheryakov Yaroslav

2018-01-01

Full Text Available This article is show to the development of an automated monitoring and positioning system for functional nodes of mining technological machines. It describes the structure, element base, algorithms for identifying the operating states of a walking excavator; various types of errors in the functioning of microelectromechanical gyroscopes and accelerometers, as well as methods for their correction based on the Madgwick fusion filter. The results of industrial tests of an automated monitoring and positioning system for functional units on one of the opencast coal mines of Kuzbass are presented. This work is addressed to specialists working in the fields of the development of embedded systems and control systems, radio electronics, mechatronics, and robotics.

Machine Learning Techniques for Arterial Pressure Waveform Analysis

Directory of Open Access Journals (Sweden)

João Cardoso

2013-05-01

Full Text Available The Arterial Pressure Waveform (APW can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1 a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2 the acquired position and amplitude of onset, Systolic Peak (SP, Point of Inflection (Pi and Dicrotic Wave (DW were used for the computation of some morphological attributes; (3 pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4 classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic, J48 (decision tree and RIPPER (rule-based induction; and (5 we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx. Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95% and high area under the curve (AUC of a Receiver Operating Characteristic (ROC curve (0.961. Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

Expedite random structure searching using objects from Wyckoff positions

Science.gov (United States)

Wang, Shu-Wei; Hsing, Cheng-Rong; Wei, Ching-Ming

2018-02-01

Random structure searching has been proved to be a powerful approach to search and find the global minimum and the metastable structures. A true random sampling is in principle needed yet it would be highly time-consuming and/or practically impossible to find the global minimum for the complicated systems in their high-dimensional configuration space. Thus the implementations of reasonable constraints, such as adopting system symmetries to reduce the independent dimension in structural space and/or imposing chemical information to reach and relax into low-energy regions, are the most essential issues in the approach. In this paper, we propose the concept of "object" which is either an atom or composed of a set of atoms (such as molecules or carbonates) carrying a symmetry defined by one of the Wyckoff positions of space group and through this process it allows the searching of global minimum for a complicated system to be confined in a greatly reduced structural space and becomes accessible in practice. We examined several representative materials, including Cd3As2 crystal, solid methanol, high-pressure carbonates (FeCO3), and Si(111)-7 × 7 reconstructed surface, to demonstrate the power and the advantages of using "object" concept in random structure searching.

Do warning signs on electronic gaming machines influence irrational cognitions?

Science.gov (United States)

Monaghan, Sally; Blaszczynski, Alex; Nower, Lia

2009-08-01

Electronic gaming machines are popular among problem gamblers; in response, governments have introduced "responsible gaming" legislation incorporating the mandatory display of warning signs on or near electronic gaming machines. These signs are designed to correct irrational and erroneous beliefs through the provision of accurate information on probabilities of winning and the concept of randomness. There is minimal empirical data evaluating the effectiveness of such signs. In this study, 93 undergraduate students were randomly allocated to standard and informative messages displayed on an electronic gaming machine during play in a laboratory setting. Results revealed that a majority of participants incorrectly estimated gambling odds and reported irrational gambling-related cognitions prior to play. In addition, there were no significant between-group differences, and few participants recalled the content of messages or modified their gambling-related cognitions. Signs placed on electronic gaming machines may not modify irrational beliefs or alter gambling behaviour.

Cluster-Randomized, Crossover Trial of Head Positioning in Acute Stroke.

Science.gov (United States)

Anderson, Craig S; Arima, Hisatomi; Lavados, Pablo; Billot, Laurent; Hackett, Maree L; Olavarría, Verónica V; Muñoz Venturelli, Paula; Brunser, Alejandro; Peng, Bin; Cui, Liying; Song, Lily; Rogers, Kris; Middleton, Sandy; Lim, Joyce Y; Forshaw, Denise; Lightbody, C Elizabeth; Woodward, Mark; Pontes-Neto, Octavio; De Silva, H Asita; Lin, Ruey-Tay; Lee, Tsong-Hai; Pandian, Jeyaraj D; Mead, Gillian E; Robinson, Thompson; Watkins, Caroline

2017-06-22

The role of supine positioning after acute stroke in improving cerebral blood flow and the countervailing risk of aspiration pneumonia have led to variation in head positioning in clinical practice. We wanted to determine whether outcomes in patients with acute ischemic stroke could be improved by positioning the patient to be lying flat (i.e., fully supine with the back horizontal and the face upwards) during treatment to increase cerebral perfusion. In a pragmatic, cluster-randomized, crossover trial conducted in nine countries, we assigned 11,093 patients with acute stroke (85% of the strokes were ischemic) to receive care in either a lying-flat position or a sitting-up position with the head elevated to at least 30 degrees, according to the randomization assignment of the hospital to which they were admitted; the designated position was initiated soon after hospital admission and was maintained for 24 hours. The primary outcome was degree of disability at 90 days, as assessed with the use of the modified Rankin scale (scores range from 0 to 6, with higher scores indicating greater disability and a score of 6 indicating death). The median interval between the onset of stroke symptoms and the initiation of the assigned position was 14 hours (interquartile range, 5 to 35). Patients in the lying-flat group were less likely than patients in the sitting-up group to maintain the position for 24 hours (87% vs. 95%, P<0.001). In a proportional-odds model, there was no significant shift in the distribution of 90-day disability outcomes on the global modified Rankin scale between patients in the lying-flat group and patients in the sitting-up group (unadjusted odds ratio for a difference in the distribution of scores on the modified Rankin scale in the lying-flat group, 1.01; 95% confidence interval, 0.92 to 1.10; P=0.84). Mortality within 90 days was 7.3% among the patients in the lying-flat group and 7.4% among the patients in the sitting-up group (P=0.83). There were

A Clustered Randomized Controlled Trial of the Positive Prevention PLUS Adolescent Pregnancy Prevention Program.

Science.gov (United States)

LaChausse, Robert G

2016-09-01

To determine the impact of Positive Prevention PLUS, a school-based adolescent pregnancy prevention program on delaying sexual intercourse, birth control use, and pregnancy. I randomly assigned a diverse sample of ninth grade students in 21 suburban public high schools in California into treatment (n = 2483) and control (n = 1784) groups that participated in a clustered randomized controlled trial. Between October 2013 and May 2014, participants completed baseline and 6-month follow-up surveys regarding sexual behavior and pregnancy. Participants in the treatment group were offered Positive Prevention PLUS, an 11-lesson adolescent pregnancy prevention program. The program had statistically significant impacts on delaying sexual intercourse and increasing the use of birth control. However, I detected no program effect on pregnancy rates at 6-month follow-up. The Positive Prevention PLUS program demonstrated positive impacts on adolescent sexual behavior. This suggests that programs that focus on having students practice risk reduction skills may delay sexual activity and increase birth control use.

Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization.

Science.gov (United States)

Xing, Haifeng; Hou, Bo; Lin, Zhihui; Guo, Meifeng

2017-10-13

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.

Science.gov (United States)

Forsyth, Alexander W; Barzilay, Regina; Hughes, Kevin S; Lui, Dickson; Lorenz, Karl A; Enzinger, Andrea; Tulsky, James A; Lindvall, Charlotta

2018-02-27

Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications. Copyright © 2018 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Effect of Randomness in Element Position on Performance of Communication Array Antennas in Internet of Things

Directory of Open Access Journals (Sweden)

Congsi Wang

2018-01-01

Full Text Available As a critical component for wireless communication, active phased array antennas face the restrictions of creating effective performance with the effect of randomness in the position of the array element, which are inevitably produced in the manufacturing and operating process of antenna. A new method for efficiently and effectively evaluating the statistic performance of antenna is presented, with consideration of randomness in element position. A coupled structural-electromagnetic statistic model for array antenna is proposed from the viewpoint of electromechanical coupling. Lastly, a 12×12 planar array is illustrated to evaluate the performance of antenna with the saddle-shaped distortion and random position error. The results show that the presented model can obtain the antenna performance quickly and effectively, providing an advantageous guidance for structural design and performance optimization for array antennas in wireless application.

High Classification Rates for Continuous Cow Activity Recognition using Low-cost GPS Positioning Sensors and Standard Machine Learning Techniques

DEFF Research Database (Denmark)

Godsk, Torben; Kjærgaard, Mikkel Baun

2011-01-01

activities. By preprocessing the raw cow position data, we obtain high classification rates using standard machine learning techniques to recognize cow activities. Our objectives were to (i) determine to what degree it is possible to robustly recognize cow activities from GPS positioning data, using low...... and their activities manually logged to serve as ground truth. For our dataset we managed to obtain an average classification success rate of 86.2% of the four activities: eating/seeking (90.0%), walking (100%), lying (76.5%), and standing (75.8%) by optimizing both the preprocessing of the raw GPS data...

Cleaning and can end chamfering special machine MSCS-04

International Nuclear Information System (INIS)

Negulescu, D.; Rusu, A.; Dragomir, I.; Turcanu, V.; Bailescu, V.; Burcea, Gh.; Chitu, I.

2001-01-01

The MSCS-04 machine executes cleaning and can end chamfering of the CANDU 6 fuel element can through the following technologic chain: - manual positioning of the workpiece in the transporter feeding location; - the transport of the workpiece in front of the cleaning machine and workpiece orientation checking; - automatic loading of the workpiece in the cleaning machine; - bonding the workpiece in the cleaning machine; - cleaning the ends of the workpiece with graphite dust aspiration; - automatic disconnection of the workpiece from the cleaning machine; - automatic unloading of the cleaning machine; - disposal of the workpiece on the transporter in front of cleaning machine; workpiece's transport in front of the chamfering machine; - automatic checking of the workpiece orientation; - automatic loading of the workpiece in the chamfering machine; - axial positioning and bounding of the workpiece in the chamfering machine; chamfering the workpiece's ends with graphite dust and splinter aspiration; - disconnecting the workpiece from the chamfering machine; - automatic unloading of the workpiece from the chamfering machine with splinter blow from the workpiece interior; - workpiece disposal on transporter and the piece transport to the outlet. Details about the technological system, transport system, manipulators, cleaning and chamfering machines are given. Novel elements are highlighted and the technical characteristics are presented

Occupational position and its relation to mental distress in a random sample of Danish residents

DEFF Research Database (Denmark)

Rugulies, Reiner Ernst; Madsen, Ida E H; Nielsen, Maj Britt D

2010-01-01

PURPOSE: To analyze the distribution of depressive, anxiety, and somatization symptoms across different occupational positions in a random sample of Danish residents. METHODS: The study sample consisted of 591 Danish residents (50% women), aged 20-65, drawn from an age- and gender-stratified random...... sample of the Danish population. Participants filled out a survey that included the 92 item version of the Hopkins Symptom Checklist (SCL-92). We categorized occupational position into seven groups: high- and low-grade non-manual workers, skilled and unskilled manual workers, high- and low-grade self...

Robotic refueling machine

International Nuclear Information System (INIS)

Challberg, R.C.; Jones, C.R.

1996-01-01

One of the longest critical path operations performed during the outage is removing and replacing the fuel. A design is currently under development for a refueling machine which would allow faster, fully automated operation and would also allow the handling of two fuel assemblies at the same time. This design is different from current designs, (a) because of its lighter weight, making increased acceleration and speed possible, (b) because of its control system which makes locating the fuel assembly more dependable and faster, and (c) because of its dual handling system allowing simultaneous fuel movements. The new design uses two robotic arms to span a designated area of the vessel and the fuel storage area. Attached to the end of each robotic arm is a lightweight telescoping mast with a pendant attached to the end of each mast. The pendant acts as the base unit, allowing attachment of any number of end effectors depending on the servicing or inspection operation. Housed within the pendant are two television cameras used for the positioning control system. The control system is adapted from the robotics field using the technology known as machine vision, which provides both object and character recognition techniques to enable relative position control rather than absolute position control as in past designs. The pendant also contains thrusters that are used for fast, short distance, precise positioning. The new refueling machine system design is capable of a complete off load and reload of an 872 element core in about 5.3 days compared to 13 days for a conventional system

The Three Pillars of Machine Programming

OpenAIRE

Gottschlich, Justin; Solar-Lezama, Armando; Tatbul, Nesime; Carbin, Michael; Rinard, Martin; Barzilay, Regina; Amarasinghe, Saman; Tenenbaum, Joshua B; Mattson, Tim

2018-01-01

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in t...

Turbo machine tip clearance and vibration measurements using a fibre optic laser Doppler position sensor

Science.gov (United States)

Pfister, T.; Büttner, L.; Czarske, J.; Krain, H.; Schodl, R.

2006-07-01

This paper presents a novel fibre optic laser Doppler position sensor for single blade tip clearance and vibration measurements at turbo machines, which offers high temporal resolution and high position resolution simultaneously. The sensor principle is based on the generation of a measurement volume consisting of two superposed fan-like interference fringe systems with contrary fringe spacing gradients using wavelength division multiplexing. A flexible and robust measurement system with an all-passive fibre coupled measurement head has been realized employing diffractive and refractive optics. Measurements of tip clearance and rotor vibrations at a transonic centrifugal compressor performed during operation at up to 50 000 rpm (833 Hz) corresponding to 21.7 kHz blade frequency and 586 m s-1 blade tip velocity are presented. The results are in excellent agreement with those of capacitive probes. The mean uncertainty of the position measurement was around 20 µm and, thus, considerably better than for conventional tip clearance probes. Consequently, this sensor is capable of fulfilling the requirements for future active clearance control systems and has great potential for in situ and online tip clearance and vibration measurements at metallic and non-metallic turbine blades with high precision.

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Science.gov (United States)

Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

2016-03-01

Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

Evolution of new X and Y positioning system for 540 MWe PHWR fuelling machines - based on commissioning experience

International Nuclear Information System (INIS)

Gupta, Vivek; Vyas, A.K.; Gupta, K.S.; Rama Mohan, N.; Bhambra, H.S.

2006-01-01

In PHWR units, X and Y positioning system is provided to give feedback regarding the misalignment between end-fitting and Fuelling Machine (FM) Head during homing on process for carrying out the correction before clamping the Head. The existing design of X and Y Positioning System works by measuring the misalignment by sensing the tilt of the FM Head in X and Y direction caused by its mechanical interfacing with end-fitting as it is advanced in Z direction. The misalignment of Head is corrected by moving it in X and Y direction by X-fine and Y-fine drives, at Z pre-stop position. This correction is vital for achieving the satisfactory sealing of heavy water from channel at snout of FM Head with end fitting. During testing and commissioning trials, it was found that the end fitting of 540 MWe coolant channel assembly either tilts or bends due to the application of load by Fuelling Machines during the process of homing-on of FM Head. Due to this phenomenon, value of misalignment sensed by the Positioning System was considerably lower than the actual misalignment and consequently results in uncorrected misalignment. It was also observed that the high unbalanced moments caused by movement of heavier mass of B-ram in FM Head was further aggravating the misalignment problem. The problem, as an interim measure, was solved by optimising the loads acting on the end fitting to achieve the practically minimum possible uncorrected misalignment. However, to provide a lasting solution for this problem, a new X and Y Positioning System has been evolved. In this system, the misalignment between FM Head and end fitting is found by direct actuation of linear Variable Differential Transformer (LVDT) sensors by four separate alignment plates mounted on the snout. Further development to evolve a completely non-invasive technique using laser sensors has also been undertaken. This paper describes the problems encountered during commissioning of existing design of X and Y Positioning

Diamond turning machine controller implementation

Energy Technology Data Exchange (ETDEWEB)

Garrard, K.P.; Taylor, L.W.; Knight, B.F.; Fornaro, R.J.

1988-12-01

The standard controller for a Pnuemo ASG 2500 Diamond Turning Machine, an Allen Bradley 8200, has been replaced with a custom high-performance design. This controller consists of four major components. Axis position feedback information is provided by a Zygo Axiom 2/20 laser interferometer with 0.1 micro-inch resolution. Hardware interface logic couples the computers digital and analog I/O channels to the diamond turning machine`s analog motor controllers, the laser interferometer, and other machine status and control information. It also provides front panel switches for operator override of the computer controller and implement the emergency stop sequence. The remaining two components, the control computer hardware and software, are discussed in detail below.

Machine tool evaluation

International Nuclear Information System (INIS)

Lunsford, B.E.

1976-01-01

Continued improvement in numerical control (NC) units and the mechanical components used in the construction of today's machine tools, necessitate the use of more precise instrumentation to calibrate and determine the capabilities of these systems. It is now necessary to calibrate most tape-control lathes to a tool-path positioning accuracy of +-300 microinches in the full slide travel and, on some special turning and boring machines, a capability of +-100 microinches must be achieved. The use of a laser interferometer to determine tool-path capabilities is described

Randomized controlled trial comparing nasal intermittent positive pressure ventilation and nasal continuous positive airway pressure in premature infants after tracheal extubation

Directory of Open Access Journals (Sweden)

Daniela Franco Rizzo Komatsu

Full Text Available Summary Objective: To analyze the frequency of extubation failure in premature infants using conventional mechanical ventilation (MV after extubation in groups subjected to nasal intermittent positive pressure ventilation (nIPPV and continuous positive airway pressure (nCPAP. Method: Seventy-two premature infants with respiratory failure were studied, with a gestational age (GA ≤ 36 weeks and birth weight (BW > 750 g, who required tracheal intubation and mechanical ventilation. The study was controlled and randomized in order to ensure that the members of the groups used in the research were chosen at random. Randomization was performed at the time of extubation using sealed envelopes. Extubation failure was defined as the need for re-intubation and mechanical ventilation during the first 72 hours after extubation. Results: Among the 36 premature infants randomized to nIPPV, six (16.6% presented extubation failure in comparison to 11 (30.5% of the 36 premature infants randomized to nCPAP. There was no statistical difference between the two study groups regarding BW, GA, classification of the premature infant, and MV time. The main cause of extubation failure was the occurrence of apnea. Gastrointestinal and neurological complications did not occur in the premature infants participating in the study. Conclusion: We found that, despite the extubation failure of the group of premature infants submitted to nIPPV being numerically smaller than in premature infants submitted to nCPAP, there was no statistically significant difference between the two modes of ventilatory support after extubation.

Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

Science.gov (United States)

Armstrong, David J.; Günther, Maximilian N.; McCormac, James; Smith, Alexis M. S.; Bayliss, Daniel; Bouchy, François; Burleigh, Matthew R.; Casewell, Sarah; Eigmüller, Philipp; Gillen, Edward; Goad, Michael R.; Hodgkin, Simon T.; Jenkins, James S.; Louden, Tom; Metrailler, Lionel; Pollacco, Don; Poppenhaeger, Katja; Queloz, Didier; Raynard, Liam; Rauer, Heike; Udry, Stéphane; Walker, Simon R.; Watson, Christopher A.; West, Richard G.; Wheatley, Peter J.

2018-05-01

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.

The ATLAS Higgs Machine Learning Challenge

CERN Document Server

Cowan, Glen; The ATLAS collaboration; Bourdarios, Claire

2015-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 1990s with Artificial Neural Net and more recently with Boosted Decision Trees, Random Forest etc. Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, data scientists are developing new Machine Learning algorithms to extract meaning from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, and at the same time data scientists have advanced algorithms: the goal of the HiggsML project was to bring the two together by a “challenge”: participants from all over the world and any scientific background could compete online to obtain the best Higgs to tau tau signal significance on a set of ATLAS fully simulated Monte Carlo signal and background. Instead of HEP physicists browsing through machine learning papers and trying to infer which new algorithms might be useful for HEP, then c...

Machine learning with quantum relative entropy

International Nuclear Information System (INIS)

Tsuda, Koji

2009-01-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Machine learning with quantum relative entropy

Energy Technology Data Exchange (ETDEWEB)

Tsuda, Koji [Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tuebingen, 72076 (Germany)], E-mail: koji.tsuda@tuebingen.mpg.de

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Light-operated machines based on threaded molecular structures.

Science.gov (United States)

Credi, Alberto; Silvi, Serena; Venturi, Margherita

2014-01-01

Rotaxanes and related species represent the most common implementation of the concept of artificial molecular machines, because the supramolecular nature of the interactions between the components and their interlocked architecture allow a precise control on the position and movement of the molecular units. The use of light to power artificial molecular machines is particularly valuable because it can play the dual role of "writing" and "reading" the system. Moreover, light-driven machines can operate without accumulation of waste products, and photons are the ideal inputs to enable autonomous operation mechanisms. In appropriately designed molecular machines, light can be used to control not only the stability of the system, which affects the relative position of the molecular components but also the kinetics of the mechanical processes, thereby enabling control on the direction of the movements. This step forward is necessary in order to make a leap from molecular machines to molecular motors.

Source localization in an ocean waveguide using supervised machine learning.

Science.gov (United States)

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Optimizing Distributed Machine Learning for Large Scale EEG Data Set

Directory of Open Access Journals (Sweden)

M Bilal Shaikh

2017-06-01

Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

Testing links between childhood positive peer relations and externalizing outcomes through a randomized controlled intervention study

NARCIS (Netherlands)

Witvliet, M.; van Lier, P.A.C.; Cuijpers, P.; Koot, H.M.

2009-01-01

In this study, the authors used a randomized controlled trial to explore the link between having positive peer relations and externalizing outcomes in 758 children followed from kindergarten to the end of 2nd grade. Children were randomly assigned to the Good Behavior Game (GBG), a universal

Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

Directory of Open Access Journals (Sweden)

Muneki Yasuda

2018-04-01

Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

Single Machine Scheduling and Due Date Assignment with Past-Sequence-Dependent Setup Time and Position-Dependent Processing Time

Directory of Open Access Journals (Sweden)

Chuan-Li Zhao

2014-01-01

Full Text Available This paper considers single machine scheduling and due date assignment with setup time. The setup time is proportional to the length of the already processed jobs; that is, the setup time is past-sequence-dependent (p-s-d. It is assumed that a job's processing time depends on its position in a sequence. The objective functions include total earliness, the weighted number of tardy jobs, and the cost of due date assignment. We analyze these problems with two different due date assignment methods. We first consider the model with job-dependent position effects. For each case, by converting the problem to a series of assignment problems, we proved that the problems can be solved in On4 time. For the model with job-independent position effects, we proved that the problems can be solved in On3 time by providing a dynamic programming algorithm.

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

Science.gov (United States)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

Diamond turning on advanced machine tool prototypes

International Nuclear Information System (INIS)

Arnold, J.B.; Steger, P.J.

1975-01-01

Specular-quality metal mirrors are being machined for use in laser optical systems. The fabrication process incorporates special quality diamond tools and specially constructed turning machines. The machines are controlled by advanced control techniques and are housed in an environmentally controlled laboratory to insure ultimate machine stability and positional accuracy. The materials from which these mirrors are primarily produced are the softer face-center-cubic structure metals, such as gold, silver, copper, and aluminum. Mirror manufacturing by the single-point diamond machining process is in an early stage of development, but it is anticipated that this method will become the most economical way for producing high-quality metal mirrors. (U.S.)

Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

Science.gov (United States)

Wallace, Byron C; Noel-Storr, Anna; Marshall, Iain J; Cohen, Aaron M; Smalheiser, Neil R; Thomas, James

2017-11-01

Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Gaussian processes for machine learning.

Science.gov (United States)

Seeger, Matthias

2004-04-01

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

Charging machine for a fast production reactor

International Nuclear Information System (INIS)

Artem'ev, L.N.; Kurilkin, V.V.

1971-01-01

Charging machine for a fast production reactor is described. The machine contains charging mechanism, mechanism for positioning fresh fuel and spent fuel assemtlies, storage drums with sockets for control rod assemtlies and collet tongs for control rods. Recharging is conducted by means of ramp channel

Feasibility and effects of the semirecumbent position to prevent ventilator-associated pneumonia: a randomized study.

