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...

Introduction to machine learning

OpenAIRE

Baştanlar, Yalın; Özuysal, Mustafa

2014-01-01

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning app...

VIRTUAL MODELING OF A NUMERICAL CONTROL MACHINE TOOL USED FOR COMPLEX MACHINING OPERATIONS

Directory of Open Access Journals (Sweden)

POPESCU Adrian

2015-11-01

Full Text Available This paper presents the 3D virtual model of the numerical control machine Modustar 100, in terms of machine elements. This is a CNC machine of modular construction, all components allowing the assembly in various configurations. The paper focused on the design of the subassemblies specific to the axes numerically controlled by means of CATIA v5, which contained different drive kinematic chains of different translation modules that ensures translation on X, Y and Z axis. Machine tool development for high speed and highly precise cutting demands employment of advanced simulation techniques witch it reflect on cost of total development of the machine.

Introduction to machine learning.

Science.gov (United States)

Baştanlar, Yalin; Ozuysal, Mustafa

2014-01-01

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

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

Directory of Open Access Journals (Sweden)

Stephen Gang Wu

2016-04-01

Full Text Available 13C metabolic flux analysis (13C-MFA has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM, k-Nearest Neighbors (k-NN, and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

49 CFR 214.341 - Roadway maintenance machines.

Science.gov (United States)

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Roadway maintenance machines. 214.341 Section 214... Roadway maintenance machines. (a) Each employer shall include in its on-track safety program specific provisions for the safety of roadway workers who operate or work near roadway maintenance machines. Those...

An HTS machine laboratory prototype

DEFF Research Database (Denmark)

Mijatovic, Nenad; Jensen, Bogi Bech; Træholt, Chresten

2012-01-01

This paper describes Superwind HTS machine laboratory setup which is a small scale HTS machine designed and build as a part of the efforts to identify and tackle some of the challenges the HTS machine design may face. One of the challenges of HTS machines is a Torque Transfer Element (TTE) which...... conduction compared to a shaft. The HTS machine was successfully cooled to 77K and tests have been performed. The IV curves of the HTS field winding employing 6 HTS coils indicate that two of the coils had been damaged. The maximal value of the torque during experiments of 78Nm was recorded. Loaded with 33...

Inverse Problems in Geodynamics Using Machine Learning Algorithms

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

Employing finite-state machines in data integrity problems

Directory of Open Access Journals (Sweden)

Malikov Andrey

2016-01-01

Full Text Available This paper explores the issue of group integrity of tuple subsets regarding corporate integrity constraints in relational databases. A solution may be found by applying the finite-state machine theory to guarantee group integrity of data. We present a practical guide to coding such an automaton. After creating SQL queries to manipulate data and control its integrity for real data domains, we study the issue of query performance, determine the level of transaction isolation, and generate query plans.

Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

Directory of Open Access Journals (Sweden)

Jonas eKaplan

2015-03-01

Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

A Cross-Entropy-Based Admission Control Optimization Approach for Heterogeneous Virtual Machine Placement in Public Clouds

Directory of Open Access Journals (Sweden)

Li Pan

2016-03-01

Full Text Available Virtualization technologies make it possible for cloud providers to consolidate multiple IaaS provisions into a single server in the form of virtual machines (VMs. Additionally, in order to fulfill the divergent service requirements from multiple users, a cloud provider needs to offer several types of VM instances, which are associated with varying configurations and performance, as well as different prices. In such a heterogeneous virtual machine placement process, one significant problem faced by a cloud provider is how to optimally accept and place multiple VM service requests into its cloud data centers to achieve revenue maximization. To address this issue, in this paper, we first formulate such a revenue maximization problem during VM admission control as a multiple-dimensional knapsack problem, which is known to be NP-hard to solve. Then, we propose to use a cross-entropy-based optimization approach to address this revenue maximization problem, by obtaining a near-optimal eligible set for the provider to accept into its data centers, from the waiting VM service requests in the system. Finally, through extensive experiments and measurements in a simulated environment with the settings of VM instance classes derived from real-world cloud systems, we show that our proposed cross-entropy-based admission control optimization algorithm is efficient and effective in maximizing cloud providers’ revenue in a public cloud computing environment.

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2015-01-01

Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2016-01-01

Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

Establishment of the BOSPOR-80 machine library of evaluated threshold reaction cross-sections and its testing by means of integral experiments

International Nuclear Information System (INIS)

Bychkov, V.M.; Zolotarev, K.I.; Pashchenko, A.B.; Plyaskin, V.I.

1982-08-01

A paper was published in 1979 containing a compilation of experimental data on the cross-sections of (n,p), (n,α) and (n,2n) threshold reactions and recommended excitation functions. A further paper considered the development of evaluation methods based on the use of theoretical model calculations, an increase in the number of recommended excitation functions, correction of the recommended cross-sections on the basis of integral experiments and allowance for recent experimental data. To satisfy the wide circle of users, BOSPOR-80 - a machine library of evaluated threshold reaction cross-sections - was set up

Automatic Classification of the Sub-Techniques (Gears Used in Cross-Country Ski Skating Employing a Mobile Phone

Directory of Open Access Journals (Sweden)

Thomas Stöggl

2014-10-01

Full Text Available The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC ski-skating gears (G using Smartphone accelerometer data. Eleven XC skiers (seven men, four women with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01 with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

Automatic Classification of the Sub-Techniques (Gears) Used in Cross-Country Ski Skating Employing a Mobile Phone

Science.gov (United States)

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-01-01

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear. PMID:25365459

On-Demand Associative Cross-Language Information Retrieval

Science.gov (United States)

Geraldo, André Pinto; Moreira, Viviane P.; Gonçalves, Marcos A.

This paper proposes the use of algorithms for mining association rules as an approach for Cross-Language Information Retrieval. These algorithms have been widely used to analyse market basket data. The idea is to map the problem of finding associations between sales items to the problem of finding term translations over a parallel corpus. The proposal was validated by means of experiments using queries in two distinct languages: Portuguese and Finnish to retrieve documents in English. The results show that the performance of our proposed approach is comparable to the performance of the monolingual baseline and to query translation via machine translation, even though these systems employ more complex Natural Language Processing techniques. The combination between machine translation and our approach yielded the best results, even outperforming the monolingual baseline.

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

Science.gov (United States)

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

2018-04-01

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

Nontraditional machining processes research advances

CERN Document Server

2013-01-01

Nontraditional machining employs processes that remove material by various methods involving thermal, electrical, chemical and mechanical energy or even combinations of these. Nontraditional Machining Processes covers recent research and development in techniques and processes which focus on achieving high accuracies and good surface finishes, parts machined without burrs or residual stresses especially with materials that cannot be machined by conventional methods. With applications to the automotive, aircraft and mould and die industries, Nontraditional Machining Processes explores different aspects and processes through dedicated chapters. The seven chapters explore recent research into a range of topics including laser assisted manufacturing, abrasive water jet milling and hybrid processes. Students and researchers will find the practical examples and new processes useful for both reference and for developing further processes. Industry professionals and materials engineers will also find Nontraditional M...

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

Machining of Complex Sculptured Surfaces

CERN Document Server

2012-01-01

The machining of complex sculptured surfaces is a global technological topic in modern manufacturing with relevance in both industrialized and emerging in countries particularly within the moulds and dies sector whose applications include highly technological industries such as the automotive and aircraft industry. Machining of Complex Sculptured Surfaces considers new approaches to the manufacture of moulds and dies within these industries. The traditional technology employed in the manufacture of moulds and dies combined conventional milling and electro-discharge machining (EDM) but this has been replaced with  high-speed milling (HSM) which has been applied in roughing, semi-finishing and finishing of moulds and dies with great success. Machining of Complex Sculptured Surfaces provides recent information on machining of complex sculptured surfaces including modern CAM systems and process planning for three and five axis machining as well as explanations of the advantages of HSM over traditional methods ra...

An abstract machine for module replacement

OpenAIRE

Walton, Chris; Krl, Dilsun; Gilmore, Stephen

1998-01-01

In this paper we define an abstract machine model for the mλ typed intermediate language. This abstract machine is used to give a formal description of the operation of run-time module replacement from the programming language Dynamic ML. The essential technical device which we employ for module replacement is a modification of two-space copying garbage collection.

Production Machine Shop Employment Competencies. Part Three: The Engine Lathe.

Science.gov (United States)

Bishart, Gus; Werner, Claire

Competencies for production machine shop are provided for the third of four topic areas: the engine lathe. Each competency appears in a one-page format. It is presented as a goal statement followed by one or more "indicator" statements, which are performance objectives describing an ability that, upon attainment, will establish…

Variable cross-section windings for efficiency improvement of electric machines

Science.gov (United States)

Grachev, P. Yu; Bazarov, A. A.; Tabachinskiy, A. S.

2018-02-01

Implementation of energy-saving technologies in industry is impossible without efficiency improvement of electric machines. The article considers the ways of efficiency improvement and mass and dimensions reduction of electric machines with electronic control. Features of compact winding design for stators and armatures are described. Influence of compact winding on thermal and electrical process is given. Finite element method was used in computer simulation.

Psychological effects of (non)employment: A cross-national comparison of the United States and Japan.

Science.gov (United States)

Gnambs, Timo; Stiglbauer, Barbara; Selenko, Eva

2015-12-01

The involuntary loss of employment has been shown to deteriorate subjective well-being. Adopting a cross-cultural perspective on Jahoda's (1982) deprivation model this study examines several latent and manifest benefits of work that were expected to mediate the effects of employment status on well-being. It was hypothesized that in more collectivistic societies the decline in subjective well-being would be a consequence of a diminished sense of collective purpose for the non-employed, whereas in individualistic societies the crucial factors would be a loss of social status and financial benefits. The findings from two representative national surveys conducted in the United States (N = 1,093) and Japan (N = 647) provided partial support for these hypotheses. Cultural differences moderated the effects of employment status on the benefits of work. As a consequence, different processes mediated the decline in well-being for the non-employed in the two countries. These results are embedded within the wider discourse on culture and its effect on unemployment. © 2015 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines

International Nuclear Information System (INIS)

Frauenhofer, Joachim; Arndt, Tabea; Grundmann, Joern

2013-01-01

The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO 2 emissions.

Performance of a Folded-Strip Toroidally Wound Induction Machine

DEFF Research Database (Denmark)

Jensen, Bogi Bech; Jack, Alan G.; Atkinson, Glynn J.

2011-01-01

This paper presents the measured experimental results from a four-pole toroidally wound induction machine, where the stator is constructed as a pre-wound foldable strip. It shows that if the machine is axially restricted in length, the toroidally wound induction machine can have substantially...... shorter stator end-windings than conventionally wound induction machines, and hence that a toroidally wound induction machine can have lower losses and a higher efficiency. The paper also presents the employed construction method, which emphasizes manufacturability, and highlights the advantages...

Positional reference system for ultraprecision machining

International Nuclear Information System (INIS)

Arnold, J.B.; Burleson, R.R.; Pardue, R.M.

1982-01-01

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlledmultiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of position interferometers and part contour description data inputs to calculate error components for each axis of movement and output them to corresponding axis drives with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base

Positional reference system for ultraprecision machining

Science.gov (United States)

Arnold, J.B.; Burleson, R.R.; Pardue, R.M.

1980-09-12

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of positions interferometers and part contour description data input to calculate error components for each axis of movement and output them to corresponding axis driven with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Directory of Open Access Journals (Sweden)

Ole Marius Hoel Rindal

2017-12-01

Full Text Available The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

Interpretation and qualification of short-term employment in cross-border situation at Article 15(2)OECD MC

OpenAIRE

Asllani, Shkumbin

2017-01-01

The free movement of people and capital has enabled individuals and businesses to engage in cross-border transactions. Global economy and the competitiveness between international groups have acknowledged the necessity for a dynamic human workforce and openness of the labour market for mobility of workers within affiliated companies and different multinational enterprises. Cross-border short-term employment[1] has been crucial part of modern development of the international business.[2]  Mobi...

Cross-Age Mentoring to Support A-Level Pupils' Transition into Higher Education and Undergraduate Students' Employability

Science.gov (United States)

James, Alana I.

2014-01-01

Two challenges identified for psychology higher education are supporting entry students' transition, and supporting graduates' transition into employment. The evaluation of the first phase of a cross-age mentoring action research project targeting these issues is presented; eight psychology undergraduates mentored 20 A-level psychology pupils in…

Machine learning for evolution strategies

CERN Document Server

Kramer, Oliver

2016-01-01

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

Thermal models of pulse electrochemical machining

International Nuclear Information System (INIS)

Kozak, J.

2004-01-01

Pulse electrochemical machining (PECM) provides an economical and effective method for machining high strength, heat-resistant materials into complex shapes such as turbine blades, die, molds and micro cavities. Pulse Electrochemical Machining involves the application of a voltage pulse at high current density in the anodic dissolution process. Small interelectrode gap, low electrolyte flow rate, gap state recovery during the pulse off-times lead to improved machining accuracy and surface finish when compared with ECM using continuous current. This paper presents a mathematical model for PECM and employs this model in a computer simulation of the PECM process for determination of the thermal limitation and energy consumption in PECM. The experimental results and discussion of the characteristics PECM are presented. (authors)

Modelling injection moulding machines for micro manufacture applications through functional analysis

DEFF Research Database (Denmark)

Fantoni, G.; Tosello, Guido; Gabelloni, D.

2012-01-01

The paper presents the analysis of an injection moulding machine using functional analysis to identify both its critical components and possible working problems when such a machine is employed for the production of polymer-based micro products. The step-by-step procedure starts from the study...... of the process phases of a machine and then it employs functional analysis to decompose the phases and attributes functions to part features. Part features are subsequently analyzed to understand the causal chains bringing either to the desired behaviour or to failures to avoid. The assessment of the design...... solution is finally performed by gathering quantitative data from experiments. The case study investigates the design motivations and functional drivers of a micro injection moulding machine. The analysis allows identifying the correlations between failures and advantages with the design of the machine...

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

Science.gov (United States)

Janssen, Ronald J; Mourão-Miranda, Janaina; Schnack, Hugo G

2018-04-22

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine-learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine-learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Cross-training in a cellular manufacturing environment

NARCIS (Netherlands)

Slomp, J.; Bokhorst, J.A.C.; Molleman, E.

This study addresses the need for cross-training in a cellular manufacturing environment. It is demonstrated that an effective cross-training situation results if workers and machines are connected, directly or indirectly, by task assignment decisions. The connections between workers and machines

Machine Imperfection Studies of the RAON Superconducting Linac

Science.gov (United States)

Jeon, D.; Jang, J.-H.; Jin, H.

2018-05-01

Studies of the machine imperfections in the RAON superconducting linac (SCL) that employs normal conducting (NC) quadrupoles were done to assess the tolerable error budgets of the machine imperfections that ensure operation of the beam. The studies show that the beam loss requirement is met even before the orbit correction and that the beam loss requirement is met even without the MHB (multi-harmonic buncher) and VE (velocity equalizer) thanks to the RAON's radio-frequency quadrupole (RFQ) design feature. For the low energy section of the linac (SCL3), a comparison is made between the two superconducting linac lattice types: one lattice that employs NC quadrupoles and the other that employs SC solenoids. The studies show that both lattices meet the beam loss requirement after the orbit correction. However, before the orbit correction, the lattice employing SC solenoids does not meet the beam loss requirement and can cause a significant beam loss, while the lattice employing NC quadrupoles meets the requirement. For the lattice employing SC solenoids, care must be taken during the beam commissioning.

Next generation of electron-positron colliding beam machines

International Nuclear Information System (INIS)

Richter, B.

1979-03-01

The contribution of electron-positron colliding beam experiments to high-energy physics in the 1970's has been prodigious. From the research done with the two highest-energy e + e - machines of the present generation of these devices, have come such things as the discovery and illumination of the properties of the psi family, charmed particles, a new heavy lepton, non-ambigious evidence for hadronic jets, etc. The rapid pace of new developments in physics from such machines comes about for two reasons. First, the electron-positron annihilation process at present energies is particularly simple and well understood, making the problem of determining the quantum numbers and properties of new particles particularly simple. Second, in electron-positron annihilation all final states are on a relatively equal footing, and small production cross sections are compensated for by a lack of confusing background. For example, the rate of production of charmed particles at the SPEAR storage ring at SLAC and the DORIS storage ring at DESY is 3 or 4 orders of magnitude less than the rate of production at FNAL and the SPS. Yet these particles were first found at the storage rings where the background cross sections are comparable to the signal cross section, and have not yet been observed directly by their hadronic decays at the proton machines where the background cross sections are 4 orders of magnitude larger than the signal cross sections. The machines PEP at SLAC and PETRA at DESY will soon be operating at 35 to 40 GeV cm to explore new regions of energy. Studies of electron-positron annihilation at much higher energies than presently planned have a great deal to teach, not only about particle structure and dynamics, but also about the nature of the weak interaction. Some of the physics which can be done with such machines is discussed with a view toward getting an idea of the minimum required energy for the new generation of colliding beam devices

Self-Employment Dynamics, State Dependence and Cross-Mobility Patterns

OpenAIRE

Caliendo, Marco; Uhlendorff, Arne

2008-01-01

This paper analyzes the mobility between self-employment, wage employment and non-employment. Using data for men in West Germany, we find strong true state dependence in all three states. Moreover, compared to wage employment, non-employment increases the probability of self-employment significantly, and self-employment goes along with a higher risk of future non-employment.

THE AGGREGATE IMPLICATIONS OF MACHINE REPLACEMENT: THEORY AND EVIDENCE

OpenAIRE

John Haltiwanger; Russell Cooper

1992-01-01

The authors study an economy in which producers incur resource costs to replace depreciated machines. The process of costly replacement and depreciation creates endogenous fluctuations in productivity, employment, and output of a single producer. The authors explore the spillover effects of machine replacement on other sectors of the economy and provide conditions for synchronized machine replacement by multiple independent producers. The implications of their model are generally consistent w...

Optimization of machining parameters of hard porcelain on a CNC ...

African Journals Online (AJOL)

Optimization of machining parameters of hard porcelain on a CNC machine by Taguchi-and RSM method. ... Journal Home > Vol 10, No 1 (2018) > ... The conduct of experiments was made by employing the Taguchi's L27 Orthogonal array to ...

Cross-sectional imaging with rotational panoramic X-ray machine for preoperative assessment of dental implant site. Comparisons of imaging properties with conventional film tomography and computed tomography

International Nuclear Information System (INIS)

Makihara, Masahiro; Nishikawa, Keiichi; Kuroyanagi, Kinya

2001-01-01

To clarify the validity of cross-sectional imaging with rotational panoramic x-ray machine for preoperative assessment of the dental implant site, the imaging properties were compared with those of spiral tomography and multi-planer reconstruction (MPR) manipulation of x-ray computed tomography. Cross-sectional imaging of the maxilla and mandible of an edentulous dry skull was performed by each technique at an image layer thickness of 1 mm. Steel spheres were used to identify cross-sectional planes and measure distance. Six oral radiologists scored the image clarity of structures with 5-grade rating scales and measured the distance between images of 2 steel spheres. Each measured distance was divided by the magnification factor. The actual distance was also measured on the skull. The score and the distance were statistically compared. The Spearman's rank correlation coefficients for the score and the absolute values of the difference in distances measured by different observers were calculated as test units to compare inter-observer agreements statistically. The same observation and measurement were repeated to compare intra-observer agreement. Image clarity of the linear tomography available with a panoramic machine was comparable to spiral tomography and superior to MPR, except for the cortical bone on the lingual side. The inter- and intra-observer agreements were comparable. The accuracy for measurement of distance, the inter- and intra-observer agreements were also comparable to the spiral tomography and superior to those of MPR. Therefore, it is concluded that cross-sectional imaging with a rotational panoramic x-ray machine is useful for preoperative assessment of the dental implant site. (author)

A Design to Digitalize Hydraulic Cylinder Control of a Machine Tool ...

African Journals Online (AJOL)

Conventionally hydraulic piston - cylinder servos are actuated using analogue controls for machine tool axis drives. In this paper a design of the axis control system of an NC milling machine which employs a small stepping motor to digitally actuated hydraulic piston - cylinder servo drives existing on the machines Y-axis is ...

Application of support vector machines to breast cancer screening using mammogram and history data

Science.gov (United States)

Land, Walker H., Jr.; Akanda, Anab; Lo, Joseph Y.; Anderson, Francis; Bryden, Margaret

2002-05-01

Support Vector Machines (SVMs) are a new and radically different type of classifiers and learning machines that use a hypothesis space of linear functions in a high dimensional feature space. This relatively new paradigm, based on Statistical Learning Theory (SLT) and Structural Risk Minimization (SRM), has many advantages when compared to traditional neural networks, which are based on Empirical Risk Minimization (ERM). Unlike neural networks, SVM training always finds a global minimum. Furthermore, SVMs have inherent ability to solve pattern classification without incorporating any problem-domain knowledge. In this study, the SVM was employed as a pattern classifier, operating on mammography data used for breast cancer detection. The main focus was to formulate the best learning machine configurations for optimum specificity and positive predictive value at very high sensitivities. Using a mammogram database of 500 biopsy-proven samples, the best performing SVM, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 45.0% and a positive predictive value (PPV) of 50.1% at 100% sensitivity. At 97% sensitivity, a specificity of 55.8% and a PPV of 55.2% were obtained.

Employing Machine-Learning Methods to Study Young Stellar Objects

Science.gov (United States)

Moore, Nicholas

2018-01-01

Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

Directory of Open Access Journals (Sweden)

Ruben Ruiz-Gonzalez

2014-11-01

Full Text Available The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Employing a virtual reality tool to explicate tacit knowledge of machine operations

NARCIS (Netherlands)

Vasenev, Alexandr; Hartmann, Timo; Doree, Andries G.; Hassani, F.

2013-01-01

The quality and durability of asphalted roads strongly depends on the final step in the road construction process; the profiling and compaction of the fresh spread asphalt. During compaction machine operators continuously make decisions on how to proceed with the compaction accounting for

Results and problems in the development of machines employed in pressing technology

Energy Technology Data Exchange (ETDEWEB)

Dietrich, P; Hinne, H; Linke, L; Nerger, R

1980-08-01

Features specifications and technical improvements of nine GDR made briquetting presses from the Zemag Zeitz company. Briquetting presses have been produced by the company for more than 100 years, the present capacity of the machines ranges from 5.1 t/h to 20.4 t/h of nominal briquet production. Development trends are directed toward larger presses. The prototype PSA 400, based on the design of the four channel press PSA 300, will be tested in industrial operation during 1980/81. Various technical details on the general briquetting press design are enumerated, including experiences gained with steam or electric power operated presses, regulation of the pressing speed with a patented switch gear system, maintenance of crank gear bearings, greasing of steam driven presses, investigation of crack damages to the machine block, further measures for reducing wear of the pressing channel and mechanized welding methods for the channel overhaul. (In German)

Agile machining and inspection thrust area team-on-machine probing / compatibility assessment of Parametric Technology Corporation (PTC) pro/CMM DMIS with Zeiss DMISEngine.

Energy Technology Data Exchange (ETDEWEB)

Wade, James Rokwel; Tomlinson, Kurt; Bryce, Edwin Anthony

2008-09-01

The charter goal of the Agile Machining and Inspection Thrust Area Team is to identify technical requirements, within the nuclear weapons complex (NWC), for Agile Machining and Inspection capabilities. During FY 2008, the team identified Parametric Technology Corporation (PTC) Pro/CMM as a software tool for use in off-line programming of probing routines--used for measurement--for machining and turning centers. The probing routine would be used for in-process verification of part geometry. The same Pro/CMM program used on the machine tool could also be employed for program validation / part verification using a coordinate measuring machine (CMM). Funding was provided to determine the compatibility of the Pro/CMM probing program with CMM software (Zeiss DMISEngine).