Science.gov (United States)

van Nieuwenhoven, Christianne A; Vandenbroucke-Grauls, Christine; van Tiel, Frank H; Joore, Hans C A; van Schijndel, Rob J M Strack; van der Tweel, Ingeborg; Ramsay, Graham; Bonten, Marc J M

2006-02-01

Reducing aspiration of gastric contents by placing mechanically ventilated patients in a semirecumbent position has been associated with lower incidences of ventilator-associated pneumonia (VAP). The feasibility and efficacy of this intervention in a larger patient population, however, are unknown. Assessment of the feasibility of the semirecumbent position for intensive care unit patients and its influence on development of VAP. In a prospective multicentered trial, critically ill patients undergoing mechanical ventilation were randomly assigned to the semirecumbent position, with a target backrest elevation of 45 degrees , or standard care (i.e., supine position) with a backrest elevation of 10 degrees . Backrest elevation was measured continuously during the first week of ventilation with a monitor-linked device. A deviation of position was defined as a change of the randomized position >5 degrees . Diagnosis of VAP was made by quantitative cultures of samples obtained by bronchoscopic techniques. One hundred nine patients were assigned to the supine group and 112 to the semirecumbent group. Baseline characteristics were comparable for both groups. Average elevations were 9.8 degrees and 16.1 degrees at day 1 and day 7, respectively, for the supine group and 28.1 degrees and 22.6 degrees at day 1 and day 7, respectively, for the semirecumbent group (p position of 45 degrees was not achieved for 85% of the study time, and these patients more frequently changed position than supine-positioned patients. VAP was diagnosed in eight patients (6.5%) in the supine group and in 13 (10.7%) in the semirecumbent group (NS), after a mean of 6 (range, 3-9) and 7 (range, 3-12) days, respectively. There were no differences in numbers of patients undergoing enteral feeding, receiving stress ulcer prophylaxis, or developing pressure sores or in mortality rates or duration of ventilation and intensive care unit stay between the groups. The targeted backrest elevation of 45 degrees

Extreme learning machines 2013 algorithms and applications

CERN Document Server

Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi

2014-01-01

In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.   This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...

The Riddle of the Smart Machines

Science.gov (United States)

Howell, Dusti D.

2010-01-01

Hundreds of graduate students were introduced to the fields of instructional design and educational technology with the riddle of the smart machines, yet over the years no one has answered it correctly. After revealing the surprising answer to this riddle, both the negative and positive impacts of smart machines are analyzed. An example of this is…

Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity using Modern Machine Learning Algorithms.

Science.gov (United States)

Kryshchyshyn, Anna; Devinyak, Oleg; Kaminskyy, Danylo; Grellier, Philippe; Lesyk, Roman

2017-11-14

This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR-models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q 2 ext =0.812 and Q 2 ext =0.830. The obtained models can be used further for in silico screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure-activity relationships. The presence of phenyl rings with electron-withdrawing atoms or groups in para-position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1-substituted 2-indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization of coded random access with compressive sensing based multi user detection

DEFF Research Database (Denmark)

Ji, Yalei; Stefanovic, Cedomir; Bockelmann, Carsten

2014-01-01

The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods...... coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol....

Reliability analysis in intelligent machines

Science.gov (United States)

Mcinroy, John E.; Saridis, George N.

1990-01-01

Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.

The signature of positive selection at randomly chosen loci.

Science.gov (United States)

Przeworski, Molly

2002-03-01

In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequency alleles drift to fixation and no longer contribute to polymorphism, while linkage disequilibrium is broken down by recombination. As a result, loci chosen without independent evidence of recent selection are not expected to exhibit either of these features, even if they have been affected by numerous sweeps in their genealogical history. How then can we explain the patterns in the data? One possibility is population structure, with unequal sampling from different subpopulations. Alternatively, positive selection may not operate as is commonly modeled. In particular, the rate of fixation of advantageous mutations may have increased in the recent past.

Slide system for machine tools

Science.gov (United States)

Douglass, Spivey S.; Green, Walter L.

1982-01-01

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

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.

Predictors of return rate discrimination in slot machine play.

Science.gov (United States)

Coates, Ewan; Blaszczynski, Alex

2014-09-01

The purpose of this study was to investigate the extent to which accurate estimates of payback percentages and volatility combined with prior learning, enabled players to successfully discriminate between multi-line/multi-credit slot machines that provided differing rates of reinforcement. The aim was to determine if the capacity to discriminate structural characteristics of gaming machines influenced player choices in selecting 'favourite' slot machines. Slot machine gambling history, gambling beliefs and knowledge, impulsivity, illusions of control, and problem solving style were assessed in a sample of 48 first year undergraduate psychology students. Participants were subsequently exposed to a choice paradigm where they could freely select to play either of two concurrently presented PC-simulated slot machines programmed to randomly differ in expected player return rates (payback percentage) and win frequency (volatility). Results suggest that prior learning and cognitions (particularly gambler's fallacy) but not payback, were major contributors to the ability of a player to discriminate volatility between slot machines. Participants displayed a general tendency to discriminate payback, but counter-intuitively placed more bets on the slot machine with lower payback percentage rates.

Duality-based algorithms for scheduling on unrelated parallel machines

NARCIS (Netherlands)

van de Velde, S.L.; van de Velde, S.L.

1993-01-01

We consider the following parallel machine scheduling problem. Each of n independent jobs has to be scheduled on one of m unrelated parallel machines. The processing of job J[sub l] on machine Mi requires an uninterrupted period of positive length p[sub lj]. The objective is to find an assignment of

Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls

Directory of Open Access Journals (Sweden)

Deanna eGreenstein

2012-06-01

Full Text Available Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI. However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest, we classified 98 COS patients and 99 age, sex, and ethnicity-matched healthy controls. We also used Random Forest to determine the likelihood of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p= 0.0004 and fewer developmental delays (p=0.02. Presence of copy number variation (CNV was associated with lower probability of being classified as schizophrenia (p=0.001. The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusions: Schizophrenia and control groups can be well classified using Random Forest and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.

Prone positioning in hypoxemic respiratory failure: meta-analysis of randomized controlled trials.

Science.gov (United States)

Kopterides, Petros; Siempos, Ilias I; Armaganidis, Apostolos

2009-03-01

Prone positioning is used to improve oxygenation in patients with hypoxemic respiratory failure (HRF). However, its role in clinical practice is not yet clearly defined. The aim of this meta-analysis was to assess the effect of prone positioning on relevant clinical outcomes, such as intensive care unit (ICU) and hospital mortality, days of mechanical ventilation, length of stay, incidence of ventilator-associated pneumonia (VAP) and pneumothorax, and associated complications. We used literature search of MEDLINE, Current Contents, and Cochrane Central Register of Controlled Trials. We focused only on randomized controlled trials reporting clinical outcomes in adult patients with HRF. Four trials met our inclusion criteria, including 662 patients randomized to prone ventilation and 609 patients to supine ventilation. The pooled odds ratio (OR) for the ICU mortality in the intention-to-treat analysis was 0.97 (95% confidence interval [CI], 0.77-1.22), for the comparison between prone and supine ventilated patients. Interestingly, the pooled OR for the ICU mortality in the selected group of the more severely ill patients favored prone positioning (OR, 0.34; 95% CI, 0.18-0.66). The duration of mechanical ventilation and the incidence of pneumothorax were not different between the 2 groups. The incidence of VAP was lower but not statistically significant in patients treated with prone positioning (OR, 0.81; 95% CI, 0.61-1.10). However, prone positioning was associated with a higher risk of pressure sores (OR, 1.49; 95% CI, 1.17-1.89) and a trend for more complications related to the endotracheal tube (OR, 1.30; 95% CI, 0.94-1.80). Despite the inherent limitations of the meta-analytic approach, it seems that prone positioning has no discernible effect on mortality in patients with HRF. It may decrease the incidence of VAP at the expense of more pressure sores and complications related to the endotracheal tube. However, a subgroup of the most severely ill patients may

Reliable self-replicating machines in asynchronous cellular automata.

Science.gov (United States)

Lee, Jia; Adachi, Susumu; Peper, Ferdinand

2007-01-01

We propose a self-replicating machine that is embedded in a two-dimensional asynchronous cellular automaton with von Neumann neighborhood. The machine dynamically encodes its shape into description signals, and despite the randomness of cell updating, it is able to successfully construct copies of itself according to the description signals. Self-replication on asynchronously updated cellular automata may find application in nanocomputers, where reconfigurability is an essential property, since it allows avoidance of defective parts and simplifies programming of such computers.

A machine learning-based framework to identify type 2 diabetes through electronic health records.

Science.gov (United States)

Zheng, Tao; Xie, Wei; Xu, Liling; He, Xiaoying; Zhang, Ya; You, Mingrong; Yang, Gong; Chen, You

2017-01-01

To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature

Positive Family Intervention for Severe Challenging Behavior I: A Multisite Randomized Clinical Trial

Science.gov (United States)

Durand, V. Mark; Hieneman, Meme; Clarke, Shelley; Wang, Mo; Rinaldi, Melissa L.

2013-01-01

The present study was a multisite randomized clinical trial assessing the effects of adding a cognitive-behavioral intervention to positive behavior support (PBS). Fifty-four families who met the criteria of (a) having a child with a developmental disability, (b) whose child displayed serious challenging behavior (e.g., aggression, self-injury,…

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

Science.gov (United States)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

Effects of systematic prone positioning in hypoxemic acute respiratory failure: a randomized controlled trial.

Science.gov (United States)

Guerin, Claude; Gaillard, Sandrine; Lemasson, Stephane; Ayzac, Louis; Girard, Raphaele; Beuret, Pascal; Palmier, Bruno; Le, Quoc Viet; Sirodot, Michel; Rosselli, Sylvaine; Cadiergue, Vincent; Sainty, Jean-Marie; Barbe, Philippe; Combourieu, Emmanuel; Debatty, Daniel; Rouffineau, Jean; Ezingeard, Eric; Millet, Olivier; Guelon, Dominique; Rodriguez, Luc; Martin, Olivier; Renault, Anne; Sibille, Jean-Paul; Kaidomar, Michel

2004-11-17

A recent trial showed that placing patients with acute lung injury in the prone position did not increase survival; however, whether those results hold true for patients with hypoxemic acute respiratory failure (ARF) is unclear. To determine whether prone positioning improves mortality in ARF patients. Prospective, unblinded, multicenter controlled trial of 791 ARF patients in 21 general intensive care units in France using concealed randomization conducted from December 14, 1998, through December 31, 2002. To be included, patients had to be at least 18 years, hemodynamically stable, receiving mechanical ventilation, and intubated and had to have a partial pressure of arterial oxygen (PaO2) to fraction of inspired oxygen (FIO2) ratio of 300 or less and no contraindications to lying prone. Patients were randomly assigned to prone position placement (n = 413), applied as early as possible for at least 8 hours per day on standard beds, or to supine position placement (n = 378). The primary end point was 28-day mortality; secondary end points were 90-day mortality, duration of mechanical ventilation, incidence of ventilator-associated pneumonia (VAP), and oxygenation. The 2 groups were comparable at randomization. The 28-day mortality rate was 32.4% for the prone group and 31.5% for the supine group (relative risk [RR], 0.97; 95% confidence interval [CI], 0.79-1.19; P = .77). Ninety-day mortality for the prone group was 43.3% vs 42.2% for the supine group (RR, 0.98; 95% CI, 0.84-1.13; P = .74). The mean (SD) duration of mechanical ventilation was 13.7 (7.8) days for the prone group vs 14.1 (8.6) days for the supine group (P = .93) and the VAP incidence was 1.66 vs 2.14 episodes per 100-patients days of intubation, respectively (P = .045). The PaO2/FIO2 ratio was significantly higher in the prone group during the 28-day follow-up. However, pressure sores, selective intubation, and endotracheal tube obstruction incidences were higher in the prone group. This trial

Moved range monitor of a refueling machine

International Nuclear Information System (INIS)

Nakajima, Masaaki; Sakanaka, Tadao; Kayano, Hiroyuki.

1976-01-01

Purpose: To incorporate light receiving and emitting elements in a face monitor to thereby increase accuracy and reliability to facilitate handling in the refueling of a BWR power plant. Constitution: In the present invention, a refueling machine and a face monitoring light receiving and emitting elements are analogously coupled whereby the face monitoring light receiving and emitting elements may be moved so as to be analogous to a route along which the refueling machine has moved. A shielding plate is positioned in the middle of the light receiving and emitting elements, and the shielding plate is machined so as to be outside of action. The range of action of the refueling machine may be monitored depending on the light receiving state of the light receiving element. Since the present invention utilizes the permeating light as described above, it is possible to detect positions more accurately than the mechanical switch. In addition, the detection section is of the non-contact system and the light receiving element comprises a hot cell, and therefore the service life is extended and the reliability is high. (Nakamura, S.)

A survey on queues in machining system: Progress from 2010 to 2017

Directory of Open Access Journals (Sweden)

Shekhar C.

2017-01-01

Full Text Available The aim of the present article is to give a historical survey of some important research works related to queues in machining system since 2010. Queues of failed machines in machine repairing problem occur due to the failure of machines at random in the manufacturing industries, where different jobs are performed on machining stations. Machines are subject to failure what may result in significant loss of production, revenue, or goodwill. In addition to the references on queues in machining system, which is also called `Machine Repair Problem' (MRP or `Machine Interference Problem' (MIP, a meticulous list of books and survey papers is also prepared so as to provide a detailed catalog for understanding the research in queueing domain. We have classified the relevant literature according to a year of publishing, methodological, and modeling aspects. The author(s hope that this survey paper could be of help to learners contemplating research on queueing domain.

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...

Lead-position dependent regular oscillations and random fluctuations of conductance in graphene quantum dots

International Nuclear Information System (INIS)

Huang Liang; Yang Rui; Lai Yingcheng; Ferry, David K

2013-01-01

Quantum interference causes a wavefunction to have sensitive spatial dependence, and this has a significant effect on quantum transport. For example, in a quantum-dot system, the conductance can depend on the lead positions. We investigate, for graphene quantum dots, the conductance variations with the lead positions. Since for graphene the types of boundaries, e.g., zigzag and armchair, can fundamentally affect the quantum transport characteristics, we focus on rectangular graphene quantum dots, for which the effects of boundaries can be systematically studied. For both zigzag and armchair horizontal boundaries, we find that changing the positions of the leads can induce significant conductance variations. Depending on the Fermi energy, the variations can be either regular oscillations or random conductance fluctuations. We develop a physical theory to elucidate the origin of the conductance oscillation/fluctuation patterns. In particular, quantum interference leads to standing-wave-like-patterns in the quantum dot which, in the absence of leads, are regulated by the energy-band structure of the corresponding vertical graphene ribbon. The observed ‘coexistence’ of regular oscillations and random fluctuations in the conductance can be exploited for the development of graphene-based nanodevices. (paper)

Risk estimation using probability machines

Science.gov (United States)

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

A tape-controlled remote automatic diameter measurement machine

International Nuclear Information System (INIS)

Jennison, W.; Salmon, A.M.

1978-01-01

The machine is designed for the automatic measurement of fuel pins after irradiation in the fast reactors and is a modified version of a machine which has been in use for several years. These modifications consist of mechanical improvements and solid state control circuitry but the design criteria are unchanged. Irradiated fuel pins with diameters up to 0.875 in. are measured at fixed axial positions and angular intervals. Axial stepping of either 1 cm or 1 in. with a standard deviation of 5 x 10 -4 in. and angular rotation by multiples of 18 0 with a non-cumulative error of 1 0 can be selected. Data on axial position to 0.1 in. or 0.1 cm and fuel element diameter to 5 x 10 -5 in. are both punched and printed out for computer evaluation. The standard deviation of a single measurement on cylindrical specimens with an eccentricity of up to at least 0.1 in. should be no worse than 1 x 10 -4 in. No operator attention is required after the pin is positioned in the machine and 40 sets of 10 diameter readings at 36 0 intervals can be performed in an hour. Switches can be set between 1 and 99 to terminate an examination when power is switched off with the machine in its rest position. (author)

Position of the physician's nametag--a randomized, blinded trial.

Directory of Open Access Journals (Sweden)

Samuel Luca Schmid

Full Text Available The patient-physician relation begins when the physician introduces himself with name and function. Most institutions request a nametag with name and function to be worn. Although nametags are consequently worn, the optimal position for the nametag is unknown. It was the purpose of this study to identify whether positioning the nametag on the right or the left chest side provides better visibility to the patient.One hundred volunteers, blinded to the experimental setup, presented for an orthopedic consultation in a standardized manner. The nametag of the physician was randomly positioned on the left chest side and presented to 50 individuals (age 35 years (range 17 to 83 or the right chest side and then presented to 50 other individuals (35 years (range 16 to 59. The time of the participant noticing the nametag was documented. Subsequently, the participant was questioned concerning the relevance of a nametag and verbal self-introduction of the physician.38% of the participants noticed the nametag on the right as opposed to 20% who noticed it if placed on the left upper chest (p = 0.0473. The mean time to detection was 9 (range 1-40 seconds for nametags on the right and 25.2 seconds (range 3 to 49, p = 0.006 on the left. For 87% of the participants, a nametag is expected and important and nearly all participants (96% expected the physician to introduce himself verbally.It is expected that a physician wears a nametag and introduce himself verbally at the first encounter. Positioning the nametag on the right chest side results in better and faster visibility.

Human-machine interface upgrade

International Nuclear Information System (INIS)

Kropik, M.; Matejka, K.; Sklenka, L.; Chab, V.

2002-01-01

The article describes a new human-machine interface that was installed at the VR-1 training reactor. The human-machine interface upgrade was completed in the summer 2001. The interface was designed with respect to functional, ergonomic and aesthetic requirements. The interface is based on a personal computer equipped with two displays. One display enables alphanumeric communication between the reactor operator and the nuclear reactor I and C. The second display is a graphical one. It presents the status of the reactor, principal parameters (as power, period), control rods positions, course of the reactor power. Furthermore, it is possible to set parameters, to show the active core configuration, to perform reactivity calculations, etc. The software for the new human-machine interface was produced with the InTouch developing tool of the Wonder-Ware Company. It is possible to switch the language of the interface between Czech and English because of many foreign students and visitors to the reactor. Microcomputer based communication units with proper software were developed to connect the new human-machine interface with the present reactor I and C. The new human-machine interface at the VR-1 training reactor improves the comfort and safety of the reactor utilisation, facilitates experiments and training, and provides better support for foreign visitors. (orig.)

The ATLAS Higgs machine learning challenge

CERN Document Server

Davey, W; The ATLAS collaboration; Rousseau, D; Cowan, G; Kegl, B; Germain-Renaud, C; Guyon, I

2014-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 90's with Artificial Neural Net for example, more recently with Boosted Decision Trees, Random Forest etc... Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, Data Scientists are developing new Machine Learning algorithms to extract sense from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, data scientists have advanced algorithms: the goal of the HiggsML project is to bring the two together by a “challenge”: participants from all over the world and any scientific background can compete online ( https://www.kaggle.com/c/higgs-boson ) to obtain the best Higgs to tau tau signal significance on a set of ATLAS full simulated Monte Carlo signal and background. Winners with the best scores will receive money prizes ; authors of the best method (most usable) will be invited t...

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.

Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

Science.gov (United States)

Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

2018-05-01

The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

Science.gov (United States)

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

Research progress in machine learning methods for gene-gene interaction detection.

Science.gov (United States)

Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

2018-03-20

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Sensorless Suitability Analysis of Hybrid PM Machines for Electric Vehicles

DEFF Research Database (Denmark)

Matzen, Torben Nørregaard; Rasmussen, Peter Omand

2009-01-01

Electrical machines for traction in electric vehicles are an essential component which attract attention with respect to machine design and control as a part of the emerging renewable industry. For the hybrid electric machine to replace the familiar behaviour of the combustion engine torque......, control seems necessary to implement. For hybrid permanent magnet (PM) machines torque control in an indirect fashion using dq-current control is frequently done. This approach requires knowledge about the machine shaft position which may be obtained sensorless. In this article a method based on accurate...

Chuck for machining armature casings and angles

International Nuclear Information System (INIS)

Tashlitskii, A.I.; Matskevich, A.I.

1984-01-01

When machining T-joints and angles, the test specimen must be fixed before being placed in the desired position. This is quite a complex operation and is achieved in a few stages. At the Scientific Production Combine ''Kislorodmash,'' a new chuck was designed which in one pressing of the jaws seats and fixes the specimen. In the clamped condition, the chuck helps rotate and fix the specimen in one of the four positions. Rotating and fixing are manual. The chuck developed ensured a distinct interdependence of the axes of the branches being machined as the specimen remains fixed throughout the period of machining, and provides reliable fixing of the specimen, and there are no clearances when the specimen is fixed with a special wedge. When using the chuck, the ancillary movements of the operator are reduced to a minimum thus increasing the labor productivity

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

Science.gov (United States)

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

2016-06-21

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

KAUST Repository

McCabe, Matthew

2017-12-06

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

Science.gov (United States)

Houborg, Rasmus; McCabe, Matthew F.

2018-01-01

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory 'predictor' variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

KAUST Repository

McCabe, Matthew; McCabe, Matthew

2017-01-01

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association

Testing links between childhood positive peer relations and externalizing outcomes through a randomized controlled intervention study.

Science.gov (United States)

Witvliet, Miranda; van Lier, Pol A C; Cuijpers, Pim; Koot, Hans M

2009-10-01

In this study, the authors used a randomized controlled trial to explore the link between having positive peer relations and externalizing outcomes in 758 children followed from kindergarten to the end of 2nd grade. Children were randomly assigned to the Good Behavior Game (GBG), a universal classroom-based preventive intervention, or a control condition. Children's acceptance by peers, their number of mutual friends, and their proximity to others were assessed annually through peer ratings. Externalizing behavior was annually rated by teachers. Reductions in children's externalizing behavior and improvements in positive peer relations were found among GBG children, as compared with control-group children. Reductions in externalizing behavior appeared to be partly mediated by the improvements in peer acceptance. This mediating role of peer acceptance was found for boys only. The results suggest that positive peer relations are not just markers, but they are environmental mediators of boys' externalizing behavior development. Implications for research and prevention are discussed. (c) 2009 APA, all rights reserved.

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.

Power training using pneumatic machines vs. plate-loaded machines to improve muscle power in older adults.

Science.gov (United States)

Balachandran, Anoop T; Gandia, Kristine; Jacobs, Kevin A; Streiner, David L; Eltoukhy, Moataz; Signorile, Joseph F

2017-11-01

Power training has been shown to be more effective than conventional resistance training for improving physical function in older adults; however, most trials have used pneumatic machines during training. Considering that the general public typically has access to plate-loaded machines, the effectiveness and safety of power training using plate-loaded machines compared to pneumatic machines is an important consideration. The purpose of this investigation was to compare the effects of high-velocity training using pneumatic machines (Pn) versus standard plate-loaded machines (PL). Independently-living older adults, 60years or older were randomized into two groups: pneumatic machine (Pn, n=19) and plate-loaded machine (PL, n=17). After 12weeks of high-velocity training twice per week, groups were analyzed using an intention-to-treat approach. Primary outcomes were lower body power measured using a linear transducer and upper body power using medicine ball throw. Secondary outcomes included lower and upper body muscle muscle strength, the Physical Performance Battery (PPB), gallon jug test, the timed up-and-go test, and self-reported function using the Patient Reported Outcomes Measurement Information System (PROMIS) and an online video questionnaire. Outcome assessors were blinded to group membership. Lower body power significantly improved in both groups (Pn: 19%, PL: 31%), with no significant difference between the groups (Cohen's d=0.4, 95% CI (-1.1, 0.3)). Upper body power significantly improved only in the PL group, but showed no significant difference between the groups (Pn: 3%, PL: 6%). For balance, there was a significant difference between the groups favoring the Pn group (d=0.7, 95% CI (0.1, 1.4)); however, there were no statistically significant differences between groups for PPB, gallon jug transfer, muscle muscle strength, timed up-and-go or self-reported function. No serious adverse events were reported in either of the groups. Pneumatic and plate

10 CFR 707.7 - Random drug testing requirements and identification of testing designated positions.

Science.gov (United States)

2010-01-01

... contractor, to have the potential to significantly affect the environment, public health and safety, or... evidence of the use of illegal drugs of employees in testing designated positions identified in this... section shall provide for random tests at a rate equal to 30 percent of the total number of employees in...

Sonographic evaluation of the fetal spine position and success rate of manual rotation of the fetus in occiput posterior position: A randomized controlled trial.

Science.gov (United States)

Masturzo, Bianca; Farina, Antonio; Attamante, Lorenza; Piazzese, Annalisa; Rolfo, Alessandro; Gaglioti, Pietro; Todros, Tullia

2017-10-01

To evaluate whether sonographic (US) diagnosis of the fetal spine position could increase the success rate of manual rotation of the fetal occiput (MRFO) in second-stage arrest in persistent occiput posterior position (OPP). In this randomized controlled parallel single-center trial, 58 nulliparous in second-stage arrest of labor with fetus in cephalic presentation and OPP diagnosed by US were randomly assigned to group A where the fetal spine position was not known by the operator or to group B where the operator knew it. The main outcome was the success of MRFO in the two groups. Secondary outcomes were perineal injuries, blood loss, duration of expulsive period, and neonatal APGAR at 5 minutes. A priori knowledge of the spine position improves the success of the MRFO (41.4% group A versus 82.8% group B, p value < 0.001), the percentage of spontaneous deliveries (27.6% group A versus 69% group B, p value = 0.01), and maternal outcome (intact perineum and blood loss). No differences were detected on the neonatal side. MRFO is a safe and useful procedure that should be performed in second-stage arrest in OPP. A better performance was observed when supported by the US knowledge of the spine position. © 2017 Wiley Periodicals, Inc. J Clin Ultrasound 45:472-476, 2017. © 2017 Wiley Periodicals, Inc.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Flocking small smart machines: An experiment in cooperative, multi-machine control

International Nuclear Information System (INIS)

Klarer, P.R.

1998-03-01

The intent and purpose of this work was to investigate and demonstrate cooperative behavior among a group of mobile robot machines. The specific goal of this work was to build a small swarm of identical machines and control them in such a way as to show a coordinated movement of the group in a flocking manner, similar to that observed in nature. Control of the swarm's individual members and its overall configuration is available to the human user via a graphic man-machine interface running on a base station control computer. Any robot may be designated as the nominal leader through the interface tool, which then may be commanded to proceed to a particular geographic destination. The remainder of the flock follows the leader by maintaining their relative positions in formation, as specified by the human controller through the interface. The formation's configuration can be altered manually through an interactive graphic-based tool. An alternative mode of control allows for teleoperation of one robot, with the flock following along as described above

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

Directory of Open Access Journals (Sweden)

Santana Isabel

2011-08-01

Full Text Available Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI, but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery

Science.gov (United States)

Piragnolo, Marco; Masiero, Andrea; Pirotti, Francesco

2017-04-01

Since recent years surveying with unmanned aerial vehicles (UAV) is getting a great amount of attention due to decreasing costs, higher precision and flexibility of usage. UAVs have been applied for geomorphological investigations, forestry, precision agriculture, cultural heritage assessment and for archaeological purposes. It can be used for land use and land cover classification (LULC). In literature, there are two main types of approaches for classification of remote sensing imagery: pixel-based and object-based. On one hand, pixel-based approach mostly uses training areas to define classes and respective spectral signatures. On the other hand, object-based classification considers pixels, scale, spatial information and texture information for creating homogeneous objects. Machine learning methods have been applied successfully for classification, and their use is increasing due to the availability of faster computing capabilities. The methods learn and train the model from previous computation. Two machine learning methods which have given good results in previous investigations are Random Forest (RF) and Support Vector Machine (SVM). The goal of this work is to compare RF and SVM methods for classifying LULC using images collected with a fixed wing UAV. The processing chain regarding classification uses packages in R, an open source scripting language for data analysis, which provides all necessary algorithms. The imagery was acquired and processed in November 2015 with cameras providing information over the red, blue, green and near infrared wavelength reflectivity over a testing area in the campus of Agripolis, in Italy. Images were elaborated and ortho-rectified through Agisoft Photoscan. The ortho-rectified image is the full data set, and the test set is derived from partial sub-setting of the full data set. Different tests have been carried out, using a percentage from 2 % to 20 % of the total. Ten training sets and ten validation sets are obtained from

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...

Design of Control System for Kiwifruit Automatic Grading Machine

Directory of Open Access Journals (Sweden)

Xingjian Zuo

2013-05-01

Full Text Available The kiwifruit automatic grading machine is an important machine for postharvest processing of kiwifruit, and the control system ensures that the machine realizes intelligence. The control system for the kiwifruit automatic grading machine designed in this paper comprises a host computer and a slave microcontroller. The host computer provides a visual grading interface for the machine with a LabVIEW software, the slave microcontroller adopts an STC89C52 microcontroller as its core, and C language is used to write programs for controlling a position sensor module, push-pull type electromagnets, motor driving modules and a power supply for controlling the operation of the machine as well as the rise or descend of grading baffle plates. The ideal control effect is obtained through test, and the intelligent operation of the machine is realized.

Reporting of Positive Results in Randomized Controlled Trials of Mindfulness-Based Mental Health Interventions.

Directory of Open Access Journals (Sweden)

Stephanie Coronado-Montoya

Full Text Available A large proportion of mindfulness-based therapy trials report statistically significant results, even in the context of very low statistical power. The objective of the present study was to characterize the reporting of "positive" results in randomized controlled trials of mindfulness-based therapy. We also assessed mindfulness-based therapy trial registrations for indications of possible reporting bias and reviewed recent systematic reviews and meta-analyses to determine whether reporting biases were identified.CINAHL, Cochrane CENTRAL, EMBASE, ISI, MEDLINE, PsycInfo, and SCOPUS databases were searched for randomized controlled trials of mindfulness-based therapy. The number of positive trials was described and compared to the number that might be expected if mindfulness-based therapy were similarly effective compared to individual therapy for depression. Trial registries were searched for mindfulness-based therapy registrations. CINAHL, Cochrane CENTRAL, EMBASE, ISI, MEDLINE, PsycInfo, and SCOPUS were also searched for mindfulness-based therapy systematic reviews and meta-analyses.108 (87% of 124 published trials reported ≥1 positive outcome in the abstract, and 109 (88% concluded that mindfulness-based therapy was effective, 1.6 times greater than the expected number of positive trials based on effect size d = 0.55 (expected number positive trials = 65.7. Of 21 trial registrations, 13 (62% remained unpublished 30 months post-trial completion. No trial registrations adequately specified a single primary outcome measure with time of assessment. None of 36 systematic reviews and meta-analyses concluded that effect estimates were overestimated due to reporting biases.The proportion of mindfulness-based therapy trials with statistically significant results may overstate what would occur in practice.

DESIGN EVALUATIONS OF DOUBLE ROTOR SWITCHED RELUCTANCE MACHINE

Directory of Open Access Journals (Sweden)

C.V. ARAVIND

2016-02-01

Full Text Available The absence of magnets makes the reluctance machine typical for low cogging operations with the torque depending on the stator rotor interaction area. The air gap between stator pole and rotor pole gives a huge effect on the reluctance variation. The primitive double rotor switched reluctance machine lags to improvise the effect of the ripple value though the torque density is higher compared to conventional machines. An optimised circular hole position and dimensioned in the stator pole of lowers the torque ripple and reduce the acoustic noise as presented in this paper. A comparative evaluation of the conventional double rotor machine with this improved structure is done through numerical design and evaluations for the same sizing. It is found that the motor constant square density. It is found that the double rotor switched reluctance machine is improved by 140% to conventional machine.

Fully automatic CNC machining production system

Directory of Open Access Journals (Sweden)

Lee Jeng-Dao

2017-01-01

Full Text Available Customized manufacturing is increasing years by years. The consumption habits change has been cause the shorter of product life cycle. Therefore, many countries view industry 4.0 as a target to achieve more efficient and more flexible automated production. To develop an automatic loading and unloading CNC machining system via vision inspection is the first step in industrial upgrading. CNC controller is adopted as the main controller to command to the robot, conveyor, and other equipment in this study. Moreover, machine vision systems are used to detect position of material on the conveyor and the edge of the machining material. In addition, Open CNC and SCADA software will be utilized to make real-time monitor, remote system of control, alarm email notification, and parameters collection. Furthermore, RFID has been added to employee classification and management. The machine handshaking has been successfully proposed to achieve automatic vision detect, edge tracing measurement, machining and system parameters collection for data analysis to accomplish industrial automation system integration with real-time monitor.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.

Science.gov (United States)

Pasupa, Kitsuchart; Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets-Maximum Unbiased Validation Dataset-which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.

Effect of machining parameters on surface integrity of silicon carbide ceramic using end electric discharge milling and mechanical grinding hybrid machining

International Nuclear Information System (INIS)

Ji, Renjie; Liu, Yonghong; Zhang, Yanzhen; Cai, Baoping; Li, Xiaopeng; Zheng, Chao

2013-01-01

A novel hybrid process that integrates end electric discharge (ED) milling and mechanical grinding is proposed. The process is able to effectively machine a large surface area on SiC ceramic with good surface quality and fine working environmental practice. The polarity, pulse on-time, and peak current are varied to explore their effects on the surface integrity, such as surface morphology, surface roughness, micro-cracks, and composition on the machined surface. The results show that positive tool polarity, short pulse on-time, and low peak current cause a fine surface finish. During the hybrid machining of SiC ceramic, the material is mainly removed by end ED milling at rough machining mode, whereas it is mainly removed by mechanical grinding at finish machining mode. Moreover, the material from the tool can transfer to the workpiece, and a combination reaction takes place during machining.

MONITORING DIAGNOSTIC INDICATORS DURING OPERATION OF A PRINT MACHIN

Directory of Open Access Journals (Sweden)

Jozef Dobránsky

2015-11-01

Full Text Available This article deals with monitoring diagnostic indicators during the operation of a machine used for production of packing materials with a print. It analyses low-frequency vibrations measured in individual spherical roller bearings in eight print positions. The rollers in these positions have a different pressure based on positioning these rollers in relation to the central roller. As a result, the article also deals with a correlation of pressure and level of measured low-frequency vibrations. The speed of the print machine (the speed of a line in meters per minute is a very important variable during its operation, this is why it is important to verify the values of vibrations in various speeds of the line, what can lead to revelation of one or more resonance areas. Moreover, it examines vibrations of the central roller drive and measurement of backlash of transmission cogs of this drive. Based on performed analyses recommendations for an operator of the machine have been conceived.

Multiple-Machine Scheduling with Learning Effects and Cooperative Games

Directory of Open Access Journals (Sweden)

Yiyuan Zhou

2015-01-01

Full Text Available Multiple-machine scheduling problems with position-based learning effects are studied in this paper. There is an initial schedule in this scheduling problem. The optimal schedule minimizes the sum of the weighted completion times; the difference between the initial total weighted completion time and the minimal total weighted completion time is the cost savings. A multiple-machine sequencing game is introduced to allocate the cost savings. The game is balanced if the normal processing times of jobs that are on the same machine are equal and an equal number of jobs are scheduled on each machine initially.

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

Science.gov (United States)

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

Machine Perfusion or Cold Storage in Deceased-Donor Kidney Transplantation

NARCIS (Netherlands)

Moers, Cyril; Smits, Jacqueline M.; Maathuis, Mark-Hugo J.; Treckmann, Juergen; van Gelder, Frank; Napieralski, Bogdan P.; van Kasterop-Kutz, Margitta; van der Heide, Jaap J. Homan; Squifflet, Jean-Paul; van Heurn, Ernest; Kirste, Guenter R.; Rahmel, Axel; Leuvenink, Henri G. D.; Paul, Andreas; Pirenne, Jacques; Ploeg, Rutger J.

2009-01-01

BACKGROUND Static cold storage is generally used to preserve kidney allografts from deceased donors. Hypothermic machine perfusion may improve outcomes after transplantation, but few sufficiently powered prospective studies have addressed this possibility. METHODS In this international randomized,

ALOHA Random Access that Operates as a Rateless Code

DEFF Research Database (Denmark)

Stefanovic, Cedomir; Popovski, Petar

2013-01-01

Various applications of wireless Machine-to-Machine (M2M) communications have rekindled the research interest in random access protocols, suitable to support a large number of connected devices. Slotted ALOHA and its derivatives represent a simple solution for distributed random access in wireless...... the contention when the instantaneous throughput is maximized. The paper presents the related analysis, providing heuristic criteria for terminating the contention period and showing that very high throughputs can be achieved, even for a low number for contending users. The demonstrated results potentially have...

Construction machine control guidance implementation strategy.

Science.gov (United States)

2010-07-01

Machine Controlled Guidance (MCG) technology may be used in roadway and bridge construction to improve construction efficiencies, potentially resulting in reduced project costs and accelerated schedules. The technology utilizes a Global Positioning S...

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

Science.gov (United States)

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

Maintenance Strategies to Reduce Downtime Due to\\ud Machine Positional Errors

OpenAIRE

Shagluf, Abubaker; Longstaff, Andrew P.; Fletcher, Simon

2014-01-01

Manufacturing strives to reduce waste and increase\\ud Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime\\ud competently and similarly identify the machines’ performance.\\ud Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a pow...

Classification of large-sized hyperspectral imagery using fast machine learning algorithms

Science.gov (United States)

Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira

2017-07-01

We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.

[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.

DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

Directory of Open Access Journals (Sweden)

J. Rhee

2016-06-01

Full Text Available The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6 and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6. An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

Aerobic training in aquatic environment improves the position sense of stroke patients: A randomized clinical trial

Directory of Open Access Journals (Sweden)

Flávia de Andrade e Souza Mazuchi

2018-03-01

Full Text Available Abstract AIMS (Stroke patients often present sensory-motor alterations and less aerobic capacity. Joint position sense, which is crucial for balance and gait control, is also affected in stroke patients. To compare the effect of two exercise training protocols (walking in deep water and on a treadmill on the knee position sense of stroke patients. METHODS This study was designed as a randomized controlled clinical trial. Twelve adults, who suffered a stroke at least one year prior to the start of the study, were randomly assigned to one of two groups: 1 pool group submitted to aerobic deep water walking training; and 2 the treadmill group which was submitted to aerobic walk on a treadmill. Measurements: The position sense, absolute error and variable error, of the knee joint was evaluated prior to and after nine weeks of aerobic training. RESULTS The pool group presented smaller absolute (13.9o versus 6.1o; p < 0.05 and variable (9.2o versus 3.9o; p < 0.05 errors after nine-weeks gait training than the treadmill group. CONCLUSIONS Nine-week aerobic exercise intervention in aquatic environment improved precision in the position sense of the knee joint of stroke patients, suggesting a possible application in a rehabilitation program.

Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System

Directory of Open Access Journals (Sweden)

Yun Mo

2014-01-01

Full Text Available With the fast developing of mobile terminals, positioning techniques based on fingerprinting method draw attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve the system performance, on the one hand, in the paper, we propose a coarse positioning method based on random forest, which is able to customize several subregions, and classify test point to the region with an outstanding accuracy compared with some typical clustering algorithms. On the other hand, through the mathematical analysis in engineering, the proposed kernel principal component analysis algorithm is applied for radio map processing, which may provide better robustness and adaptability compared with linear feature extraction methods and manifold learning technique. We build both theoretical model and real environment for verifying the feasibility and reliability. The experimental results show that the proposed indoor positioning system could achieve 99% coarse locating accuracy and enhance 15% fine positioning accuracy on average in a strong noisy environment compared with some typical fingerprinting based methods.

Torque characteristics of double-stator permanent magnet synchronous machines

Directory of Open Access Journals (Sweden)

Awah Chukwuemeka Chijioke

2017-12-01

Full Text Available The torque profile of a double-stator permanent magnet (PM synchronous machine of 90 mm stator diameter having different rotor pole numbers as well as dual excitation is investigated in this paper. The analysis includes a comparative study of the machine’s torque and power-speed curves, static torque and inductance characteristics, losses and unbalanced magnetic force. The most promising flux-weakening potential is revealed in 13- and 7-rotor pole machines. Moreover, the machines having different rotor/stator (Nr/Ns pole combinations of the form Nr = Ns ± 1 have balanced and symmetric static torque waveforms variation with the rotor position in contrast to the machines having Nr = Ns ± 2. Further, the inductance results of the analyzed machines reveal that the machines with odd rotor pole numbers have better fault-tolerant capability than their even rotor pole equivalents. A prototype of the developed double-stator machine having a 13-pole rotor is manufactured and tested for verification.

Machine learning a probabilistic perspective

CERN Document Server

Murphy, Kevin P

2012-01-01

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic method...

Locking devices on cigarette vending machines: evaluation of a city ordinance.

Science.gov (United States)

Forster, J L; Hourigan, M E; Kelder, S

1992-01-01

OBJECTIVES. Policymakers, researchers, and citizens are beginning to recognize the need to limit minors' access to tobacco by restricting the sale of cigarettes through vending machines. One policy alternative that has been proposed by the tobacco industry is a requirement that vending machines be fitted with electronic locking devices. This study evaluates such a policy as enacted in St. Paul, Minn. METHODS. A random sample of vending machine locations was selected for cigarette purchase attempts conducted before implementation and at 3 and 12 months postimplementation. RESULTS. The rate of noncompliance by merchants was 34% after 3 months and 30% after 1 year. The effect of the law was to reduce the ability of a minor to purchase cigarettes from locations originally selling cigarettes through vending machines from 86% at baseline to 36% at 3 months. The purchase rate at these locations rose to 48% at 1 year. CONCLUSIONS. Our results suggest that cigarette vending machine locking devices may not be as effective as vending machine bans and require additional enforcement to ensure compliance with the law. PMID:1503160

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

Directory of Open Access Journals (Sweden)

Rita Melo

2016-07-01

Full Text Available Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM, for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Predicting the dissolution kinetics of silicate glasses using machine learning

Science.gov (United States)

Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

2018-05-01

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

Corneal Neovascularization with Associated Lipid Keratopathy in a Patient with Obstructive Sleep Apnea-Hypopnea Syndrome Using a Continuous Positive Airway Pressure Machine

Directory of Open Access Journals (Sweden)

Konstantinos Oikonomakis

2017-08-01

Full Text Available Objective: To report a case of corneal neovascularization with secondary lipid keratopathy in a patient treated with continuous positive airway pressure (CPAP for obstructive sleep apnea-hypopnea syndrome (OSAHS. Case Report: A 49-year-old male had been diagnosed with obstructive sleep apnea syndrome 10 years ago and has been treated with the application of a CPAP machine during night sleep ever since. For the past year, the patient had been complaining for ocular irritation and excessive tearing of the left eye on awakening. Slit-lamp biomicroscopy revealed the presence of neovascularization and lipid exudation in the inferior third of the cornea of the left eye. Ocular patching during night sleep resulted in recession of the reported symptoms and shrinkage of the neovascularization, while the area of lipid exudation ceased to enlarge. Conclusion: To the best of our knowledge, this is the first report of corneal neovascularization in a patient using a CPAP machine for OSAHS.

Corneal Neovascularization with Associated Lipid Keratopathy in a Patient with Obstructive Sleep Apnea-Hypopnea Syndrome Using a Continuous Positive Airway Pressure Machine.

Science.gov (United States)

Oikonomakis, Konstantinos; Petrelli, Myrsini; Andreanos, Konstantinos; Mouchtouris, Andreas; Petrou, Petros; Georgalas, Ilias; Papaconstantinou, Dimitrios; Kymionis, George

2017-01-01

To report a case of corneal neovascularization with secondary lipid keratopathy in a patient treated with continuous positive airway pressure (CPAP) for obstructive sleep apnea-hypopnea syndrome (OSAHS). A 49-year-old male had been diagnosed with obstructive sleep apnea syndrome 10 years ago and has been treated with the application of a CPAP machine during night sleep ever since. For the past year, the patient had been complaining for ocular irritation and excessive tearing of the left eye on awakening. Slit-lamp biomicroscopy revealed the presence of neovascularization and lipid exudation in the inferior third of the cornea of the left eye. Ocular patching during night sleep resulted in recession of the reported symptoms and shrinkage of the neovascularization, while the area of lipid exudation ceased to enlarge. To the best of our knowledge, this is the first report of corneal neovascularization in a patient using a CPAP machine for OSAHS.

Classification of Cytochrome P450 1A2 Inhibitors and Non-Inhibitors by Machine Learning Techniques

DEFF Research Database (Denmark)

Vasanthanathan, Poongavanam; Taboureau, Olivier; Oostenbrink, Chris

2009-01-01

of CYP1A2 inhibitors and non-inhibitors. Training and test sets consisted of about 400 and 7000 compounds, respectively. Various machine learning techniques, like binary QSAR, support vector machine (SVM), random forest, kappa nearest neighbors (kNN), and decision tree methods were used to develop...

Manufacturing Methods for Cutting, Machining and Drilling Composites. Volume 1. Composites Machining Handbook

Science.gov (United States)

1978-08-01

12°±30’ 1180±2° OPTIONAL .0005 IN./IN. BACK TAPER 015 RAD LIPS TO BE WITHIN .002 OF TRUE ANGULAR POSITION NOTES: 1. LAND WIDTH: 28% ± .005... horoscope and dye-penetrant requirements. 79 PHASE 1 PHASE II PHASE III PHASE IV CUTTING DRILLING MACHINING NONDESTRUCTIVE EVALUATION METHOD MATERIAL

Learning About Climate and Atmospheric Models Through Machine Learning

Science.gov (United States)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

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 ...

Hydraulic Power Plant Machine Dynamic Diagnosis

Directory of Open Access Journals (Sweden)

Hans Günther Poll

2006-01-01

Full Text Available A method how to perform an entire structural and hydraulic diagnosis of prototype Francis power machines is presented and discussed in this report. Machine diagnosis of Francis units consists on a proper evaluation of acquired mechanical, thermal and hydraulic data obtained in different operating conditions of several rotary and non rotary machine components. Many different physical quantities of a Francis machine such as pressure, strains, vibration related data, water flow, air flow, position of regulating devices and displacements are measured in a synchronized way so that a relation of cause an effect can be developed for each operating condition and help one to understand all phenomena that are involved with such kind of machine. This amount of data needs to be adequately post processed in order to allow correct interpretation of the machine dynamics and finally these data must be compared with the expected calculated data not only to fine tuning the calculation methods but also to accomplish fully understanding of the influence of the water passages on such machines. The way how the power plant owner has to operate its Francis machines, many times also determined by a central dispatcher, has a high influence on the fatigue life time of the machine components. The diagnostic method presented in this report helps one to understand the importance of adequate operation to allow a low maintenance cost for the entire power plant. The method how to acquire these quantities is discussed in details together with the importance of correct sensor balancing, calibration and adequate correlation with the physical quantities. Typical results of the dynamic machine behavior, with adequate interpretation, obtained in recent measurement campaigns of some important hydraulic turbines were presented. The paper highlights the investigation focus of the hydraulic machine behavior and how to tailor the measurement strategy to accomplish all goals. Finally some

Prediction of Baseflow Index of Catchments using Machine Learning Algorithms

Science.gov (United States)

Yadav, B.; Hatfield, K.

2017-12-01

We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized

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.

Classification of Listeria monocytogenes persistence in retail delicatessen environments using expert elicitation and machine learning.

Science.gov (United States)

Vangay, P; Steingrimsson, J; Wiedmann, M; Stasiewicz, M J

2014-10-01

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts' classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence. © 2014 Society for Risk Analysis.

A machine protection beam position monitor system

International Nuclear Information System (INIS)

Medvedko, E.; Smith, S.; Fisher, A.

1998-01-01

Loss of the stored beam in an uncontrolled manner can cause damage to the PEP-II B Factory. We describe here a device which detects large beam position excursions or unexpected beam loss and triggers the beam abort system to extract the stored beam safely. The bad-orbit abort trigger beam position monitor (BOAT BPM) generates a trigger when the beam orbit is far off the center (>20 mm), or rapid beam current loss (dI/dT) is detected. The BOAT BPM averages the input signal over one turn (136 kHz). AM demodulation is used to convert input signals at 476 MHz to baseband voltages. The detected signal goes to a filter section for suppression of the revolution frequency, then on to amplifiers, dividers, and comparators for position and current measurements and triggering. The derived current signal goes to a special filter, designed to perform dI/dT monitoring at fast, medium, and slow current loss rates. The BOAT BPM prototype test results confirm the design concepts. copyright 1998 American Institute of Physics

Aerobic training in aquatic environment improves the position sense of stroke patients: A randomized clinical trial

OpenAIRE

Flávia de Andrade e Souza Mazuchi; Aline Bigongiari; Juliana Valente Francica; Patricia Martins Franciulli; Luis Mochizuki; Joseph Hamill; Ulysses Fernandes Ervilha

2018-01-01

Abstract AIMS (Stroke patients often present sensory-motor alterations and less aerobic capacity. Joint position sense, which is crucial for balance and gait control, is also affected in stroke patients). To compare the effect of two exercise training protocols (walking in deep water and on a treadmill) on the knee position sense of stroke patients. METHODS This study was designed as a randomized controlled clinical trial. Twelve adults, who suffered a stroke at least one year prior to the ...