Machinability of cast commercial titanium alloys.

Science.gov (United States)

Watanabe, I; Kiyosue, S; Ohkubo, C; Aoki, T; Okabe, T

2002-01-01

This study investigated the machinability of cast orthopedic titanium (metastable beta) alloys for possible application to dentistry and compared the results with those of cast CP Ti, Ti-6Al-4V, and Ti-6Al-7Nb, which are currently used in dentistry. Machinability was determined as the amount of metal removed with the use of an electric handpiece and a SiC abrasive wheel turning at four different rotational wheel speeds. The ratios of the amount of metal removed and the wheel volume loss (machining ratio) were also evaluated. Based on these two criteria, the two alpha + beta alloys tested generally exhibited better results for most of the wheel speeds compared to all the other metals tested. The machinability of the three beta alloys employed was similar or worse, depending on the speed of the wheel, compared to CP Ti. Copyright 2002 Wiley Periodicals, Inc.

Employment status of patients receiving maintenance dialysis – peritoneal and hemodialysis: A cross-sectional study

Directory of Open Access Journals (Sweden)

B S Lakshmi

2017-01-01

Full Text Available The long-term dialysis therapy for end-stage renal disease takes a heavy toll of quality of life of the patient. Several factors such as fatigue and decreased physical capability, impaired social and mental functioning, contribute to this forlorn state. To meld maintenance dialysis treatment with a regular employment can be a serious test. A cross-sectional study of employment of patients on hemodialysis and peritoneal dialysis in a state government tertiary institute in South India was performed between June 2015 and December 2015. Patients who completed 3 months of regular dialysis were only included in the study. The number of patients on hemodialysis was 157 and on peritoneal dialysis was 69. The employment status before the initiation of dialysis was 60% (93 out of 155 and 63.7% (44 out of 69 in hemodialysis and peritoneal dialysis, respectively. After initiation, the loss of employment was observed in 44% (41 out of 93 in hemodialysis and 51.2% (26 out of 44 in peritoneal dialysis (P = 0.2604. Even though there was fall of absolute number of job holders in both the blue and white collar jobs, the proportion of jobholders in the white collar job holders improved. On univariate analysis, the factors which influenced the loss of employment were males, age between 50 and 60 years, number of comorbidities >2, illiteracy and blue collar versus white collar job before the initiation of dialysis. The majority of patients had the scores above 80 on Karnofsky performance scale and the majority belonged upper and middle classes than lower classes on modified Kuppuswamy's socioeconomic status scale; however, the loss of employment was also disproportionately high. There appeared a substantial difference in the attitude of the patients toward the employment. There was no difference between hemodialysis and peritoneal dialysis in the loss of employment of our patients.

Employment type, workplace interpersonal conflict, and insomnia: a cross-sectional study of 37,646 employees in Japan.

Science.gov (United States)

Sakurai, Kenji; Nakata, Aknori; Ikeda, Tomoko; Otsuka, Yasumasa; Kawahito, Junko

2014-01-01

This study explored whether workplace interpersonal conflict (WIC) is associated with insomnia, and whether the relationship between WIC and insomnia differs across different employment groups. A total of 37,646 Japanese full-time employees participated in a cross-sectional survey. Employment types included permanent employment and 2 forms of temporary employment: direct-hire and temporary work agent (TWA). Insomnia symptoms, including difficulty initiating sleep, difficulty maintaining sleep, and early morning awakening were measured. Insomnia was defined as having experienced 1 or more of these symptoms on ≥3 nights per week over the past 12 months. Results showed that WIC was significantly associated with an increased risk of insomnia (odds ratio OR = 1.63; 95% confidence interval CI = 1.55-1.71), controlling for confounders. However, the relationship between WIC and the risk of insomnia was significantly stronger for TWAs than for permanent employees (OR = 1.97; 95% CI = 1.13-3.45). A frequent exposure to WIC may increase the risk of insomnia, particularly for TWAs.

Towards Semantic Analysis of Training-Learning Relationships within Human-Machine Interactions

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

In this article First-Order Predicate Logic (FOL) is employed for analysing some relationships between human beings and machines. Based on FOL, I will be conceptually and logically concerned with semantic analysis of training-learning relationships in human-machine interaction. The central focus...

Employing post-DEA cross-evaluation and cluster analysis in a sample of Greek NHS hospitals.

Science.gov (United States)

Flokou, Angeliki; Kontodimopoulos, Nick; Niakas, Dimitris

2011-10-01

To increase Data Envelopment Analysis (DEA) discrimination of efficient Decision Making Units (DMUs), by complementing "self-evaluated" efficiencies with "peer-evaluated" cross-efficiencies and, based on these results, to classify the DMUs using cluster analysis. Healthcare, which is deprived of such studies, was chosen as the study area. The sample consisted of 27 small- to medium-sized (70-500 beds) NHS general hospitals distributed throughout Greece, in areas where they are the sole NHS representatives. DEA was performed on 2005 data collected from the Ministry of Health and the General Secretariat of the National Statistical Service. Three inputs -hospital beds, physicians and other health professionals- and three outputs -case-mix adjusted hospitalized cases, surgeries and outpatient visits- were included in input-oriented, constant-returns-to-scale (CRS) and variable-returns-to-scale (VRS) models. In a second stage (post-DEA), aggressive and benevolent cross-efficiency formulations and clustering were employed, to validate (or not) the initial DEA scores. The "maverick index" was used to sort the peer-appraised hospitals. All analyses were performed using custom-made software. Ten benchmark hospitals were identified by DEA, but using the aggressive and benevolent formulations showed that two and four of them respectively were at the lower end of the maverick index list. On the other hand, only one 100% efficient (self-appraised) hospital was at the higher end of the list, using either formulation. Cluster analysis produced a hierarchical "tree" structure which dichotomized the hospitals in accordance to the cross-evaluation results, and provided insight on the two-dimensional path to improving efficiency. This is, to our awareness, the first study in the healthcare domain to employ both of these post-DEA techniques (cross efficiency and clustering) at the hospital (i.e. micro) level. The potential benefit for decision-makers is the capability to examine high

Machine learning in cardiovascular medicine: are we there yet?

Science.gov (United States)

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

About the walking machine motion stability

Directory of Open Access Journals (Sweden)

V. V. Lapshin

2014-01-01

Full Text Available The use of legs as propulsive devices of the machine will increase its capability to cross rough and deformable terrain as compared with wheeled and trucked machines. Today it is already possible to speak about design of statically stable walking robots to be used in the certain areas of application. The most promising areas of their application are exploration and emergency-rescue operations in extremely complicated situations (e.g. in the zone of destruction after earthquakes, technogenic catastrophe, etc..In such dangerous situations there is a possibility for the walking machine to be overturned either because of loosing a support to one or several legs or due to significant displacement of the leg support points, which are caused by deformation or destruction of the terrain in the points of the legs support. Therefore, it is necessary to design motion control algorithms that enable teaching the motion control system of a walking robot: How to decrease the possibility of the robot overturning? How to stop the robot as quickly as possible keeping its static stability? What must be done if static stability is lost? Note that the loss of static stability does not inevitably result in the robot falling down. How to fall down better (with minimal robot destruction in inevitable case?This work investigates the first abovementioned problems, i.e. preventing a walking machine from overturning in dangerous situations. For this purpose it suggests to use a special cautious (safe gait, which allows the machine to remain statically stable if it suddenly looses support to its any leg. The natural price for the increased safety to prevent from overturning is the reduced capabilities of robot kinematics and, as a consequence, its capability to cross rough terrain. It is also suggested to reconsider the general definition of a walking machine static stability margin in order to obtain an adequate estimation of the robot overturning possibility

Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

Science.gov (United States)

Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

2015-04-01

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

Directory of Open Access Journals (Sweden)

Ickwon Choi

2015-04-01

Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Prediction on sunspot activity based on fuzzy information granulation and support vector machine

Science.gov (United States)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

Analysis of a Novel Transverse Flux Machine with a Tubular Cross-section for Free Piston Energy Converter Application

Energy Technology Data Exchange (ETDEWEB)

Cosic, Alija

2010-07-01

Constantly growing need for oil, all over the world, has caused oil price to rise rapidly during the last decade. High oil prices have made fuel economy as one of the most important factors when consumers are buying their cars today. Realizing this, many car manufacturers have developed or are looking for some alternative solutions in order to decrease fuel consumption. Combining two different technologies in a vehicle, the so called hybrid vehicle, can be seen as the first step toward a better and more sustainable development.There are several different solutions for hybrid vehicles today, among the best known are the Serie Electric Hybrid Vehicle (SEHV), the Parallel Electric Hybrid Vehicle (PEHV) and the Serie-Parallel Hybrid Electric Vehicle (SPEHV). By integrating a combustion engine with a linear electric machine into one unit, a system that is called Free Piston Energy Converter (FPEC) is achieved. The FPEC is suitable for use in a SEHV. Other application areas like stand alone generator are also possible. In this report a novel Transverse Flux Machine (TFM) with a tubular cross section of the translator has been investigated. Application of the machine in a FPEC has put tough requirement on the translator weight, specific power and force density. Different configurations of the winding arrangements as well as the magnet arrangement have been investigated. It has been concluded that the buried magnet design suffers from high leakage flux and is thus not a suitable TFM concept. Instead the surface mounted magnet design has been chosen for further investigation. An analytical model has been developed and a prototype machine has been built based on the analytical results. In order to have a better understanding of the machine characteristic a 3D-FEM analysis has been performed. The results from the analytical model, FEM model and measurements are analyzed and compared. The comparison between the measured and FEM-simulated results shows very good agreement

Study on ultra-fine w-EDM with on-machine measurement-assisted

International Nuclear Information System (INIS)

Chen Shuntong; Yang Hongye

2011-01-01

The purpose of this study was to develop the on-machine measurement techniques so as to precisely fabricate micro intricate part using ultra-fine w-EDM. The measurement-assisted approach which employs an automatic optical inspection (AOI) is incorporated to ultra-fine w-EDM process to on-machine detect the machining error for next re-machining. The AOI acquires the image through a high resolution CCD device from the contour of the workpiece after roughing in order to further process and recognize the image for determining the residual. This facilitates the on-machine error detection and compensation re-machining. The micro workpiece and electrode are not repositioned during machining. A fabrication for a micro probe of 30-μm diameter is rapidly machined and verified successfully. Based on the proposed technique, on-machine measurement with AOI has been realized satisfactorily.

75 FR 41894 - Wapakoneta Machine Company, Currently Known as EF Industrial Technologies, Inc., Wapakoneta, OH...

Science.gov (United States)

2010-07-19

... of early 2010, Wapakoneta Machine Company is currently known as EF Industrial Technologies, Inc. Some... Wapakoneta Machine Company, currently known as EF Industrial Technologies, Inc., Wapakoneta, Ohio became... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-73,211] Wapakoneta Machine...

Machinability evaluation of titanium alloys.

Science.gov (United States)

Kikuchi, Masafumi; Okuno, Osamu

2004-03-01

In the present study, the machinability of titanium, Ti-6Al-4V, Ti-6A1-7Nb, and free-cutting brass was evaluated using a milling machine. The metals were slotted with square end mills under four cutting conditions. The cutting force and the rotational speed of the spindle were measured. The cutting forces for Ti-6Al-4V and Ti-6Al-7Nb were higher and that for brass was lower than that for titanium. The rotational speed of the spindle was barely affected by cutting. The cross sections of the Ti-6Al-4V and Ti-6Al-7Nb chips were more clearly serrated than those of titanium, which is an indication of difficult-to-cut metals. There was no marked difference in the surface roughness of the cut surfaces among the metals. Cutting force and the appearance of the metal chips were found to be useful as indices of machinability and will aid in the development of new alloys for dental CAD/CAM and the selection of suitable machining conditions.

Magnet management in electric machines

Science.gov (United States)

Reddy, Patel Bhageerath; El-Refaie, Ayman Mohamed Fawzi; Huh, Kum Kang

2017-03-21

A magnet management method of controlling a ferrite-type permanent magnet electrical machine includes receiving and/or estimating the temperature permanent magnets; determining if that temperature is below a predetermined temperature; and if so, then: selectively heating the magnets in order to prevent demagnetization and/or derating the machine. A similar method provides for controlling magnetization level by analyzing flux or magnetization level. Controllers that employ various methods are disclosed. The present invention has been described in terms of specific embodiment(s), and it is recognized that equivalents, alternatives, and modifications, aside from those expressly stated, are possible and within the scope of the appending claims.

Cross-age mentoring to support A-level pupils’ transition into Higher Education and undergraduate students’ employability

OpenAIRE

James, Alana I.

2014-01-01

Two challenges identified for psychology higher education are supporting entry students’ transition, and supporting graduates’ transition into employment. The evaluation of the first phase of a cross-age mentoring action research project targeting these issues is presented; eight psychology undergraduates mentored 20 A-level psychology pupils in two schools. Mentors showed significant increases in two of nine psychological literacies, in self-efficacy but not self-esteem, were highly satisfie...

An Overall Perspective of Machine Translation with its Shortcomings

Directory of Open Access Journals (Sweden)

Alireza Akbari

2014-01-01

Full Text Available The petition for language translation has strikingly augmented recently due to cross-cultural communication and exchange of information. In order to communicate well, text should be translated correctly and completely in each field such as legal documents, technical texts, scientific texts, publicity leaflets, and instructional materials. In this connection, Machine translation is of great importance in translation. The term “Machine Translation” was first proposed by George Artsrouni and Smirnov Troyanski (1933 to design a storage design on paper tape. This paper sought to investigate an overall perspective of Machine Translation models and its metrics in detail. Finally, it scrutinized the ins and outs shortcomings of Machine Translation.

Grinding machine: Friend or Foe | Adigun | West African Journal of ...

African Journals Online (AJOL)

Few cases of traumatic loses of the external genitalia have been reported in this part of the world. In a developing country like ours, grinding machines are commonly being used by the people for domestic purposes. Children in their mid fifteens are usually employed to man and operate the machine without proper training ...

A Machine Learning Framework for Plan Payment Risk Adjustment.

Science.gov (United States)

Rose, Sherri

2016-12-01

To introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment. 2011-2012 Truven MarketScan database. We compare the performance of multiple statistical approaches within a broad machine learning framework for estimation of risk adjustment formulas. Total annual expenditure was predicted using age, sex, geography, inpatient diagnoses, and hierarchical condition category variables. The methods included regression, penalized regression, decision trees, neural networks, and an ensemble super learner, all in concert with screening algorithms that reduce the set of variables considered. The performance of these methods was compared based on cross-validated R 2 . Our results indicate that a simplified risk adjustment formula selected via this nonparametric framework maintains much of the efficiency of a traditional larger formula. The ensemble approach also outperformed classical regression and all other algorithms studied. The implementation of cross-validated machine learning techniques provides novel insight into risk adjustment estimation, possibly allowing for a simplified formula, thereby reducing incentives for increased coding intensity as well as the ability of insurers to "game" the system with aggressive diagnostic upcoding. © Health Research and Educational Trust.

Do flexicurity policies protect workers from the adverse health consequences of temporary employment? A cross-national comparative analysis

Directory of Open Access Journals (Sweden)

Faraz Vahid Shahidi

2016-12-01

Full Text Available Flexicurity policies comprise a relatively novel approach to the regulation of work and welfare that aims to combine labour market flexibility with social security. Advocates of this approach argue that, by striking the right balance between flexibility and security, flexicurity policies allow firms to take advantage of loose contractual arrangements in an increasingly competitive economic environment while simultaneously protecting workers from the adverse health and social consequences of flexible forms of employment. In this study, we use multilevel Poisson regression models to test the theoretical claim of the flexicurity approach using data for 23 countries across three waves of the European Social Survey. We construct an institutional typology of labour market regulation and social security to evaluate whether inequalities in self-reported health and limiting longstanding illness between temporary workers and their permanent counterparts are smaller in countries that most closely approximate the ideal type described by advocates of the flexicurity approach. Our results indicate that, while the association between temporary employment and health varies across countries, institutional configurations of labour market regulation and social security do not provide a meaningful explanation for this cross-national variation. Contrary to the expectations of the flexicurity hypothesis, our data do not indicate that employment-related inequalities are smaller in countries that approximate the flexicurity approach. We discuss potential explanations for these findings and conclude that there remains a relative lack of evidence in support of the theoretical claims of the flexicurity approach. Keywords: Health inequalities, Cross-national, Temporary, Employment, Flexicurity, Multilevel

Thin type inverter for machine-room-less elevator; Machine roomless elevator yo usugata inverter

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-01-10

In the elevator industry, a machine-room-less elevator, which does not necessitate a machine room usually installed on the roof, has come into the spotlight in the domain of low and intermediate speed elevators. The lack of a machine room, however, will necessarily limit the space for the installation of the traction motor and control panel. Fuji Electric Co., Ltd., in order to properly cope with the situation, has developed in cooperation with Fujitec Co., Ltd., a very thin type inverter installable on an elevator hall floor. The inverter, based on Fuji Electric's high-performance vector control inverter FRENIC5000VG5, is as thin as 100mm, and is available in three series up to 11kW. For the embodiment of such a thin structure, a cooling structure of Fuji Electric's own is employed, and prudence is exercised as required at many locations so that maintainability will not be impaired throughout the very thin control panel design. (translated by NEDO)

Employment status, employment functioning, and barriers to employment among VA primary care patients.

Science.gov (United States)

Zivin, Kara; Yosef, Matheos; Levine, Debra S; Abraham, Kristen M; Miller, Erin M; Henry, Jennifer; Nelson, C Beau; Pfeiffer, Paul N; Sripada, Rebecca K; Harrod, Molly; Valenstein, Marcia

2016-03-15

Prior research found lower employment rates among working-aged patients who use the VA than among non-Veterans or Veterans who do not use the VA, with the lowest reported employment rates among VA patients with mental disorders. This study assessed employment status, employment functioning, and barriers to employment among VA patients treated in primary care settings, and examined how depression and anxiety were associated with these outcomes. The sample included 287 VA patients treated in primary care in a large Midwestern VA Medical Center. Bivariate and multivariable analyses were conducted examining associations between socio-demographic and clinical predictors of six employment domains, including: employment status, job search self-efficacy, work performance, concerns about job loss among employed Veterans, and employment barriers and likelihood of job seeking among not employed Veterans. 54% of respondents were employed, 36% were not employed, and 10% were economically inactive. In adjusted analyses, participants with depression or anxiety (43%) were less likely to be employed, had lower job search self-efficacy, had lower levels of work performance, and reported more employment barriers. Depression and anxiety were not associated with perceived likelihood of job loss among employed or likelihood of job seeking among not employed. Single VA primary care clinic; cross-sectional study. Employment rates are low among working-aged VA primary care patients, particularly those with mental health conditions. Offering primary care interventions to patients that address mental health issues, job search self-efficacy, and work performance may be important in improving health, work, and economic outcomes. Published by Elsevier B.V.

Man-machine interfaces analysis system based on computer simulation

International Nuclear Information System (INIS)

Chen Xiaoming; Gao Zuying; Zhou Zhiwei; Zhao Bingquan

2004-01-01

The paper depicts a software assessment system, Dynamic Interaction Analysis Support (DIAS), based on computer simulation technology for man-machine interfaces (MMI) of a control room. It employs a computer to simulate the operation procedures of operations on man-machine interfaces in a control room, provides quantified assessment, and at the same time carries out analysis on operational error rate of operators by means of techniques for human error rate prediction. The problems of placing man-machine interfaces in a control room and of arranging instruments can be detected from simulation results. DIAS system can provide good technical supports to the design and improvement of man-machine interfaces of the main control room of a nuclear power plant

Python for probability, statistics, and machine learning

CERN Document Server

Unpingco, José

2016-01-01

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowl...

Prototype-based models in machine learning

NARCIS (Netherlands)

Biehl, Michael; Hammer, Barbara; Villmann, Thomas

2016-01-01

An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

Higgs Machine Learning Challenge 2014

CERN Document Server

Olivier, A-P; Bourdarios, C ; LAL / Orsay; Goldfarb, S ; University of Michigan

2014-01-01

High Energy Physics (HEP) has been using Machine Learning (ML) techniques such as boosted decision trees (paper) and neural nets since the 90s. These techniques are now routinely used for difficult tasks such as the Higgs boson search. Nevertheless, formal connections between the two research fields are rather scarce, with some exceptions such as the AppStat group at LAL, founded in 2006. In collaboration with INRIA, AppStat promotes interdisciplinary research on machine learning, computational statistics, and high-energy particle and astroparticle physics. We are now exploring new ways to improve the cross-fertilization of the two fields by setting up a data challenge, following the footsteps of, among others, the astrophysics community (dark matter and galaxy zoo challenges) and neurobiology (connectomics and decoding the human brain). The organization committee consists of ATLAS physicists and machine learning researchers. The Challenge will run from Monday 12th to September 2014.

An abstract machine model of dynamic module replacement

OpenAIRE

Walton, Chris; Kırlı, Dilsun; Gilmore, Stephen

2000-01-01

In this paper we define an abstract machine model for the mλ typed intermediate language. This abstract machine is used to give a formal description of the operation of run-time module replacement for the programming language Dynamic ML. The essential technical device which we employ for module replacement is a modification of two-space copying garbage collection. We show how the operation of module replacement could be applied to other garbage-collected languages such as Java.

Machine Learning and Parallelism in the Reconstruction of LHCb and its Upgrade

Science.gov (United States)

De Cian, Michel

2016-11-01

The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an offline-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40 MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed.

Machine Learning and Parallelism in the Reconstruction of LHCb and its Upgrade

International Nuclear Information System (INIS)

Cian, Michel De

2016-01-01

The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an offline-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40 MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed

The role of soft computing in intelligent machines.

Science.gov (United States)

de Silva, Clarence W

2003-08-15

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Worksite Food and Physical Activity Environments and Wellness Supports Reported by Employed Adults in the United States, 2013.

Science.gov (United States)

Onufrak, Stephen J; Watson, Kathleen B; Kimmons, Joel; Pan, Liping; Khan, Laura Kettel; Lee-Kwan, Seung Hee; Park, Sohyun

2018-01-01

To examine the workplace food and physical activity (PA) environments and wellness culture reported by employed United States adults, overall and by employer size. Cross-sectional study using web-based survey on wellness policies and environmental supports for healthy eating and PA. Worksites in the United States. A total of 2101 adults employed outside the home. Survey items were based on the Centers for Disease Control and Prevention Worksite Health ScoreCard and Checklist of Health Promotion Environments and included the availability and promotion of healthy food items, nutrition education, promotion of breast-feeding, availability of PA amenities and programs, facility discounts, time for PA, stairwell signage, health promotion programs, and health risk assessments. Descriptive statistics were used to examine the prevalence of worksite environmental and facility supports by employer size (<100 or ≥100 employees). Chi-square tests were used to examine the differences by employer size. Among employed respondents with workplace food or drink vending machines, approximately 35% indicated the availability of healthy items. Regarding PA, 30.9% of respondents reported that their employer provided opportunities to be physically active and 17.6% reported worksite exercise facilities. Wellness programs were reported by 53.2% working for large employers, compared to 18.1% for smaller employers. Employee reports suggested that workplace supports for healthy eating, PA, and wellness were limited and were less common among smaller employers.

Contemporary employment arrangements and mental well-being in men and women across Europe: a cross-sectional study.

Science.gov (United States)

De Moortel, Deborah; Vandenheede, Hadewijch; Vanroelen, Christophe

2014-10-28

There is the tendency in occupational health research of approximating the 'changed world of work' with a sole focus on the intrinsic characteristics of the work task, encompassing the job content and working conditions. This is insufficient to explain the mental health risks associated with contemporary paid work as not only the nature of work tasks have changed but also the terms and conditions of employment. The main aim of the present study is to investigate whether a set of indicators referring to quality of the employment arrangement is associated with the well-being of people in salaried employment. Associations between the quality of contemporary employment arrangements and mental well-being in salaried workers are investigated through a multidimensional set of indicators for employment quality (contract type; income; irregular and/or unsocial working hours; employment status; training; participation; and representation). The second and third aim are to investigate whether the relation between employment quality and mental well-being is different for employed men and women and across different welfare regimes. Cross-sectional data of salaried workers aged 15-65 from 21 EU-member states (n =11,940) were obtained from the 2010 European Social Survey. Linear regression analyses were performed. For both men and women, and irrespective of welfare regime, several sub-dimensions of low employment quality are significantly related with poor mental well-being. Most of the significant relations persist after controlling for intrinsic job characteristics. An insufficient household income and irregular and/or unsocial working hours are the strongest predictors of poor mental well-being. A differential vulnerability of employed men and women to the sub-dimensions of employment quality is found in Traditional family and Southern European welfare regimes. There are significant relations between indicators of low employment quality and poor mental well-being, also when

Education and employment status of adults with autism spectrum disorders in Germany - a cross-sectional-survey.

Science.gov (United States)

Frank, Fabian; Jablotschkin, Martina; Arthen, Tobias; Riedel, Andreas; Fangmeier, Thomas; Hölzel, Lars P; Tebartz van Elst, Ludger

2018-03-27

Adults with autism spectrum disorders (ASD) experience challenges in participating in the labour market and struggle to achieve and maintain appropriate professional positions, possibly due to impairments of communication and social interaction. Studies have shown high rates of unemployment as well as evidence of inadequate employment. As knowledge on the participation in the German labour market is scarce, the aim of our study was to examine employment status, type of occupation and inadequate employment in a sample of clinically mostly late-diagnosed and most likely not intellectually disabled adults with ASD in Germany. We conducted a cross-sectional-survey in clinically mostly late-diagnosed adults with ASD. Employment status, type of occupation, and the level of formal education and training were examined through a postal questionnaire. Inadequate employment regarding participants' current and longest practised occupation was assessed by transforming participants' information into skill levels of the "Classification of Occupations 2010" of the German Federal Employment Agency, and comparing these with participants' level of formal education and training. The response rate was 43.2% (N = 185 of N = 428 potential participants). 94.6% were first-time diagnosed when being 18 years of age or older. 56.8% held a general university entrance-level qualification and 24.9% had obtained a Masters' or diploma degree as their highest vocational qualification. 94.1% had been employed at some time. Of these, 68.4% reported being currently employed, 13.5% being currently unemployed and 17.0% being retired for health reasons. Regarding the longest-practised and the current occupation, the highest proportion of participants was found in the occupational area "health and social sector, teaching and education" (22.4% and 23.3%, respectively). With respect to inadequate employment, 22.1% were found to be overeducated in relation to their longest-practised occupation and 31

The Machinic Temporality of Metadata

Directory of Open Access Journals (Sweden)

Claudio Celis

2015-03-01

Full Text Available In 1990 Deleuze introduced the hypothesis that disciplinary societies are gradually being replaced by a new logic of power: control. Accordingly, Matteo Pasquinelli has recently argued that we are moving towards societies of metadata, which correspond to a new stage of what Deleuze called control societies. Societies of metadata are characterised for the central role that meta-information acquires both as a source of surplus value and as an apparatus of social control. The aim of this article is to develop Pasquinelli’s thesis by examining the temporal scope of these emerging societies of metadata. In particular, this article employs Guattari’s distinction between human and machinic times. Through these two concepts, this article attempts to show how societies of metadata combine the two poles of capitalist power formations as identified by Deleuze and Guattari, i.e. social subjection and machinic enslavement. It begins by presenting the notion of metadata in order to identify some of the defining traits of contemporary capitalism. It then examines Berardi’s account of the temporality of the attention economy from the perspective of the asymmetric relation between cyber-time and human time. The third section challenges Berardi’s definition of the temporality of the attention economy by using Guattari’s notions of human and machinic times. Parts four and five fall back upon Deleuze and Guattari’s notions of machinic surplus labour and machinic enslavement, respectively. The concluding section tries to show that machinic and human times constitute two poles of contemporary power formations that articulate the temporal dimension of societies of metadata.

Clinical and cognitive correlates of employment among patients with schizophrenia: a cross-sectional study in Malaysia

Directory of Open Access Journals (Sweden)

Radzi Rozhan SM

2011-05-01

Full Text Available Abstract Background Gainful employment is one major area of functioning which is becoming an important goal in psychiatric rehabilitation of patients with schizophrenia. Studies in western countries are pointing to evidence that certain sociodemographic and clinical factors may contribute to employment outcomes in this group of people. However, the area is still largely unexplored in Malaysia. The aim of this study was to examine the sociodemographic, clinical and cognitive correlates of employment status among patients with Schizophrenia. Methods This was a cross-sectional study. All participants who fulfilled the requirements of the study according to the inclusion and exclusion criteria were enrolled. Study instruments included a demographic data questionnaire, Positive and Negative Symptom Scale (PANSS, Trail Making Tests, Rey's Auditory Verbal Learning Test (RAVLT and Digit Span. Bivariate analyses were done using chi-square for categorical data and t-test for continuous data and multiple logistic regression analysis was done to identify predictors of employment status. Results A total of 95 participants who fulfilled the inclusion criteria were enrolled into the study. Among the sociodemographic, clinical and cognitive variables studied marital status, educational level, mean scores of negative symptoms, Digit Span and RAVLT and Trail Making Tests were found to show significant association with employment status on bivariate analyses. However, when entered into a logistic regression model, only cognitive variables ie. Trail A and B, Digit Span and RAVLT were significant predictors of employment status. Conclusions The results from this study support the role of cognitive function, particularly, attention, working memory and executive functioning on attaining and maintaining employment in persons with schizophrenia as measured by the RAVLT, Digit Span and Trail Making Tests. These findings may act as preliminary evidence suggesting the

75 FR 60141 - International Business Machines (IBM), Global Technology Services Delivery Division, Including On...

Science.gov (United States)

2010-09-29

... 25, 2010, applicable to workers of International Business Machines (IBM), Global Technology Services... hereby issued as follows: All workers of International Business Machines (IBM), Global Technology... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,164] International Business...

Accessible engineering drawings for visually impaired machine operators.

Science.gov (United States)

Ramteke, Deepak; Kansal, Gayatri; Madhab, Benu

2014-01-01

An engineering drawing provides manufacturing information to a machine operator. An operator plans and executes machining operations based on this information. A visually impaired (VI) operator does not have direct access to the drawings. Drawing information is provided to them verbally or by using sample parts. Both methods have limitations that affect the quality of output. Use of engineering drawings is a standard practice for every industry; this hampers employment of a VI operator. Accessible engineering drawings are required to increase both independence, as well as, employability of VI operators. Today, Computer Aided Design (CAD) software is used for making engineering drawings, which are saved in CAD files. Required information is extracted from the CAD files and converted into Braille or voice. The authors of this article propose a method to make engineering drawings information directly accessible to a VI operator.

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...

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.

76 FR 174 - International Business Machines (IBM), Global Sales Operations Organization, Sales and...

Science.gov (United States)

2011-01-03

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,575; TA-W-74,575D] International Business Machines (IBM), Global Sales Operations Organization, Sales and Distribution Business Manager Roles; One Teleworker Located in Charleston, WV; International Business Machines (IBM), Global Sales Operations Organization, Sales and...

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.

Microstructure, Morphology, and Nanomechanical Properties Near Fine Holes Produced by Electro-Discharge Machining

Science.gov (United States)

Blau, P. J.; Howe, J. Y.; Coffey, D. W.; Trejo, R. M.; Kenik, E. D.; Jolly, B. C.; Yang, N.

2012-08-01

Fine holes in metal alloys are employed for many important technological purposes, including cooling and the precise atomization of liquids. For example, they play an important role in the metering and delivery of fuel to the combustion chambers in energy-efficient, low-emission diesel engines. Electro-discharge machining (EDM) is one process employed to produce such holes. Since the hole shape and bore morphology can affect fluid flow, and holes also represent structural discontinuities in the tips of the spray nozzles, it is important to understand the microstructures adjacent to these holes, the features of the hole walls, and the nanomechanical properties of the material that was in some manner altered by the EDM hole-making process. Several techniques were used to characterize the structure and properties of spray-holes in a commercial injector nozzle. These include scanning electron microscopy, cross sectioning and metallographic etching, bore surface roughness measurements by optical interferometry, scanning electron microscopy, and transmission electron microscopy of recast EDM layers extracted with the help of a focused ion beam.

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

OpenAIRE

Andy Feng; Georg Graetz

2015-01-01

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

Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

Directory of Open Access Journals (Sweden)

Ryan Suderman

Full Text Available Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively

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....

Method and apparatus for characterizing and enhancing the functional performance of machine tools

Science.gov (United States)

Barkman, William E; Babelay, Jr., Edwin F; Smith, Kevin Scott; Assaid, Thomas S; McFarland, Justin T; Tursky, David A; Woody, Bethany; Adams, David

2013-04-30

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

Transient thermal analysis of flux switching PM machines

NARCIS (Netherlands)

Ilhan, E.; Kremers, M.F.J.; Motoasca, T.E.; Paulides, J.J.H.; Lomonova, E.

2013-01-01

Flux switching permanent magnet (FSPM) machines bring together the merits of switched reluctance and PM synchronous motors. FSPM employs armature windings and PMs together in the stator region, therefore the proximity of the windings PMs makes a thermal model mandatory. In literature, thermal

Evaluation influence of machining parameters on shape form errors in turning of machine parts clamped in the chuck with adaptive jaws

Directory of Open Access Journals (Sweden)

I.V. Lutsiv

2017-12-01

Full Text Available The paper deals with the derivation problem of the dependence of machine part geometric form deviation in cross section area on clamping diameter as well as cutting speed, feed and cutting depth in semi finish machining. The analysis of single factor circular deviation dependences on machining conditions values is performed. Using the special software application package the laboratory conditions experiment results are analyzed. The dispersion analysis including options for main linear and quadratic effects evaluation is given and the simplification model of experiment results is obtained. It presents the evaluation empiric dependence of cutting conditions and clamping diameter influence on shape error forming (dynamic error. It is found that to obtain the necessary form accuracy in machining with lathe chuck equipped with the adaptive clamping jaws it is desirable to control the most statistically significant factors that actually are the cutting depth and feed.

Machine Learning Methods for Attack Detection in the Smart Grid.

Science.gov (United States)

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

77 FR 33490 - Long Elevator & Machine Company, Inc., Including Workers Whose Unemployment Insurance (UI) Wages...

Science.gov (United States)

2012-06-06

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-80,525] Long Elevator & Machine Company, Inc., Including Workers Whose Unemployment Insurance (UI) Wages Were Reported Through Kone, Inc... former workers of Long Elevator & Machine Company, Inc., including workers whose unemployment insurance...

Code-Expanded Random Access for Machine-Type Communications

DEFF Research Database (Denmark)

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

2012-01-01

Abstract—The random access methods used for support of machine-type communications (MTC) in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. Motivated by the random access method employed in LTE, we propose...

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.

Sales of healthy snacks and beverages following the implementation of healthy vending standards in City of Philadelphia vending machines.

Science.gov (United States)

Pharis, Meagan L; Colby, Lisa; Wagner, Amanda; Mallya, Giridhar

2018-02-01

We examined outcomes following the implementation of employer-wide vending standards, designed to increase healthy snack and beverage options, on the proportion of healthy v. less healthy sales, sales volume and revenue for snack and beverage vending machines. A single-arm evaluation of a policy utilizing monthly sales volume and revenue data provided by the contracted vendor during baseline, machine conversion and post-conversion time periods. Study time periods are full calendar years unless otherwise noted. Property owned or leased by the City of Philadelphia, USA. Approximately 250 vending machines over a 4-year period (2010-2013). At post-conversion, the proportion of sales attributable to healthy items was 40 % for snacks and 46 % for beverages. Healthy snack sales were 323 % higher (38·4 to 162·5 items sold per machine per month) and total snack sales were 17 % lower (486·8 to 402·1 items sold per machine per month). Healthy beverage sales were 33 % higher (68·2 to 90·6 items sold per machine per month) and there was no significant change in total beverage sales (213·2 to 209·6 items sold per machine per month). Revenue was 11 % lower for snacks ($US 468·30 to $US 415·70 per machine per month) and 21 % lower for beverages ($US 344·00 to $US 270·70 per machine per month). Sales of healthy vending items were significantly higher following the implementation of employer-wide vending standards for snack and beverage vending machines. Entities receiving revenue-based commission payments from vending machines should employ strategies to minimize potential revenue losses.

Machine Learning Interface for Medical Image Analysis.

Science.gov (United States)

Zhang, Yi C; Kagen, Alexander C

2017-10-01

TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting.

Machine Learning and Inverse Problem in Geodynamics

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

Investigation of permanent magnet machines for downhole applications: Design, prototype and testing of a flux-switching permanent magnet machine

Energy Technology Data Exchange (ETDEWEB)

Chen, Anyuan

2011-01-15

The current standard electrical downhole machine is the induction machine which is relatively inefficient. Permanent magnet (PM) machines, having higher efficiencies, higher torque densities and smaller volumes, have widely employed in industrial applications to replace conventional machines, but few have been developed for downhole applications due to the high ambient temperatures in deep wells and the low temperature stability of PM materials over time. Today, with the development of variable speed drives and the applications of high temperature magnet materials, it is increasingly interesting for oil and gas industries to develop PM machines for downhole applications. Recently, some PM machines applications have been presented for downhole applications, which are normally addressed on certain specific downhole case. In this thesis the focus has been put on the performance investigation of different PM machines for general downhole cases, in which the machine outer diameter is limited to be small by well size, while the machine axial length may be relatively long. The machine reliability is the most critical requirement while high torque density and high efficiency are also desirable. The purpose is to understand how the special constraints in downhole condition affect the performances of different machines. First of all, three basic machine concepts, which are the radial, axial and transverse flux machines, are studied in details by analytical method. Their torque density, efficiency, power factor and power capability are investigated with respect to the machine axial length and pole number. The presented critical performance comparisons of the machines provide an indication of machines best suitable with respect to performance and size for downhole applications. Conventional radial flux permanent magnet (RFPM) machines with the PMs on the rotor can provide high torque density and high efficiency. This type of machine has been suggested for several different

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...

Machine Learning Phases of Strongly Correlated Fermions

Directory of Open Access Journals (Sweden)

Kelvin Ch’ng

2017-08-01

Full Text Available Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions on cubic lattices. We show that a three-dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling. We then use the network, trained at half filling, to explore the trend in the transition temperature as the system is doped away from half filling. This transfer learning approach predicts that the instability to the magnetic phase extends to at least 5% doping in this region. Our results pave the way for other machine learning applications in correlated quantum many-body systems.

Office Machine and Computer Occupations. Reprinted from the Occupational Outlook Handbook, 1978-79 Edition.

Science.gov (United States)

Bureau of Labor Statistics (DOL), Washington, DC.

Focusing on office machine and computer occupations, this document is one in a series of forty-one reprints from the Occupational Outlook Handbook providing current information and employment projections for individual occupations and industries through 1985. The specific occupations covered in this document include business machine repairers,…

PENGEMBANGAN EMPLOYABILITY SKILLS SISWA SMK DITINJAU DARI IMPLEMENTASI PENDEKATAN SAINTIFIK

Directory of Open Access Journals (Sweden)

Sunardi Sunardi

2016-07-01

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

Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification

Directory of Open Access Journals (Sweden)

Qingbo Li

2013-01-01

Full Text Available Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.

Age and gender differential relationship between employment status and body mass index among middle-aged and elderly adults : a cross-sectional study

NARCIS (Netherlands)

Noh, Jin-Won; Kim, Jinseok; Park, Jumin; Oh, In-Hwan; Kwon, Young Dae

2016-01-01

Objective: To determine the influence of age and gender, respectively, on the association between employment status and body mass index (BMI) in Korean adults using a large, nationally representative sample. Design: Cross-sectional study. Setting: South Korea. Participants: 7228 from fourth wave of

Low temperature wetting and cleanup of alkali metal-advanced electrical machine systems

International Nuclear Information System (INIS)

Gass, W.R.; Witkowski, R.E.; Burrow, G.C.

1980-01-01

Advanced homopolar electrical machines employing high electrical current density, liquid metal sliprings for current transfer utilize NaK/sub 78/ (78 w/o potassium, 22 w/o sodium) for the conducting fluid. Experiments have been performed to improve alkali metal/oxide clean-up procedures. Studies have also confirmed chemical and materials compatibility between barium doped NaK/sub 78/ and typical machine structural materials. 4 refs

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

Science.gov (United States)

Thabtah, Fadi

2018-02-13

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

Achieving precision in high density batch mode micro-electro-discharge machining

International Nuclear Information System (INIS)

Richardson, Mark T; Gianchandani, Yogesh B

2008-01-01

This paper reports a parametric study of batch mode micro-electro-discharge machining (µEDM) of high density features in stainless steel. Lithographically fabricated copper tools with single cross, parallel line and 8 × 8 circle/square array features of 5–100 µm width and 5–75 µm spacing were used to quantify trends in machining tolerance and the impact of debris accumulation. As the tool feature density is increased, debris accumulation effects begin to dominate, eventually degrading both tool and workpiece. Two independent techniques for mitigating this debris buildup are separately investigated. The first is a passivation coating which suppresses spurious discharges triggered from the sidewalls of the machining tool. By this method, the mean tool wear rate decreases from a typical of about 34% to 1.7% and machining non-uniformity reduces from 4.9 µm to 1.1 µm across the workpiece. The second technique involves a two-step machining process that enhances the hydrodynamic removal of machining debris compared to standard methods. This improves surface and edge finish, machining time and tool wear

Delay dynamical systems and applications to nonlinear machine-tool chatter

International Nuclear Information System (INIS)

Fofana, M.S.

2003-01-01

The stability behaviour of machine chatter that exhibits Hopf and degenerate bifurcations has been examined without the assumption of small delays between successive cuts. Delay dynamical system theory leading to the reduction of the infinite-dimensional character of the governing delay differential equations (DDEs) to a finite-dimensional set of ordinary differential equations have been employed. The essential mathematical arguments for these systems in the context of retarded DDEs are summarized. Then the application of these arguments in the stability study of machine-tool chatter with multiple time delays is presented. Explicit analytical expressions ensuring stable and unstable machining when perturbations are periodic, stochastic and nonlinear have been derived using the integral averaging method and Lyapunov exponents

Experimental Research into Technology of Abrasive Flow Machining Nonlinear Tube Runner

Directory of Open Access Journals (Sweden)

Junye Li

2014-06-01

Full Text Available In the fields of military and civil uses, some special passages exist in many major parts, such as non-linear tubes. The overall performance is usually decided by the surface quality. Abrasive flow machining (AFM technology can effectively improve the surface quality of the parts. In order to discuss the mechanism and technology of abrasive flow machining nonlinear tube, the nozzle is picked up as the researching object, and the self-designed polishing liquid is employed to make research on the key technological parameters of abrasive flow machining linear tube. Technological parameters’ impact on surface quality of the parts through the nozzle surface topography and scanning electron microscopy (SEM map is explored. It is experimentally confirmed that abrasive flow machining can significantly improve surface quality of nonlinear runner, and experimental results can provide technical reference to optimizing study of abrasive flow machining theory.

Optimization of Milling Parameters Employing Desirability Functions

Science.gov (United States)

Ribeiro, J. L. S.; Rubio, J. C. Campos; Abrão, A. M.

2011-01-01

The principal aim of this paper is to investigate the influence of tool material (one cermet and two coated carbide grades), cutting speed and feed rate on the machinability of hardened AISI H13 hot work steel, in order to identify the cutting conditions which lead to optimal performance. A multiple response optimization procedure based on tool life, surface roughness, milling forces and the machining time (required to produce a sample cavity) was employed. The results indicated that the TiCN-TiN coated carbide and cermet presented similar results concerning the global optimum values for cutting speed and feed rate per tooth, outperforming the TiN-TiCN-Al2O3 coated carbide tool.

77 FR 62265 - Long Elevator & Machine Company, Inc., Including Workers Whose Wages Were Reported Through Kone...

Science.gov (United States)

2012-10-12

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-80,525] Long Elevator & Machine... Determination Regarding Application for Reconsideration for workers and former workers of Long Elevator... (hereafter referred to as Long Elevator & Machine Company or the subject firm). The Department's Notice was...

[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.

Population and Employment in China

OpenAIRE

Keyfitz, N.

1982-01-01

China's effectiveness in population control can be credited to the direct line of command through party and administrative cadres that extends from the leadership in Beijing down to the production team in a distant rural commune. The reason that the administrative machine has devoted so much attention to population control is twofold: the perceived limits of the natural environment, as indicated by slowness of growth of food supplies, and the difficulty of arranging productive employment for ...

Performance Evaluation of a Mechanical Draft Cross Flow Cooling Towers Employed in a Subtropical Region

Science.gov (United States)

Muthukumar, Palanisamy; Naik, Bukke Kiran; Goswami, Amarendra

2018-02-01

Mechanical draft cross flow cooling towers are generally used in a large-scale water cooled condenser based air-conditioning plants for removing heat from warm water which comes out from the condensing unit. During this process considerable amount of water in the form of drift (droplets) and evaporation is carried away along with the circulated air. In this paper, the performance evaluation of a standard cross flow induced draft cooling tower in terms of water loss, range, approach and cooling tower efficiency are presented. Extensive experimental studies have been carried out in three cooling towers employed in a water cooled condenser based 1200 TR A/C plant over a period of time. Daily variation of average water loss and cooling tower performance parameters have been reported for some selected days. The reported average water loss from three cooling towers is 4080 l/h and the estimated average water loss per TR per h is about 3.1 l at an average relative humidity (RH) of 83%. The water loss during peak hours (2 pm) is about 3.4 l/h-TR corresponding to 88% of RH and the corresponding efficiency of cooling towers varied between 25% and 45%.

Comparing health-related quality of life of employed women and housewives: a cross sectional study from southeast Iran

Directory of Open Access Journals (Sweden)

Saravi Fatihe Kerman

2012-11-01

Full Text Available Abstract Background Quality of life differs for different people in different situations and is related to one's self-satisfaction with life. Considering the role of women in family and social health and the specific cultural characteristics of our province, we aimed to compare the quality of life of employed women with housewives in Zahedan, Iran. Methods This cross-sectional study was carried out during 2009–2010 in Zahedan, Iran. The sample consisted of 110 housewives and 110 employed women selected randomly from ten health care centers. Health-related quality of life was assessed using the SF-36. Analysis of covariance (ANCOVA was used to compare quality of life in housewives and employed women while controlling for age, education and income. Results The mean (±SD age of participants was 33.87± 8.95 years. Eighty-eight women (40% had a university degree with a mean (±SD official education of 10.8 (±4.9 years. The results indicated that employed women scored higher than housewives in all measures except for physical functioning. The differences were found to be remarkable for vitality, mental health and role emotional. However, after controlling for age, education and family income, none of differences reached significant level. Conclusion After controlling for potential confounders, the findings from this study indicated that there were no significant differences in quality of life between employed women and housewives. However, employed women scored higher on the SF-36, especially on the role emotional, vitality, and mental health. The findings suggest that associations exist between some aspects of health-related quality of life and employment. Indeed improving health-related quality of life among housewives seems essential.