Migration of supervisory machine control architectures

NARCIS (Netherlands)

Graaf, B.; Weber, S.; Deursen, van A.; Nord, R.; Medvidovic, N.; Krikhaar, R.; Stafford, J.; Bosch, J.

2005-01-01

In this position paper, we discuss a first step towards an approach for the migration of supervisory machine control (SMC) architectures. This approach is based on the identification of SMC concerns and the definition of corresponding transformation rules.

Positive Psychology Interventions for Patients With Heart Disease: A Preliminary Randomized Trial.

Science.gov (United States)

Nikrahan, Gholam Reza; Suarez, Laura; Asgari, Karim; Beach, Scott R; Celano, Christopher M; Kalantari, Mehrdad; Abedi, Mohammad Reza; Etesampour, Ali; Abbas, Rezaei; Huffman, Jeff C

2016-01-01

Positive psychologic characteristics have been linked to superior cardiac outcomes. Accordingly, in this exploratory study, we assessed positive psychology interventions in patients who had recently undergone a procedure to treat cardiovascular disease. Participants were randomly assigned to receive 1 of 3 different 6-week face-to-face interventions or a wait-list control condition. We assessed intervention feasibility and compared changes in psychologic outcome measures postintervention (7wk) and at follow-up (15wk) between intervention and control participants. Across the interventions, 74% of assigned sessions were completed. When comparing outcomes between interventions and control participants (N = 55 total), there were no between-group differences post-intervention, but at follow-up intervention participants had greater improvements in happiness (β = 14.43, 95% CI: 8.66-20.2, p psychology intervention for cardiac patients. Copyright © 2016 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

High power linear electric machine - made possible by gas springs

Energy Technology Data Exchange (ETDEWEB)

Hoff, E.; Brennvall, J.E.; Nilssen, R.; Norum, L.

2004-07-01

In some applications, such as compressors, free piston linear machines have several advantages compared to rotating machines. The power level of linear machines has been limited, mainly due to difficulties with the spring. A solution for this has now been found and will be described in this paper. It can open up new areas of applications, where the power level exceeds the present power limit of about 2 kW. This machine needs special regulators in order to work efficiently. Two regulator algorithms for piston phase and one for position amplitude are therefore implemented for this prototype. (author)

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

Science.gov (United States)

Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

2016-04-01

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

Tensor manifold-based extreme learning machine for 2.5-D face recognition

Science.gov (United States)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

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)

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

Science.gov (United States)

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Machine Learning-Empowered Biometric Methods for Biomedicine Applications

Directory of Open Access Journals (Sweden)

Qingxue Zhang

2017-07-01

Full Text Available Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG and electroencephalogram (EEG, considering that they are both unique to individuals, and more reliable than token (identity card and knowledge-based (username/password methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.

Application of a 16-bit microprocessor to the digital control of machine tools

International Nuclear Information System (INIS)

Issaly, Alain

1979-01-01

After an overview of machine tools (various types, definition standardization, associated technologies for motors and position sensors), this research thesis describes the principles of computer-based digital control: classification of machine tool command systems, machining programming, programming languages, dialog function, interpolation function, servo-control function, tool compensation function. The author reports the application of a 16-bit microprocessor to the computer-based digital control of a machine tool: feasibility, selection of microprocessor, hardware presentation, software development and description, machining mode, translation-loading mode

Large-scale Ising-machines composed of magnetic neurons

Science.gov (United States)

Mizushima, Koichi; Goto, Hayato; Sato, Rie

2017-10-01

We propose Ising-machines composed of magnetic neurons, that is, magnetic bits in a recording track. In large-scale machines, the sizes of both neurons and synapses need to be reduced, and neat and smart connections among neurons are also required to achieve all-to-all connectivity among them. These requirements can be fulfilled by adopting magnetic recording technologies such as race-track memories and skyrmion tracks because the area of a magnetic bit is almost two orders of magnitude smaller than that of static random access memory, which has normally been used as a semiconductor neuron, and the smart connections among neurons are realized by using the read and write methods of these technologies.

Voice based gender classification using machine learning

Science.gov (United States)

Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

2017-11-01

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

Machine learning in geosciences and remote sensing

Directory of Open Access Journals (Sweden)

David J. Lary

2016-01-01

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

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.

Matrix stochastic analysis of the maintainability of a machine under shocks

International Nuclear Information System (INIS)

Montoro-Cazorla, Delia; Pérez-Ocón, Rafael

2014-01-01

We study the maintenance of a machine operating under environmental conditions producing shocks affecting the lifetime of the machine. The shocks cause different types of damage depending on their strength and eventually the total failure. The maintenance of the machine is performed by repairs and replacement. The interarrival times of shocks are dependent. We introduce a multidimensional stochastic model for simulating the evolution of the lifetime of the machine. This model implies the application of the matrix-analytic methods, that are being used in stochastic modelling with interesting results. Under this methodology, the availability, the reliability, and the rates of occurrence of the different types of failures and of the replacements are calculated, obtaining mathematically tractable expressions. The results are applied to a numerical example. - Highlights: • A machine under random environmental conditions producing shocks and wear is studied under matrix-analytic methods. • There is dependence in the interarrival times of shocks. • Different types of failure producing damage in the internal and external structure of the machine are considered. • Maintenance is performed by repair and replacement. • Explicit expressions for the main reliability performance measures are given

A New Approach to Spindle Radial Error Evaluation Using a Machine Vision System

Directory of Open Access Journals (Sweden)

Kavitha C.

2017-03-01

Full Text Available The spindle rotational accuracy is one of the important issues in a machine tool which affects the surface topography and dimensional accuracy of a workpiece. This paper presents a machine-vision-based approach to radial error measurement of a lathe spindle using a CMOS camera and a PC-based image processing system. In the present work, a precisely machined cylindrical master is mounted on the spindle as a datum surface and variations of its position are captured using the camera for evaluating runout of the spindle. The Circular Hough Transform (CHT is used to detect variations of the centre position of the master cylinder during spindle rotation at subpixel level from a sequence of images. Radial error values of the spindle are evaluated using the Fourier series analysis of the centre position of the master cylinder calculated with the least squares curve fitting technique. The experiments have been carried out on a lathe at different operating speeds and the spindle radial error estimation results are presented. The proposed method provides a simpler approach to on-machine estimation of the spindle radial error in machine tools.

A linear maglev guide for machine tools

Energy Technology Data Exchange (ETDEWEB)

Tieste, K D [Inst. of Mechanics, Univ. of Hannover (Germany); Popp, K [Inst. of Mechanics, Univ. of Hannover (Germany)

1996-12-31

Machine tools require linear guides with high slide velocity and very high position accuracy. The three tasks of a linear guide - supporting, guiding and driving - shall be realised by means of active magnetic bearings (AMB). The resulting linear magnetically levitated (maglev) guide has to accomplish the following characteristics: High stiffness, good damping and low noise as well as low heat production. First research on a one degree-of-freedom (DOF) support magnet unit aimed at the development of components and efficient control strategies for the linear maglev guide. The actual research is directed to realise a five DOF linear maglev guide for machine tools without drive to answer the question whether the maglev principle can be used for a linear axis in a machine tool. (orig.)

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

Directory of Open Access Journals (Sweden)

Shuibo Hu

2018-03-01

Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

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.

A quantum speedup in machine learning: finding an N-bit Boolean function for a classification

International Nuclear Information System (INIS)

Yoo, Seokwon; Lee, Jinhyoung; Bang, Jeongho; Lee, Changhyoup

2014-01-01

We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution. (paper)

Manufacture of mirrors by NC machining of EEM

International Nuclear Information System (INIS)

Hongo, Toshio; Azuma, Yasuo; Kato, Haruo; Hoshino, Hideo

1981-01-01

In the X-ray optical system for the photon factory facility being constructed now in the National Laboratory for High Energy Physics, total reflection mirrors occupy important position. The shapes of mirrors are both plane and curved surface, and the sizes are various. Especially concerning hard X-ray, the required accuracy of the shapes and surface roughness is high. Thereupon mirrors were machined by elastic emission machining (EEM) developed by Mori et al. of Osaka University, and the flatness and surface roughness were examined. The materials machined were Pyrex and copper, the mirror finish of which is difficult. The results are reported. In this machining method, the liquid in which very fine powder is uniformly dispersed and suspended in water was used. By approaching a rotating urethane ball to a work surface, the gap of about 1 μm was formed between them utilizing fluid bearing-like flow arising there. The machining was carried out by colliding the fine particles in suspension to a minute region of the work surface. In order to obtain an arbitrary curved surface, the numerical control according to the variable controling the amount of machining was made. In the case of glasses, the amount of machining was able to be controlled to about 0.01 μm. As for polycrystalline copper, the machining was difficult, and the suitable conditions must be sought hereafter. (Kako, I.)

Traceability of On-Machine Tool Measurement: A Review

Science.gov (United States)

Gomez-Acedo, Eneko; Kortaberria, Gorka; Olarra, Aitor

2017-01-01

Nowadays, errors during the manufacturing process of high value components are not acceptable in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power need complex and accurate components that demand close measurements and fast feedback into their manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They provide the possibility to measure the workpiece in-machine during or after its manufacture, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable enough for process control or product validation. The scientific objective is to determine the uncertainty on a machine tool measurement and, therefore, convert it into a machine integrated traceable measuring process. For that purpose, an error budget should consider error sources such as the machine tools, components under measurement and the interactions between both of them. This paper reviews all those uncertainty sources, being mainly focused on those related to the machine tool, either on the process of geometric error assessment of the machine or on the technology employed to probe the measurand. PMID:28696358

Traceability of On-Machine Tool Measurement: A Review.

Science.gov (United States)

Mutilba, Unai; Gomez-Acedo, Eneko; Kortaberria, Gorka; Olarra, Aitor; Yagüe-Fabra, Jose A

2017-07-11

Nowadays, errors during the manufacturing process of high value components are not acceptable in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power need complex and accurate components that demand close measurements and fast feedback into their manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They provide the possibility to measure the workpiece in-machine during or after its manufacture, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable enough for process control or product validation. The scientific objective is to determine the uncertainty on a machine tool measurement and, therefore, convert it into a machine integrated traceable measuring process. For that purpose, an error budget should consider error sources such as the machine tools, components under measurement and the interactions between both of them. This paper reviews all those uncertainty sources, being mainly focused on those related to the machine tool, either on the process of geometric error assessment of the machine or on the technology employed to probe the measurand.

Foods Sold in School Vending Machines are Associated with Overall Student Dietary Intake

Science.gov (United States)

Rovner, Alisha J.; Nansel, Tonja R.; Wang, Jing; Iannotti, Ronald J.

2010-01-01

Purpose To examine the association between foods sold in school vending machines and students’ dietary behaviors. Methods The 2005-2006 US Health Behavior in School Aged Children (HBSC) survey was administered to 6th to 10th graders and school administrators. Students’ dietary intake was estimated with a brief food frequency measure. Administrators completed questions about foods sold in vending machines. For each food intake behavior, a multilevel regression analysis modeled students (level 1) nested within schools (level 2), with the corresponding food sold in vending machines as the main predictor. Control variables included gender, grade, family affluence and school poverty. Analyses were conducted separately for 6th to 8th and 9th to 10th grades. Results Eighty-three percent of schools (152 schools, 5,930 students) had vending machines which primarily sold foods of minimal nutritional values (soft drinks, chips and sweets). In younger grades, availability of fruits/vegetables and chocolate/sweets was positively related to the corresponding food intake, with vending machine content and school poverty explaining 70.6% of between-school variation in fruit/vegetable consumption, and 71.7% in sweets consumption. In older grades, there was no significant effect of foods available in vending machines on reported consumption of those foods. Conclusions Vending machines are widely available in US public schools. In younger grades, school vending machines were related to students’ diets positively or negatively, depending on what was sold in them. Schools are in a powerful position to influence children’s diets; therefore attention to foods sold in them is necessary in order to try to improve children’s diets. PMID:21185519

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

Science.gov (United States)

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Research on cylindrical indexing cam’s unilateral machining

Directory of Open Access Journals (Sweden)

Junhua Chen

2015-08-01

Full Text Available The cylindrical cam ridge of the indexer is a spatial curved surface, which is difficult to design and machine. The cylindrical cam has some defects after machining because conventional machining methods have inaccuracies. This article aims at proposing a precise way to machine an indexing cam, using basic motion analysis and analytic geometry approach. Analytical methodology is first applied in the cam’s motion analysis, to obtain an error-free cam follower’s trajectory formula, and then separate the continuous trajectory curve by thousandth resolution, to create a three-dimensional discrete trajectory curve. Planar formulae and spherical formulae can be built on the loci. Based on the machine principle, the cutting cutter’s position and orientation will be taken into account. This article calculates the formula set as presented previously and obtains the ultimate cutter path coordinate value. The new error-free cutter path trajectory is called the unilateral machining trajectory. The earned results will compile into numerical control processing schedule. This processing methodology gives a convenient and precision way to manufacture a cylindrical indexing cam. Experimental results are also well supported.

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

Science.gov (United States)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

Position Paper: Applying Machine Learning to Software Analysis to Achieve Trusted, Repeatable Scientific Computing

Energy Technology Data Exchange (ETDEWEB)

Prowell, Stacy J [ORNL; Symons, Christopher T [ORNL

2015-01-01

Producing trusted results from high-performance codes is essential for policy and has significant economic impact. We propose combining rigorous analytical methods with machine learning techniques to achieve the goal of repeatable, trustworthy scientific computing.

Determination of cut front position in laser cutting

Science.gov (United States)

Pereira, M.; Thombansen, U.

2016-07-01

Laser cutting has a huge importance to manufacturing industry. Laser cutting machines operate with fixed technological parameters and this does not guarantee the best productivity. The adjustment of the cutting parameters during operation can improve the machine performance. Based on a coaxial measuring device it is possible to identify the cut front position during the cutting process. This paper describes the data analysis approach used to determine the cut front position for different feed rates. The cut front position was determined with good resolution, but improvements are needed to make the whole process more stable.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

Science.gov (United States)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

A comparison of machine learning techniques for predicting downstream acid mine drainage

CSIR Research Space (South Africa)

van Zyl, TL

2014-07-01

Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

The validation and assessment of machine learning: a game of prediction from high-dimensional data

DEFF Research Database (Denmark)

Pers, Tune Hannes; Albrechtsen, A; Holst, C

2009-01-01

In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often...... the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively....

Coordinated joint motion control system with position error correction

Science.gov (United States)

Danko, George L.

2016-04-05

Disclosed are an articulated hydraulic machine supporting, control system and control method for same. The articulated hydraulic machine has an end effector for performing useful work. The control system is capable of controlling the end effector for automated movement along a preselected trajectory. The control system has a position error correction system to correct discrepancies between an actual end effector trajectory and a desired end effector trajectory. The correction system can employ one or more absolute position signals provided by one or more acceleration sensors supported by one or more movable machine elements. Good trajectory positioning and repeatability can be obtained. A two joystick controller system is enabled, which can in some cases facilitate the operator's task and enhance their work quality and productivity.

Impact of patient position on the incidence of ventilator-associated pneumonia: a meta-analysis of randomized controlled trials.

Science.gov (United States)

Alexiou, Vangelis G; Ierodiakonou, Vrettos; Dimopoulos, George; Falagas, Matthew E

2009-12-01

The aim of this study is to summarize the effect of position (prone and semirecumbent 45 degrees ) of mechanically ventilated patients on the incidence of ventilator-associated pneumonia (VAP) and other outcomes. A systematic search for randomized control trials (RCTs) was done. We estimated pooled odds ratios (ORs) and 95% confidence intervals (CIs) using fixed effects model or random effects model, where appropriate. For continuous variables, we calculated the estimation of weighted mean differences. We analyzed data extracted from 3 RCTs studying the semirecumbent 45 degrees and 4 RCTs studying the prone position with a total of 337 and 1018 patients, respectively. The odds of developing clinically diagnosed VAP were significantly lower among patients in the semirecumbent 45 degrees position compared to patients in the supine position (OR = 0.47; 95% CI, 0.27-0.82; 337 patients). The comparison of prone vs supine position group showed a moderate trend toward better outcomes regarding the incidence of clinically diagnosed VAP among patients in the prone position (OR = 0.80; 95% CI, 0.60-1.08; 1018 patients). The subanalysis regarding the incidence of microbiologically documented VAP, the length of intensive care unit stay, and the duration of mechanical ventilation showed that patients in the semirecumbent 45 degrees position have a moderate trend toward better clinical outcomes. This meta-analysis provides additional evidence that the usual practice of back-rest elevation of 15 degrees to 30 degrees is not sufficient to prevent VAP in mechanically ventilated patients. Patients positioned semirecumbently 45 degrees have significantly lower incidence of clinically diagnosed VAP compared to patients positioned supinely. On the other hand, the incidence of clinically diagnosed VAP among patients positioned pronely does not differ significantly from the incidence of clinically diagnosed VAP among patients positioned supinely.

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.

Anesthesia machine checkout and room setup: a randomized, single-blind, comparison of two teaching modalities.

Science.gov (United States)

Spofford, Christina M; Bayman, Emine O; Szeluga, Debra J; From, Robert P

2012-01-01

Novel methods for teaching are needed to enhance the efficiency of academic anesthesia departments as well as provide approaches to learning that are aligned with current trends and advances in technology. A video was produced that taught the key elements of anesthesia machine checkout and room set up. Novice learners were randomly assigned to receive either the new video format or traditional lecture-based format for this topic during their regularly scheduled lecture series. Primary outcome was the difference in written examination score before and after teaching between the two groups. Secondary outcome was the satisfaction score of the trainees in the two groups. Forty-two students assigned to the video group and 36 students assigned to the lecture group completed the study. Students in each group similar interest in anesthesia, pre-test scores, post-test scores, and final exam scores. The median posttest to pretest difference was greater in the video groups (3.5 (3.0-5.0) vs 2.5 (2.0-3.0), for video and lecture groups respectively, p 0.002). Despite improved test scores, students reported higher satisfaction the traditional, lecture-based format (22.0 (18.0-24.0) vs 24.0 (20.0-28.0), for video and lecture groups respectively, p students in the video-based teaching group, however students rated traditional, live lectures higher than newer video-based teaching.

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Science.gov (United States)

Zdravevski, Eftim; Risteska Stojkoska, Biljana; Standl, Marie; Schulz, Holger

2017-01-01

Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Directory of Open Access Journals (Sweden)

Eftim Zdravevski

Full Text Available Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers.The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be

A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

Science.gov (United States)

Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

2013-01-01

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Directory of Open Access Journals (Sweden)

Ching Lee Koo

2013-01-01

Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A Comparison of Machine Learning Approaches for Corn Yield Estimation

Science.gov (United States)

Kim, N.; Lee, Y. W.

2017-12-01

Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.

SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

International Nuclear Information System (INIS)

Jia, J; Cao, R; Pei, X; Wang, H; Hu, L

2015-01-01

Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy

SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Jia, J; Cao, R; Pei, X; Wang, H; Hu, L [Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031 (China); Engineering Technology Research Center of Accurate Radiotherapy of Anhui Province, Hefei 230031 (China); Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, SuZhou (China)

2015-06-15

Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.

Machine for compacting solid residues

International Nuclear Information System (INIS)

Herzog, J.

1981-11-01

Machine for compacting solid residues, particularly bulky radioactive residues, constituted of a horizontally actuated punch and a fixed compression anvil, in which the residues are first compacted horizontally and then vertically. Its salient characteristic is that the punch and the compression anvil have embossments on the compression side and interpenetrating plates in the compression position [fr

Large Neighborhood Search and Adaptive Randomized Decompositions for Flexible Jobshop Scheduling

DEFF Research Database (Denmark)

Pacino, Dario; Van Hentenryck, Pascal

2011-01-01

This paper considers a constraint-based scheduling approach to the flexible jobshop, a generalization of the traditional jobshop scheduling where activities have a choice of machines. It studies both large neighborhood (LNS) and adaptive randomized de- composition (ARD) schemes, using random...

An incremental anomaly detection model for virtual machines.

Directory of Open Access Journals (Sweden)

Hancui Zhang

Full Text Available Self-Organizing Map (SOM algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

An incremental anomaly detection model for virtual machines

Science.gov (United States)

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

TMI-2 core boring machine

International Nuclear Information System (INIS)

Croft, K.M.; Helbert, H.J.; Laney, W.M.

1986-01-01

An important and essential aspect of the TMI-2 defueling effort is to determine what occurred in the core region during the accident. Remote cameras and probes only portray a portion of the overall picture. What lies beneath the rubble bed and solidified sublayer is, as yet, unknown. This paper discusses the TMI-2 Core Boring Machine, which has been developed to drill into the damaged core of the TMI-2 reactor and extract stratified samples of the core. This machine, its unique support structure, positioning and leveling systems, and specially designed drill bits, combine to provide a unique mechanical system. In addition, the machine is controlled by a microprocessor; which actually controls the drilling operation, allowing relatively inexperienced operators to drill the core samples. A data acquisition system is data integral with the controlling system and collects data relative to system conditions and monitored parameters during drilling. Data obtained during the actual drilling operations are collected in a data base which will be used for actual mapping of the core region, identifying materials and stratification levels that are present

Sine-Bar Attachment For Machine Tools

Science.gov (United States)

Mann, Franklin D.

1988-01-01

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

Optimization on robot arm machining by using genetic algorithms

Science.gov (United States)

Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En

2007-12-01

In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.

A comparison of free weight squat to Smith machine squat using electromyography.

Science.gov (United States)

Schwanbeck, Shane; Chilibeck, Philip D; Binsted, Gordon

2009-12-01

The purpose of this experiment was to determine whether free weight or Smith machine squats were optimal for activating the prime movers of the legs and the stabilizers of the legs and the trunk. Six healthy participants performed 1 set of 8 repetitions (using a weight they could lift 8 times, i.e., 8RM, or 8 repetition maximum) for each of the free weight squat and Smith machine squat in a randomized order with a minimum of 3 days between sessions, while electromyographic (EMG) activity of the tibialis anterior, gastrocnemius, vastus medialis, vastus lateralis, biceps femoris, lumbar erector spinae, and rectus abdominus were simultaneously measured. Electromyographic activity was significantly higher by 34, 26, and 49 in the gastrocnemius, biceps femoris, and vastus medialis, respectively, during the free weight squat compared to the Smith machine squat (p free weight and Smith machine squat for any of the other muscles; however, the EMG averaged over all muscles during the free weight squat was 43% higher when compared to the Smith machine squat (p free weight squat may be more beneficial than the Smith machine squat for individuals who are looking to strengthen plantar flexors, knee flexors, and knee extensors.

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

Science.gov (United States)

Sakiyama, Yojiro

2009-02-01

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

Food sold in school vending machines is associated with overall student dietary intake.

Science.gov (United States)

Rovner, Alisha J; Nansel, Tonja R; Wang, Jing; Iannotti, Ronald J

2011-01-01

To examine the association between food sold in school vending machines and the dietary behaviors of students. The 2005-2006 U.S. Health Behavior in School-aged Children survey was administered to 6th to 10th graders and school administrators. Dietary intake in students was estimated with a brief food frequency measure. School administrators completed questions regarding food sold in vending machines. For each food intake behavior, a multilevel regression analysis modeled students (level 1) nested within schools (level 2), with the corresponding food sold in vending machines as the main predictor. Control variables included gender, grade, family affluence, and school poverty index. Analyses were conducted separately for 6th to 8th and 9th-10th grades. In all, 83% of the schools (152 schools; 5,930 students) had vending machines that primarily sold food of minimal nutritional values (soft drinks, chips, and sweets). In younger grades, availability of fruit and/or vegetables and chocolate and/or sweets was positively related to the corresponding food intake, with vending machine content and school poverty index providing an explanation for 70.6% of between-school variation in fruit and/or vegetable consumption and 71.7% in sweets consumption. Among the older grades, there was no significant effect of food available in vending machines on reported consumption of those food. Vending machines are widely available in public schools in the United States. In younger grades, school vending machines were either positively or negatively related to the diets of the students, depending on what was sold in them. Schools are in a powerful position to influence the diets of children; therefore, attention to the food sold at school is necessary to try to improve their diets. Copyright © 2011 Society for Adolescent Health and Medicine. All rights reserved.