Linear parallel processing machines I

Energy Technology Data Exchange (ETDEWEB)

Von Kunze, M

1984-01-01

As is well-known, non-context-free grammars for generating formal languages happen to be of a certain intrinsic computational power that presents serious difficulties to efficient parsing algorithms as well as for the development of an algebraic theory of contextsensitive languages. In this paper a framework is given for the investigation of the computational power of formal grammars, in order to start a thorough analysis of grammars consisting of derivation rules of the form aB ..-->.. A/sub 1/ ... A /sub n/ b/sub 1/...b /sub m/ . These grammars may be thought of as automata by means of parallel processing, if one considers the variables as operators acting on the terminals while reading them right-to-left. This kind of automata and their 2-dimensional programming language prove to be useful by allowing a concise linear-time algorithm for integer multiplication. Linear parallel processing machines (LP-machines) which are, in their general form, equivalent to Turing machines, include finite automata and pushdown automata (with states encoded) as special cases. Bounded LP-machines yield deterministic accepting automata for nondeterministic contextfree languages, and they define an interesting class of contextsensitive languages. A characterization of this class in terms of generating grammars is established by using derivation trees with crossings as a helpful tool. From the algebraic point of view, deterministic LP-machines are effectively represented semigroups with distinguished subsets. Concerning the dualism between generating and accepting devices of formal languages within the algebraic setting, the concept of accepting automata turns out to reduce essentially to embeddability in an effectively represented extension monoid, even in the classical cases.

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.

Man-machine interface builders at the Advanced Photon Source

International Nuclear Information System (INIS)

Anderson, M.D.

1991-01-01

Argonne National Laboratory is constructing a 7-GeV Advanced Photon Source for use as a synchrotron radiation source in basic and applied research. The controls and computing environment for this accelerator complex includes graphical operator interfaces to the machine based on Motif, X11, and PHIGS/PEX. Construction and operation of the control system for this accelerator relies upon interactive interface builder and diagram/editor type tools, as well as a run-time environment for the constructed displays which communicate with the physical machine via network connections. This paper discusses our experience with several commercial CUI builders, the inadequacies found in these, motivation for the development of an application- specific builder, and design and implementation strategies employed in the development of our own Man-Machine Interface builder. 5 refs

Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines; Energieeffiziente elektrische Maschinen durch neue Materialien. Supraleitung in grossen elektrischen Maschinen

Energy Technology Data Exchange (ETDEWEB)

Frauenhofer, Joachim [Siemens, Nuernberg (Germany); Arndt, Tabea; Grundmann, Joern [Siemens, Erlangen (Germany)

2013-07-01

The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO{sub 2} emissions.

What’s up with the self-employed? A cross-national perspective on the self-employed’s work-related mental well-being

Directory of Open Access Journals (Sweden)

Jessie Gevaert

2018-04-01

Full Text Available Although many governments actively stimulate self-employment, their work-related mental well-being remains understudied. The aim of current study is to investigate the mental well-being of different types of self-employed, testing whether mental well-being differences among self-employed are explained by the presence of work characteristics that are in accordance with the ideal-typical image of the “successful entrepreneur” (e.g. creativity, willingness to take risks, innovativeness, high intrinsic motivation, skilfulness and the ability of recognizing opportunities. Moreover, we investigate the relation of country-level “entrepreneurial climate” and the individual mental well-being of self-employed. For this purpose, data from the European Working Conditions Survey, round 6 (2015 was analysed, including 5448 cases, originating from the 28 EU-member states. Multilevel random intercepts modelling was used to investigate associations of both individual- and country-level characteristics with mental well-being. We found that motivation, the ability to recognize opportunities, and finding it easy to be self-employed positively influences the mental well-being of self-employed. Respondents with these characteristics are often medium-big employers, while farmers, dependent freelancers and own account workers generally have less of these features and tend to have lower levels of mental well-being. At the country-level, positive entrepreneurship perception relates to more advantageous mental health scores in self-employed. These results implicate that policies promoting self-employment should be (more concerned with the work-related characteristics of (future self-employed. Keywords: Self-employment, Mental well-being, Cross-national, Entrepreneurial characteristics, Entrepreneurial ecosystems, EU 28

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.

Effect of the Machining Processes on Low Cycle Fatigue Behavior of a Powder Metallurgy Disk

Science.gov (United States)

Telesman, J.; Kantzos, P.; Gabb, T. P.; Ghosn, L. J.

2010-01-01

A study has been performed to investigate the effect of various machining processes on fatigue life of configured low cycle fatigue specimens machined out of a NASA developed LSHR P/M nickel based disk alloy. Two types of configured specimen geometries were employed in the study. To evaluate a broach machining processes a double notch geometry was used with both notches machined using broach tooling. EDM machined notched specimens of the same configuration were tested for comparison purposes. Honing finishing process was evaluated by using a center hole specimen geometry. Comparison testing was again done using EDM machined specimens of the same geometry. The effect of these machining processes on the resulting surface roughness, residual stress distribution and microstructural damage were characterized and used in attempt to explain the low cycle fatigue results.

Feasibility studies on the potential of employing electron beam machine for non-medical products irradiation in Malaysia

International Nuclear Information System (INIS)

Siti Aiasah Hashim; Sarada Idris

2012-01-01

In Malaysia, two 10 MeV irradiators were installed by private companies as part of in-house manufacturing or as third party sterilization service provider. At the same time, the 3 MeV EPS 3000 machine at Nuclear Malaysia is providing irradiation services for various purposes and products. With the current increase in demand in automotive manufacturing for better quality harnesses and components, the irradiation service at Nuclear Malaysia had to provide extended time to cope with the requests. This paper looks at the potential of setting up a commercial irradiation facility to cater for non-medical products such as automotive wires and tubing, food, fruits, cosmetic and semiconductors. Intensive interviews with related industries were carried out throughout Malaysia to evaluate the potential of installing electron beam machine for commercial irradiation. The results show that a majority of non-medical industries are not aware of the irradiation service provided by Nuclear Malaysia, although many understand the need for it. A multipurpose irradiator is desired in order to optimize the usage since a single dedicated machine may be too costly to sustain. (author)

76 FR 46853 - International Business Machines Corporation, ITD Business Unit, Division 7, E-mail and...

Science.gov (United States)

2011-08-03

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-73,218; TA-W-73,218A] International Business Machines Corporation, ITD Business Unit, Division 7, E-mail and Collaboration Group, Including Workers Off-Site From Various States in the United States Reporting to Armonk, NY; International Business Machines Corporation, Web Strategy...

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 ...

Artificial Intelligence, Employment, and Income

OpenAIRE

Nilsson, Nils J.

1984-01-01

Artificial intelligence (AI) will have profound societal effects. It promises potential benefits (and may also pose risks) in education, defense, business, law and science. In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of human toil. We also note that some people fear the automation of work by machines and the resulting of unemployment. Yet, since the majority of us probably would rathe...

Constrained state-feedback control of an externally excited synchronous machine

NARCIS (Netherlands)

Carpiuc, S.C.; Lazar, M.

2013-01-01

State-feedback control of externally excited synchronous machines employed in applications such as hybrid electric vehicles and full electric vehicles is a challenging problem. Indeed, these applications are characterized by fast dynamics that are subject to hard physical and control constraints.

Reliability of measuring abductor hallucis muscle parameters using two different diagnostic ultrasound machines

Directory of Open Access Journals (Sweden)

Cameron Alyse FM

2009-11-01

Full Text Available Abstract Background Diagnostic ultrasound provides a method of analysing soft tissue structures of the musculoskeletal system effectively and reliably. The aim of this study was to evaluate within and between session reliability of measuring muscle dorso-plantar thickness, medio-lateral length and cross-sectional area, of the abductor hallucis muscle using two different ultrasound machines, a higher end Philips HD11 Ultrasound machine and clinically orientated Chison 8300 Deluxe Digital Portable Ultrasound System. Methods The abductor hallucis muscle of both the left and right feet of thirty asymptomatic participants was imaged and then measured using both ultrasound machines. Interclass correlation coefficients (ICC with 95% confidence intervals (CI were used to calculate both within and between session intra-tester reliability. Standard error of the measurement (SEM calculations were undertaken to assess difference between the actual measured score across trials and the smallest real difference (SRD was calculated from the SEM to indicate the degree of change that would exceed the expected trial to trial variability. Results The ICCs, SEM and SRD for dorso-plantar thickness and medial-lateral length were shown to have excellent to high within and between-session reliability for both ultrasound machines. The between-session reliability indices for cross-sectional area were acceptable for both ultrasound machines. Conclusion The results of the current study suggest that regardless of the type ultrasound machine, intra-tester reliability for the measurement the abductor hallucis muscle parameters is very high.

What's up with the self-employed? A cross-national perspective on the self-employed's work-related mental well-being.

Science.gov (United States)

Gevaert, Jessie; Moortel, Deborah De; Wilkens, Mathijn; Vanroelen, Christophe

2018-04-01

Although many governments actively stimulate self-employment, their work-related mental well-being remains understudied. The aim of current study is to investigate the mental well-being of different types of self-employed, testing whether mental well-being differences among self-employed are explained by the presence of work characteristics that are in accordance with the ideal-typical image of the "successful entrepreneur" (e.g. creativity, willingness to take risks, innovativeness, high intrinsic motivation, skilfulness and the ability of recognizing opportunities). Moreover, we investigate the relation of country-level "entrepreneurial climate" and the individual mental well-being of self-employed. For this purpose, data from the European Working Conditions Survey, round 6 (2015) was analysed, including 5448 cases, originating from the 28 EU-member states. Multilevel random intercepts modelling was used to investigate associations of both individual- and country-level characteristics with mental well-being. We found that motivation, the ability to recognize opportunities, and finding it easy to be self-employed positively influences the mental well-being of self-employed. Respondents with these characteristics are often medium-big employers, while farmers, dependent freelancers and own account workers generally have less of these features and tend to have lower levels of mental well-being. At the country-level, positive entrepreneurship perception relates to more advantageous mental health scores in self-employed. These results implicate that policies promoting self-employment should be (more) concerned with the work-related characteristics of (future) self-employed.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.

Science.gov (United States)

Manavalan, Balachandran; Shin, Tae H; Lee, Gwang

2018-01-01

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Directory of Open Access Journals (Sweden)

Balachandran Manavalan

2018-03-01

Full Text Available Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

Support vector machine for automatic pain recognition

Science.gov (United States)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Employment status and mental health care use in times of economic contraction: a repeated cross-sectional study in Europe, using a three-level model.

Science.gov (United States)

Buffel, Veerle; van de Straat, Vera; Bracke, Piet

2015-03-11

Framed within the recent economic crisis, in this study we investigate the medical mental health care use of the unemployed compared with that of the employed in Europe, and whether the relationship between employment status and mental health care use varies across macro-economic conditions. We examine whether the macro-economic context and changes therein are related to mental health care use, via their impact on mental health, or more directly, irrespective of mental health. We use data from three waves of the Eurobarometer (2002, 2005/2006, and 2010), which has a repeated cross-sectional and cross-national design. Linear and logistic multilevel regression analyses are performed with mental health, contacting a general practitioner, and contacting a psychiatrist for mental health problems as dependent variables. The multilevel design has three levels (the individual, the period-country, and the country), which allows us to estimate both longitudinal and cross-sectional macro-effects. The macro-economic context and changes therein are assessed using national unemployment rates and growth rates in Gross Domestic Product (GDP). The mean unemployment rate is negatively related to mental health, although for women, this effect only applies to the employed. Among women, no relationship is found between changes in the macro-economic context and mental health. The unemployment rate, and changes in both the unemployment rate and the real GDP growth rate, are associated with men's care use, regardless of their mental health, whereas this does not hold for women. In countries with an increase in the unemployment rate, both unemployed and employed men tend to medicalize their problems more by contacting a general practitioner, irrespective of their mental health, while the likelihood of contacting a psychiatrist is lower among employed men. Our findings stress the importance of taking the macro-economic context and changes therein into account when studying the mental health

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)

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Science.gov (United States)

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Synchrotron phase transition crossing using an rf harmonic

International Nuclear Information System (INIS)

Griffin, J.E.

1991-03-01

This paper describes a new method of transition crossing in strong focusing proton or heavy ion synchrotrons. Such accelerators have the property that at some energy, frequently within the operating range of the machine, the rotation period of particles within the momentum acceptance range of the machine becomes independent of momentum. 19 refs., 10 figs

Work, work environments and other factors influencing nurse faculty intention to remain employed: a cross-sectional study.

Science.gov (United States)

Tourangeau, Ann; Saari, Margaret; Patterson, Erin; Ferron, Era Mae; Thomson, Heather; Widger, Kimberley; MacMillan, Kathleen

2014-06-01

Given the role nurse faculty have in educating nurses, little is known about what influences their intention to remain employed (ITR) in academic settings. Findings from a nurse faculty survey administered to test a conceptual model of factors hypothesized as influencing nurse faculty ITR are reported. A cross-sectional survey design was employed. We included colleges and universities in Ontario, Canada. The population of Ontario nurse faculty who reported being employed as nurse faculty with the College of Nurses of Ontario (Canada) was included. Of the 1328 nurse faculty who were surveyed, 650 participated. Participants completed a questionnaire with measures of work, work environment, job satisfaction, burnout and ITR. Regression analyses were conducted to test the model. Ten of 26 independent variables explained 25.4% of variance in nurse faculty ITR for five years. These variables included: proximity to retirement, quality of relationships with colleagues, being employed full time, having dependents, satisfaction with work-life balance, quality of education, satisfaction with job status, access to financial support for education from organization, access to required human resources and being unionized. Although not all influencing factors are modifiable, academic leadership should develop strategies that encourage nurse faculty ITR. Strategies that support collegial relationships among faculty, increase the number of full time positions, promote work-life balance, engage faculty in assessing and strengthening education quality, support faculty choice between full-time and part-time work, and ensure adequate human resources required to teach effectively will lead to heightened nurse faculty ITR. © 2013.

Modernity of parts in casting machines and coefficients of total productive maintenance

Directory of Open Access Journals (Sweden)

S. Borkowski

2010-10-01

Full Text Available The goal of this study is to investigate the impact of equipment efficiency in casting machines on the quality of die castings made of Al-Si alloys in consideration of their modernity. Analysis focused on two cold-chamber die-casting machines. The assessment of modernity ofthe equipment was made based on ABC analysis of technology and Parker’s scale. Then, the coefficients of total productive maintenance(TPM were employed for assessment of the efficiency of both machines. Using correlation coefficients r allowed authors to demonstrate the relationships between individual TPM coefficients and the number of non-conforming products. The finding of the study is pointing to the differences between the factors which determine the quality of castings resulting from the level of modernity of machines.

Assessing the Need for Personal Protective Equipment: A Guide for Small Business Employers

National Research Council Canada - National Science Library

2000-01-01

The Occupational Safety and Health Administration (OSHA) requires employers to protect their employees from workplace hazards such as machines, work procedures, and hazardous substances that can cause injury...

Materiality of a simulation: Scratch reading machine, 1931

Directory of Open Access Journals (Sweden)

Craig Saper

2009-12-01

Full Text Available Using Bob Brown's reading machine and the prepared texts for his machine, called readies, both designed in 1930, as an example of scratch turntablist techniques, suggests an alternative to narrow definitions of literacy and new ways to appreciate the history of scratch techniques. Brown's machine resembles the turntablist’s ability to rapidly shift reading (its direction, speed, and repetition rather than slowly flipping the pages of a book. Punctuation marks, in the readies, become visual analogies. For movement we see em-dashes (— that also, by definition, indicate that the sentence was interrupted or cut short. The old uses of punctuation, such as employment of periods to mark the end of a sentence, disappear. The result looks like a script for a turntablist’s performance, and dj Herc starts to sound like a reading teacher. An online simulation of Brown's machine, http://www.readies.org, reproduce, or approximate, the motion, scratch, jerking, flickering, and visual effects produced or illuminated with the machine. Those supplemental aspects of reading are always already part of reading. The supplement (movement, visuality, mechanicity to traditional notions of literacy usually remain part of an implicate process. The reading machine and scratch techniques are not simply a new conduit for the same supposedly natural process. The scratch reading highlights what Jacques Derrida calls the "virtual multimedia" (of reading print on paper. The increasing prevalence, even omnipresent and [to some critics] epidemic, use of text(ing machines, something outside or beside traditional literacy, the scratch-meaning becomes foregrounded. Brown's machine puts the natural process of reading under erasure or scratch (simply by adjusting the speed, direction, and layout. dj Herc did the same for music.

Accounting for female employment in Africa

OpenAIRE

Anyanwu, John C.

2012-01-01

Women employment has become a critical development challenge globally. This is because the exclusion of women in employment has potential negative effects on both sustainable inclusive development and poverty reduction. In this paper, we examine the characteristics and the key determinants of female employment in Africa. Our empirical estimates, using available cross-sectional data over the period, 1991 and 2009 suggest that in the all-Africa estimation, quadratic levels of real per capita GD...

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.

Interaction between groundwater and TBM (Tunnel Boring Machine) excavated tunnels

OpenAIRE

Font Capó, Jordi

2012-01-01

A number of problems, e.g. sudden inflows are encountered during tunneling under the piezometric level, especially when the excavation crosses high transmissivity areas. These inflows may drag materials when the tunnel crosses low competent layers, resulting in subsidence, chimney formation and collapses. Moreover, inflows can lead to a decrease in head level because of aquifer drainage. Tunnels can be drilled by a tunnel boring machine (TBM) to minimize inflows and groundwater impacts, restr...

EFFECT OF SILICON CONTENT ON MACHINABILITY OF Al-Si ALLOYS

Directory of Open Access Journals (Sweden)

Birol Akyüz

2016-09-01

Full Text Available In this study the effect of the change in the amount of Silicon (Si occuring in Al-Si alloys on mechanical and machinability properties of the alloy was investigated. The change in mechanical properties and microstructure, which depends on the increase in Si percentage, and the effects of this change on Flank Build-up (FBU, wear on the cutting edge, surface roughness, and machinability were also studied. Alloys in different ratios of Si (i.e. 2 to 12 wt %, were employed in the study. The specimens for tests were obtained by casting into metal moulds. The results obtained from experimental studies indicate improved mechanical properties and machinability, depending on the rise in Si percentage in Al-Si alloys. It is also observed that the increase in Si percentage enhanced surface quality.

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.

Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

DEFF Research Database (Denmark)

Zibar, Darko; de Carvalho, Luis Henrique Hecker; Piels, Molly

2015-01-01

In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms...

Different protein-protein interface patterns predicted by different machine learning methods.

Science.gov (United States)

Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

2017-11-22

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

Science.gov (United States)

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Surface and elemental alterations of dental alloys induced by electro discharge machining (EDM).

Science.gov (United States)

Zinelis, Spiros

2007-05-01

To evaluate the surface and elemental alterations induced by electro discharge machining (EDM) on the surface of dental cast alloys used for the fabrication of implant retained meso- and super-structures. A completed cast model of an arch that received dental implants was used for the preparation of six wax patterns which were divided into three groups (Au, Co and Ti). The wax patterns of the Au and Co groups were invested with conventional phosphate-bonded silica-based investment material and the Ti group with magnesia-based investment material. The investment rings of the Au and Co groups were cast with an Au-Ag alloy (Stabilor G) and a Co-Cr base alloy (Okta C), respectively, while the investment rings of group Ti were cast with cp Ti (Biotan). One casting of each group was subjected to electro discharge machining (EDM); the other was conventionally ground and polished. The surface morphology and the elemental compositions of conventionally and EDM-finished surfaces were studied by SEM/X-ray EDS analysis. Six spectra were collected from each surface employing the area scan mode and the mean value of each element between conventionally and EDM-finished surfaces was statistically analyzed by t-test (a=0.05). Then the specimens of each group were cut perpendicular to their longitudinal axis and after metallographic grinding and polishing the cross-sections studied under the SEM. The EDM surfaces showed a significant increase in C due to the decomposition of the dielectric fluid during spark erosion. Moreover, a significant Cu uptake was noted on these surfaces from the decomposition of the Cu electrodes used for EDM. Cross-sectional analysis showed that all alloys developed a superficial zone (recast layer) varying from 2 microm for Au-Ag to 10 microm for Co-Cr alloy. The elemental composition of dental alloy surfaces is significantly altered after EDM treatment.

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

Science.gov (United States)

Batchelor, Bruce G.; Whelan, Paul F.

1995-10-01

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

Determination of stability of epimetamorphic rock slope using Minimax Probability Machine

Directory of Open Access Journals (Sweden)

Manoj Kumar

2016-01-01

Full Text Available The article employs Minimax Probability Machine (MPM for the prediction of the stability status of epimetamorphic rock slope. The MPM gives a worst-case bound on the probability of misclassification of future data points. Bulk density (d, height (H, inclination (β, cohesion (c and internal friction angle (φ have been used as input of the MPM. This study uses the MPM as a classification technique. Two models {Linear Minimax Probability Machine (LMPM and Kernelized Minimax Probability Machine (KMPM} have been developed. The generalization capability of the developed models has been checked by a case study. The experimental results demonstrate that MPM-based approaches are promising tools for the prediction of the stability status of epimetamorphic rock slope.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Science.gov (United States)

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Electronic gaming machines and gambling disorder: a cross-cultural comparison between Brazil and the United States

Science.gov (United States)

Medeiros, Gustavo Costa; Leppink, Eric W.; Yaemi, Ana; Mariani, Mirella; Tavares, Hermano; Grant, Jon E.

2015-01-01

Aims The objective of this paper is to perform a cross-cultural comparison of gambling disorder (GD) due to electronic gaming machines (EGM), a form of gambling that may have a high addictive potential. Our goal is to investigate two treatment-seeking samples of adults collected in Brazil and the United States, countries with different socio-cultural backgrounds. This comparison may lead to a better understanding of cultural influences on GD. Methods The total studied sample involved 733 treatment-seeking subjects: 353 men and 380 women (average age = 45.80, standard deviation ±10.9). The Brazilian sample had 517 individuals and the American sample 216. Subjects were recruited by analogous strategies. Results We found that the Brazilian sample was younger, predominantly male, less likely to be Caucasian, more likely to be partnered, had a faster progression from recreational gambling to GD, and were more likely to endorse chasing losses. Conclusion This study demonstrated that there are significant differences between treatment-seeking samples of adults presenting GD due to EGM in Brazil and in the United States. These findings suggest that cultural aspects may have a relevant role in GD due to EGM. PMID:26474662

Applications of machine learning in cancer prediction and prognosis.

Science.gov (United States)

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Electrochemical machining of internal built-up surfaces of large-sized vessels for nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Ryabchenko, N N; Pulin, V Ya [Vsesoyuznyj Proektno-Tekhnologicheskij Inst. Atomnogo Mashinostroeniya i Kotlostroeniya, Rostov-na-Donu (USSR)

1977-01-01

Electrochemical machining (ECM) has been employed for finishing of mechanically processed inner surfaces of large lateral parts of construction bodies with welded 0Kh18N10T steel overlayer. The finishing technology developed reduces the surface roughness from 10 mcm to the standard 2.5 mcm at the efficiency of machining of 2-4 m/sup 2/ per hour.

Application for vibration monitoring of aspheric surface machining based on wireless sensor networks

Science.gov (United States)

Han, Chun Guang; Guo, Yin Biao; Jiang, Chen

2010-05-01

Any kinds of tiny vibration of machine tool parts will have a great influence on surface quality of the workpiece at ultra-precise machining process of aspheric surface. At present the major way for decreasing influence of vibration is machining compensation technology. Therefore it is important for machining compensation control to acquire and transmit these vibration signals effectively. This paper presents a vibration monitoring system of aspheric surface machining machine tool based on wireless sensor networks (WSN). Some key issues of wireless sensor networks for vibration monitoring system of aspheric surface machining are discussed. The reliability of data transmission, network communication protocol and synchronization mechanism of wireless sensor networks are studied for the vibration monitoring system. The proposed system achieves multi-sensors vibration monitoring involving the grinding wheel, the workpiece and the workbench spindle. The wireless transmission of vibration signals is achieved by the combination with vibration sensor nodes and wireless network. In this paper, these vibration sensor nodes are developed. An experimental platform is structured which employs wireless sensor networks to the vibration monitoring system in order to test acquisition and wireless transmission of vibration signal. The test results show that the proposed system can achieve vibration data transmission effectively and reliability and meet the monitoring requirements of aspheric surface machining machine tool.