A Multicenter, Randomized Trial of Ramped Position vs Sniffing Position During Endotracheal Intubation of Critically Ill Adults.

Science.gov (United States)

Semler, Matthew W; Janz, David R; Russell, Derek W; Casey, Jonathan D; Lentz, Robert J; Zouk, Aline N; deBoisblanc, Bennett P; Santanilla, Jairo I; Khan, Yasin A; Joffe, Aaron M; Stigler, William S; Rice, Todd W

2017-10-01

Hypoxemia is the most common complication during endotracheal intubation of critically ill adults. Intubation in the ramped position has been hypothesized to prevent hypoxemia by increasing functional residual capacity and decreasing the duration of intubation, but has never been studied outside of the operating room. Multicenter, randomized trial comparing the ramped position (head of the bed elevated to 25°) with the sniffing position (torso supine, neck flexed, and head extended) among 260 adults undergoing endotracheal intubation by pulmonary and critical care medicine fellows in four ICUs between July 22, 2015, and July 19, 2016. The primary outcome was lowest arterial oxygen saturation between induction and 2 minutes after intubation. Secondary outcomes included Cormack-Lehane grade of glottic view, difficulty of intubation, and number of laryngoscopy attempts. The median lowest arterial oxygen saturation was 93% (interquartile range [IQR], 84%-99%) with the ramped position vs 92% (IQR, 79%-98%) with the sniffing position (P = .27). The ramped position appeared to increase the incidence of grade III or IV view (25.4% vs 11.5%, P = .01), increase the incidence of difficult intubation (12.3% vs 4.6%, P = .04), and decrease the rate of intubation on the first attempt (76.2% vs 85.4%, P = .02), respectively. In this multicenter trial, the ramped position did not improve oxygenation during endotracheal intubation of critically ill adults compared with the sniffing position. The ramped position may worsen glottic view and increase the number of laryngoscopy attempts required for successful intubation. ClinicalTrials.gov; No.: NCT02497729; URL: www.clinicaltrials.gov. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

VOLUMETRIC ERROR COMPENSATION IN FIVE-AXIS CNC MACHINING CENTER THROUGH KINEMATICS MODELING OF GEOMETRIC ERROR

Directory of Open Access Journals (Sweden)

Pooyan Vahidi Pashsaki

2016-06-01

Full Text Available Accuracy of a five-axis CNC machine tool is affected by a vast number of error sources. This paper investigates volumetric error modeling and its compensation to the basis for creation of new tool path for improvement of work pieces accuracy. The volumetric error model of a five-axis machine tool with the configuration RTTTR (tilting head B-axis and rotary table in work piece side A΄ was set up taking into consideration rigid body kinematics and homogeneous transformation matrix, in which 43 error components are included. Volumetric error comprises 43 error components that can separately reduce geometrical and dimensional accuracy of work pieces. The machining accuracy of work piece is guaranteed due to the position of the cutting tool center point (TCP relative to the work piece. The cutting tool is deviated from its ideal position relative to the work piece and machining error is experienced. For compensation process detection of the present tool path and analysis of the RTTTR five-axis CNC machine tools geometrical error, translating current position of component to compensated positions using the Kinematics error model, converting newly created component to new tool paths using the compensation algorithms and finally editing old G-codes using G-code generator algorithm have been employed.

Work stress of women in sewing machine operation.

Science.gov (United States)

Nag, A; Desai, H; Nag, P K

1992-06-01

The study examined the work stresses of 107 women who were engaged in sewing machine operation in small garment manufacturing units. Of the three types of sewing machines (motor-operated, full and half shuttle foot-operated), 74% of the machines were foot-operated, where throttle action of the lower limb is required to move the shuttle of the machine. The motor-operated machines were faster than the foot-operated machines. The short cycle sewing work involves repetitive action of hand and feet. The women had to maintain a constant seated position on a stool without backrest and the body inclined forward. Long-term sewing work had a cumulative load on the musculo-skeletal structures, including the vertebral column and reflected in the form of high prevalence of discomfort and pain in different body parts. About 68% of the women complained of back pain, among whom 35% reported a persistent low back pain. Common sewing work accident is piercing of the needle through the fingers, particularly the right forefingers. Unsatisfactory man-machine incompatibility, work posture and fatigue, improper coordination of eye, leg and hand are the major problems of the operators. The design mis-match of the work place may be significantly improved by taking women's anthropometric dimensions in modifying the workplace, i.e. the seat surface, seat height, work height, backrest, etc.

Machine learning and pattern recognition from surface molecular architectures.

Science.gov (United States)

Maksov, Artem; Ziatdinov, Maxim; Fujii, Shintaro; Sumpter, Bobby; Kalinin, Sergei

The ability to utilize molecular assemblies as data storage devices requires capability to identify individual molecular states on a scale of thousands of molecules. We present a novel method of applying machine learning techniques for extraction of positional and rotational information from ultra-high vacuum scanning tunneling microscopy (STM) images and apply it to self-assembled monolayer of π-bowl sumanene molecules on gold. From density functional theory (DFT) simulations, we assume existence of distinct polar and multiple azimuthal rotational states. We use DFT-generated templates in conjunction with Markov Chain Monte Carlo (MCMC) sampler and noise modeling to create synthetic images representative of our model. We extract positional information of each molecule and use nearest neighbor criteria to construct a graph input to Markov Random Field (MRF) model to identify polar rotational states. We train a convolutional Neural Network (cNN) on a synthetic dataset and combine it with MRF model to classify molecules based on their azimuthal rotational state. We demonstrate effectiveness of such approach compared to other methods. Finally, we apply our approach to experimental images and achieve complete rotational class information extraction. This research was sponsored by the Division of Materials Sciences and Engineering, Office of Science, Basic Energy Sciences, US DOE.

Design of precision position adjustable scoop

International Nuclear Information System (INIS)

Li Zhili; Zhang Kai; Dong Jinping

2014-01-01

In isotopes separation technologies, the centrifuge method has been the most popular technology now. Separation performance of centrifugal machines is greatly influenced by the flow field in the centrifugal machines. And the position of scoops in the centrifuges has a significant influence on the flow field. To obtain a better flow field characteristic and find the best position of scoops in the centrifuges, a position adjustable scoop system was studied. A micro stage and a linear encoder were used in the system to improve the position accuracy of the scoop. Eddy current sensors had been used in a position calibration measurement. The measurement result showed the sensitivity and stability of the position system could meet the performance expectation. But as the driving mean, the steel wire and pulley limit the control precision. On the basis of this scheme, an ultrasonic motor was used as driving mean. Experimental results showed the control accuracy was improved. This scheme laid a foundation to obtain internal flow field parameters of centrifuge and get the optimal feeding tube position. (authors)

Effect of Positioning and Early Ambulation on Coronary Angiography Complications: a Randomized Clinical Trial.

Science.gov (United States)

Abdollahi, Ali Akbar; Mehranfard, Shahzad; Behnampour, Nasser; Kordnejad, Abdol Mohamad

2015-06-01

After coronary angiography to prevent potential complications, patients are restricted to 4-24 hours bed rest in the supine position due to the complications. This study was designed to assess the effect of changing position and early ambulation on low back pain, urinary retention, bleeding and hematoma after cardiac catheterization. In this clinical trial, 140 patients by using a convenience sampling randomly divided into four 35-individual groups. The patients in the control group were in the supine position for 6 hours without a movement. Change position was applied to the second group (based on a specific protocol), early ambulation was applied to the third group and both early ambulation and change position were applied to the fourth group. Then, severity of bleeding, hematoma, back pain and urinary retention were measured at zero, 1, 2, 4, 6, and 24 hours after angiography. The data was collected through an individual data questionnaire, Numerical Rating Scale (NRS) of pain and Kristin Swain's check list was applied to evaluate the severity of bleeding and hematoma. None of patients developed vascular complications. Incidence of urinary retention was higher in the control group, although this difference was not significant. The mean of pain intensity in the fourth and sixth hours showed a significant difference. Based on the findings of this study, changing patients' position can be safe and they can be ambulated early after angiography.

Approximating prediction uncertainty for random forest regression models

Science.gov (United States)

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Machine vision based quality inspection of flat glass products

Science.gov (United States)

Zauner, G.; Schagerl, M.

2014-03-01

This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little `image structure', i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

The dynamic analysis of drum roll lathe for machining of rollers

Science.gov (United States)

Qiao, Zheng; Wu, Dongxu; Wang, Bo; Li, Guo; Wang, Huiming; Ding, Fei

2014-08-01

An ultra-precision machine tool for machining of the roller has been designed and assembled, and due to the obvious impact which dynamic characteristic of machine tool has on the quality of microstructures on the roller surface, the dynamic characteristic of the existing machine tool is analyzed in this paper, so is the influence of circumstance that a large scale and slender roller is fixed in the machine on dynamic characteristic of the machine tool. At first, finite element model of the machine tool is built and simplified, and based on that, the paper carries on with the finite element mode analysis and gets the natural frequency and shaking type of four steps of the machine tool. According to the above model analysis results, the weak stiffness systems of machine tool can be further improved and the reasonable bandwidth of control system of the machine tool can be designed. In the end, considering the shock which is caused by Z axis as a result of fast positioning frequently to feeding system and cutting tool, transient analysis is conducted by means of ANSYS analysis in this paper. Based on the results of transient analysis, the vibration regularity of key components of machine tool and its impact on cutting process are explored respectively.

Determination of cut front position in laser cutting

International Nuclear Information System (INIS)

Pereira, M; Thombansen, U

2016-01-01

Laser cutting has a huge importance to manufacturing industry. Laser cutting machines operate with fixed technological parameters and this does not guarantee the best productivity. The adjustment of the cutting parameters during operation can improve the machine performance. Based on a coaxial measuring device it is possible to identify the cut front position during the cutting process. This paper describes the data analysis approach used to determine the cut front position for different feed rates. The cut front position was determined with good resolution, but improvements are needed to make the whole process more stable. (paper)

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.

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper

Science.gov (United States)

Luo, Gang

2017-01-01

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic. PMID:29177022

Mineral mining machines

Energy Technology Data Exchange (ETDEWEB)

Mc Gaw, B H

1984-01-01

A machine for mining minerals is patented. It is a cutter loader with a drum actuating element of the worm type equipped with a multitude of cutting teeth reinforced with tungsten carbide. A feature of the patented machine is that all of the cutting teeth and holders on the drum have the identical design. This is achieved through selecting a slant angle for the cutting teeth which is the mean between the slant angle of the conventional radial teeth and the slant angle of the advance teeth. This, in turn, is provided thanks to the corresponding slant of the holders relative to the drum and (or) the slant of the cutting part of the teeth relative to their stems. Thus, the advance teeth projecting beyond the surface of the drum on the face side and providing upper and lateral clearances have the same angle of attack as the radial teeth, that is, from 20 to 35 degrees. A series of modifications of the cutting teeth is patented. One of the designs allows the cutting tooth to occupy a varying position relative to the drum, from the conventional vertical to an inverted, axially projecting position. In the last case the tooth in the extraction process provides the upper and lateral clearances for the drum on the face side. Among the different modifications of the cutting teeth, a design is proposed which provides for the presence of a stem which is shaped like a truncated cone. This particular stem is designed for use jointly with a wedge which unfastens the teeth and is placed in a holder. The latter is completed in a transverse slot thanks to which the rear end of the stem is compressed, which simplifies replacement of a tooth. Channels are provided in the patented machine for feeding water to the worm spiral, the holders and the cutting teeth themselves in order to deal with dust.

Modelling, Construction, and Testing of a Simple HTS Machine Demonstrator

DEFF Research Database (Denmark)

Jensen, Bogi Bech; Abrahamsen, Asger Bech

2011-01-01

This paper describes the construction, modeling and experimental testing of a high temperature superconducting (HTS) machine prototype employing second generation (2G) coated conductors in the field winding. The prototype is constructed in a simple way, with the purpose of having an inexpensive way...... of validating finite element (FE) simulations and gaining a better understanding of HTS machines. 3D FE simulations of the machine are compared to measured current vs. voltage (IV) curves for the tape on its own. It is validated that this method can be used to predict the critical current of the HTS tape...... installed in the machine. The measured torque as a function of rotor position is also reproduced by the 3D FE model....

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.

Positive post-disaster images: A daydream machine?

Science.gov (United States)

Hancock, Nicola J; de Joux, Neil R; Wingreen, Stephen C; Kemp, Simon; Thomas, Jared; Helton, William S

2017-08-01

This study explores the impact of post-earthquake images inserted in a vigilance task, in terms of performance, self-reports of task-focus, and cerebral activity using functional near-infrared spectroscopy (fNIRS). Vigilance tasks present a sequence of stimuli in which only a few are pre-designated critical or target stimuli requiring an overt response from the participant. Seventy-one residents participated (51 women, 20 men) by taking part in a vigilance task with task-irrelevant images inserted in the sequence. There were three conditions consisting positive (emotive inducing), negative (emotive inducing), and control (devoid of meaning) images embedded in the vigilance task to assess possible impacts on vigilance performance. The images were obtained through crowdsourcing and represented parts of the city 3-4 years post-earthquake. Task performance was assessed with signal detection theory metrics of sensitivity A' and bias β''. This enables the separation of an individual's ability to accurately discriminate critical signals from non-critical stimuli (sensitivity) and shifts in their willingness to respond to any stimuli whether critical or not (bias). Individuals viewing the positive images, relating to progress, rebuild, or aesthetic aspects within the city, had a more conservative response bias (they responded less to both rare critical and distractor stimuli) than those in the other conditions. These individuals also reported lower task-focus, as would be expected. However, contrary to expectations, indicators of cerebral activity (fNIRS) did not differ significantly between the experimental groups. These results, when combined, suggest that mind wandering events may be being generated when exposed to positive post-earthquake images. © 2016 The British Psychological Society.

Methods and apparatus for controlling rotary machines

Science.gov (United States)

Bagepalli, Bharat Sampathkumaran [Niskayuna, NY; Jansen, Patrick Lee [Scotia, NY; Barnes, Gary R [Delanson, NY; Fric, Thomas Frank [Greer, SC; Lyons, James Patrick Francis [Niskayuna, NY; Pierce, Kirk Gee [Simpsonville, SC; Holley, William Edwin [Greer, SC; Barbu, Corneliu [Guilderland, NY

2009-09-01

A control system for a rotary machine is provided. The rotary machine has at least one rotating member and at least one substantially stationary member positioned such that a clearance gap is defined between a portion of the rotating member and a portion of the substantially stationary member. The control system includes at least one clearance gap dimension measurement apparatus and at least one clearance gap adjustment assembly. The adjustment assembly is coupled in electronic data communication with the measurement apparatus. The control system is configured to process a clearance gap dimension signal and modulate the clearance gap dimension.

Random Positional Variation Among the Skull, Mandible, and Cervical Spine With Treatment Progression During Head-and-Neck Radiotherapy

International Nuclear Information System (INIS)

Ahn, Peter H.; Ahn, Andrew I.; Lee, C. Joe; Shen Jin; Miller, Ekeni; Lukaj, Alex; Milan, Elissa; Yaparpalvi, Ravindra; Kalnicki, Shalom; Garg, Madhur K.

2009-01-01

Purpose: With 54 o of freedom from the skull to mandible to C7, ensuring adequate immobilization for head-and-neck radiotherapy (RT) is complex. We quantify variations in skull, mandible, and cervical spine movement between RT sessions. Methods and Materials: Twenty-three sequential head-and-neck RT patients underwent serial computed tomography. Patients underwent planned rescanning at 11, 22, and 33 fractions for a total of 93 scans. Coordinates of multiple bony elements of the skull, mandible, and cervical spine were used to calculate rotational and translational changes of bony anatomy compared with the original planning scan. Results: Mean translational and rotational variations on rescanning were negligible, but showed a wide range. Changes in scoliosis and lordosis of the cervical spine between fractions showed similar variability. There was no correlation between positional variation and fraction number and no strong correlation with weight loss or skin separation. Semi-independent rotational and translation movement of the skull in relation to the lower cervical spine was shown. Positioning variability measured by means of vector displacement was largest in the mandible and lower cervical spine. Conclusions: Although only small overall variations in position between head-and-neck RT sessions exist on average, there is significant random variation in patient positioning of the skull, mandible, and cervical spine elements. Such variation is accentuated in the mandible and lower cervical spine. These random semirigid variations in positioning of the skull and spine point to a need for improved immobilization and/or confirmation of patient positioning in RT of the head and neck

Random Positional Variation Among the Skull, Mandible, and Cervical Spine With Treatment Progression During Head-and-Neck Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Ahn, Peter H. [Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY (United States)], E-mail: phahn@mdanderson.org; Ahn, Andrew I [Albert Einstein College of Medicine of Yeshiva University, Bronx, NY (United States); Lee, C Joe; Jin, Shen; Miller, Ekeni; Lukaj, Alex; Milan, Elissa; Yaparpalvi, Ravindra; Kalnicki, Shalom; Garg, Madhur K [Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY (United States)

2009-02-01

Purpose: With 54{sup o} of freedom from the skull to mandible to C7, ensuring adequate immobilization for head-and-neck radiotherapy (RT) is complex. We quantify variations in skull, mandible, and cervical spine movement between RT sessions. Methods and Materials: Twenty-three sequential head-and-neck RT patients underwent serial computed tomography. Patients underwent planned rescanning at 11, 22, and 33 fractions for a total of 93 scans. Coordinates of multiple bony elements of the skull, mandible, and cervical spine were used to calculate rotational and translational changes of bony anatomy compared with the original planning scan. Results: Mean translational and rotational variations on rescanning were negligible, but showed a wide range. Changes in scoliosis and lordosis of the cervical spine between fractions showed similar variability. There was no correlation between positional variation and fraction number and no strong correlation with weight loss or skin separation. Semi-independent rotational and translation movement of the skull in relation to the lower cervical spine was shown. Positioning variability measured by means of vector displacement was largest in the mandible and lower cervical spine. Conclusions: Although only small overall variations in position between head-and-neck RT sessions exist on average, there is significant random variation in patient positioning of the skull, mandible, and cervical spine elements. Such variation is accentuated in the mandible and lower cervical spine. These random semirigid variations in positioning of the skull and spine point to a need for improved immobilization and/or confirmation of patient positioning in RT of the head and neck.

Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures

Directory of Open Access Journals (Sweden)

Stéphane Saux Picart

2018-02-01

Full Text Available Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO, Generalised Additive Model (GAM and random forest. In the case of geostationary satellites for which a large number of collocations is available, results show that the random forest model is the best model to predict the systematic errors and it is computationally fast, making it a good candidate for operational processing. It is able to explain nearly 31% of the total variance of the bias (in comparison to about 24% for the multi-linear regression model.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

Science.gov (United States)

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

A Simple and General Approach to Determination of Self and Mutual Inductances for AC machines

DEFF Research Database (Denmark)

Lu, Kaiyuan; Rasmussen, Peter Omand; Ritchie, Ewen

2011-01-01

Modelling of AC electrical machines plays an important role in electrical engineering education related to electrical machine design and control. One of the fundamental requirements in AC machine modelling is to derive the self and mutual inductances, which could be position dependant. Theories...... developed so far for inductance determination are often associated with complicated machine magnetic field analysis, which exhibits a difficulty for most students. This paper describes a simple and general approach to the determination of self and mutual inductances of different types of AC machines. A new...... determination are given for a 3-phase, salient-pole synchronous machine, and an induction machine....

Does providing nutrition information at vending machines reduce calories per item sold?

Science.gov (United States)

Dingman, Deirdre A; Schulz, Mark R; Wyrick, David L; Bibeau, Daniel L; Gupta, Sat N

2015-02-01

In 2010, the United States (US) enacted a restaurant menu labeling law. The law also applied to vending machine companies selling food. Research suggested that providing nutrition information on menus in restaurants might reduce the number of calories purchased. We tested the effect of providing nutrition information and 'healthy' designations to consumers where vending machines were located in college residence halls. We conducted our study at one university in Southeast US (October-November 2012). We randomly assigned 18 vending machines locations (residence halls) to an intervention or control group. For the intervention we posted nutrition information, interpretive signage, and sent a promotional email to residents of the hall. For the control group we did nothing. We tracked sales over 4 weeks before and 4 weeks after we introduced the intervention. Our intervention did not change what the residents bought. We recommend additional research about providing nutrition information where vending machines are located, including testing formats used to present information.

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.

Effect of Positioning and Early Ambulation on Coronary Angiography Complications: a Randomized Clinical Trial

Directory of Open Access Journals (Sweden)

Ali Akbar Abdollahi

2015-06-01

Full Text Available Introduction: After coronary angiography to prevent potential complications, patients are restricted to 4-24 hours bed rest in the supine position due to the complications. This study was designed to assess the effect of changing position and early ambulation on low back pain, urinary retention, bleeding and hematoma after cardiac catheterization. Methods: In this clinical trial, 140 patients by using a convenience sampling randomly divided into four 35-individual groups. The patients in the control group were in the supine position for 6 hours without a movement. Change position was applied to the second group (based on a specific protocol, early ambulation was applied to the third group and both early ambulation and change position were applied to the fourth group. Then, severity of bleeding, hematoma, back pain and urinary retention were measured at zero, 1, 2, 4, 6, and 24 hours after angiography. The data was collected through an individual data questionnaire, Numerical Rating Scale (NRS of pain and Kristin Swain’s check list was applied to evaluate the severity of bleeding and hematoma. Results: None of patients developed vascular complications. Incidence of urinary retention was higher in the control group, although this difference was not significant. The mean of pain intensity in the fourth and sixth hours showed a significant difference.Conclusion: Based on the findings of this study, changing patients’ position can be safe and they can be ambulated early after angiography.

Machine Learning Based Localization and Classification with Atomic Magnetometers

Science.gov (United States)

Deans, Cameron; Griffin, Lewis D.; Marmugi, Luca; Renzoni, Ferruccio

2018-01-01

We demonstrate identification of position, material, orientation, and shape of objects imaged by a Rb 85 atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating the use of hidden data. Localization 2.6 times better than the spatial resolution of the imaging system and successful classification up to 97% are obtained. This circumvents the need of solving the inverse problem and demonstrates the extension of machine learning to diffusive systems, such as low-frequency electrodynamics in media. Automated collection of task-relevant information from quantum-based electromagnetic imaging will have a relevant impact from biomedicine to security.

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...

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Science.gov (United States)

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification

Directory of Open Access Journals (Sweden)

Longjun Dong

2014-01-01

Full Text Available The discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF, Support Vector Machines (SVM, and Naïve Bayes Classifier (NBC were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were discussed and compared. The result of RF method clearly shows the best predictive power with a maximum area of 0.975 under the ROC among RF, SVM, and NBC. The discriminant accuracies of RF, SVM, and NBC for test samples are 92.86%, 85.71%, and 92.86%, respectively. It has been demonstrated that the presented RF model can not only identify seismic event automatically with high accuracy, but also can sort the discriminant indicators according to calculated values of weights.

Machine Learning Methods for Production Cases Analysis

Science.gov (United States)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

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

Machine printed text and handwriting identification in noisy document images.

Science.gov (United States)

Zheng, Yefeng; Li, Huiping; Doermann, David

2004-03-01

In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications. Experimental results show that our approach is robust and can significantly improve page segmentation in noisy document collections.

Machine learning application in online lending risk prediction

OpenAIRE

Yu, Xiaojiao

2017-01-01

Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

Musical feedback during exercise machine workout enhances mood

Directory of Open Access Journals (Sweden)

Thomas Hans Fritz

2013-12-01

Full Text Available Music making has a number of beneficial effects for motor tasks compared to passive music listening. Given that recent research suggests that high energy musical activities elevate positive affect more strongly than low energy musical activities, we here investigated a recent method that combined music making with systematically increasing physiological arousal by exercise machine workout. We compared mood and anxiety after two exercise conditions on non-cyclical exercise machines, one with passive music listening and the other with musical feedback (where participants could make music with the exercise machines. The results showed that agency during exercise machine workout (an activity we previously labeled jymmin—a cross between jammin and gym had an enhancing effect on mood compared to workout with passive music listening. Furthermore, the order in which the conditions were presented mediated the effect of musical agency for this subscale When participants first listened passively, the difference in mood between the two conditions was greater, suggesting that a stronger increase in hormone levels (e.g. endorphins during the active condition may have caused the observed effect. Given an enhanced mood after training with musical feedback compared to passively listening to the same type of music during workout, the results suggest that exercise machine workout with musical feedback (jymmin makes the act of exercise machine training more desirable.

Experimental investigation of the tip based micro/nano machining

Science.gov (United States)

Guo, Z.; Tian, Y.; Liu, X.; Wang, F.; Zhou, C.; Zhang, D.

2017-12-01

Based on the self-developed three dimensional micro/nano machining system, the effects of machining parameters and sample material on micro/nano machining are investigated. The micro/nano machining system is mainly composed of the probe system and micro/nano positioning stage. The former is applied to control the normal load and the latter is utilized to realize high precision motion in the xy plane. A sample examination method is firstly introduced to estimate whether the sample is placed horizontally. The machining parameters include scratching direction, speed, cycles, normal load and feed. According to the experimental results, the scratching depth is significantly affected by the normal load in all four defined scratching directions but is rarely influenced by the scratching speed. The increase of scratching cycle number can increase the scratching depth as well as smooth the groove wall. In addition, the scratching tests of silicon and copper attest that the harder material is easier to be removed. In the scratching with different feed amount, the machining results indicate that the machined depth increases as the feed reduces. Further, a cubic polynomial is used to fit the experimental results to predict the scratching depth. With the selected machining parameters of scratching direction d3/d4, scratching speed 5 μm/s and feed 0.06 μm, some more micro structures including stair, sinusoidal groove, Chinese character '田', 'TJU' and Chinese panda have been fabricated on the silicon substrate.

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.

Media-Augmented Exercise Machines

Science.gov (United States)

Krueger, T.

2002-01-01

Cardio-vascular exercise has been used to mitigate the muscle and cardiac atrophy associated with adaptation to micro-gravity environments. Several hours per day may be required. In confined spaces and long duration missions this kind of exercise is inevitably repetitive and rapidly becomes uninteresting. At the same time, there are pressures to accomplish as much as possible given the cost- per-hour for humans occupying orbiting or interplanetary. Media augmentation provides a the means to overlap activities in time by supplementing the exercise with social, recreational, training or collaborative activities and thereby reducing time pressures. In addition, the machine functions as an interface to a wide range of digital environments allowing for spatial variety in an otherwise confined environment. We hypothesize that the adoption of media augmented exercise machines will have a positive effect on psycho-social well-being on long duration missions. By organizing and supplementing exercise machines, data acquisition hardware, computers and displays into an interacting system this proposal increases functionality with limited additional mass. This paper reviews preliminary work on a project to augment exercise equipment in a manner that addresses these issues and at the same time opens possibilities for additional benefits. A testbed augmented exercise machine uses a specialty built cycle trainer as both input to a virtual environment and as an output device from it using spatialized sound, and visual displays, vibration transducers and variable resistance. The resulting interactivity increases a sense of engagement in the exercise, provides a rich experience of the digital environments. Activities in the virtual environment and accompanying physiological and psychological indicators may be correlated to track and evaluate the health of the crew.

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.

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...

Modelling tick abundance using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Kjær, Lene Jung; Korslund, L.; Kjelland, V.

satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

Design, Construction and Evaluation of a Row Crop Thinning Machine

Directory of Open Access Journals (Sweden)

M Gol Mohammadi

2014-04-01

Full Text Available Equipment availability is necessary in the development of Agriculture mechanization. Crop thinning is one of the most important stages in row crop production which is laborious and costly. The objective of this project is design and construction of a row crop thinning machine. Four main system units are plant sensors, ground sensors, control and thinning platforms. In this machine the unwanted plants on the rows are randomly removed by employing a pneumatically system. A blade on a vertical arm with pendulum motion removes the plant from the rows. The machine control system consists of an arm and a blade which is activated by a double acting cylinder and equipped with a relay and a timer. The pneumatic cylinder is controlled via a solenoid valve. Laboratory tests were conducted to validate the machine performance. Some other preliminary tests also were performed for optimization of parameters such as cinematic index and cutting length of blades. The laboratory tests (totally 9 tests were performed with a constant forward speed and three levels of plant density, using artificial plants. The data were analyzed using SPSS software. The results show that satisfactory performance of the machine is achieved when the plant density is moderate i.e. the thinning performance reduces with higher plant distance in the row. The other effective variable on machine performance is the adjustment of sensor sensitivity, which is used to distinguish between week and strong plants. In general the machine performance is sensitive to plant shape and morphology, plant distribution pattern in the field, growing stage of the plants, time of thinning and the effectiveness of previous weeding operations

Statistical process control and verifying positional accuracy of a cobra motion couch using step-wedge quality assurance tool.

Science.gov (United States)

Binny, Diana; Lancaster, Craig M; Trapp, Jamie V; Crowe, Scott B

2017-09-01

This study utilizes process control techniques to identify action limits for TomoTherapy couch positioning quality assurance tests. A test was introduced to monitor accuracy of the applied couch offset detection in the TomoTherapy Hi-Art treatment system using the TQA "Step-Wedge Helical" module and MVCT detector. Individual X-charts, process capability (cp), probability (P), and acceptability (cpk) indices were used to monitor a 4-year couch IEC offset data to detect systematic and random errors in the couch positional accuracy for different action levels. Process capability tests were also performed on the retrospective data to define tolerances based on user-specified levels. A second study was carried out whereby physical couch offsets were applied using the TQA module and the MVCT detector was used to detect the observed variations. Random and systematic variations were observed for the SPC-based upper and lower control limits, and investigations were carried out to maintain the ongoing stability of the process for a 4-year and a three-monthly period. Local trend analysis showed mean variations up to ±0.5 mm in the three-monthly analysis period for all IEC offset measurements. Variations were also observed in the detected versus applied offsets using the MVCT detector in the second study largely in the vertical direction, and actions were taken to remediate this error. Based on the results, it was recommended that imaging shifts in each coordinate direction be only applied after assessing the machine for applied versus detected test results using the step helical module. User-specified tolerance levels of at least ±2 mm were recommended for a test frequency of once every 3 months to improve couch positional accuracy. SPC enables detection of systematic variations prior to reaching machine tolerance levels. Couch encoding system recalibrations reduced variations to user-specified levels and a monitoring period of 3 months using SPC facilitated in detecting

Effects of changing the random number stride in Monte Carlo calculations

International Nuclear Information System (INIS)

Hendricks, J.S.

1991-01-01

This paper reports on a common practice in Monte Carlo radiation transport codes which is to start each random walk a specified number of steps up the random number sequence from the previous one. This is called the stride in the random number sequence between source particles. It is used for correlated sampling or to provide tree-structured random numbers. A new random number generator algorithm for the major Monte Carlo code MCNP has been written to allow adjustment of the random number stride. This random number generator is machine portable. The effects of varying the stride for several sample problems are examined

Design of an Electric Commutated Frog-Leg Winding Permanent-Magnet DC Machine

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Hang Zhang

2014-03-01

Full Text Available An electric commutated frog-leg winding permanent-magnet (PM DC machine is proposed in this paper. It has a semi-closed slotted stator with a classical type of mesh winding introduced from the conventional brushed DC machine and a polyphase electric commutation besides a PM excitation rotor and a circular arrayed Hall position sensor. Under the cooperation between the position sensor and the electric commutation, the proposed machine is basically operated on the same principle of the brushed one. Because of its simplex frog-leg winding, the combination between poles and slots is designed as 4/22, and the number of phases is set as 11. By applying an exact analytical method, which is verified comparable with the finite element analyses (FEA, to the prediction of its instantaneous magnetic field, electromotive force (EMF, cogging torque and output torque, it is well designed with a series of parameters in dimension aiming at the lowest cogging torque. A 230 W, 4-pole, and 22-slot new machine is prototyped and tested to verify the analysis.

Subjective randomness as statistical inference.

Science.gov (United States)

Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B

2018-06-01

Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.

CT crown for on-machine scale calibration in Computed Tomography

DEFF Research Database (Denmark)

Stolfi, Alessandro; De Chiffre, Leonardo

2016-01-01

A novel artefact for on-machine calibration of the scale in 3D X-ray Computed Tomography (CT) is presented. The artefact comprises an invar disc on which several reference ruby spheres are positioned at different heights using carbon fibre rods. The artefact is positioned and scanned together...

Learning Random Numbers: A Matlab Anomaly

Czech Academy of Sciences Publication Activity Database

Savický, Petr; Robnik-Šikonja, M.

2008-01-01

Roč. 22, č. 3 (2008), s. 254-265 ISSN 0883-9514 R&D Projects: GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : random number s * machine learning * classification * attribute evaluation * regression Subject RIV: BA - General Mathematics Impact factor: 0.795, year: 2008

Machine Learning of Fault Friction

Science.gov (United States)

Johnson, P. A.; Rouet-Leduc, B.; Hulbert, C.; Marone, C.; Guyer, R. A.

2017-12-01

We are applying machine learning (ML) techniques to continuous acoustic emission (AE) data from laboratory earthquake experiments. Our goal is to apply explicit ML methods to this acoustic datathe AE in order to infer frictional properties of a laboratory fault. The experiment is a double direct shear apparatus comprised of fault blocks surrounding fault gouge comprised of glass beads or quartz powder. Fault characteristics are recorded, including shear stress, applied load (bulk friction = shear stress/normal load) and shear velocity. The raw acoustic signal is continuously recorded. We rely on explicit decision tree approaches (Random Forest and Gradient Boosted Trees) that allow us to identify important features linked to the fault friction. A training procedure that employs both the AE and the recorded shear stress from the experiment is first conducted. Then, testing takes place on data the algorithm has never seen before, using only the continuous AE signal. We find that these methods provide rich information regarding frictional processes during slip (Rouet-Leduc et al., 2017a; Hulbert et al., 2017). In addition, similar machine learning approaches predict failure times, as well as slip magnitudes in some cases. We find that these methods work for both stick slip and slow slip experiments, for periodic slip and for aperiodic slip. We also derive a fundamental relationship between the AE and the friction describing the frictional behavior of any earthquake slip cycle in a given experiment (Rouet-Leduc et al., 2017b). Our goal is to ultimately scale these approaches to Earth geophysical data to probe fault friction. References Rouet-Leduc, B., C. Hulbert, N. Lubbers, K. Barros, C. Humphreys and P. A. Johnson, Machine learning predicts laboratory earthquakes, in review (2017). https://arxiv.org/abs/1702.05774Rouet-LeDuc, B. et al., Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning (2017), AGU Fall Meeting Session S025

Random a-adic groups and random net fractals

Energy Technology Data Exchange (ETDEWEB)

Li Yin [Department of Mathematics, Nanjing University, Nanjing 210093 (China)], E-mail: Lyjerry7788@hotmail.com; Su Weiyi [Department of Mathematics, Nanjing University, Nanjing 210093 (China)], E-mail: suqiu@nju.edu.cn

2008-08-15

Based on random a-adic groups, this paper investigates the relationship between the existence conditions of a positive flow in a random network and the estimation of the Hausdorff dimension of a proper random net fractal. Subsequently we describe some particular random fractals for which our results can be applied. Finally the Mauldin and Williams theorem is shown to be very important example for a random Cantor set with application in physics as shown in E-infinity theory.

Automatic Compensation of Workpiece Positioning Tolerances for Precise Laser

Directory of Open Access Journals (Sweden)

N. C. Stache

2008-01-01

Full Text Available Precise laser welding plays a fundamental role in the production of high-tech goods, particularly in precision engineering. In this working field, precise adjustment and compensation of positioning tolerances of the parts to be welded with respect to the laser beam is of paramount importance. This procedure mostly requires tedious and error-prone manual adjustment, which additionally results in a sharp increase in production costs. We therefore developed a system which automates and thus accelerates this procedure significantly. To this end, the welding machine is equipped with a camera to acquire high resolution images of the parts to be welded. In addition, a software framework is developed which enables precise automatic position detection of these parts and adjusts the position of the welding contour correspondingly. As a result, the machine is rapidly prepared for welding, and it is much more flexible in adapting to unknown parts.This paper describes the entire concept of extending a conventional welding machine with means for image acquisition and position estimation. In addition to this description, the algorithms, the results of an evaluation of position estimation, and a final welding result are presented. 

Machinability of Minor Wooden Species before and after Modification with Thermo-Vacuum Technology.

Science.gov (United States)

Sandak, Jakub; Goli, Giacomo; Cetera, Paola; Sandak, Anna; Cavalli, Alberto; Todaro, Luigi

2017-01-28

The influence of the thermal modification process on wood machinability was investigated with four minor species of low economic importance. A set of representative experimental samples was machined to the form of disks with sharp and dull tools. The resulting surface quality was visually evaluated by a team of experts according to the American standard procedure ASTM D-1666-87. The objective quantification of the surface quality was also done by means of a three dimensions (3D) surface scanner for the whole range of grain orientations. Visual assessment and 3D surface analysis showed a good agreement in terms of conclusions. The best quality of the wood surface was obtained when machining thermally modified samples. The positive effect of the material modification was apparent when cutting deodar cedar, black pine and black poplar in unfavorable conditions (i.e., against the grain). The difference was much smaller for an easy-machinability specie such as Italian alder. The use of dull tools resulted in the worst surface quality. Thermal modification has shown a very positive effect when machining with dull tools, leading to a relevant increment of the final surface smoothness.

The Effects of Positive or Neutral Communication during Acupuncture for Relaxing Effects: A Sham-Controlled Randomized Trial

Directory of Open Access Journals (Sweden)

Annelie Rosén

2016-01-01

Full Text Available Introduction. The link between patient-clinician communication and its effect on clinical outcomes is an important clinical issue that is yet to be elucidated. Objective. Investigating if communication type (positive or neutral about the expected treatment outcome affected (i participants’ expectations and (ii short-term relaxation effects in response to genuine or sham acupuncture and investigating if expectations were related to outcome. Methods. Healthy volunteers (n=243, mean age of 42 were randomized to one treatment with genuine or sham acupuncture. Within groups, participants were randomized to positive or neutral communication, regarding expected treatment effects. Visual Analogue Scales (0–100 millimeters were used to measure treatment expectations and relaxation, directly before and after treatment. Results. Participants in the positive communication group reported higher treatment expectancy, compared to the neutral communication group (md 12 versus 6 mm, p=0.002. There was no difference in relaxation effects between acupuncture groups or between communication groups. Participants with high baseline expectancy perceived greater improvement in relaxation, compared to participants with low baseline levels (md 27 versus 15 mm, p=0.022. Conclusion. Our data highlights the importance of expectations for treatment outcome and demonstrates that expectations can be effectively manipulated using a standardized protocol that in future research may be implemented in clinical trials.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

Science.gov (United States)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

A Forging Hardness Dispersion Effect on the Energy Consumption of Machining

Directory of Open Access Journals (Sweden)

L. D. Mal'kova

2015-01-01

Full Text Available The aim of the work is to evaluate a hardness dispersion of forgings to be further machined, and analyse the impact of this dispersion on the resulting power consumption when cutting.The paper studies the hardness values of three kinds of parts for automotive manufacturing. Sample of each part was n = 100 pieces. Analysis of measurements showed that 46% - 93% of parts meet requirements for a range defined by the work-piece working drawing. It was found that hardness of one batch of forgings is under dispersion, which distribution is governed by the normal law.The work provides calculations for machining the external cylindrical surfaces of the considered parts. In the context of calculating are adopted parameters of the enterprise-processing rate. It is found that power consumption of machining because of the dispersion values of the work-piece hardness is a function of the random BH variable and it itself is a random variable. Two types of samples are considered, namely: the full sample and that of the values that meet requirements for hardness. The coefficient of variation for samples that meet the technical requirements for hardness is lower than for the full samples, so their average value is more reliable characteristic of a set. It was also found that to ensure a reliable prediction of power consumption in designing the manufacturing processes it is necessary to reduce a tolerance range of workpiece hardness to the limit.The work gives a comparative evaluation of electric power consumption per unit cylindrical surface of the parts under consideration. A relative change in the electric power consumed at the minimum and maximum levels of the hardness value was introduced as an evaluation criterion. It is found that with changing hardness of machined work-pieces within the tolerance, the change in power consumption in machining the unit surface reaches 16% while in the case its being out of the specified range it does 47%.

Machine learning paradigms applications in recommender systems

CERN Document Server

Lampropoulos, Aristomenis S

2015-01-01

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and ...

Modelling Machine Tools using Structure Integrated Sensors for Fast Calibration

Directory of Open Access Journals (Sweden)

Benjamin Montavon

2018-02-01

Full Text Available Monitoring of the relative deviation between commanded and actual tool tip position, which limits the volumetric performance of the machine tool, enables the use of contemporary methods of compensation to reduce tolerance mismatch and the uncertainties of on-machine measurements. The development of a primarily optical sensor setup capable of being integrated into the machine structure without limiting its operating range is presented. The use of a frequency-modulating interferometer and photosensitive arrays in combination with a Gaussian laser beam allows for fast and automated online measurements of the axes’ motion errors and thermal conditions with comparable accuracy, lower cost, and smaller dimensions as compared to state-of-the-art optical measuring instruments for offline machine tool calibration. The development is tested through simulation of the sensor setup based on raytracing and Monte-Carlo techniques.

Efficacy of a Multicomponent Positive Psychology Self-Help Intervention: Study Protocol of a Randomized Controlled Trial

Science.gov (United States)

Drossaert, Constance HC; Pieterse, Marcel E; Walburg, Jan A; Bohlmeijer, Ernst T

2015-01-01

Background Positive psychology interventions have been found to enhance well-being and decrease clinical symptomatology. However, it is still unknown how flourishing can also be increased. Although multicomponent interventions seem to be necessary for this purpose, different formats can be used. A cost-effective approach could be a positive psychology-based self-help book with tailored email support to reach large target groups and to prevent dropout. Objective This study will evaluate the efficacy of a comprehensive multicomponent self-help intervention with or without email support on well-being and flourishing, and will seek to determine the working mechanisms underlying the intervention. Methods In this 3-armed, parallel, randomized controlled trial, 396 participants with low or moderate levels of well-being and without clinical symptomatology will be randomly assigned to (1) a self-help book condition with weekly email support, (2) a self-help book condition without email support but with a weekly information email, or (3) a waiting list control condition. Online measurements will be assessed at baseline, at post-test (3 months after baseline), and at 6 and 12 months after baseline. Results The primary outcomes are well-being and flourishing (ie, high levels of well-being). Secondary outcomes are the well-being components included in the intervention: positive emotion, use of strengths, optimism, self-compassion, resilience, and positive relations. Other measures include depressive and anxiety symptoms, personality traits, direct medical and non-medical costs, life-events, and client satisfaction. Conclusions This study will add knowledge to the efficacy and cost-effectiveness of a multicomponent positive psychology intervention. We will also explore who can benefit most from this intervention. If the intervention is found to be effective, our results will be especially relevant for public mental health services, governments, and primary care. Trial

Machine learning algorithms for the creation of clinical healthcare enterprise systems

Science.gov (United States)

Mandal, Indrajit

2017-10-01

Clinical recommender systems are increasingly becoming popular for improving modern healthcare systems. Enterprise systems are persuasively used for creating effective nurse care plans to provide nurse training, clinical recommendations and clinical quality control. A novel design of a reliable clinical recommender system based on multiple classifier system (MCS) is implemented. A hybrid machine learning (ML) ensemble based on random subspace method and random forest is presented. The performance accuracy and robustness of proposed enterprise architecture are quantitatively estimated to be above 99% and 97%, respectively (above 95% confidence interval). The study then extends to experimental analysis of the clinical recommender system with respect to the noisy data environment. The ranking of items in nurse care plan is demonstrated using machine learning algorithms (MLAs) to overcome the drawback of the traditional association rule method. The promising experimental results are compared against the sate-of-the-art approaches to highlight the advancement in recommendation technology. The proposed recommender system is experimentally validated using five benchmark clinical data to reinforce the research findings.

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

Science.gov (United States)

Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko

2018-05-04

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Antibiotic Residues in Milk from Three Popular Kenyan Milk Vending Machines.

Science.gov (United States)

Kosgey, Amos; Shitandi, Anakalo; Marion, Jason W

2018-05-01

Milk vending machines (MVMs) are growing in popularity in Kenya and worldwide. Milk vending machines dispense varying quantities of locally sourced, pasteurized milk. The Kenya Dairy Board has a regulatory framework, but surveillance is weak because of several factors. Milk vending machines' milk is not routinely screened for antibiotics, thereby increasing potential for antibiotic misuse. To investigate, a total of 80 milk samples from four commercial providers ( N = 25), street vendors ( N = 21), and three MVMs ( N = 34) were collected and screened in Eldoret, Kenya. Antibiotic residue surveillance occurred during December 2016 and January 2017 using Idexx SNAP ® tests for tetracyclines, sulfamethazine, beta-lactams, and gentamicin. Overall, 24% of MVM samples and 24% of street vendor samples were presumably positive for at least one antibiotic. No commercial samples were positive. Research into cost-effective screening methods and increased monitoring by food safety agencies are needed to uphold hazard analysis and critical control point for improving antibiotic stewardship throughout the Kenyan private dairy industry.

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.

Paging memory from random access memory to backing storage in a parallel computer

Science.gov (United States)

Archer, Charles J; Blocksome, Michael A; Inglett, Todd A; Ratterman, Joseph D; Smith, Brian E

2013-05-21

Paging memory from random access memory (`RAM`) to backing storage in a parallel computer that includes a plurality of compute nodes, including: executing a data processing application on a virtual machine operating system in a virtual machine on a first compute node; providing, by a second compute node, backing storage for the contents of RAM on the first compute node; and swapping, by the virtual machine operating system in the virtual machine on the first compute node, a page of memory from RAM on the first compute node to the backing storage on the second compute node.

High-speed micro-electro-discharge machining.

Energy Technology Data Exchange (ETDEWEB)

Chandrasekar, Srinivasan Dr. (.School of Industrial Engineering, West Lafayette, IN); Moylan, Shawn P. (School of Industrial Engineering, West Lafayette, IN); Benavides, Gilbert Lawrence

2005-09-01

When two electrodes are in close proximity in a dielectric liquid, application of a voltage pulse can produce a spark discharge between them, resulting in a small amount of material removal from both electrodes. Pulsed application of the voltage at discharge energies in the range of micro-Joules results in the continuous material removal process known as micro-electro-discharge machining (micro-EDM). Spark erosion by micro-EDM provides significant opportunities for producing small features and micro-components such as nozzle holes, slots, shafts and gears in virtually any conductive material. If the speed and precision of micro-EDM processes can be significantly enhanced, then they have the potential to be used for a wide variety of micro-machining applications including fabrication of microelectromechanical system (MEMS) components. Toward this end, a better understanding of the impacts the various machining parameters have on material removal has been established through a single discharge study of micro-EDM and a parametric study of small hole making by micro-EDM. The main avenues for improving the speed and efficiency of the micro-EDM process are in the areas of more controlled pulse generation in the power supply and more controlled positioning of the tool electrode during the machining process. Further investigation of the micro-EDM process in three dimensions leads to important design rules, specifically the smallest feature size attainable by the process.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

Science.gov (United States)

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

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

Morphological and mechanical analyses of laminates manufactured from randomly positioned carbon fibre/epoxy resin prepreg scraps

Science.gov (United States)

Souza, Christiane S. R.; Cândido, Geraldo M.; Alves, Wellington; Marlet, José Maria F.; Rezende, Mirabel C.

2017-10-01

This study aims to contribute to sustainability by proposing the reuse of composite prepreg scrap as an added value from discards. The research evaluates the microstructure and mechanical properties of laminates processed by the reuse of uncured carbon fibre/F155-epoxy resin prepreg scraps, waste from the ply cutting area of an aeronautical industry. The composite scraps were used as collected and were randomly positioned to produce laminates to be cured at an autoclave. The mechanical characterization shows a decrease of 39% for the compression property due to the discontinuous fibres in the laminate and an increase of 34% for the interlaminar shear strength, when compared to continuous fibre laminates. This increase is attributed to the higher crosslink density of the epoxy resin, as a result of the cure temperature used in autoclave (60 °C higher than suggested by supplier) and also to the randomly positioned scraps. Microscopic analyses confirm the consolidation of laminates, although show resin rich areas with different sizes and shapes attributed to the overlapping of the scraps with different sizes and shapes. These resin rich areas may contribute to decrease the mechanical properties of laminates. The correlation between mechanical and morphological results shows potential to be used on non-critical structural application, as composite jigs, contributing to sustainability.