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.

Adaptation of machine translation for multilingual information retrieval in the medical domain.

Science.gov (United States)

Pecina, Pavel; Dušek, Ondřej; Goeuriot, Lorraine; Hajič, Jan; Hlaváčová, Jaroslava; Jones, Gareth J F; Kelly, Liadh; Leveling, Johannes; Mareček, David; Novák, Michal; Popel, Martin; Rosa, Rudolf; Tamchyna, Aleš; Urešová, Zdeňka

2014-07-01

We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets. The search query translation results achieved in our experiments are outstanding - our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

DEFF Research Database (Denmark)

Mølgaard, Lasse Lohilahti; Buus, Ole Thomsen; Larsen, Jan

2017-01-01

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling...... of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction...... in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions...

Parallelization of the ROOT Machine Learning Methods

CERN Document Server

Vakilipourtakalou, Pourya

2016-01-01

Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

Does self-employment really raise job satisfaction? Adaptation and anticipation effects on self-employment and general job changes

OpenAIRE

Hanglberger, Dominik; Merz, Joachim

2015-01-01

Empirical analyses using cross-sectional and panel data found significantly higher levels of job satisfaction for the self-employed than for employees. We argue that by neglecting anticipation and adaptation effects estimates in previous studies might be misleading. To test this, we specify models accounting for anticipation and adaptation to self-employment and general job changes. In contrast to recent literature we find no specific long-term effect of self-employment on job satisfaction. A...

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Science.gov (United States)

Chekroud, Adam Mourad; Zotti, Ryan Joseph; Shehzad, Zarrar; Gueorguieva, Ralitza; Johnson, Marcia K; Trivedi, Madhukar H; Cannon, Tyrone D; Krystal, John Harrison; Corlett, Philip Robert

2016-03-01

Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram. We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863). We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms. Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant. Yale University. Copyright © 2016 Elsevier Ltd. All rights reserved.

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 %.

Design and development of a cross-European employability training for older jobseekers

NARCIS (Netherlands)

Janssen, José; Hale, Hilary; Sanders, Val; Boon, Jo; Van der Klink, Marcel; Stoyanov, Slavi

2015-01-01

The importance of social networking for effective job-search has been clearly established in various studies. Social networks (social capital) represent a constituent component of employability: the potential of an individual to gain employment and to stay employed. However, not everyone feels

Trial of accelerator cells machining with high precision and high efficiency at Okayama region

International Nuclear Information System (INIS)

Yoshikawa, Mitsuo; Yoden, Hiroyuki; Yokomizo, Seiichi; Sumida, Tsuneto; Kunishida, Jun; Oshita, Isao

2005-01-01

In the framework of the project 'Promotion of Science and Technology in Regional Areas' by the Ministry of Education, Culture, Sports, Science and Technology, we have prepared a special apparatus for machining accelerator cells with a high precision and a high efficiency for the future linear collider. A machining with as small an error as 2 micrometers has been realized. Necessary time to finish one accelerator cell is reduced from 128 minutes to 34 minutes due to the suppression of the heating of the object at the machining. If newly developed one chuck method was employed, the precision and efficiency would be further improved. By cutting at both sides of the spindle, the necessary time for machining would be reduced by half. (author)

Precarious Employment and Quality of Employment in Relation to Health and Well-being in Europe.

Science.gov (United States)

Julià, Mireia; Vanroelen, Christophe; Bosmans, Kim; Van Aerden, Karen; Benach, Joan

2017-07-01

This article presents an overview of the recent work on precarious employment and employment quality in relation to workers' health and well-being. More specifically, the article mainly reviews the work performed in the E.U. 7th Framework project, SOPHIE. First, we present our overarching conceptual framework. Then, we provide a compiled overview of the evidence on the sociodemographic and European cross-country distribution of employment quality and employment precariousness. Subsequently, we provide the current evidence regarding the relations with health and broader worker well-being indicators. A final section summarizes current insights on the pathways relating precarious employment and health and well-being. The article concludes with a plea for further data collection and research into the longitudinal effects of employment precariousness among emerging groups of workers. Based on the evidence compiled in this article, policymakers should be convinced of the harmful health and well-being effects of employment precariousness and (further) labor market flexibilization.

Co-Simulation of an Inverter Fed Permanent Magnet Synchronous Machine

Directory of Open Access Journals (Sweden)

Kiss Gergely Máté

2014-10-01

Full Text Available Co-simulation is a method which makes it possible to study the electric machine and its drive at once, as one system. By taking into account the actual inverter voltage waveforms in a finite element model instead of using only the fundamental, we are able to study the electrical machine's behavior in more realistic scenario. The recent increase in the use of variable speed drives justifies the research on such simulation techniques. In this paper we present the co-simulation of an inverter fed permanent magnet synchronous machine. The modelling method employs an analytical variable speed drive model and a finite element electrical machine model. By linking the analytical variable speed drive model together with a finite element model the complex simulation model enables the investigation of the electrical machine during actual operation. The methods are coupled via the results. This means that output of the finite element model serves as an input to the analytical model, and the output of the analytical model provides the input of the finite element model for a different simulation, thus enabling the finite element simulation of an inverter fed machine. The resulting speed and torque characteristics from the analytical model and the finite element model show a good agreement. The experiences with the co-simulation technique encourage further research and effort to improve the method.

MAINTENANCE PLANNING OF THE SEWING NEEDLES OF SIMPLE SEWING MACHINES

Directory of Open Access Journals (Sweden)

ŞUTEU Marius Darius

2017-05-01

Full Text Available The effectiveness of simple sewing machines can be increased through the planning of predictive maintenance activities. The monitoring of the technical condition of the sewing needles of simple sewing machines was based on the measurement of their noise level. For this purpose a Center 322 sonometer was used, while the data obtained during the monitoring process was analyzed through the E322 software. The working speed of the simple sewing machine that was used for obtaining the experimental results varied from 200 stitches/minute to 4000 stitches/minute. The noise levels of a new needle at the working speed of 200 stitches/minute and 4000 stitches/minute were measured. The noise levels for a fault needle at the same working speed of 200 stitches/minute, respectively 4000 stitches/minute were also measured. Using Fuzzy Logic Toolbox ™ module of Matlab®, a decision-making system for determining when replacement of the sewing needles of simple sewing machines should be performed was developed. A case study illustrates the employment of the decision-making system based on fuzzy logic for a simple sewing machine. By replacing the sewing needles of simple sewing machines at the time specified through the decision-making system based on fuzzy logic, the occurrence of the failure can be prevented and the quality of textile products can be improved.

Risk assessment of atmospheric emissions using machine learning

OpenAIRE

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

2008-01-01

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

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

The changing employment relationship in the chemical industry : the role of the employment- and psychological contract / Elsabé Keyser.

OpenAIRE

Keyser, Elsabé

2010-01-01

Understanding the employment relationship in the chemical industry in South Africa and organisational change within it is crucial to the understanding of the changing employment and psychological contract within this industry. This study focused on the employment- and psychological contracts, as well as employees ' work-outcomes (organisational commitment, job insecurity, job performance and intention to quit). Employees from the chemical industry were targeted and a cross-sectional survey...

Crossed molecular beams

International Nuclear Information System (INIS)

Lee, Y.T.

1976-01-01

Research activities with crossed molecular beams at Lawrence Berkeley Laboratory during 1976 are described. Topics covered include: scattering of Ar*, Kr*, with Xe; metastable rare gas interactions, He* + H 2 ; an atomic and molecular halogen beam source; a crossed molecular beam study of the Cl + Br 2 → BrCl + Br reaction; O( 3 P) reaction dynamics, development of the high pressure plasma beam source; energy randomization in the Cl + C 2 H 3 Br → Br + C 2 H 3 Cl reaction; high resolution photoionization studies of NO and ICl; photoionization of (H 2 O)/sub n/ and (NH 3 ) 2 ; photoionization mass spectroscopy of NH 3 + and O 3 + ; photo fragmentation of bromine; and construction of chemiluminescence-laser fluorescence crossed molecular beam machine

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Directory of Open Access Journals (Sweden)

Saerom Park

Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

The Design of EMG Measurement System for Arm Strength Training Machine

Directory of Open Access Journals (Sweden)

Tze-Yee Ho

2015-01-01

Full Text Available The setup of interactive communication between arm strength training machine and the people will make exercise and rehabilitation therapy become more friendly. The employment of electromyographic not only can help physical therapy but also can achieve more effective rehabilitation. Both of the system hardware and software of the arm strength training machine with EMG system are well designed and described. The fundamental design of electromyographic measurement system based on a microcontroller is analyzed and discussed. The software programming is developed in MPLAB integrated development environment from the Microchip Technology Inc. and the friendly user interface is created as well. Finally, an arm strength training machine with electromyographic control system is realized and demonstrated. The experimental results show the feasibility and fidelity of the complete designed system.

Self-commissioning of permanent magnet synchronous machine drives using hybrid approach

DEFF Research Database (Denmark)

Basar, Mehmet Sertug

2016-01-01

Self-commissioning of permanent-magnet (PM) synchronous machines (PMSMs) is of prime importance in an industrial drive system because control performance and system stability depend heavily on the accurate machine parameter information. This article focuses on a combination of offline and online...... parameter estimation for a non-salient pole PMSM which eliminates the need for any prior knowledge on machine parameters. Stator resistance and inductance are first identified at standstill utilising fundamental and high-frequency excitation signals, respectively. A novel method has been developed...... and employed for inductance estimation. Then, stator resistance, inductance and PM flux are updated online using a recursive least-squares (RLS) algorithm. The proposed controllers are designed using MATLAB/Simulink® and implemented on d-Space® real-time system incorporating a commercially available PMSM drive....

Nutritional value of foods sold in vending machines in a UK University: Formative, cross-sectional research to inform an environmental intervention.

Science.gov (United States)

Park, Hanla; Papadaki, Angeliki

2016-01-01

Vending machine use has been associated with low dietary quality among children but there is limited evidence on its role in food habits of University students. We aimed to examine the nutritional value of foods sold in vending machines in a UK University and conduct formative research to investigate differences in food intake and body weight by vending machine use among 137 University students. The nutrient content of snacks and beverages available at nine campus vending machines was assessed by direct observation in May 2014. Participants (mean age 22.5 years; 54% males) subsequently completed a self-administered questionnaire to assess vending machine behaviours and food intake. Self-reported weight and height were collected. Vending machine snacks were generally high in sugar, fat and saturated fat, whereas most beverages were high in sugar. Seventy three participants (53.3%) used vending machines more than once per week and 82.2% (n 60) of vending machine users used them to snack between meals. Vending machine accessibility was positively correlated with vending machine use (r = 0.209, P = 0.015). Vending machine users, compared to non-users, reported a significantly higher weekly consumption of savoury snacks (5.2 vs. 2.8, P = 0.014), fruit juice (6.5 vs. 4.3, P = 0.035), soft drinks (5.1 vs. 1.9, P = 0.006), meat products (8.3 vs. 5.6, P = 0.029) and microwave meals (2.0 vs. 1.3, P = 0.020). No between-group differences were found in body weight. Most foods available from vending machines in this UK University were of low nutritional quality. In this sample of University students, vending machine users displayed several unfavourable dietary behaviours, compared to non-users. Findings can be used to inform the development of an environmental intervention that will focus on vending machines to improve dietary behaviours in University students in the UK. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

Science.gov (United States)

Yavuz, Ahmet Sinan; Sezerman, Osman Ugur

2014-01-01

Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

Applications of Machine Learning in Cancer Prediction and Prognosis

Directory of Open Access Journals (Sweden)

Joseph A. Cruz

2006-01-01

Full Text Available Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25% improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Employment status and psychological distress in a population-based cross-sectional study in Sweden: the impact of migration.

Science.gov (United States)

Sidorchuk, Anna; Engström, Karin; Johnson, Charisse M; Kayser Leeoza, Naima; Möller, Jette

2017-04-07

Unemployment and temporary employment are known to impact psychological health. However, the extent to which the effect is altered by migration-related and sociodemographic determinants is less clear. The purpose of this study was to investigate whether the association between employment status and psychological distress differs between immigrants and Swedish-born and to what extent, the association is modified by gender and reason for immigration. Cross-sectional survey study. Data from public health surveys undertaken in 2002, 2006 and 2010 from random samples of Stockholm County residents, Sweden, were used to analyse a weighted sample of 51 118 individuals aged 18-64 (43 444 Swedish-born, 4055 non-refugees, 3619 refugees). According to their activity in the labour market, the participants were categorised into permanently/self-employed, temporarily employed and unemployed. Associations between self-reported employment and psychological distress measured by a 12-item version of the General Health Questionnaire were explored across individuals with different migration status and reasons for immigration using logistic regression and pairwise comparisons. The analyses were stratified by gender and adjusted for age, socioeconomic characteristics and survey year. Unemployment was associated with elevated likelihood of psychological distress across the study population, regardless of migration status and gender. Fully adjusted models revealed nearly a 3-fold higher odds of distress in unemployed Swedish-born (OR 3.05, 95% CI 2.66 to 3.51), non-refugees (OR 3.51, 95% CI 2.44 to 5.05) and refugees (OR 2.91, 95% CI 2.20 to 3.85) when compared with permanently/self-employed. Temporary employment also increased the likelihood of distress, particularly among refugees and Swedish-born. The effect of unemployment on increased likelihood of poor psychological well-being overcomes gender-specific and migration-specific differences and is equally pronounced for Swedish

Supporting and guiding device that is leak-tight and can be dismantled for the shaft of a rotating machine

International Nuclear Information System (INIS)

Tigoulet, Bernard; Fanchtein, J.P.; Dubost, Rene.

1982-01-01

This device includes a removable bearing casing crossed by at least one shaft of the machine, facilities for guiding this casing in parallel with the axis of the shaft so as to facilitate its removal and refitting, a system for supporting the shaft when the removable casing is not fitted in the machine frame. Application to machines for the extrusion of coating bitumen for radioactive waste [fr

Employment protection legislation in Croatia

Directory of Open Access Journals (Sweden)

Marina Kunovac

2014-06-01

Full Text Available According to business climate and competitiveness indicators published by international organisations, Croatia is a country with a rigid labour market and a high level of the legal protection of employees. Given that an Act on Amendments to the Labour Act (OG 73/13 entered into force in Croatia in June 2013, this paper examines changes in employment protection legislation in Croatia and Central and Eastern European (CEE countries, as well as in Croatia's main trading partners during the period between 2008 and 2013. A cross-country comparison shows a strong downward trend in legal employment protection in most CEE countries during the observed period, primarily as concerns individual dismissal in the cases of regular employment contracts, while in the case of temporary employment the protection strengthened slightly. On the other hand, despite the adoption of amendments to the Labour Act (LA, Croatian labour legislation governing employment protection for regular employment contracts remains relatively inflexible compared to that in other countries.

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.

Factors associated with persons with disability employment in India: a cross-sectional study.

Science.gov (United States)

Naraharisetti, Ramya; Castro, Marcia C

2016-10-07

Over twenty million persons with disability in India are increasingly being offered poverty alleviation strategies, including employment programs. This study employs a spatial analytic approach to identify correlates of employment among persons with disability in India, considering sight, speech, hearing, movement, and mental disabilities. Based on 2001 Census data, this study utilizes linear regression and spatial autoregressive models to identify factors associated with the proportion employed among persons with disability at the district level. Models stratified by rural and urban areas were also considered. Spatial autoregressive models revealed that different factors contribute to employment of persons with disability in rural and urban areas. In rural areas, having mental disability decreased the likelihood of employment, while being female and having movement, or sight impairment (compared to other disabilities) increased the likelihood of employment. In urban areas, being female and illiterate decreased the likelihood of employment but having sight, mental and movement impairment (compared to other disabilities) increased the likelihood of employment. Poverty alleviation programs designed for persons with disability in India should account for differences in employment by disability types and should be spatially targeted. Since persons with disability in rural and urban areas have different factors contributing to their employment, it is vital that government and service-planning organizations account for these differences when creating programs aimed at livelihood development.

Social firms: building cross-sectoral partnerships to create employment opportunity and supportive workplaces for people with mental illness.

Science.gov (United States)

Paluch, Tamar; Fossey, Ellie; Harvey, Carol

2012-01-01

A major barrier to employment for people with mental illness is limited access to supportive and non-discriminatory workplaces. Social firms are businesses committed to employing up to 50% of people with a disability or other disadvantage and to providing supportive work environments that benefit workers. Little research has been conducted to understand the features and social processes that support the vocational experiences of employees with mental health issues in social firms. This ethnographic study sought to explore the experiences of nine employees at one Australian social firm. Nine employees of a social firm, with and without mental illness. Study methods used included participant observation, interviewing and document analysis. The study highlights the complexity of running a socially-invested business, and the importance of cross-sectoral partnerships to support their operational success. Natural workplace supports, adequate training and support infrastructure and enabling participation in the business, were identified as important to creating a supportive workplace. Partnerships within the workplace and in support of the workplace are discussed. Future growth and development of partnerships are recommended to support the establishment of social firms.

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.

Process capability improvement through DMAIC for aluminum alloy wheel machining

Science.gov (United States)

Sharma, G. V. S. S.; Rao, P. Srinivasa; Babu, B. Surendra

2017-07-01

This paper first enlists the generic problems of alloy wheel machining and subsequently details on the process improvement of the identified critical-to-quality machining characteristic of A356 aluminum alloy wheel machining process. The causal factors are traced using the Ishikawa diagram and prioritization of corrective actions is done through process failure modes and effects analysis. Process monitoring charts are employed for improving the process capability index of the process, at the industrial benchmark of four sigma level, which is equal to the value of 1.33. The procedure adopted for improving the process capability levels is the define-measure-analyze-improve-control (DMAIC) approach. By following the DMAIC approach, the C p, C pk and C pm showed signs of improvement from an initial value of 0.66, -0.24 and 0.27, to a final value of 4.19, 3.24 and 1.41, respectively.

Testing machine for fatigue crack kinetic investigation in specimens under bending

International Nuclear Information System (INIS)

Panasyuk, V.V.; Ratych, L.V.; Dmytrakh, I.N.

1978-01-01

A kinematic diagram of testing mashine for the investigation of fatigue crack kinetics in prismatic specimens, subjected to pure bending is described. Suggested is a technique of choosing an optimum ratio of the parameters of ''the testing machine-specimen'' system, which provide the stabilization of the stress intensity coefficient for a certain region of crack development under hard loading. On the example of the 40KhS and 15Kh2MFA steel specimens the pliability of the machine constructed according to the described diagram and designed for the 30ONxm maximum bending moment. The results obtained can be used in designing of the testing machines for studying pure bending under hard loading and in choosing the sizes of specimens with rectangular cross sections for investigations into the kinetics of the fatigue crack

Dynamic behaviour of nominal and PACMAN bunches for different LHC crossing schemes

CERN Document Server

Herr, Werner

2005-01-01

To avoid unwanted beam-beam interactions, the two LHC beams cross at an angle in all experimental interaction regions. The choice of the crossing plane in the different areas has fundamental consequences on the effects of the long range beam-beam interaction. An alternating crossing scheme, i.e. horizontal and vertical crossing planes in the two high luminosity experiments, is presently foreseen to partly compensate and minimize first order effects on PACMAN bunches. Recent studies with a simplified model of the machine suggested that an alternating crossing scheme may be less favourable due to a reduced dynamic aperture. In this report I investigate this assertion with a detailed tracking study. The head on and long range beam-beam effects are taken into account as well as machine imperfections. The effects on the dynamic aperture of nominal and PACMAN bunches are evaluated.

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.

Machine Learning for Precision Psychiatry: Opportunities and Challenges.

Science.gov (United States)

Bzdok, Danilo; Meyer-Lindenberg, Andreas

2018-03-01

The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine.

Science.gov (United States)

Yan, Jun; Huang, Jian-Hua; He, Min; Lu, Hong-Bing; Yang, Rui; Kong, Bo; Xu, Qing-Song; Liang, Yi-Zeng

2013-08-01

Retention indices for frequently reported compounds of plant essential oils on three different stationary phases were investigated. Multivariate linear regression, partial least squares, and support vector machine combined with a new variable selection approach called random-frog recently proposed by our group, were employed to model quantitative structure-retention relationships. Internal and external validations were performed to ensure the stability and predictive ability. All the three methods could obtain an acceptable model, and the optimal results by support vector machine based on a small number of informative descriptors with the square of correlation coefficient for cross validation, values of 0.9726, 0.9759, and 0.9331 on the dimethylsilicone stationary phase, the dimethylsilicone phase with 5% phenyl groups, and the PEG stationary phase, respectively. The performances of two variable selection approaches, random-frog and genetic algorithm, are compared. The importance of the variables was found to be consistent when estimated from correlation coefficients in multivariate linear regression equations and selection probability in model spaces. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

Science.gov (United States)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

Differences in sickness absence between self-employed and employed doctors: a cross-sectional study on national sample of Norwegian doctors in 2010

Science.gov (United States)

2014-01-01

Background Doctors have a low prevalence of sickness absence. Employment status is a determinant in the multifactorial background of sickness absence. The effect of doctors’ employment status on sickness absence is unexplored. The study compares the number of sickness absence days during the last 12 months and the impact of employment status, psychosocial work stress, self-rated health and demographics on sickness absence between self-employed practitioners and employed hospital doctors in Norway. Methods The study population consisted of a representative sample of 521 employed interns and consultants and 313 self-employed GPs and private practice specialists in Norway, who received postal questionnaires in 2010. The questionnaires contained items on sickness absence days during the last 12 months, employment status, demographics, self-rated health, professional autonomy and psychosocial work stress. Results 84% (95% CI 80 to 88%) of self-employed and 60% (95% CI 55 to 64%) of employed doctors reported no absence at all last year. In three multivariate logistic regression models with sickness absence as response variable, employment category was a highly significant predictor for absence vs. no absence, 1 to 3 days of absence vs. no absence and 4 to 99 days of absence vs. no absence), while in a model with 100 or more days of absence vs. no absence, there was no difference between employment categories, suggesting that serious chronic disease or injury is less dependent on employment category. Average or poor self-rated health and low professional autonomy, were also significant predictors of sickness absence, while psychosocial work stress, age and gender were not. Conclusion Self-employed GPs and private practice specialist reported lower sickness absence than employed hospital doctors. Differences in sickness compensation, and organisational and individual factors may to a certain extent explain this finding. PMID:24885230

Analysis of Effects of Cutting Parameters of Wire Electrical Discharge Machining on Material Removal Rate and Surface Integrity

Science.gov (United States)

Tonday, H. R.; Tigga, A. M.

2016-02-01

As wire electrical discharge machining is pioneered as a vigorous, efficient and precise and complex nontraditional machining technique, research is needed in this area for efficient machining. In this paper, the influence of various input factors of wire electrical discharge machining (WEDM) on output variable has been analyzed by using Taguchi technique and analysis of variance. The design of experiments has been done and by applying L8 orthogonal arrays method and experiments have been conducted and collected required data. The objectives of the research are to maximize the material removal rate and to minimize the surface roughness value (Ra). Surface morphology of machined workpiece has been obtained and examined by employing scanning electron microscopy (SEM) technique.

Analysis of Effects of Cutting Parameters of Wire Electrical Discharge Machining on Material Removal Rate and Surface Integrity

International Nuclear Information System (INIS)

Tonday, H. R.; Tigga, A. M.

2016-01-01

As wire electrical discharge machining is pioneered as a vigorous, efficient and precise and complex nontraditional machining technique, research is needed in this area for efficient machining. In this paper, the influence of various input factors of wire electrical discharge machining (WEDM) on output variable has been analyzed by using Taguchi technique and analysis of variance. The design of experiments has been done and by applying L8 orthogonal arrays method and experiments have been conducted and collected required data. The objectives of the research are to maximize the material removal rate and to minimize the surface roughness value (Ra). Surface morphology of machined workpiece has been obtained and examined by employing scanning electron microscopy (SEM) technique. (paper)

Effect of electric discharge machining on the fatigue life of Inconel 718

Science.gov (United States)

Jeelani, S.; Collins, M. R.

1988-01-01

The effect of electric discharge machining on the fatigue life of Inconel 718 alloy at room temperature was investigated. Data were generated in the uniaxial tension fatigue mode at ambient temperature using flat 3.175 mm thick specimens. The specimens were machined on a wire-cut electric discharge machine at cutting speeds ranging from 0.5 to 2 mm per minute. The specimens were fatigued at a selected stress, and the resulting fatigue lives compared with that of the virgin material. The surfaces of the fatigued specimens were examined under optical and scanning electron microscopes, and the roughness of the surfaces was measured using a standard profilometer. From the results of the investigation, it was concluded that the fatigue life of the specimens machined using EDM decreased slightly as compared with that of the virgin material, but remained unchanged as the cutting speed was changed. The results are explained using data produced employing microhardness measurements, profilometry, and optical and scanning microscopy.

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...

Steady-State Characteristics Analysis of Hybrid-Excited Flux-Switching Machines with Identical Iron Laminations

Directory of Open Access Journals (Sweden)

Gan Zhang

2015-11-01

Full Text Available Since the air-gap field of flux-switching permanent magnet (FSPM machines is difficult to regulate as it is produced by the stator-magnets alone, a type of hybrid-excited flux-switching (HEFS machine is obtained by reducing the magnet length of an original FSPM machine and introducing a set of field windings into the saved space. In this paper, the steady-state characteristics, especially for the loaded performances of four prototyped HEFS machines, namely, PM-top, PM-middle-1, PM-middle-2, and PM-bottom, are comprehensively compared and evaluated based on both 2D and 3D finite element analysis. Also, the influences of PM materials including ferrite and NdFeB, respectively, on the characteristics of HEFS machines are covered. Particularly, the impacts of magnet movement in the corresponding slot on flux-regulating performances are studied in depth. The best overall performances employing NdFeB can be obtained when magnets are located near the air-gap. The FEA predictions are validated by experimental measurements on corresponding machine prototypes.

Preliminary study on AC superconducting machines

International Nuclear Information System (INIS)

Yamamoto, M.; Ishigohka, T.; Shimohka, T.; Mizukami, N.; Yamaguchi, M.

1988-01-01

This paper describes the issues involved in developing AC superconducting machines. In the first phase, as a preliminary experiment, a 4kVa AC superconducting coil which employs 100A class 50/60Hz superconductors is made and tested. And, in the second phase, as an extension of the 4kVa coil, a model superconducting transformer is made and examined. The transformer has a novel quench protection system with an auxiliary coil only in the low voltage side. The behavior of the overcurrent protection system is confirmed

Design of an ultraprecision computerized numerical control chemical mechanical polishing machine and its implementation

Science.gov (United States)

Zhang, Chupeng; Zhao, Huiying; Zhu, Xueliang; Zhao, Shijie; Jiang, Chunye

2018-01-01

The chemical mechanical polishing (CMP) is a key process during the machining route of plane optics. To improve the polishing efficiency and accuracy, a CMP model and machine tool were developed. Based on the Preston equation and the axial run-out error measurement results of the m circles on the tin plate, a CMP model that could simulate the material removal at any point on the workpiece was presented. An analysis of the model indicated that lower axial run-out error led to lower material removal but better polishing efficiency and accuracy. Based on this conclusion, the CMP machine was designed, and the ultraprecision gas hydrostatic guideway and rotary table as well as the Siemens 840Dsl numerical control system were incorporated in the CMP machine. To verify the design principles of machine, a series of detection and machining experiments were conducted. The LK-G5000 laser sensor was employed for detecting the straightness error of the gas hydrostatic guideway and the axial run-out error of the gas hydrostatic rotary table. A 300-mm-diameter optic was chosen for the surface profile machining experiments performed to determine the CMP efficiency and accuracy.

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

Science.gov (United States)

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

2016-09-01

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

A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

Science.gov (United States)

Lee, Yu-Hao; Hsieh, Ya-Ju; Shiah, Yung-Jong; Lin, Yu-Huei; Chen, Chiao-Yun; Tyan, Yu-Chang; GengQiu, JiaCheng; Hsu, Chung-Yao; Chen, Sharon Chia-Ju

2017-04-01

To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

Assessing a Novel Method to Reduce Anesthesia Machine Contamination: A Prospective, Observational Trial.

Science.gov (United States)

Biddle, Chuck J; George-Gay, Beverly; Prasanna, Praveen; Hill, Emily M; Davis, Thomas C; Verhulst, Brad

2018-01-01

Anesthesia machines are known reservoirs of bacterial species, potentially contributing to healthcare associated infections (HAIs). An inexpensive, disposable, nonpermeable, transparent anesthesia machine wrap (AMW) may reduce microbial contamination of the anesthesia machine. This study quantified the density and diversity of bacterial species found on anesthesia machines after terminal cleaning and between cases during actual anesthesia care to assess the impact of the AMW. We hypothesized reduced bioburden with the use of the AMW. In a prospective, experimental research design, the AMW was used in 11 surgical cases (intervention group) and not used in 11 control surgical cases. Cases were consecutively assigned to general surgical operating rooms. Seven frequently touched and difficult to disinfect "hot spots" were cultured on each machine preceding and following each case. The density and diversity of cultured colony forming units (CFUs) between the covered and uncovered machines were compared using Wilcoxon signed-rank test and Student's t -tests. There was a statistically significant reduction in CFU density and diversity when the AMW was employed. The protective effect of the AMW during regular anesthetic care provides a reliable and low-cost method to minimize the transmission of pathogens across patients and potentially reduces HAIs.

Design of salient pole PM synchronous machines for a vehicle traction application. Analysis and implementation

Energy Technology Data Exchange (ETDEWEB)

Rilla, M.

2012-07-01

This doctoral thesis presents a study on the development of a liquid-cooled frame salient pole permanent-magnet-exited traction machine for a four-wheel-driven electric car. The emphasis of the thesis is put on a radial flux machine design in order to achieve a light-weight machine structure for traction applications. The design features combine electromagnetic and thermal design methods, because traction machine operation does not have a strict operating point. Arbitrary load cycles and the flexible supply require special attention in the design process. It is shown that accurate modelling of the machine magnetic state is essential for high-performance operation. The saturation effect related to the cross-saturation has to be taken carefully into account in order to achieve the desired operation. Two prototype machines have been designed and built for testing: one totally enclosed machine with a special magnet module pole arrangement and another through-ventilated machine with a more traditional embedded magnet structure. Both structures are built with magnetically salient structures in order to increase the torque production capability with the reluctance torque component. Both machine structures show potential for traction usage. However, the traditional embedded magnet design turns out to be mechanically the more secure one of these two machine options. (orig.)

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

Effect of machining parameters on surface finish of Inconel 718 in end milling

Directory of Open Access Journals (Sweden)

Sarkar Bapi

2017-01-01

Full Text Available Surface finish is an important criteria in machining process and selection of proper machining parameters is important to obtain good surface finish. In the present work effects of the machining parameters in end milling of Inconel 718 were investigated. Central composite design was used to design the total number of experiments. A Mathematical model for surface roughness has been developed using response surface methodology. In this study, the influence of cutting parameters such as cutting speed, feed rate and depth of cut on surface roughness was analyzed. The study includes individual effect of cutting parameters on surface roughness as well as their interaction. The analysis of variance (ANOVA was employed to find the validity of the developed model. The results show that depth of cut mostly affected the surface roughness. It is also observed that surface roughness values are comparable in both dry and wet machining conditions.

Cross-sectional associations of active transport, employment status and objectively measured physical activity: analyses from the National Health and Nutrition Examination Survey.

Science.gov (United States)

Yang, Lin; Hu, Liang; Hipp, J Aaron; Imm, Kellie R; Schutte, Rudolph; Stubbs, Brendon; Colditz, Graham A; Smith, Lee

2018-05-05

To investigate associations between active transport, employment status and objectively measured moderate-to-vigorous physical activity (MVPA) in a representative sample of US adults. Cross-sectional analyses of data from the National Health and Nutrition Examination Survey. A total of 5180 adults (50.2 years old, 49.0% men) were classified by levels of active transportation and employment status. Outcome measure was weekly time spent in MVPA as recorded by the Actigraph accelerometer. Associations between active transport, employment status and objectively measured MVPA were examined using multivariable linear regression models adjusted for age, body mass index, race and ethnicity, education level, marital status, smoking status, working hour duration (among the employed only) and self-reported leisure time physical activity. Patterns of active transport were similar between the employed (n=2897) and unemployed (n=2283), such that 76.0% employed and 77.5% unemployed engaged in no active transport. For employed adults, those engaging in high levels of active transport (≥90 min/week) had higher amount of MVPA than those who did not engage in active transport. This translated to 40.8 (95% CI 15.7 to 65.9) additional minutes MVPA per week in men and 57.9 (95% CI 32.1 to 83.7) additional minutes MVPA per week in women. Among the unemployed adults, higher levels of active transport were associated with more MVPA among men (44.8 min/week MVPA, 95% CI 9.2 to 80.5) only. Findings from the present study support interventions to promote active transport to increase population level physical activity. Additional strategies are likely required to promote physical activity among unemployed women. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Semiautomatic machine for turning inside out industrial leather gloves

International Nuclear Information System (INIS)

Aragón-Gonzalez, G; Cano-Blanco, M; León-Galicia, A; Medrano-Sierra, L F; Morales-Gómez, J R

2015-01-01

The last step in the industrial leather gloves manufacturing is to turn the inside out so that the sewing be in the inside of the glove. This work presents the design and testing of a machine for that purpose. In order to quantify the relevant variables, testing was performed with a prototype glove. The employed devices and the testing proceeding were developed experimentally. The obtained information was used to build the turning inside out machine. This machine works with pneumatic power to carry the inside out turning by means of double effect lineal actuators. It has two independent work stations that could be operated simultaneously by two persons, one in each station or in single mode operating one station by one person. The turning inside out cycle is started by means of directional control valves operated with pedals. The velocity and developed force by the actuators is controlled with typical pneumatic resources. The geometrical dimensions of the machine are: 1.15 m length; 0.71 m width and 2.15 m high. Its approximated weight is 120 kg. The air consumption is 5.4 fps by each working station with 60 psig work pressure. The turning inside out operation is 40 s for each industrial leather glove

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Science.gov (United States)

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

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.

75 FR 20388 - International Business Machines Corporation, Global Technology Services Business Unit, Integrated...

Science.gov (United States)

2010-04-19

... Machines Corporation, Global Technology Services Business Unit, Integrated Technology Services, Cost and... Technology Services Business Unit, Integrated Technology Services, Cost and Expense Team working from various... Technology Services Business Unit. The company reports that workers leased from Datrose, Inc., were employed...

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...

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).

Amplifying human ability through autonomics and machine learning in IMPACT

Science.gov (United States)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

Do flexicurity policies protect workers from the adverse health consequences of temporary employment? A cross-national comparative analysis.

Science.gov (United States)

Shahidi, Faraz Vahid; De Moortel, Deborah; Muntaner, Carles; Davis, Owen; Siddiqi, Arjumand

2016-12-01

Flexicurity policies comprise a relatively novel approach to the regulation of work and welfare that aims to combine labour market flexibility with social security. Advocates of this approach argue that, by striking the right balance between flexibility and security, flexicurity policies allow firms to take advantage of loose contractual arrangements in an increasingly competitive economic environment while simultaneously protecting workers from the adverse health and social consequences of flexible forms of employment. In this study, we use multilevel Poisson regression models to test the theoretical claim of the flexicurity approach using data for 23 countries across three waves of the European Social Survey. We construct an institutional typology of labour market regulation and social security to evaluate whether inequalities in self-reported health and limiting longstanding illness between temporary workers and their permanent counterparts are smaller in countries that most closely approximate the ideal type described by advocates of the flexicurity approach. Our results indicate that, while the association between temporary employment and health varies across countries, institutional configurations of labour market regulation and social security do not provide a meaningful explanation for this cross-national variation. Contrary to the expectations of the flexicurity hypothesis, our data do not indicate that employment-related inequalities are smaller in countries that approximate the flexicurity approach. We discuss potential explanations for these findings and conclude that there remains a relative lack of evidence in support of the theoretical claims of the flexicurity approach.

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

Finding Translation Examples for Under-Resourced Language Pairs or for Narrow Domains; the Case for Machine Translation

Directory of Open Access Journals (Sweden)

Dan Tufis

2012-07-01

Full Text Available The cyberspace is populated with valuable information sources, expressed in about 1500 different languages and dialects. Yet, for the vast majority of WEB surfers this wealth of information is practically inaccessible or meaningless. Recent advancements in cross-lingual information retrieval, multilingual summarization, cross-lingual question answering and machine translation promise to narrow the linguistic gaps and lower the communication barriers between humans and/or software agents. Most of these language technologies are based on statistical machine learning techniques which require large volumes of cross lingual data. The most adequate type of cross-lingual data is represented by parallel corpora, collection of reciprocal translations. However, it is not easy to find enough parallel data for any language pair might be of interest. When required parallel data refers to specialized (narrow domains, the scarcity of data becomes even more acute. Intelligent information extraction techniques from comparable corpora provide one of the possible answers to this lack of translation data.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

Science.gov (United States)

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods.

Science.gov (United States)

Legrain, Fleur; Carrete, Jesús; van Roekeghem, Ambroise; Madsen, Georg K H; Mingo, Natalio

2018-01-18

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71 178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high-throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.

Outsmarting neural networks: an alternative paradigm for machine learning

Energy Technology Data Exchange (ETDEWEB)

Protopopescu, V.; Rao, N.S.V.

1996-10-01

We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

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

A Supervised Machine Learning Study of Online Discussion Forums about Type-2 Diabetes

DEFF Research Database (Denmark)

Reichert, Jonathan-Raphael; Kristensen, Klaus Langholz; Mukkamala, Raghava Rao

2017-01-01

supervised machine learning techniques to analyze the online conversations. In order to analyse these online textual conversations, we have chosen four domain specific models (Emotions, Sentiment, Personality Traits and Patient Journey). As part of text classification, we employed the ensemble learning...... method by using 5 different supervised machine learning algorithms to build a set of text classifiers by using the voting method to predict most probable label for a given textual conversation from the online discussion forums. Our findings show that there is a high amount of trust expressed by a subset...

Australian dentists: characteristics of those who employ or are willing to employ oral health therapists.

Science.gov (United States)

Kempster, C; Luzzi, L; Roberts-Thomson, K

2015-06-01

There has been an increase in the availability of oral health therapists (OHTs) in the oral health workforce in the last decade. The impact these clinicians will have on the oral health of the general public is dependent on access pathways and utilization. This study aimed to profile Australian dentists who employ or are willing to employ OHTs and to explore the degree of association between dentist characteristics and employment decisions. This cross-sectional study used a random sample of Australian dentists (n = 1169) from the Federal Australian Dental Association register in 2009. Participants were sent a postal questionnaire capturing dentist characteristics and oral health practitioner employment information. An adjusted response rate of 55% was obtained. Dentists willing to employ OHTs included non-metropolitan dentists, dentists in multiple surgery practices and those considering practice expansion. Age, gender and sector of practice were not significantly associated with retrospective employment decisions or willingness to employ in the future. Certain characteristics of dentists or of their practice are associated with their history of employment and willingness to employ OHTs. Employment decisions are more commonly related to entrepreneurial aspirations (expressed as a willingness to expand), sector of practice, surgery capacity and regionality over gender and age. Understanding the factors that influence the employment of OHTs is important in enhancing access pathways to the services provided by OHTs. © 2015 Australian Dental Association.

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

Science.gov (United States)

Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto

2017-04-01

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

Directory of Open Access Journals (Sweden)

Yudong Zhang

2013-01-01

Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.

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

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...

Gambling-machines and the Automation of Desire

Directory of Open Access Journals (Sweden)

César Albarrán Torres

2014-10-01

Full Text Available This paper deals with the relationship between gamblers and Electronic Gaming Machines (EGMs, which leads to the automation of desire through procedures. "Pokies", as EGMs are known in the Australian context, are both desiring­machines (Deleuze and Guattari and cross­platform media where digital gambling and videogame conventions and procedures intersect. I make a case that, as desiring­machines (entities that are defined by their connections, pokies and gamblers form couplings that generate assemblages, which are "passional", "a composition of desire" (Deleuze and Guattari. I also argue that pokies share aesthetic and procedural similarities with videogames and that the gameplay's objective is not always to merely win money, but to fulfil a desire to accomplish missions and embark on adventures. I also argue that these "missions" are related to chasing, the overarching procedure that defines EGM consumption and allows for the automation of gambler­pokie couplings. The aesthetics of most of these procedure­ images can be traced back to a postcolonial disposition over foreign lands, peoples and cultures ­faux Chinese and Aboriginal lore, exotic deserts, untamed jungles and Arctic landscapes that need to be conquered. This disposition echoes notions such as class­ related aspiration (desire and exotica. I analyse the imagery in some of the pokies that circulate in the New South Wales (Australia EGM market. This reading of Electronic Gaming Machines adds a ludic dimension to the analysis of a highly class­bound social practice that is part of a wider socioeconomic trend that points towards a new and contradictory consumption ethic. The application of videogame theory is absent in current discussions on poker machine interfaces and legislation, which generally focus on the figure of the pathological gambler and disregard the complexities of gambling platforms.

Zero waste machine coolant management strategy at Los Alamos National Laboratory

International Nuclear Information System (INIS)

Carlson, B.; Algarra, F.; Wilburn, D.

1998-01-01

Machine coolants are used in machining equipment including lathes, grinders, saws and drills. The purpose of coolants is to wash away machinery debris in the form of metal fines, lubricate, and disperse heat between the part and the machine tool. An effective coolant prolongs tool life and protects against part rejection, commonly due to scoring or scorching. Traditionally, coolants have a very short effective life in the machine, often times being disposed of as frequently as once per week. The cause of coolant degradation is primarily due to the effects of bacteria, which thrive in the organic rich coolant environment. Bacteria in this environment reproduce at a logarithmic rate, destroying the coolant desirable aspects and causing potential worker health risks associated with the use of biocides to control the bacteria. The strategy described in this paper has effectively controlled bacterial activity without the use of biocides, avoided disposal of a hazardous waste, and has extended coolant life indefinitely. The Machine Coolant Management Strategy employed a combination of filtration, heavy lubricating oil removal, and aeration, which maintained the coolant peak performance without the use of biocides. In FY96, the Laboratory generated and disposed of 19,880 kg of coolants from 9 separate sites at a cost of $145K. The single largest generator was the main machine shop producing an average 14,000 kg annually. However, in FY97, the waste generation for the main machine shop dropped to 4,000 kg after the implementation of the zero waste strategy. It is expected that this value will be further reduced in FY98

Academic Globalization: Universality of Cross-Cultural And Cross-Disciplinary LMR Perspectives

Directory of Open Access Journals (Sweden)

Marta Szabo White

2010-10-01

Full Text Available The contribution of this paper suggests that previous research underscoring cross-cultural differences may be misleading, when in fact it is cross-professional rather than cross-cultural differences that should be emphasized. Employing the LMR framework, this paper concludes that business or non-business predisposition has a more direct impact on one's individual cultural profile than does nationality. Regardless of culture, persons involved in business are characterized primarily by linear-active modes of communication, and persons not involved in business typically employ less linear and more multi-active/hybrid modes of communication. The linkages among individual characteristics, communication styles, work behaviors, and the extent to which the LMR constructs can facilitate and predict leadership, negotiating styles, individual behaviors, etc. are central to academic globalization and preparing global business leaders.

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

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

Energy Technology Data Exchange (ETDEWEB)

Stocki, Trevor J., E-mail: trevor_stocki@hc-sc.gc.c [Radiation Protection Bureau, 775 Brookfield Road, A.L. 6302D1, Ottawa, ON, K1A 1C1 (Canada); Li, Guichong; Japkowicz, Nathalie [School of Information Technology and Engineering, University of Ottawa, 800 King Edward Avenue, Ottawa, ON, K1N 6N5 (Canada); Ungar, R. Kurt [Radiation Protection Bureau, 775 Brookfield Road, A.L. 6302D1, Ottawa, ON, K1A 1C1 (Canada)

2010-01-15

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of {sup 131m}Xe, {sup 133}Xe, {sup 133m}Xe, and {sup 135}Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naive Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

International Nuclear Information System (INIS)

Stocki, Trevor J.; Li, Guichong; Japkowicz, Nathalie; Ungar, R. Kurt

2010-01-01

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of 131m Xe, 133 Xe, 133m Xe, and 135 Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naive Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.

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.

Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

Science.gov (United States)

Zhang, Lei; Zhang, David

2016-08-10

We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

Contemporary machine learning: techniques for practitioners in the physical sciences

Science.gov (United States)

Spears, Brian

2017-10-01

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

Hazard perception and occupational injuries in the welders and lathe machine operators of Rawalpindi and Islamabad.

Science.gov (United States)

Shaikh, M A

2001-02-01

To study the prevalence of occupational injuries in the welders and lathe machine operators and their hazard perception. This study was conducted in the welders and lathe machine operators working in the welding and metal working shops in Rawalpindi and Islamabad. A cross-sectional survey was conducted by two trained health interviewers using uniform questionnaire with both close and open-ended questions. Two hundred and eight welders and 104 lathe machine operators were interviewed. Thirty nine (18.7%) welders and 27 (26%) lathe machine operators reported an injury in the past three months, while 63 (30.3%) welders and 76 (73.8%) lathe machine operators reported sustaining an injury in the past twelve months. However, only half of the welders and 31 (29.8%) lathe machine operators believed that their occupation was hazardous for health. For effective public health policy there is a need preventive education and enforcement of safety regulations for the informal occupational sector in Pakistan.

The Roots of Low European Employment : Family Culture?

OpenAIRE

Yann Algan; Pierre Cahuc

2005-01-01

OECD countries faced largely divergent employment rates during the last decades. But the whole bulk of the cross-national and cross-temporal heterogeneity relies on specific demographic groups: prime-age women and younger and older individuals. This paper argues that family labour supply interactions and cross-country heterogeneity in family culture are key for explaining these stylized facts. First we provide a simple labour supply model in which heterogeneity in family preferences can accou...

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

Engagement techniques and playing level impact the biomechanical demands on rugby forwards during machine-based scrummaging

OpenAIRE

Preatoni, Ezio; Stokes, Keith A.; England, Michael E.; Trewartha, Grant

2014-01-01

Objectives This cross-sectional study investigated the factors that may influence the physical loading on rugby forwards performing a scrum by studying the biomechanics of machine-based scrummaging under different engagement techniques and playing levels.Methods 34 forward packs from six playing levels performed repetitions of five different types of engagement techniques against an instrumented scrum machine under realistic training conditions. Applied forces and body movements were recorded...

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.

Apron strings of working mothers: Maternal employment and housework in cross-national perspective.

Science.gov (United States)

Treas, Judith; Tai, Tsui-O

2012-07-01

This paper asks whether maternal employment has a lasting influence on the division of household labor for married women and men. Employing multi-level models with 2002 ISSP survey data for 31 countries, we test the lagged accommodation hypothesis that a long societal history of maternal employment contributes to more egalitarian household arrangements. Our results find that living in a country with a legacy of high maternal employment is positively associated with housework task-sharing, even controlling for the personal socialization experience of growing up with a mother who worked for pay. In formerly socialist countries, however, there is less gender parity in housework than predicted by the high historical level of maternal employment. Copyright © 2012 Elsevier Inc. All rights reserved.