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder.

Directory of Open Access Journals (Sweden)

Matthew J Maenner

Full Text Available The Autism and Developmental Disabilities Monitoring (ADDM Network conducts population-based surveillance of autism spectrum disorder (ASD among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children's developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods. The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria. We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive. The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD "prevalence" was 1.46% compared to the published (clinician-determined estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

Science.gov (United States)

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

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.

Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Directory of Open Access Journals (Sweden)

Forni Selma

2011-02-01

Full Text Available Abstract Background Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates small n (number of observations problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance. It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. Methods This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO and two machine learning algorithms (boosting and random forest to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability. Results The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. Conclusions The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different

Development and testing of high performance pseudo random number generator for Monte Carlo simulation

International Nuclear Information System (INIS)

Chakraborty, Brahmananda

2009-01-01

Random number plays an important role in any Monte Carlo simulation. The accuracy of the results depends on the quality of the sequence of random numbers employed in the simulation. These include randomness of the random numbers, uniformity of their distribution, absence of correlation and long period. In a typical Monte Carlo simulation of particle transport in a nuclear reactor core, the history of a particle from its birth in a fission event until its death by an absorption or leakage event is tracked. The geometry of the core and the surrounding materials are exactly modeled in the simulation. To track a neutron history one needs random numbers for determining inter collision distance, nature of the collision, the direction of the scattered neutron etc. Neutrons are tracked in batches. In one batch approximately 2000-5000 neutrons are tracked. The statistical accuracy of the results of the simulation depends on the total number of particles (number of particles in one batch multiplied by the number of batches) tracked. The number of histories to be generated is usually large for a typical radiation transport problem. To track a very large number of histories one needs to generate a long sequence of independent random numbers. In other words the cycle length of the random number generator (RNG) should be more than the total number of random numbers required for simulating the given transport problem. The number of bits of the machine generally limits the cycle length. For a binary machine of p bits the maximum cycle length is 2 p . To achieve higher cycle length in the same machine one has to use either register arithmetic or bit manipulation technique

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

Integrating human and machine intelligence in galaxy morphology classification tasks

Science.gov (United States)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

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…

Man-machine cooperation in remote handling for fusion plants

International Nuclear Information System (INIS)

Leinemann, K.

1984-01-01

Man-machine cooperation in remote handling for fusion plants comprises cooperation for design of equipment and planning of procedures using a CAD system, and cooperation during operation of the equipment with computer aided telemanipulation systems (CAT). This concept is presently being implemented for support of slave positioning, camera tracking, and camera alignment in the KfK manipulator test facility. The pilot implementation will be used to test various man-machine interface layouts, and to establish a set of basic buildings blocks for future implementations of advanced remote handling control systems. (author)

A comparison of machine learning and Bayesian modelling for molecular serotyping.

Science.gov (United States)

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological

Machine-Learning Algorithms to Code Public Health Spending Accounts.

Science.gov (United States)

Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

THE ANALYSIS OF MACHINES FOR THE FOOTWEAR PULL ON BOOT TREE

Directory of Open Access Journals (Sweden)

I. Pascari

2011-01-01

Full Text Available The ways of manufacturing of footwear have undergone a long evolutionary way, beginning with the manual work and finishing with the automated. The first sewing machine that had come to help the manappeared in ХVIII century. The Industrial progress of development and of the equipment it is possible to divide conditionally into three stages: from 60 till 90th years where it is attempt the two-operational power engine; from 90 till 2000 two machines were applied to an inhaling; and the modern period when inhaling processallows using less machines. The new machines had new patented system that allows the operator to put the position for preparation of the top of footwear according to the design of model that is applied to each size of footwear. The system provides an automatic centering of a narrow site of footwear that by means of a followingbeam of light the operator could guarantee each time exact width of capture of a long edge in beam parts ofpreparation of top. In a similar way the system provides exact positioning of the sample in toe cap footwear parts. Except the equipment by the modern electronic devices, the new machines have fashionable design, in particular, the polished black panel and the automated tray for the footwear, covered with genuine leather. Theappointment of these machines is universal: on them it is possible to carry out process of an inhaling for various types of footwear (boots, low shoes, shoes, etc. both as daily socks as model sports. Thus the footwear can be various groups of people– man, female, and children.

Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Cuellar, Ana Carolina; Kjær, Lene Jung; Skovgaard, Henrik

2017-01-01

coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month...... and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group. Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability...

Disruption Warning Database Development and Exploratory Machine Learning Studies on Alcator C-Mod

Science.gov (United States)

Montes, Kevin; Rea, Cristina; Granetz, Robert

2017-10-01

A database of about 1800 shots from the 2015 campaign on the Alcator C-Mod tokamak is assembled, including disruptive and non-disruptive discharges. The database consists of 40 relevant plasma parameters with data taken from 160k time slices. In order to investigate the possibility of developing a robust disruption prediction algorithm that is tokamak-independent, we focused machine learning studies on a subset of dimensionless parameters such as βp, n /nG , etc. The Random Forests machine learning algorithm provides insight on the available data set by ranking the relative importance of the input features. Its application on the C-Mod database, however, reveals that virtually no one parameter has more importance than any other, and that its classification algorithm has a low rate of successfully predicted samples, as well as poor false positive and false negative rates. Comparing the analysis of this algorithm on the C-Mod database with its application to a similar database on DIII-D, we conclude that disruption prediction may not be feasible on C-Mod. This conclusion is supported by empirical observations that most C-Mod disruptions are caused by radiative collapse due to molybdenum from the first wall, which happens on just a 1-2ms timescale. Supported by the US Dept. of Energy under DE-FC02-99ER54512 and DE-FC02-04ER54698.

Selected problems relating to the dynamics of block-type foundations for machines

Directory of Open Access Journals (Sweden)

Marek Zombroń

2014-07-01

Full Text Available Atypical but real practical problems relating to the dynamics of block-type foundations for machines are considered using the deterministic approach and assuming that the determined parameters are random variables. A foundation model in the form of an undeformable solid on which another undeformable solid modelling a machine is mounted via viscoelastic constraints was adopted. The dynamic load was defined by a harmonically varying signal and by a series of short duration signals. The vibration of the system was investigated for the case when stratified ground (groundwater occurred within the side backfill was present. Calculation results illustrating the theoretical analyses are presented.

Fault tolerant operation of switched reluctance machine

Science.gov (United States)

Wang, Wei

The energy crisis and environmental challenges have driven industry towards more energy efficient solutions. With nearly 60% of electricity consumed by various electric machines in industry sector, advancement in the efficiency of the electric drive system is of vital importance. Adjustable speed drive system (ASDS) provides excellent speed regulation and dynamic performance as well as dramatically improved system efficiency compared with conventional motors without electronics drives. Industry has witnessed tremendous grow in ASDS applications not only as a driving force but also as an electric auxiliary system for replacing bulky and low efficiency auxiliary hydraulic and mechanical systems. With the vast penetration of ASDS, its fault tolerant operation capability is more widely recognized as an important feature of drive performance especially for aerospace, automotive applications and other industrial drive applications demanding high reliability. The Switched Reluctance Machine (SRM), a low cost, highly reliable electric machine with fault tolerant operation capability, has drawn substantial attention in the past three decades. Nevertheless, SRM is not free of fault. Certain faults such as converter faults, sensor faults, winding shorts, eccentricity and position sensor faults are commonly shared among all ASDS. In this dissertation, a thorough understanding of various faults and their influence on transient and steady state performance of SRM is developed via simulation and experimental study, providing necessary knowledge for fault detection and post fault management. Lumped parameter models are established for fast real time simulation and drive control. Based on the behavior of the faults, a fault detection scheme is developed for the purpose of fast and reliable fault diagnosis. In order to improve the SRM power and torque capacity under faults, the maximum torque per ampere excitation are conceptualized and validated through theoretical analysis and

Analytical N beam position monitor method

Directory of Open Access Journals (Sweden)

A. Wegscheider

2017-11-01

Full Text Available Measurement and correction of focusing errors is of great importance for performance and machine protection of circular accelerators. Furthermore LHC needs to provide equal luminosities to the experiments ATLAS and CMS. High demands are also set on the speed of the optics commissioning, as the foreseen operation with β^{*}-leveling on luminosity will require many operational optics. A fast measurement of the β-function around a storage ring is usually done by using the measured phase advance between three consecutive beam position monitors (BPMs. A recent extension of this established technique, called the N-BPM method, was successfully applied for optics measurements at CERN, ALBA, and ESRF. We present here an improved algorithm that uses analytical calculations for both random and systematic errors and takes into account the presence of quadrupole, sextupole, and BPM misalignments, in addition to quadrupolar field errors. This new scheme, called the analytical N-BPM method, is much faster, further improves the measurement accuracy, and is applicable to very pushed beam optics where the existing numerical N-BPM method tends to fail.

Analytical N beam position monitor method

Science.gov (United States)

Wegscheider, A.; Langner, A.; Tomás, R.; Franchi, A.

2017-11-01

Measurement and correction of focusing errors is of great importance for performance and machine protection of circular accelerators. Furthermore LHC needs to provide equal luminosities to the experiments ATLAS and CMS. High demands are also set on the speed of the optics commissioning, as the foreseen operation with β*-leveling on luminosity will require many operational optics. A fast measurement of the β -function around a storage ring is usually done by using the measured phase advance between three consecutive beam position monitors (BPMs). A recent extension of this established technique, called the N-BPM method, was successfully applied for optics measurements at CERN, ALBA, and ESRF. We present here an improved algorithm that uses analytical calculations for both random and systematic errors and takes into account the presence of quadrupole, sextupole, and BPM misalignments, in addition to quadrupolar field errors. This new scheme, called the analytical N-BPM method, is much faster, further improves the measurement accuracy, and is applicable to very pushed beam optics where the existing numerical N-BPM method tends to fail.

Ergonomic principles for the design of combined drilling and loading machines

Energy Technology Data Exchange (ETDEWEB)

Mason, S.; Simpson, G.C.

1990-08-08

Underground investigations of development machines have revealed a number of limitations in ergonomics aspects of their design which could influence both the safety and efficiency of the operation. This handbook is intended to provide designers of Combined Drilling and Loading machines with the ergonomic information which can be used to eliminate or reduce such problems. The following criteria were examined: workspace position; operator clearances; operator protection; operator visual communications; operator visual machine monitoring; operator visual safety information; operator seating; operature posture; operator access to workspace; control types; control operating forces; control-response stereotypes; safety controls; control dynamics; control layout; control clearances; control protection; visual displays.

RAW MILK IN AUTOMATIC SALE MACHINES: MONITORING PLAN IN PIEDEMONT REGION

Directory of Open Access Journals (Sweden)

S. Gallina

2010-06-01

Full Text Available Raw milk at vending machine is surging in popularity amongst consumers of Northern Italy; indeed in Piedmont Region there are more than 100 vending machines. In June 2008 Piedmont Region set out a specific monitoring plan to check the milk quality. From June to December 2008, 113 raw milk samples were collected at vending machines. Samples were analysed for Listeria monocytogenes, Salmonella spp., coagulase positive staphylococci, Staphylococcus aureus and Campylobacter. Moreover, 100 samples were analysed for the quantification of aflatoxin M1. 26 samples have been resulted Not Conform for the hygienic criteria and 1 exceeded the aflatoxin M1 limit.

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.

Randomized random walk on a random walk

International Nuclear Information System (INIS)

Lee, P.A.

1983-06-01

This paper discusses generalizations of the model introduced by Kehr and Kunter of the random walk of a particle on a one-dimensional chain which in turn has been constructed by a random walk procedure. The superimposed random walk is randomised in time according to the occurrences of a stochastic point process. The probability of finding the particle in a particular position at a certain instant is obtained explicitly in the transform domain. It is found that the asymptotic behaviour for large time of the mean-square displacement of the particle depends critically on the assumed structure of the basic random walk, giving a diffusion-like term for an asymmetric walk or a square root law if the walk is symmetric. Many results are obtained in closed form for the Poisson process case, and these agree with those given previously by Kehr and Kunter. (author)

Micro Electro Discharge Machining for Nonconductive Ceramic Materials

Directory of Open Access Journals (Sweden)

Mohammad Yeakub Ali

2018-03-01

Full Text Available In micro-electro discharge machining (micro-EDM of nonconductive ceramics, material is removed mainly by spalling due to the dominance of alternating thermal load. The established micro-EDM models established for single spark erosion are not applicable for nonconductive ceramics because of random spalling. Moreover, it is difficult to create single spark on a nonconductive ceramic workpiece when the spark is initiated by the assisting electrode. In this paper, theoretical model of material removal rate (MRR as the function of capacitance and voltage is developed for micro-EDM of nonconductive zirconium oxide (ZrO2. It is shown that the charging and discharging duration depend on the capacitance and resistances of the circuit. The number of sparks per unit time is estimated from the single spark duration s derived from heat transfer fundamentals. The model showed that both the capacitance and voltage are significant process parameters where any increase of capacitance and voltage increases the MRR. However, capacitance was found to be the dominating parameter over voltage. As in case of higher capacitances, the creation of a conductive carbonic layer on the machined surface was not stable; the effective window of machining 101 - 103 pF capacitance and 80 - 100 V gap voltage or 10 - 470 pF capacitance and 80 - 110 V gap voltage. This fact was confirmed EDX analysis where the presence of high carbon content was evident. Conversely, the spark was found to be inconsistent using parameters beyond these ranges and consequently insignificant MRR. Nevertheless, the effective number of sparks per second were close to the predicted numbers when machining conductive copper material. In addition, higher percentage of ineffective pulses was observed during the machining which eventually reduced the MRR. In case of validation, average deviations between the predicted and experimental values were found to be around 10%. Finally, micro-channels were machined on

Overall design concepts for the APS storage ring machine protection system

International Nuclear Information System (INIS)

Lumpkin, A.; Fuja, R.; Votaw, A.; Wang, X.; Shu, D.; Stepp, J.; Arnold, N.; Nawrocki, G.; Decker, G.; Chung, Y.

1995-01-01

The basic design and status of the machine protection system for the Advanced Photon Source (APS) storage ring are discussed. The machine is passively safe to the bending magnet sources, but the high power of the insertion devices requires missteering conditions to be identified and the beam aborted in less than one millisecond. The basic aspects of waterflow, temperature, beam position, etc. monitoring are addressed. Initial commissioning of subsystems and sensors is statused

Beam position monitors for the high brightness lattice

International Nuclear Information System (INIS)

Ring, T.

1985-06-01

Engineering developments associated with the high brightness lattice and the projected change in machine operating parameters will inherently affect the diagnostics systems and devices installed at present in the storage ring. This is particularly true of the beam position monitoring (BPI) system. The new sixteen unit cell lattice with its higher betatron tune values and the limited space available in the redesigned machine straights for fitting standard BPI vessels forces a fundamental re-evaluation of the beam position monitor system. The design aims for the new system are based on accepting the space limitations imposed while still providing the monitor points required to give good radial and vertical closed orbit plots. The locations of BPI's in the redesigned machine straights is illustrated. A description of the new BPI assemblies and their calibration is given. The BPI's use capacitance button type pick-ups; their response is described. (U.K.)

Machine learning-based dual-energy CT parametric mapping.

Science.gov (United States)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-05-22

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z_eff), relative electron density (ρ_e), mean excitation energy (I_x), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 seconds. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency. . © 2018 Institute of Physics and Engineering in

A variable-mode stator consequent pole memory machine

Science.gov (United States)

Yang, Hui; Lyu, Shukang; Lin, Heyun; Zhu, Z. Q.

2018-05-01

In this paper, a variable-mode concept is proposed for the speed range extension of a stator-consequent-pole memory machine (SCPMM). An integrated permanent magnet (PM) and electrically excited control scheme is utilized to simplify the flux-weakening control instead of relatively complicated continuous PM magnetization control. Due to the nature of memory machine, the magnetization state of low coercive force (LCF) magnets can be easily changed by applying either a positive or negative current pulse. Therefore, the number of PM poles may be changed to satisfy the specific performance requirement under different speed ranges, i.e. the machine with all PM poles can offer high torque output while that with half PM poles provides wide constant power range. In addition, the SCPMM with non-magnetized PMs can be considered as a dual-three phase electrically excited reluctance machine, which can be fed by an open-winding based dual inverters that provide direct current (DC) bias excitation to further extend the speed range. The effectiveness of the proposed variable-mode operation for extending its operating region and improving the system reliability is verified by both finite element analysis (FEA) and experiments.

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.

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.)

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.

Comparative adoption of cone beam computed tomography and panoramic radiography machines across Australia.

Science.gov (United States)

Zhang, A; Critchley, S; Monsour, P A

2016-12-01

The aim of the present study was to assess the current adoption of cone beam computed tomography (CBCT) and panoramic radiography (PR) machines across Australia. Information regarding registered CBCT and PR machines was obtained from radiation regulators across Australia. The number of X-ray machines was correlated with the population size, the number of dentists, and the gross state product (GSP) per capita, to determine the best fitting regression model(s). In 2014, there were 232 CBCT and 1681 PR machines registered in Australia. Based on absolute counts, Queensland had the largest number of CBCT and PR machines whereas the Northern Territory had the smallest number. However, when based on accessibility in terms of the population size and the number of dentists, the Australian Capital Territory had the most CBCT machines and Western Australia had the most PR machines. The number of X-ray machines correlated strongly with both the population size and the number of dentists, but not with the GSP per capita. In 2014, the ratio of PR to CBCT machines was approximately 7:1. Projected increases in either the population size or the number of dentists could positively impact on the adoption of PR and CBCT machines in Australia. © 2016 Australian Dental Association.

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.

Effects of two different feeding positions on physiological characteristics and feeding performance of preterm infants: A randomized controlled trial.

Science.gov (United States)

Girgin, Burcu Aykanat; Gözen, Duygu; Karatekin, Güner

2018-04-01

The aim of this randomized controlled study was to determine the effect of semielevated side-lying (ESL) and semielevated supine (ESU) positions, which are used to bottle-feed preterm infants, on their physiological characteristics and feeding performance. The sample consisted of preterm infants who were born in the 31st gestational week and below, and met the inclusion criteria. A randomization was provided in the sample group with a total of 80 infants including 38 infants in the ESL (experimental) group and 42 infants in the ESU (control) group. Both groups were compared in terms of their SpO2 values, heart rates, and feeding performances before, during, and after the feeding. The data were obtained by using a form for infant descriptive characteristics, feeding follow-up form, a Masimo Radical-7 pulse oximeter device, and a video camera. It was determined that the infants in the ESL group had statistically significantly higher SpO2 values (ESL: 96.77 ± 2.51; ESU: 93.48 ± 5.63) and lower heart rates (ESL: 155.87 ± 11.18; ESU: 164.35 ± 6.00) during the feeding compared to the infants in the ESU group (p ESL group. The ESL position has a more positive effect on oxygen saturation and heart rate of infants and it is more effective in providing a physiological stabilization during the feeding, compared to the ESU position. According to these results, the ESL position can be recommended for preterm feeding. © 2018 Wiley Periodicals, Inc.

HUMAN DECISIONS AND MACHINE PREDICTIONS.

Science.gov (United States)

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-02-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior).

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.

Non-linear hybrid control oriented modelling of a digital displacement machine

DEFF Research Database (Denmark)

Pedersen, Niels Henrik; Johansen, Per; Andersen, Torben O.

2017-01-01

Proper feedback control of digital fluid power machines (Pressure, flow, torque or speed control) requires a control oriented model, from where the system dynamics can be analyzed, stability can be proven and design criteria can be specified. The development of control oriented models for hydraulic...... Digital Displacement Machines (DDM) is complicated due to non-smooth machine behavior, where the dynamics comprises both analog, digital and non-linear elements. For a full stroke operated DDM the power throughput is altered in discrete levels based on the ratio of activated pressure chambers....... In this paper, a control oriented hybrid model is established, which combines the continuous non-linear pressure chamber dynamics and the discrete shaft position dependent activation of the pressure chambers. The hybrid machine model is further extended to describe the dynamics of a Digital Fluid Power...

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)

Multivariate Mapping of Environmental Data Using Extreme Learning Machines

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2014-05-01

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

Global patterns and predictions of seafloor biomass using random forests

Digital Repository Service at National Institute of Oceanography (India)

Wei, Chih-Lin; Rowe, G.T.; Escobar-Briones, E.; Boetius, A; Soltwedel, T.; Caley, M.J.; Soliman, Y.; Huettmann, F.; Qu, F.; Yu, Z.; Pitcher, C.R.; Haedrich, R.L.; Wicksten, M.K.; Rex, M.A; Baguley, J.G.; Sharma, J.; Danovaro, R.; MacDonald, I.R.; Nunnally, C.C.; Deming, J.W.; Montagna, P.; Levesque, M.; Weslawsk, J.M.; Wlodarska-Kowalczuk, M.; Ingole, B.S.; Bett, B.J.; Billett, D.S.M.; Yool, A; Bluhm, B.A; Iken, K.; Narayanaswamy, B.E.

A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model...

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets.

Directory of Open Access Journals (Sweden)

Igor Shuryak

Full Text Available The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms. Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1 adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2 adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3 adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4 running a selected machine learning method multiple times (with different random-number seeds to test the robustness of the detected "signal"; (5 using several machine learning methods to test the "signal's" sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine, and (II bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA. We show that the proposed

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

Science.gov (United States)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

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

Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

Directory of Open Access Journals (Sweden)

Marc Wieland

2014-03-01

Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

Efficient thermal error prediction in a machine tool using finite element analysis

International Nuclear Information System (INIS)

Mian, Naeem S; Fletcher, Simon; Longstaff, Andrew P; Myers, Alan

2011-01-01

Thermally induced errors have a major significance on the positional accuracy of a machine tool. Heat generated during the machining process produces thermal gradients that flow through the machine structure causing linear and nonlinear thermal expansions and distortions of associated complex discrete structures, producing deformations that adversely affect structural stability. The heat passes through structural linkages and mechanical joints where interfacial parameters such as the roughness and form of the contacting surfaces affect the thermal resistance and thus the heat transfer coefficients. This paper presents a novel offline technique using finite element analysis (FEA) to simulate the effects of the major internal heat sources such as bearings, motors and belt drives of a small vertical milling machine (VMC) and the effects of ambient temperature pockets that build up during the machine operation. Simplified models of the machine have been created offline using FEA software and evaluated experimental results applied for offline thermal behaviour simulation of the full machine structure. The FEA simulated results are in close agreement with the experimental results ranging from 65% to 90% for a variety of testing regimes and revealed a maximum error range of 70 µm reduced to less than 10 µm

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.

Machine learning strategies for systems with invariance properties

Science.gov (United States)

Ling, Julia; Jones, Reese; Templeton, Jeremy

2016-08-01

In many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds Averaged Navier Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high performance computing has led to a growing availability of high fidelity simulation data. These data open up the possibility of using machine learning algorithms, such as random forests or neural networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these empirical models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first method, a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance at significantly reduced computational training costs.

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.

Non-Linguistic Vocal Event Detection Using Online Random

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

2014-01-01

areas such as object detection, face recognition, and audio event detection. This paper proposes to use online random forest technique for detecting laughter and filler and for analyzing the importance of various features for non-linguistic vocal event classification through permutation. The results...... show that according to the Area Under Curve measure the online random forest achieved 88.1% compared to 82.9% obtained by the baseline support vector machines for laughter classification and 86.8% to 83.6% for filler classification....

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

Catalytic aided electrical discharge machining of polycrystalline diamond - parameter analysis of finishing condition

Science.gov (United States)

Haikal Ahmad, M. A.; Zulafif Rahim, M.; Fauzi, M. F. Mohd; Abdullah, Aslam; Omar, Z.; Ding, Songlin; Ismail, A. E.; Rasidi Ibrahim, M.