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

Bias correction for selecting the minimal-error classifier from many machine learning models.

Science.gov (United States)

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Assessing a Novel Method to Reduce Anesthesia Machine Contamination: A Prospective, Observational Trial

Directory of Open Access Journals (Sweden)

Chuck J. Biddle

2018-01-01

Full Text Available Background. Anesthesia machines are known reservoirs of bacterial species, potentially contributing to healthcare associated infections (HAIs. An inexpensive, disposable, nonpermeable, transparent anesthesia machine wrap (AMW may reduce microbial contamination of the anesthesia machine. This study quantified the density and diversity of bacterial species found on anesthesia machines after terminal cleaning and between cases during actual anesthesia care to assess the impact of the AMW. We hypothesized reduced bioburden with the use of the AMW. Methods. In a prospective, experimental research design, the AMW was used in 11 surgical cases (intervention group and not used in 11 control surgical cases. Cases were consecutively assigned to general surgical operating rooms. Seven frequently touched and difficult to disinfect “hot spots” were cultured on each machine preceding and following each case. The density and diversity of cultured colony forming units (CFUs between the covered and uncovered machines were compared using Wilcoxon signed-rank test and Student’s t-tests. Results. There was a statistically significant reduction in CFU density and diversity when the AMW was employed. Conclusion. The protective effect of the AMW during regular anesthetic care provides a reliable and low-cost method to minimize the transmission of pathogens across patients and potentially reduces HAIs.

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…

Recent development in the design of hard rock tunnel boring machines for the mining industry

International Nuclear Information System (INIS)

Snyder, L.L.; Williams, R.I.

1991-01-01

Underground development for nuclear waste storage will possibly require tunnels to be excavated in a variety of rock conditions and configurations. Recent innovations in Tunnel Boring Machine (TBM) design have allowed for an evolved style of TBM which has distinct advantages over the standard machines. Present day conventional hard rock TBM's were developed primarily for the long, relatively straight tunnels of the civil construction industry, thereby making them for the most part, unsuitable for the sharp curves, turnouts, declines, inclines and ramps required in many underground environments. The five foot to 36 foot (1.52 to 11 m) diameter machines are capable of boring tunnels with curve radiuses as small as 40 to 90 feet (12.2 to 27.5 m) depending on size. These short turning radiuses can be accomplished while gripping the tunnel walls horizontally in the traditional manner or vertically as required when intersecting existing tunnels, or making turnouts from the tunnel that the machine has just bored. The machine's length is approximately half of a traditional machine's length while still employing a full measure of thrust, horsepower and rock cutting ability. The machine's short length, combined with a patented machine structure allows it to steer while boring without causing harmful eccentric loads on the cutterhead and main bearing assembly. The machine configuration is versatile and can be easily modified to operate in a wide variety of conditions

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Directory of Open Access Journals (Sweden)

Alfonso F. Torres-Rua

2016-04-01

Full Text Available Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed.

Cross contamination of turkey carcasses by Salmonella species during defeathering.

Science.gov (United States)

Nde, C W; McEvoy, J M; Sherwood, J S; Logue, C M

2007-01-01

Salmonella present on the feathers of live birds could be a source of contamination to carcass skin during defeathering. In this study, the possibility of transfer of Salmonella from the feathers of live turkeys to carcass tissue during the defeathering process at a commercial turkey processing plant was investigated. The contribution of scald water and the fingers of the picker machines to cross contamination were also examined. Over 4 visits, swab samples were collected from 174 randomly selected tagged birds before and after defeathering. Two swab samples from the fingers of the picker machines and a sample of scald water were also collected during each visit. Detection of Salmonella was carried out following standard cultural and identification methods. The DNA fingerprints obtained from pulsed field gel electrophoresis of Salmonella serotypes isolated before and after defeathering, from scald water, and from the fingers of the picker machines were compared to trace cross contamination routes. Salmonella prevalence was similar before and after defeathering during visits 2 and 3 and significantly increased after defeathering during visits 1 and 4. Over the 4 visits, all Salmonella subtypes obtained after defeathering were also isolated before defeathering. The results of this study suggest that Salmonella was transferred from the feathers to carcass skin during each visit. On each visit, the Salmonella subtypes isolated from the fingers of the picker machines were similar to subtypes isolated before and after defeathering, indicating that the fingers facilitate carcass cross contamination during defeathering. Salmonella isolated from scald water during visit 4 was related to isolates obtained before and after defeathering, suggesting that scald water is also a vehicle for cross contamination during defeathering. By using molecular subtyping, this study demonstrated the relationship between Salmonella present on the feathers of live turkeys and carcass skin after

Numerical modelling of micro-machining of f.c.c. single crystal: Influence of strain gradients

KAUST Repository

Demiral, Murat; Roy, Anish; El Sayed, Tamer S.; Silberschmidt, Vadim V.

2014-01-01

of orthogonal micro-machining of f.c.c. single crystal copper was developed. The model was implemented in a commercial software ABAQUS/Explicit employing a user-defined subroutine VUMAT. Strain-gradient crystal-plasticity and conventional crystal

Numerical approach for optimum electromagnetic parameters of electrical machines used in vehicle traction applications

International Nuclear Information System (INIS)

Fodorean, D.; Giurgea, S.; Djerdir, A.; Miraoui, A.

2009-01-01

A large speed variation is an essential request in the automobile industry. In order to compete with diesel engines, the flux weakening technique has to be employed on the electrical machines. In this way, appropriate electromagnetic and geometrical parameters can give the desired speed. Using the inverse problem method coupled with numerical analysis by finite element method (FEM), the authors propose an optimum parameters configuration that maximizes the speed domain operation. Several types of electrical machines are under study: induction, synchronous permanent magnet, variable reluctance and transverse flux machines, respectively. With a proper non-linear model, by using analytical and numerical calculation, the authors propose an optimum solution for the speed variation of the studied drives, which will be standing for a final comparison.

A Machine Learning Concept for DTN Routing

Science.gov (United States)

Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

2017-01-01

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

The future machine with electrons of 15-30 GeV

International Nuclear Information System (INIS)

Tkatchenko, A.

1992-01-01

This article presents the project of european linear accelerator with a continuous beam of high energy electrons for the Nuclear Physics researches. Based on a superconducting linear accelerator crossed several times, this machine will be able to produce beams of 15 GeV in a first time, then 30 GeV, by increasing of accelerator cavity field without modifying the beam circulation system

Support vector machine for the diagnosis of malignant mesothelioma

Science.gov (United States)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

Machine Repairers and Operators. Reprinted from the Occupational Outlook Handbook, 1978-79 Edition.

Science.gov (United States)

Bureau of Labor Statistics (DOL), Washington, DC.

Focusing on machine repairers and operators, this document is one in a series of forty-one reprints from the Occupational Outlook Handbook providing current information and employment projections for individual occupations and industries through 1985. The specific occupations covered in this document include appliance repairers,…

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.

Micro Machining Enhances Precision Fabrication

Science.gov (United States)

2007-01-01

Advanced thermal systems developed for the Space Station Freedom project are now in use on the International Space Station. These thermal systems employ evaporative ammonia as their coolant, and though they employ the same series of chemical reactions as terrestrial refrigerators, the space-bound coolers are significantly smaller. Two Small Business Innovation Research (SBIR) contracts between Creare Inc. of Hanover, NH and Johnson Space Center developed an ammonia evaporator for thermal management systems aboard Freedom. The principal investigator for Creare Inc., formed Mikros Technologies Inc. to commercialize the work. Mikros Technologies then developed an advanced form of micro-electrical discharge machining (micro-EDM) to make tiny holes in the ammonia evaporator. Mikros Technologies has had great success applying this method to the fabrication of micro-nozzle array systems for industrial ink jet printing systems. The company is currently the world leader in fabrication of stainless steel micro-nozzles for this market, and in 2001 the company was awarded two SBIR research contracts from Goddard Space Flight Center to advance micro-fabrication and high-performance thermal management technologies.

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.)

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.

Employing X-ray absorption technique for better detector resolution and measurement of low cross-section events

Science.gov (United States)

Sharma, Gaurav; Puri, Nitin K.; Kumar, Pravin; Nandi, T.

2018-03-01

The versatility of X-ray absorption technique is experimentally employed for enhancing the detector resolution and to rejuvenate the low probable transitions obscured in the pile-up region, during a beam-foil spectroscopy experiment. The multiple aluminum absorber layers (10 μm each) are used to suppress the pile-up contribution drastically and to restore a weak transition which is about 1.38 × 104 times weaker than a one-electron-one-photon transitions viz. Kα and Khα. The weak line is possibly originating from a two-electron-one-photon transition in He-like Ti. Further, the transitions, which were obscured in the spectra due to high intensity ratio, are revived by dissimilar line intensity attenuation using this technique. The measured lifetimes of Kα line with and without intensity attenuation match well within error bar. The present technique finds potential implications in understanding the structure of multiple-core-vacant ions and other low cross section processes in ion-solid collisions.

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.

SELF-EMPLOYMENT AND HEALTH: BARRIERS OR BENEFITS?

Science.gov (United States)

Rietveld, Cornelius A; van Kippersluis, Hans; Thurik, A Roy

2014-07-22

The self-employed are often reported to be healthier than wageworkers; however, the cause of this health difference is largely unknown. The longitudinal nature of the US Health and Retirement Study allows us to gauge the plausibility of two competing explanations for this difference: a contextual effect of self-employment on health (benefit effect), or a health-related selection of individuals into self-employment (barrier effect). Our main finding is that the selection of comparatively healthier individuals into self-employment accounts for the positive cross-sectional difference. The results rule out a positive contextual effect of self-employment on health, and we present tentative evidence that, if anything, engaging in self-employment is bad for one's health. Given the importance of the self-employed in the economy, these findings contribute to our understanding of the vitality of the labor force. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.

Cross-training workers in dual resource constrained systems with heterogeneous processing times

NARCIS (Netherlands)

Bokhorst, J. A. C.; Gaalman, G. J. C.

2009-01-01

In this paper, we explore the effect of cross-training workers in Dual Resource Constrained (DRC) systems with machines having different mean processing times. By means of queuing and simulation analysis, we show that the detrimental effects of pooling (cross-training) previously found in single

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)

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

Geometry and surface damage in micro electrical discharge machining of micro-holes

Science.gov (United States)

Ekmekci, Bülent; Sayar, Atakan; Tecelli Öpöz, Tahsin; Erden, Abdulkadir

2009-10-01

Geometry and subsurface damage of blind micro-holes produced by micro electrical discharge machining (micro-EDM) is investigated experimentally to explore the relational dependence with respect to pulse energy. For this purpose, micro-holes are machined with various pulse energies on plastic mold steel samples using a tungsten carbide tool electrode and a hydrocarbon-based dielectric liquid. Variations in the micro-hole geometry, micro-hole depth and over-cut in micro-hole diameter are measured. Then, unconventional etching agents are applied on the cross sections to examine micro structural alterations within the substrate. It is observed that the heat-damaged segment is composed of three distinctive layers, which have relatively high thicknesses and vary noticeably with respect to the drilling depth. Crack formation is identified on some sections of the micro-holes even by utilizing low pulse energies during machining. It is concluded that the cracking mechanism is different from cracks encountered on the surfaces when machining is performed by using the conventional EDM process. Moreover, an electrically conductive bridge between work material and debris particles is possible at the end tip during machining which leads to electric discharges between the piled segments of debris particles and the tool electrode during discharging.

Geometry and surface damage in micro electrical discharge machining of micro-holes

International Nuclear Information System (INIS)

Ekmekci, Bülent; Sayar, Atakan; Öpöz, Tahsin Tecelli; Erden, Abdulkadir

2009-01-01

Geometry and subsurface damage of blind micro-holes produced by micro electrical discharge machining (micro-EDM) is investigated experimentally to explore the relational dependence with respect to pulse energy. For this purpose, micro-holes are machined with various pulse energies on plastic mold steel samples using a tungsten carbide tool electrode and a hydrocarbon-based dielectric liquid. Variations in the micro-hole geometry, micro-hole depth and over-cut in micro-hole diameter are measured. Then, unconventional etching agents are applied on the cross sections to examine micro structural alterations within the substrate. It is observed that the heat-damaged segment is composed of three distinctive layers, which have relatively high thicknesses and vary noticeably with respect to the drilling depth. Crack formation is identified on some sections of the micro-holes even by utilizing low pulse energies during machining. It is concluded that the cracking mechanism is different from cracks encountered on the surfaces when machining is performed by using the conventional EDM process. Moreover, an electrically conductive bridge between work material and debris particles is possible at the end tip during machining which leads to electric discharges between the piled segments of debris particles and the tool electrode during discharging

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.

76 FR 21033 - International Business Machines (IBM), Sales and Distribution Business Unit, Global Sales...

Science.gov (United States)

2011-04-14

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,364] International Business Machines (IBM), Sales and Distribution Business Unit, Global Sales Solution Department, Off-Site Teleworker in Centerport, New York; Notice of Affirmative Determination Regarding Application for Reconsideration By application dated November 29, 2011,...

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.

Correlation between use time of machine and decline curve for emerging enterprise information systems

Science.gov (United States)

Chang, Yao-Chung; Lai, Chin-Feng; Chuang, Chi-Cheng; Hou, Cheng-Yu

2018-04-01

With the progress of science and technology, more and more machines are adpot to help human life better and more convenient. When the machines have been used for a longer period of time so that the machine components are getting old, the amount of power comsumption will increase and easily cause the machine to overheat. This also causes a waste of invisible resources. If the Internet of Everything (IoE) technologies are able to be applied into the enterprise information systems for monitoring the machines use time, it can not only make energy can be effectively used, but aslo create a safer living environment. To solve the above problem, the correlation predict model is established to collect the data of power consumption converted into power eigenvalues. This study takes the power eigenvalue as the independent variable and use time as the dependent variable in order to establish the decline curve. Ultimately, the scoring and estimation modules are employed to seek the best power eigenvalue as the independent variable. To predict use time, the correlation is discussed between the use time and the decline curve to improve the entire behavioural analysis of the facilitate recognition of the use time of machines.

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.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

NARCIS (Netherlands)

Pettersson-Yeo, W.; Benetti, S.; Marquand, A.F.; Joules, R.; Catani, M.; Williams, S.C.; Allen, P.; McGuire, P.; Mechelli, A.

2014-01-01

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that 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

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

Directory of Open Access Journals (Sweden)

Maolong Xi

2016-01-01

Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

Science.gov (United States)

Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

2016-01-01

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363

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.

Machine learning control taming nonlinear dynamics and turbulence

CERN Document Server

Duriez, Thomas; Noack, Bernd R

2017-01-01

This is the first book on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading r...

Servo scanning 3D micro EDM for array micro cavities using on-machine fabricated tool electrodes

Science.gov (United States)

Tong, Hao; Li, Yong; Zhang, Long

2018-02-01

Array micro cavities are useful in many fields including in micro molds, optical devices, biochips and so on. Array servo scanning micro electro discharge machining (EDM), using array micro electrodes with simple cross-sectional shape, has the advantage of machining complex 3D micro cavities in batches. In this paper, the machining errors caused by offline-fabricated array micro electrodes are analyzed in particular, and then a machining process of array servo scanning micro EDM is proposed by using on-machine fabricated array micro electrodes. The array micro electrodes are fabricated on-machine by combined procedures including wire electro discharge grinding, array reverse copying and electrode end trimming. Nine-array tool electrodes with Φ80 µm diameter and 600 µm length are obtained. Furthermore, the proposed process is verified by several machining experiments for achieving nine-array hexagonal micro cavities with top side length of 300 µm, bottom side length of 150 µm, and depth of 112 µm or 120 µm. In the experiments, a chip hump accumulates on the electrode tips like the built-up edge in mechanical machining under the conditions of brass workpieces, copper electrodes and the dielectric of deionized water. The accumulated hump can be avoided by replacing the water dielectric by an oil dielectric.

Direct Simulation Monte Carlo (DSMC) on the Connection Machine

International Nuclear Information System (INIS)

Wong, B.C.; Long, L.N.

1992-01-01

The massively parallel computer Connection Machine is utilized to map an improved version of the direct simulation Monte Carlo (DSMC) method for solving flows with the Boltzmann equation. The kinetic theory is required for analyzing hypersonic aerospace applications, and the features and capabilities of the DSMC particle-simulation technique are discussed. The DSMC is shown to be inherently massively parallel and data parallel, and the algorithm is based on molecule movements, cross-referencing their locations, locating collisions within cells, and sampling macroscopic quantities in each cell. The serial DSMC code is compared to the present parallel DSMC code, and timing results show that the speedup of the parallel version is approximately linear. The correct physics can be resolved from the results of the complete DSMC method implemented on the connection machine using the data-parallel approach. 41 refs

Part time employment and happiness: A cross-country analysis

OpenAIRE

Jenny Willson; Andy Dickerson

2010-01-01

The relationship between part time employment and job satisfaction is analysed for mothers in Germany, Denmark, the Netherlands, Finland, France, Spain and the UK. The impact of working part time on subjective life satisfaction and mental well-being is additionally analysed for British mothers. Cultural traditions concerning women´s role in society, and institutional differences between the countries are exploited. Results indicate that poor quality jobs can diminish any positive well-being r...

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

Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

Directory of Open Access Journals (Sweden)

Zhouhao Wang

2018-01-01

Full Text Available In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method.

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.

Tunnel boring machine applications

International Nuclear Information System (INIS)

Bhattacharyya, K.K.; McDonald, R.; Saunders, R.S.

1992-01-01

This paper reports that characterization of Yucca Mountain for a potential repository requires construction of an underground Exploratory Studies Facility (ESF). Mechanical excavating methods have been proposed for construction of the ESF as they offer a number of advantages over drilling and blasting at the Yucca Mountain site, including; less ground disturbance and therefore a potential for less adverse effects on the integrity of the site, creation of a more stable excavation cross section requiring less ground support, and an inherently safer and cleaner working environment. The tunnel boring machine (TBM) provides a proven technology for excavating the welded and unwelded Yucca Mountain tuffs. The access ramps and main underground tunnels form the largest part of the ESF underground construction work, and have been designed for excavation by TBM

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.

Machine Learning and Data Mining Methods in Diabetes Research.

Science.gov (United States)

Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

2017-01-01

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Multiple Property Cross Direction Control of Paper Machines

Directory of Open Access Journals (Sweden)

Markku Ohenoja

2011-07-01

Full Text Available Cross direction (CD control in sheet-forming process forms a challenging problem with high dimensions. Accounting the interactions between different properties and actuators, the dimensionality increases further and also computational issues arise. We present a multiple property controller feasible to be used especially with imaging measurements that provide high sampling frequency and therefore enable short control interval. The simulation results state the benefits of multiple property CD control over single property control and single property control using full feedforward compensation. The controller presented may also be tuned in automated manner and the results demonstrate the effect of tuning on input saturation.

Induction machine handbook

CERN Document Server

Boldea, Ion

2002-01-01

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

Chaotic Boltzmann machines

Science.gov (United States)

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

2013-01-01

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

Rotating electrical machines

CERN Document Server

Le Doeuff, René

2013-01-01

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

A Novel Dual-Permanent-Magnet-Excited Machine with Flux Strengthening Effect for Low-Speed Large-Torque Applications

Directory of Open Access Journals (Sweden)

Yujun Shi

2018-01-01

Full Text Available This paper proposes a novel dual-permanent-magnet-excited (DPME machine. It employs two sets of permanent magnets (PMs. One is on the rotor, the other is on the stator with PM arrays. When compared with the existing DPME machines, not all of the PMs are located in the slots formed by the iron teeth. Specifically, the radially magnetized PMs in the arrays are located under the short iron teeth, while the tangentially magnetized PMs are located in the slots formed by the long stator iron teeth and the radially magnetized PMs. Each long stator iron tooth is sandwiched by two tangentially magnetized PMs with opposite directions, thus resulting in the flux strengthening effect. The simulation analysis indicates that the proposed machine can offer large back EMF with low THD and large torque density with low torque ripple when compared with Machine I from a literature. Meanwhile, by comparison, the proposed machine has great potential in improving the power factor and efficiency.

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

Science.gov (United States)

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Tempo in electronic gaming machines affects behavior among at-risk gamblers.

Science.gov (United States)

Mentzoni, Rune A; Laberg, Jon Christian; Brunborg, Geir Scott; Molde, Helge; Pallesen, Ståle

2012-09-01

Background and aims Electronic gaming machines (EGM) may be a particularly addictive form of gambling, and gambling speed is believed to contribute to the addictive potential of such machines. The aim of the current study was to generate more knowledge concerning speed as a structural characteristic in gambling, by comparing the effects of three different bet-to-outcome intervals (BOI) on gamblers bet-sizes, game evaluations and illusion of control during gambling on a computer simulated slot machine. Furthermore, we investigated whether problem gambling moderates effects of BOI on gambling behavior and cognitions. Methods 62 participants played a computerized slot machine with either fast (400 ms), medium (1700 ms) or slow (3000 ms) BOI. SOGS-R was used to measure pre-existing gambling problems. Mean bet size, game evaluations and illusion of control comprised the dependent variables. Results Gambling speed had no overall effect on either mean bet size, game evaluations or illusion of control, but in the 400 ms condition, at-risk gamblers (SOGS-R score > 0) employed higher bet sizes compared to no-risk (SOGS-R score = 0) gamblers. Conclusions The findings corroborate and elaborate on previous studies and indicate that restrictions on gambling speed may serve as a harm reducing effort for at-risk gamblers.

Machine learning of the reactor core loading pattern critical parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2007-01-01

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employed a recently introduced machine learning technique, Support Vector Regression (SVR), which has a strong theoretical background in statistical learning theory. Superior empirical performance of the method has been reported on difficult regression problems in different fields of science and technology. SVR is a data driven, kernel based, nonlinear modelling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modelling. The starting set of experimental data for training and testing of the machine learning algorithm was obtained using a two-dimensional diffusion theory reactor physics computer code. We illustrate the performance of the solution and discuss its applicability, i.e., complexity, speed and accuracy, with a projection to a more realistic scenario involving machine learning from the results of more accurate and time consuming three-dimensional core modelling code. (author)

Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning.

Science.gov (United States)

Rashidi, Mohammad; Wolkow, Robert A

2018-05-23

Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen-terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases. By using multiple points of comparison and majority voting, the accuracy of the method is improved beyond 99%.

The hammer QSD-quick stop device for high speed machining and rubbing

Science.gov (United States)

Black, J. T.; James, C. R.

1980-01-01

A quick stop device (QSD) was designed for use in orthogonal machining and rubbing experiments. QSD's are used to obtain chip root samples that are representative of the deformation taking place during dynamic (actual) cutting conditions. These 'frozen' specimens are helpful in examining the plastic deformation that occurs in the regions of compression and shear which form the chip; the secondary shear at the tool-chip interface; and the nose ploughing/flank rubbing action which operates on the newly machined surface. The Hammer QSD employs a shear pin mechanism, broken by a flying hammer, which is traveling at the same velocity as the workpiece. The device has been successfully tested up to 6000 sfpm (30.48 m/sec).

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.