2018-01-01

Polycrystalline diamond (PCD) is regarded as among the hardest material in the world. Electrical Discharge Machining (EDM) typically used to machine this material because of its non-contact process nature. This investigation was purposely done to compare the EDM performances of PCD when using normal electrode of copper (Cu) and newly proposed graphitization catalyst electrode of copper nickel (CuNi). Two level full factorial design of experiment with 4 center points technique was used to study the influence of main and interaction effects of the machining parameter namely; pulse-on, pulse-off, sparking current, and electrode materials (categorical factor). The paper shows interesting discovery in which the newly proposed electrode presented positive impact to the machining performance. With the same machining parameters of finishing, CuNi delivered more than 100% better in Ra and MRR than ordinary Cu electrode.

Bench calibration of INDUS-2 beam position indicators

International Nuclear Information System (INIS)

Tyagi, Y.; Banerji, Anil; Kotaiah, S.

2005-01-01

A third generation synchrotron radiation source of energy 2.5 GeV named INDUS-2 at Centre for Advanced Technology (C.A.T), Indore (M.P) is in the advanced stage of construction. Accurate determination and correction of beam closed orbit in INDUS-2 machine within 100 of microns is a very desirable goal. Bench based calibration of Beam Position Indicators (BPI) play a very important and useful role during initial commissioning of electron machines. To precisely measure transverse position of electron beam in the Indus-2 storage ring, 56 Beam Position Indicators (BPI) will be installed in INDUS-2 machine. Out of 56 Beam Position Indicators 40 are of individual type whereas 16 are integrated with dipole vacuum chamber. The Beam Position Indicators are required to be calibrated before they can be installed. The calibration is done to determine electrical offset with respect to defined mechanical centre, to determine displacement sensitivities as well as non linearity's of BPI. Ideally when beam passes through the geometrical center of BPI's, all electrodes should have same signal strength. However due to different capacitance of electrodes and offset and drift in electronics, the electrical centre (mechanical x, y where all electrodes shows same signal strength) differs from mechanical centre of BPI. A fully automatic calibration system has been developed to carry out the calibration of Beam Position Indicators. A calibration software has been developed which has necessary utilities to process and display calibration data and results. This paper describes the calibration results of Indus-2 BPM. (author)

Machine learning of big data in gaining insight into successful treatment of hypertension.

Science.gov (United States)

Koren, Gideon; Nordon, Galia; Radinsky, Kira; Shalev, Varda

2018-06-01

Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta-analyses of these studies. The objective of this study was to test the utility of machine learning of big data in gaining insight into the treatment of hypertension. We applied machine learning techniques such as decision trees and neural networks, to identify determinants that contribute to the success of hypertension drug treatment on a large set of patients. We also identified concomitant drugs not considered to have antihypertensive activity, which may contribute to lowering blood pressure (BP) control. Higher initial BP predicts lower success rates. Among the medication options and their combinations, treatment with beta blockers appears to be more commonly effective, which is not reflected in contemporary guidelines. Among numerous concomitant drugs taken by hypertensive patients, proton pump inhibitors (PPIs), and HMG CO-A reductase inhibitors (statins) significantly improved the success rate of hypertension. In conclusions, machine learning of big data is a novel method to identify effective antihypertensive therapy and for repurposing medications already on the market for new indications. Our results related to beta blockers, stemming from machine learning of a large and diverse set of big data, in contrast to the much narrower criteria for randomized clinic trials (RCTs), should be corroborated and affirmed by other methods, as they hold potential promise for an old class of drugs which may be presently underutilized. These previously unrecognized effects of PPIs and statins have been very recently identified as effective in lowering BP in preliminary clinical observations, lending credibility to our big data results.

Design of Parameter Independent, High Performance Sensorless Controllers for Permanent Magnet Synchronous Machines

DEFF Research Database (Denmark)

Xie, Ge

. The transient fluctuation of the estimated rotor position error is around 20 degrees with a step load torque change from 0% to 100% of the rated torque. The position error in steady state is within ±2 electrical degrees for the best case. The proposed method may also be used for e.g. online machine parameter......The Permanent Magnet Synchronous Machine (PMSM) has become an attractive candidate for various industrial applications due to its high efficiency and torque density. In the PMSM drive system, simple and robust control methods play an important role in achieving satisfactory drive performances....... For reducing the cost and increasing the reliability of the drive system, eliminating the mechanical sensor brings a lot advantages to the PMSM drive system. Therefore, sensorless control was developed and has been increasingly used in different PMSM drive systems in the last 20 years. However, machine...

Technology of high-speed combined machining with brush electrode

Science.gov (United States)

Kirillov, O. N.; Smolentsev, V. P.; Yukhnevich, S. S.

2018-03-01

The new method was proposed for high-precision dimensional machining with a brush electrode when the true position of bundles of metal wire is adjusted by means of creating controlled centrifugal forces appeared due to the increased frequency of rotation of a tool. There are the ultimate values of circumferential velocity at which the bundles are pressed against a machined area of a workpiece in a stable manner despite the profile of the machined surface and variable stock of the workpiece. The special aspects of design of processing procedures for finishing standard parts, including components of products with low rigidity, are disclosed. The methodology of calculation and selection of processing modes which allow one to produce high-precision details and to provide corresponding surface roughness required to perform finishing operations (including the preparation of a surface for metal deposition) is presented. The production experience concerned with the use of high-speed combined machining with an unshaped tool electrode in knowledge-intensive branches of the machine-building industry for different types of production is analyzed. It is shown that the implementation of high-speed dimensional machining with an unshaped brush electrode allows one to expand the field of use of the considered process due to the application of a multipurpose tool in the form of a metal brush, as well as to obtain stable results of finishing and to provide the opportunities for long-term operation of the equipment without its changeover and readjustment.

Machine learning search for variable stars

Science.gov (United States)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

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.

EAST machine assembly and its measurement system

International Nuclear Information System (INIS)

Wu, S.T.

2005-01-01

The EAST (HT-7U) superconducting tokamak consists of a superconducting poloidal field magnet system, a toroidal field magnet system, a vacuum vessel and in-vessel components, thermal shields and a cryostat vessel. The main parts of the machine have been delivered to ASIPP (Institute of Plasma Physics, Chinese Academy of Sciences) successionally from 2003. For its complicated constitution and precise requirement, a reasonable assembly procedure and measurement technique should be defined carefully. Before the assembly procedure, a reference frame has been set up with reference fiducial targets on the wall of the test hall by an industrial measurement system. After the torus of TF coils is formed, a new reference frame will be set up from the position of the TF torus. The vacuum vessel with all inner parts will be installed with reference of the new reference frame. The big size and mass of components, special configuration of the superconducting machine with tight installation tolerances of the HT-7U (EAST) machine result in complicated assembly procedure. The procedure had begun with the installation of the support frame and the base of cryostat vessel last year. In this paper, the requirements of the assembly precise for some key components of the machine are described. The reference frame for the assembly and maintenance is explained. The assembly procedure is introduced

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

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.

Measurement of LHCD antenna position in Aditya tokamak

International Nuclear Information System (INIS)

Ambulkar, K K; Sharma, P K; Virani, C G; Parmar, P R; Thakur, A L; Kulkarni, S V

2010-01-01

To drive plasma current non-inductively in ADITYA tokamak, 120 kW pulsed Lower Hybrid Current Drive (LHCD) system at 3.7 GHz has been designed, fabricated and installed on ADITYA tokamak. In this system, the antenna consists of a grill structure, having two rows, each row comprising of four sub-waveguides. The coupling of LHCD power to the plasma strongly depends on the plasma density near the mouth of grill antenna. Thus the grill antenna has to be precisely positioned for efficient coupling. The movement of mechanical bellow, which contracts or expands up to 50mm, governs the movement of antenna. In order to monitor the position of the antenna precisely, the reference position of the antenna with respect to the machine/plasma position has to be accurately determined. Further a mechanical system or an electronic system to measure the relative movement of the antenna with respect to the reference position is also desired. Also due to poor accessibility inside the ADITYA machine, it is impossible to measure physically the reference position of the grill antenna with respect to machine wall, taken as reference position and hence an alternative method has to be adopted to establish these measurements reliably. In this paper we report the design and development of a mechanism, using which the antenna position measurements are made. It also describes a unique method employing which the measurements of the reference position of the antenna with respect to the inner edge of the tokamak wall is carried out, which otherwise was impossible due to poor accessibility and physical constraints. The position of the antenna is monitored using an electronic scale, which is developed and installed on the bellow. Once the reference position is derived, the linear potentiometer, attached to the bellow, measures the linear distance using position transmitter. The accuracy of measurement obtained in our setup is within +/- 0.5 % and the linearity, along with repeatability is excellent.

Safety mechanism of a lifting machine

International Nuclear Information System (INIS)

Blaive, D.; Chopinet, E.

1985-01-01

The lifting machine has at least one winch supporting a chain which passes around a chain pulley in a roller block attached to the load. At least one locking mechanism prevents the rotation of the pulley within the block. The locking mechanism can moves between an out-of-operation position and a locking position. A control system includes load sensors associated with the winch sensing the weight of the load acting through the chain. If one part of the chain should break, the load sensors detect this, and the locking mechanism is activated. The invention applies, more particularly, to the handling winches in a fast neutron nuclear power plant [fr

Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.

Science.gov (United States)

Pan, Ian; Nolan, Laura B; Brown, Rashida R; Khan, Romana; van der Boor, Paul; Harris, Daniel G; Ghani, Rayid

2017-06-01

To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions.

Effects of pole flux distribution in a homopolar linear synchronous machine

Science.gov (United States)

Balchin, M. J.; Eastham, J. F.; Coles, P. C.

1994-05-01

Linear forms of synchronous electrical machine are at present being considered as the propulsion means in high-speed, magnetically levitated (Maglev) ground transportation systems. A homopolar form of machine is considered in which the primary member, which carries both ac and dc windings, is supported on the vehicle. Test results and theoretical predictions are presented for a design of machine intended for driving a 100 passenger vehicle at a top speed of 400 km/h. The layout of the dc magnetic circuit is examined to locate the best position for the dc winding from the point of view of minimum core weight. Measurements of flux build-up under the machine at different operating speeds are given for two types of secondary pole: solid and laminated. The solid pole results, which are confirmed theoretically, show that this form of construction is impractical for high-speed drives. Measured motoring characteristics are presented for a short length of machine which simulates conditions at the leading and trailing ends of the full-sized machine. Combination of the results with those from a cylindrical version of the machine make it possible to infer the performance of the full-sized traction machine. This gives 0.8 pf and 0.9 efficiency at 300 km/h, which is much better than the reported performance of a comparable linear induction motor (0.52 pf and 0.82 efficiency). It is therefore concluded that in any projected high-speed Maglev systems, a linear synchronous machine should be the first choice as the propulsion means.

Hydraulic Modular Dosaging Systems for Machine Drives

Directory of Open Access Journals (Sweden)

A. J. Kotlobai

2005-01-01

Full Text Available The justified principle of making modular dosaging systems for positive-displacement multimotor hydraulic drives used in running gear and technological equipment of mobile construction, road and agricultural machines makes it possible to synchronize motion of running parts. The examples of the realization of modular dosaging systems and an algorithm of their operation are given in the paper.

Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer

Directory of Open Access Journals (Sweden)

Thang Diep

2010-06-01

Full Text Available This paper examines the optimization of production involving a tandem two-machine system producing a single part type, with each machine being subject to random breakdowns and repairs. An analytical model is formulated with a view to solving an optimal stochastic production problem of the system with machines having up-downtime non-exponential distributions. The model developed is obtained by using a dynamic programming approach and a semi-Markov process. The control problem aims to find the production rates needed by the machines to meet the demand rate, through a minimization of the inventory/shortage cost. Using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation, which depends on time and system states, and ultimately, leads to a feedback control. Consequently, the new model enables us to improve the coefficient of variation (CVup/down to be less than one while it is equal to one in Markov model. Heuristics methods are used to involve the problem because of the difficulty of the analytical model using several states, and to show what control law should be used in each system state (i.e., including Kanban, feedback and CONWIP control. Numerical methods are used to solve the optimality conditions and to show how a machine should produce.

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

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

Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

Science.gov (United States)

Weiss, Jeremy; Kuusisto, Finn; Boyd, Kendrick; Liu, Jie; Page, David

2015-01-01

Clinical studies model the average treatment effect (ATE), but apply this population-level effect to future individuals. Due to recent developments of machine learning algorithms with useful statistical guarantees, we argue instead for modeling the individualized treatment effect (ITE), which has better applicability to new patients. We compare ATE-estimation using randomized and observational analysis methods against ITE-estimation using machine learning, and describe how the ITE theoretically generalizes to new population distributions, whereas the ATE may not. On a synthetic data set of statin use and myocardial infarction (MI), we show that a learned ITE model improves true ITE estimation and outperforms the ATE. We additionally argue that ITE models should be learned with a consistent, nonparametric algorithm from unweighted examples and show experiments in favor of our argument using our synthetic data model and a real data set of D-penicillamine use for primary biliary cirrhosis.

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

Science.gov (United States)

Narula, Sukrit; Shameer, Khader; Salem Omar, Alaa Mabrouk; Dudley, Joel T; Sengupta, Partho P

2016-11-29

Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Health Promotion and Healthier Products Increase Vending Purchases: A Randomized Factorial Trial.

Science.gov (United States)

Hua, Sophia V; Kimmel, Lisa; Van Emmenes, Michael; Taherian, Rafi; Remer, Geraldine; Millman, Adam; Ickovics, Jeannette R

2017-07-01

The current food environment has a high prevalence of nutrient-sparse foods and beverages, most starkly seen in vending machine offerings. There are currently few studies that explore different interventions that might lead to healthier vending machine purchases. To examine how healthier product availability, price reductions, and/or promotional signs affect sales and revenue of snack and beverage vending machines. A 2×2×2 factorial randomized controlled trial was conducted. Students, staff, and employees on a university campus. All co-located snack and beverage vending machines (n=56, 28 snack and 28 beverage) were randomized into one of eight conditions: availability of healthier products and/or 25% price reduction for healthier items and/or promotional signs on machines. Aggregate sales and revenue data for the 5-month study period (February to June 2015) were compared with data from the same months 1 year prior. Analyses were conducted July 2015. The change in units sold and revenue between February through June 2014 and 2015. Linear regression models (main effects and interaction effects) and t test analyses were performed. The interaction between healthier product guidelines and promotional signs in snack vending machines documented increased revenue (Prevenue change. Price reductions alone had no effect, nor were there any effects for the three-way interaction of the factors. Examining top-selling products for all vending machines combined, pre- to postintervention, we found an overall shift to healthier purchasing. When healthier vending snacks are available, promotional signs are also important to ensure consumers purchase those items in greater amounts. Mitigating potential loss in profits is essential for sustainability of a healthier food environment. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

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.

Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners.

Directory of Open Access Journals (Sweden)

Antoine Collin

Full Text Available Epi-macrobenthic species richness, abundance and composition are linked with type, assemblage and structural complexity of seabed habitat within coastal ecosystems. However, the evaluation of these habitats is highly hindered by limitations related to both waterborne surveys (slow acquisition, shallow water and low reactivity and water clarity (turbid for most coastal areas. Substratum type/diversity and bathymetric features were elucidated using a supervised method applied to airborne bathymetric LiDAR waveforms over Saint-Siméon-Bonaventure's nearshore area (Gulf of Saint-Lawrence, Québec, Canada. High-resolution underwater photographs were taken at three hundred stations across an 8-km(2 study area. Seven models based upon state-of-the-art machine learning techniques such as Naïve Bayes, Regression Tree, Classification Tree, C 4.5, Random Forest, Support Vector Machine, and CN2 learners were tested for predicting eight epi-macrobenthic species diversity metrics as a function of the class number. The Random Forest outperformed other models with a three-discretized Simpson index applied to epi-macrobenthic communities, explaining 69% (Classification Accuracy of its variability by mean bathymetry, time range and skewness derived from the LiDAR waveform. Corroborating marine ecological theory, areas with low Simpson epi-macrobenthic diversity responded to low water depths, high skewness and time range, whereas higher Simpson diversity relied upon deeper bottoms (correlated with stronger hydrodynamics and low skewness and time range. The degree of species heterogeneity was therefore positively linked with the degree of the structural complexity of the benthic cover. This work underpins that fully exploited bathymetric LiDAR (not only bathymetrically derived by-products, coupled with proficient machine learner, is able to rapidly predict habitat characteristics at a spatial resolution relevant to epi-macrobenthos diversity, ranging from clear to

Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine

Directory of Open Access Journals (Sweden)

Alexios eKoutsoukas

2016-03-01

Full Text Available Modern drug discovery and toxicological research are under pressure, as the cost of developing and testing new chemicals for potential toxicological risk is rising. Extensive evaluation of chemical products for potential adverse effects is a challenging task, due to the large number of chemicals and the possible hazardous effects on human health. Safety regulatory agencies around the world are dealing with two major challenges. First, the growth of chemicals introduced every year in household products and medicines that need to be tested, and second the need to protect public welfare. Hence, alternative and more efficient toxicological risk assessment methods are in high demand. The Toxicology in the 21st Century (Tox21 consortium a collaborative effort was formed to develop and investigate alternative assessment methods. A collection of 10,000 compounds composed of environmental chemicals and approved drugs were screened for interference in biochemical pathways and released for crowdsourcing data analysis. The physicochemical space covered by Tox21 library was explored, measured by Molecular Weight (MW and the octanol/water partition coefficient (cLogP. It was found that on average chemical structures had MW of 272.6 Daltons. In case of cLogP the average value was 2.476. Next relationships between assays were examined based on compounds activity profiles across the assays utilizing the Pearson correlation coefficient r. A cluster was observed between the Androgen and Estrogen Receptors and their ligand bind domains accordingly indicating presence of cross talks among the receptors. The highest correlations observed were between NR.AR and NR.AR_LBD, where it was r=0.66 and between NR.ER and NR.ER_LBD, where it was r=0.5.Our approach to model the Tox21 data consisted of utilizing circular molecular fingerprints combined with Random Forest and Support Vector Machine by modeling each assay independently. In all of the 12 sub-challenges our modeling

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.

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 * ...

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

Transducer-actuator systems and methods for performing on-machine measurements and automatic part alignment

Science.gov (United States)

Barkman, William E.; Dow, Thomas A.; Garrard, Kenneth P.; Marston, Zachary

2016-07-12

Systems and methods for performing on-machine measurements and automatic part alignment, including: a measurement component operable for determining the position of a part on a machine; and an actuation component operable for adjusting the position of the part by contacting the part with a predetermined force responsive to the determined position of the part. The measurement component consists of a transducer. The actuation component consists of a linear actuator. Optionally, the measurement component and the actuation component consist of a single linear actuator operable for contacting the part with a first lighter force for determining the position of the part and with a second harder force for adjusting the position of the part. The actuation component is utilized in a substantially horizontal configuration and the effects of gravitational drop of the part are accounted for in the force applied and the timing of the contact.

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...

A Machine Learning Approach to Test Data Generation

DEFF Research Database (Denmark)

Christiansen, Henning; Dahmcke, Christina Mackeprang

2007-01-01

been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...... system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here...

Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

Directory of Open Access Journals (Sweden)

Heng Liu

2017-01-01

Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

Informed cytology for triaging HPV-positive women: substudy nested in the NTCC randomized controlled trial.

Science.gov (United States)

Bergeron, Christine; Giorgi-Rossi, Paolo; Cas, Frederic; Schiboni, Maria Luisa; Ghiringhello, Bruno; Dalla Palma, Paolo; Minucci, Daria; Rosso, Stefano; Zorzi, Manuel; Naldoni, Carlo; Segnan, Nereo; Confortini, Massimo; Ronco, Guglielmo

2015-02-01

Human papillomavirus (HPV)-based screening needs triage. In most randomized controlled trials (RCTs) on HPV testing with cytological triage, cytology interpretation has been blind to HPV status. Women age 25 to 60 years enrolled in the New Technology in Cervical Cancer (NTCC) RCT comparing HPV testing with cytology were referred to colposcopy if HPV positive and, if no cervical intraepithelial neoplasia (CIN) was detected, followed up until HPV negativity. Cytological slides taken at the first colposcopy were retrieved and independently interpreted by an external laboratory, which was only aware of patients' HPV positivity. Sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were computed for histologically proven CIN2+ with HPV status-informed cytology for women with a determination of atypical squamous cells of undetermined significance (ASCUS) or more severe. All statistical tests were two-sided. Among HPV-positive women, informed cytology had cross-sectional sensitivity, specificity, PPV and 1-NPV for CIN2+ of 85.6% (95% confidence interval [CI] = 76.6 to 92.1), 65.9% (95% CI = 63.1 to 68.6), 16.2% (95% CI = 13.0 to 19.8), and 1.7 (95% CI = 0.9 to 2.8), respectively. Cytology was also associated with subsequent risk of newly diagnosed CIN2+ and CIN3+. The cross-sectional relative sensitivity for CIN2+ vs blind cytology obtained by referring to colposcopy and following up only HPV positive women who had HPV status-informed cytology greater than or equal to ASCUS was 1.58 (95% CI = 1.22 to 2.01), while the corresponding relative referral to colposcopy was 0.95 (95% CI = 0.86 to 1.04). Cytology informed of HPV positivity is more sensitive than blind cytology and could allow longer intervals before retesting HPV-positive, cytology-negative women. © The Author 2015. Published by Oxford University Press.

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

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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

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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. 

Gait rehabilitation machines based on programmable footplates.

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Schmidt, Henning; Werner, Cordula; Bernhardt, Rolf; Hesse, Stefan; Krüger, Jörg

2007-02-09

approach. Sophisticated technical developments and positive randomized controlled trials form the basis of a growing acceptance worldwide to the benefits or our patients.

Gait rehabilitation machines based on programmable footplates

Directory of Open Access Journals (Sweden)

Bernhardt Rolf

2007-02-01

of motor relearning promoting a task-specific repetitive approach. Sophisticated technical developments and positive randomized controlled trials form the basis of a growing acceptance worldwide to the benefits or our patients.

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

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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,…

Coordinate measurement machines as an alignment tool

International Nuclear Information System (INIS)

Wand, B.T.

1991-03-01

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

Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

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Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

2018-04-01

The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

Classification of Strawberry Fruit Shape by Machine Learning

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Ishikawa, T.; Hayashi, A.; Nagamatsu, S.; Kyutoku, Y.; Dan, I.; Wada, T.; Oku, K.; Saeki, Y.; Uto, T.; Tanabata, T.; Isobe, S.; Kochi, N.

2018-05-01

Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.

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...

A community-engaged randomized controlled trial of an integrative intervention with HIV-positive, methamphetamine-using men who have sex with men

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Adam W. Carrico

2016-07-01

Full Text Available Abstract Background Contingency management (CM is an evidence-based intervention providing tangible rewards as positive reinforcement for abstinence from stimulants such as methamphetamine. Integrative approaches targeting affect regulation could boost the effectiveness of CM in community-based settings and optimize HIV/AIDS prevention efforts. Methods/Design This randomized controlled trial with HIV-positive, methamphetamine-using men who have sex with men (MSM is examining the efficacy of a 5-session, individually delivered positive affect regulation intervention – Affect Regulation Treatment to Enhance Methamphetamine Intervention Success (ARTEMIS. ARTEMIS is designed to sensitize individuals to non-drug-related sources of reward as well as assist with managing depression and other symptoms of stimulant withdrawal during CM. HIV-positive, methamphetamine-using MSM who are enrolled in a community-based, 12-week CM program are randomized to receive ARTEMIS or an attention-matched control condition. Follow-up assessments are conducted at 3, 6, 12, and 15 months after enrollment in CM. Four peripheral venous blood samples are collected over the 15-month follow-up with specimen banking for planned biomarker sub-studies. The primary outcome is mean HIV viral load. Secondary outcomes include: sustained HIV viral suppression, T-helper cell count, psychological adjustment, stimulant use, and potentially amplified transmission risk behavior. Discussion Implementation of this randomized controlled trial highlights the importance of delineating boundaries between research activities and community-based service provision. It also provides insights into best practices for integrating the distinct agendas of academic and community partners in clinical research. This trial is currently enrolling and data collection is anticipated to be completed in September of 2018. Trial registration This trial was registered on clinicaltrials.gov ( NCT01926184 on

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)

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

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