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

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 geometric process model for M/PH(M/PH)/1/K queue with new service machine procurement lead time

Science.gov (United States)

Yu, Miaomiao; Tang, Yinghui; Fu, Yonghong

2013-06-01

In this article, we consider a geometric process model for M/PH(M/PH)/1/K queue with new service machine procurement lead time. A maintenance policy (N - 1, N) based on the number of failures of the service machine is introduced into the system. Assuming that a failed service machine after repair will not be 'as good as new', and the spare service machine for replacement is only available by an order. More specifically, we suppose that the procurement lead time for delivering the spare service machine follows a phase-type (PH) distribution. Under such assumptions, we apply the matrix-analytic method to develop the steady state probabilities of the system, and then we obtain some system performance measures. Finally, employing an important Lemma, the explicit expression of the long-run average cost rate for the service machine is derived, and the direct search method is also implemented to determine the optimal value of N for minimising the average cost rate.

Paid employment and common mental disorders in 50-64-year olds: analysis of three cross-sectional nationally representative survey samples in 1993, 2000 and 2007.

Science.gov (United States)

Perera, G; Di Gessa, G; Corna, L M; Glaser, K; Stewart, R

2017-08-24

Associations between employment status and mental health are well recognised, but evidence is sparse on the relationship between paid employment and mental health in the years running up to statutory retirement ages using robust mental health measures. In addition, there has been no investigation into the stability over time in this relationship: an important consideration if survey findings are used to inform future policy. The aim of this study is to investigate the association between employment status and common mental disorder (CMD) in 50-64-year old residents in England and its stability over time, taking advantage of three national mental health surveys carried out over a 14-year period. Data were analysed from the British National Surveys of Psychiatric Morbidity of 1993, 2000 and 2007. Paid employment status was the primary exposure of interest and CMD the primary outcome - both ascertained identically in all three surveys (CMD from the revised Clinical Interview Schedule). Multivariable logistic regression models were used. The prevalence of CMD was higher in people not in paid employment across all survey years; however, this association was only present for non-employment related to poor health as an outcome and was not apparent in those citing other reasons for non-employment. Odds ratios for the association between non-employment due to ill health and CMD were 3.05 in 1993, 3.56 in 2000, and 2.80 in 2007, after adjustment for age, gender, marital status, education, social class, housing tenure, financial difficulties, smoking status, recent physical health consultation and activities of daily living impairment. The prevalence of CMD was higher in people not in paid employment for health reasons, but was not associated with non-employment for other reasons. Associations had been relatively stable in strength from 1993 to 2007 in those three cross-sectional nationally representative samples.

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.)

In vivo and in vitro performance of a China-made hemodialysis machine: a multi-center prospective controlled study.

Science.gov (United States)

Wang, Yong; Chen, Xiang-Mei; Cai, Guang-Yan; Li, Wen-Ge; Zhang, Ai-Hua; Hao, Li-Rong; Shi, Ming; Wang, Rong; Jiang, Hong-Li; Luo, Hui-Min; Zhang, Dong; Sun, Xue-Feng

2017-08-02

To evaluate the in vivo and in vitro performance of a China-made dialysis machine (SWS-4000). This was a multi-center prospective controlled study consisting of both long-term in vitro evaluations and cross-over in vivo tests in 132 patients. The China-made SWS-4000 dialysis machine was compared with a German-made dialysis machine (Fresenius 4008) with regard to Kt/V values, URR values, and dialysis-related adverse reactions in patients on maintenance hemodialysis, as well as the ultrafiltration rate, the concentration of electrolytes in the proportioned dialysate, the rate of heparin injection, the flow rate of the blood pump, and the rate of malfunction. The Kt/V and URR values at the 1st and 4th weeks of dialysis as well as the incidence of adverse effects did not differ between the two groups in cross-over in vivo tests (P > 0.05). There were no significant differences between the two groups in the error values of the ultrafiltration rate, the rate of heparin injection or the concentrations of electrolytes in the proportioned dialysate at different time points under different parameter settings. At weeks 2 and 24, with the flow rate of the blood pump set at 300 mL/min, the actual error of the SWS-4000 dialysis machine was significantly higher than that of the Fresenius 4008 dialysis machine (P  0.05). The malfunction rate was higher in the SWS-4000 group than in the Fresenius 4008 group (P Fresenius 4008 dialysis machine; however, the malfunction rate of the former is higher than that of the latter in in vitro tests. The stability and long-term accuracy of the SWS-4000 dialysis machine remain to be improved.

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

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Andres Wanner

2014-12-01

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

Machine learning techniques in disease forecasting: a case study on rice blast prediction

Directory of Open Access Journals (Sweden)

Kapoor Amar S

2006-11-01

Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

76 FR 54800 - International Business Machines (IBM), Software Group Business Unit, Quality Assurance Group, San...

Science.gov (United States)

2011-09-02

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,554] International Business Machines (IBM), Software Group Business Unit, Quality Assurance Group, San Jose, California; Notice of Negative Determination on Reconsideration On January 21, 2011, the Department of Labor (Department) issued an Affirmative Determination Regarding...

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.

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...

Machine Learning.

Science.gov (United States)

Kirrane, Diane E.

1990-01-01

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

Machining of Metal Matrix Composites

CERN Document Server

2012-01-01

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

Assessment of wear facets produced by the ACTA wear machine

DEFF Research Database (Denmark)

Benetti, Ana R; Larsen, Liselotte; Dowling, Adam H

2016-01-01

. The mean wear depth was measured using the traditionally employed 2D and compared with the 3D profilometric (digital) techniques. Data were submitted to analyses of variance, Tukey's post hoc tests and Independent Samples Student's t-tests (where appropriate) at p...OBJECTIVE: To investigate the use of a three-dimensional (3D) digital scanning method in determining the accuracy of the wear performance parameters of resin-based composites (RBCs) determined using a two-dimensional (2D) analogue methodology following in-vitro testing in an Academisch Centrum...... for Tandheelkunde Amsterdam (ACTA) wear machine. METHODS: Specimens compatible with the compartments of the ACTA wear machine specimen wheel (n=10) were prepared from one commercial and four experimental RBCs. The RBC specimens were rotated against an antagonist wheel in a food-like slurry for 220,000 wear cycles...

Femtosecond laser micro-machined polyimide films for cell scaffold applications

DEFF Research Database (Denmark)

Antanavičiute, Ieva; Šimatonis, Linas; Ulčinas, Orestas

2018-01-01

of commercially available 12.7 and 25.4μm thickness polyimide (PI) film was applied. Mechanical properties of the fabricated scaffolds, i.e. arrays of differently spaced holes, were examined via custom-built uniaxial micro-tensile testing and finite element method simulations. We demonstrate that experimental...... micro-tensile testing results could be numerically simulated and explained by two-material model, assuming that 2-6μm width rings around the holes possessed up to five times higher Young's modulus and yield stress compared with the rest of the laser intacted PI film areas of 'dog-bone'-shaped specimens......Engineering of sophisticated synthetic 3D scaffolds that allow controlling behaviour and location of the cells requires advanced micro/nano-fabrication techniques. Ultrafast laser micro-machining employing a 1030-nm wavelength Yb:KGW femtosecond laser and a micro-fabrication workstation for micro-machining...

Machine technology: a survey

International Nuclear Information System (INIS)

Barbier, M.M.

1981-01-01

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

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Object discrimination using optimized multi-frequency auditory cross-modal haptic feedback.

Science.gov (United States)

Gibson, Alison; Artemiadis, Panagiotis

2014-01-01

As the field of brain-machine interfaces and neuro-prosthetics continues to grow, there is a high need for sensor and actuation mechanisms that can provide haptic feedback to the user. Current technologies employ expensive, invasive and often inefficient force feedback methods, resulting in an unrealistic solution for individuals who rely on these devices. This paper responds through the development, integration and analysis of a novel feedback architecture where haptic information during the neural control of a prosthetic hand is perceived through multi-frequency auditory signals. Through representing force magnitude with volume and force location with frequency, the feedback architecture can translate the haptic experiences of a robotic end effector into the alternative sensory modality of sound. Previous research with the proposed cross-modal feedback method confirmed its learnability, so the current work aimed to investigate which frequency map (i.e. frequency-specific locations on the hand) is optimal in helping users distinguish between hand-held objects and tasks associated with them. After short use with the cross-modal feedback during the electromyographic (EMG) control of a prosthetic hand, testing results show that users are able to use audial feedback alone to discriminate between everyday objects. While users showed adaptation to three different frequency maps, the simplest map containing only two frequencies was found to be the most useful in discriminating between objects. This outcome provides support for the feasibility and practicality of the cross-modal feedback method during the neural control of prosthetics.

Determination of High-Frequency d- and q-axis Inductances for Surface-Mounted Permanent-Magnet Synchronous Machines

DEFF Research Database (Denmark)

Lu, Kaiyuan; Vetuschi, M.; Rasmussen, Peter Omand

2010-01-01

This paper presents a reliable method for the experimental determination of high-frequency d- and q -axis inductances for surface-mounted permanent-magnet synchronous machines (SMPMSMs). Knowledge of the high-frequency d- and q-axis inductances plays an important role in the efficient design...... of sensorless controllers using high-frequency signal injection techniques. The proposed method employs a static locked-rotor test using an ac +dc power supply. By injecting a high-frequency rotating voltage vector into the machine, the d- and q-axis inductances may simultaneously be determined with no need...

Multi-response optimization of machining characteristics in ultrasonic machining of WC-Co composite through Taguchi method and grey-fuzzy logic

Directory of Open Access Journals (Sweden)

Ravi Pratap Singh

2018-01-01

Full Text Available This article addresses the application of grey based fuzzy logic coupled with Taguchi’s approach for optimization of multi performance characteristics in ultrasonic machining of WC-Co composite material. The Taguchi’s L-36 array has been employed to conduct the experimentation and also to observe the influence of different process variables (power rating, cobalt content, tool geometry, thickness of work piece, tool material, abrasive grit size on machining characteristics. Grey relational fuzzy grade has been computed by converting the multiple responses, i.e., material removal rate and tool wear rate obtained from Taguchi’s approach into a single performance characteristic using grey based fuzzy logic. In addition, analysis of variance (ANOVA has also been attempted in a view to identify the significant parameters. Results revealed grit size and power rating as leading parameters for optimization of multi performance characteristics. From the microstructure analysis, the mode of material deformation has been observed and the critical parameters (i.e., work material properties, grit size, and power rating for the deformation mode have been established.

Improving machine operation management efficiency via improving the vehicle park structure and using the production operation information database

Science.gov (United States)

Koptev, V. Yu

2017-02-01

The work represents the results of studying basic interconnected criteria of separate equipment units of the transport network machines fleet, depending on production and mining factors to improve the transport systems management. Justifying the selection of a control system necessitates employing new methodologies and models, augmented with stability and transport flow criteria, accounting for mining work development dynamics on mining sites. A necessary condition is the accounting of technical and operating parameters related to vehicle operation. Modern open pit mining dispatching systems must include such kinds of the information database. An algorithm forming a machine fleet is presented based on multi-variation task solution in connection with defining reasonable operating features of a machine working as a part of a complex. Proposals cited in the work may apply to mining machines (drilling equipment, excavators) and construction equipment (bulldozers, cranes, pile-drivers), city transport and other types of production activities using machine fleet.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

Science.gov (United States)

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

VIRTUAL MACHINES IN EDUCATION – CNC MILLING MACHINE WITH SINUMERIK 840D CONTROL SYSTEM

Directory of Open Access Journals (Sweden)

Ireneusz Zagórski

2014-11-01

Full Text Available Machining process nowadays could not be conducted without its inseparable element: cutting edge and frequently numerically controlled milling machines. Milling and lathe machining centres comprise standard equipment in many companies of the machinery industry, e.g. automotive or aircraft. It is for that reason that tertiary education should account for this rising demand. This entails the introduction into the curricula the forms which enable visualisation of machining, milling process and virtual production as well as virtual machining centres simulation. Siemens Virtual Machine (Virtual Workshop sets an example of such software, whose high functionality offers a range of learning experience, such as: learning the design of machine tools, their configuration, basic operation functions as well as basics of CNC.

Machine Vision based Micro-crack Inspection in Thin-film Solar Cell Panel

Directory of Open Access Journals (Sweden)

Zhang Yinong

2014-09-01

Full Text Available Thin film solar cell consists of various layers so the surface of solar cell shows heterogeneous textures. Because of this property the visual inspection of micro-crack is very difficult. In this paper, we propose the machine vision-based micro-crack detection scheme for thin film solar cell panel. In the proposed method, the crack edge detection is based on the application of diagonal-kernel and cross-kernel in parallel. Experimental results show that the proposed method has better performance of micro-crack detection than conventional anisotropic model based methods on a cross- kernel.

Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

Science.gov (United States)

Hsiao, Ming-Chih; Su, Ling-Huey

2018-02-01

This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.

Microbial cross-contamination by airborne dispersion and contagion during defeathering of poultry.

Science.gov (United States)

Allen, V M; Hinton, M H; Tinker, D B; Gibson, C; Mead, G C; Wathes, C M

2003-09-01

1. A readily identifiable strain of Escherichia coli K12 was used as a 'marker' organism to determine the sources, routes and patterns of microbial cross-contamination during mechanical defeathering of broiler chicken carcases. 2. Inoculation of scald water with the marker organism led to a relatively even pattern of carcase contamination during subsequent defeathering. Microbial cross-contamination was greater by this route of inoculation than by either surface inoculation of a 'seeder' carcase or oral inoculation of a live bird one day before slaughter. 3. Dispersal of the marker organism was strongly influenced by the mechanical action of the defeathering machines. Forward transmission of the marker occurred by aerosol or large airborne droplets and particulates such as feathers. Moving carcases through the defeathering machines when these were non-operational clearly reduced backward transmission of the marker. 4. Although microbial dispersal was unaffected by increasing the spacing between individual carcases or installing a water curtain at the entry and exit of the defeathering machines, shielding of carcases with aluminium baffles reduced counts of the marker organism from contaminated carcases by > 90%. 5. The results imply that microbial cross-contamination of broiler chicken carcases during defeathering occurs mainly via the airborne route, which could be contained by physical means.

Numerical modelling of micro-machining of f.c.c. single crystal: Influence of strain gradients

KAUST Repository

Demiral, Murat

2014-11-01

A micro-machining process becomes increasingly important with the continuous miniaturization of components used in various fields from military to civilian applications. To characterise underlying micromechanics, a 3D finite-element model of orthogonal micro-machining of f.c.c. single crystal copper was developed. The model was implemented in a commercial software ABAQUS/Explicit employing a user-defined subroutine VUMAT. Strain-gradient crystal-plasticity and conventional crystal-plasticity theories were used to demonstrate the influence of pre-existing and evolved strain gradients on the cutting process for different combinations of crystal orientations and cutting directions. Crown Copyright © 2014.

Kernel methods for interpretable machine learning of order parameters

Science.gov (United States)

Ponte, Pedro; Melko, Roger G.

2017-11-01

Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

Support vector machine based diagnostic system for breast cancer using swarm intelligence.

Science.gov (United States)

Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

2012-08-01

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

Machine-to-machine communications architectures, technology, standards, and applications

CERN Document Server

Misic, Vojislav B

2014-01-01

With the number of machine-to-machine (M2M)-enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology.Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology.Details a practical scheme for the forward error correction code designInvestigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communicationsIdentifies algorithms that will ensure functionality, performance, reliability, ...

Nonplanar machines

International Nuclear Information System (INIS)

Ritson, D.

1989-05-01

This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

Study on the Optimization and Process Modeling of the Rotary Ultrasonic Machining of Zerodur Glass-Ceramic

Science.gov (United States)

Pitts, James Daniel

Rotary ultrasonic machining (RUM), a hybrid process combining ultrasonic machining and diamond grinding, was created to increase material removal rates for the fabrication of hard and brittle workpieces. The objective of this research was to experimentally derive empirical equations for the prediction of multiple machined surface roughness parameters for helically pocketed rotary ultrasonic machined Zerodur glass-ceramic workpieces by means of a systematic statistical experimental approach. A Taguchi parametric screening design of experiments was employed to systematically determine the RUM process parameters with the largest effect on mean surface roughness. Next empirically determined equations for the seven common surface quality metrics were developed via Box-Behnken surface response experimental trials. Validation trials were conducted resulting in predicted and experimental surface roughness in varying levels of agreement. The reductions in cutting force and tool wear associated with RUM, reported by previous researchers, was experimentally verified to also extended to helical pocketing of Zerodur glass-ceramic.

Ultraprecision machining. Cho seimitsu kako

Energy Technology Data Exchange (ETDEWEB)

Suga, T [The Univ. of Tokyo, Tokyo (Japan). Research Center for Advanced Science and Technology

1992-10-05

It is said that the image of ultraprecision improved from 0.1[mu]m to 0.01[mu]m within recent years. Ultraprecision machining is a production technology which forms what is called nanotechnology with ultraprecision measuring and ultraprecision control. Accuracy means average machined sizes close to a required value, namely the deflection errors are small; precision means the scattered errors of machined sizes agree very closely. The errors of machining are related to both of the above errors and ultraprecision means the combined errors are very small. In the present ultraprecision machining, the relative precision to the size of a machined object is said to be in the order of 10[sup -6]. The flatness of silicon wafers is usually less than 0.5[mu]m. It is the fact that the appearance of atomic scale machining is awaited as the limit of ultraprecision machining. The machining of removing and adding atomic units using scanning probe microscopes are expected to reach the limit actually. 2 refs.

PIMA Point of Care CD4+ Cell Count Machines in Remote MNCH Settings: Lessons Learned from Seven Districts in Zimbabwe

Science.gov (United States)

Mtapuri-Zinyowera, Sekesai; Chiyaka, Edward T.; Mushayi, Wellington; Musuka, Godfrey; Naluyinda-Kitabire, Florence; Mushavi, Angella; Chikwasha, Vasco

2013-01-01

An evaluation was commissioned to generate evidence on the impact of PIMA point-of-care CD4+ count machines in maternal and new-born child health settings in Zimbabwe; document best practices, lessons learned, challenges, and recommendations related to scale up of this new technology. A mixed methodology approach that included 31 in-depth interviews with stakeholders involved in procurement, distribution, and use of the POC machines was employed. Additionally, data was also abstracted from 207 patient records from 35 sites with the PIMA POC CD4+ count machines and 10 other comparative sites without the machine. A clearer training strategy was found to be necessary. The average time taken to initiate clients on antiretroviral treatment (ART) was substantially less, 15 days (IQR-1-149) for sites with a PIMA POC machine as compared to 32.7 days (IQR-1-192) at sites with no PIMA POC machine. There was general satisfaction because of the presence of the PIMA POC CD4+ count machine at sites that also initiated ART. PMID:24847177

Theory and practice in machining systems

CERN Document Server

Ito, Yoshimi

2017-01-01

This book describes machining technology from a wider perspective by considering it within the machining space. Machining technology is one of the metal removal activities that occur at the machining point within the machining space. The machining space consists of structural configuration entities, e.g., the main spindle, the turret head and attachments such the chuck and mandrel, and also the form-generating movement of the machine tool itself. The book describes fundamental topics, including the form-generating movement of the machine tool and the important roles of the attachments, before moving on to consider the supply of raw materials into the machining space, and the discharge of swarf from it, and then machining technology itself. Building on the latest research findings “Theory and Practice in Machining System” discusses current challenges in machining. Thus, with the inclusion of introductory and advanced topics, the book can be used as a guide and survey of machining technology for students an...

Machine learning-enabled discovery and design of membrane-active peptides.

Science.gov (United States)

Lee, Ernest Y; Wong, Gerard C L; Ferguson, Andrew L

2017-07-08

Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen sequence space and guide experiment towards promising candidates with high putative activity. In this mini-review, we provide an introduction to antimicrobial peptides and summarize recent advances in machine learning-enabled antimicrobial peptide discovery and design with a focus on a recent work Lee et al. Proc. Natl. Acad. Sci. USA 2016;113(48):13588-13593. This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides. We use this model to discover membrane activity as a multiplexed function in diverse peptide families and provide interpretable understanding of the physicochemical properties and mechanisms governing membrane activity. Experimental validation of the classifier reveals it to have learned membrane activity as a unifying signature of antimicrobial peptides with diverse modes of action. Some of the discriminating rules by which it performs classification are in line with existing "human learned" understanding, but it also unveils new previously unknown determinants and multidimensional couplings governing membrane activity. Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action. Copyright © 2017 Elsevier Ltd. All rights reserved.

Support vector machines and generalisation in HEP

Science.gov (United States)

Bevan, Adrian; Gamboa Goñi, Rodrigo; Hays, Jon; Stevenson, Tom

2017-10-01

We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for H → τ + τ - at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.

Face machines

Energy Technology Data Exchange (ETDEWEB)

Hindle, D.

1999-06-01

The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

Public Service Motivation and Employment Sector: Attraction or Socialization?

DEFF Research Database (Denmark)

Kjeldsen, Anne Mette; Jacobsen, Christian Bøtcher

2013-01-01

Numerous studies have shown that public service motivation (PSM) is positively associated with public sector employment. However, the question of whether PSM influences or is influenced by employment decisions remains open, since previous studies have mostly relied on cross-sectional samples...... with experienced employees. This article investigates the relationship between PSM and employment sector in pre-entry and post-entry settings using data from a panel of Danish physiotherapy students surveyed before and after their first job in the public or private sector. The analyses show that PSM is neither...

The effect of SCHIP expansions on health insurance decisions by employers.

Science.gov (United States)

Buchmueller, Thomas; Cooper, Philip; Simon, Kosali; Vistnes, Jessica

2005-01-01

This study uses repeated cross-sectional data from the Medical Expenditure Panel Survey-Insurance Component (MEPS-IC), a large nationally representative survey of establishments, to investigate the effect of the State Children's Health Insurance Program (SCHIP) on health insurance decisions by employers. The data span the years 1997 to 2001, the period when states were implementing SCHIP. We exploit cross-state variation in the timing of SCHIP implementation and the extent to which the program increased eligibility for public insurance. We find evidence suggesting that employers whose workers were likely to have been affected by these expansions reacted by raising employee contributions for family coverage options, and that take-up of any coverage, generally, and family coverage, specifically, dropped in these establishments. We find no evidence that employers stopped offering single or family coverage outright.

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

International Nuclear Information System (INIS)

Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

2005-01-01

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

Tattoo machines, needles and utilities.

Science.gov (United States)

Rosenkilde, Frank

2015-01-01

Starting out as a professional tattooist back in 1977 in Copenhagen, Denmark, Frank Rosenkilde has personally experienced the remarkable development of tattoo machines, needles and utilities: all the way from home-made equipment to industrial products of substantially improved quality. Machines can be constructed like the traditional dual-coil and single-coil machines or can be e-coil, rotary and hybrid machines, with the more convenient and precise rotary machines being the recent trend. This development has resulted in disposable needles and utilities. Newer machines are more easily kept clean and protected with foil to prevent crosscontaminations and infections. The machines and the tattooists' knowledge and awareness about prevention of infection have developed hand-in-hand. For decades, Frank Rosenkilde has been collecting tattoo machines. Part of his collection is presented here, supplemented by his personal notes. © 2015 S. Karger AG, Basel.

Design of rotating electrical machines

CERN Document Server

Pyrhonen , Juha; Hrabovcova , Valeria

2013-01-01

In one complete volume, this essential reference presents an in-depth overview of the theoretical principles and techniques of electrical machine design. This timely new edition offers up-to-date theory and guidelines for the design of electrical machines, taking into account recent advances in permanent magnet machines as well as synchronous reluctance machines. New coverage includes: Brand new material on the ecological impact of the motors, covering the eco-design principles of rotating electrical machinesAn expanded section on the design of permanent magnet synchronous machines, now repo

8 CFR 212.6 - Border crossing identification cards.

Science.gov (United States)

2010-01-01

... Section 212.6 Aliens and Nationality DEPARTMENT OF HOMELAND SECURITY IMMIGRATION REGULATIONS DOCUMENTARY... valid on or after October 1, 2002, the non-biometric border crossing card portion of the document is not... contain a machine-readable biometric identifier, may be admitted on the basis of the nonimmigrant visa...

Machine learning applications in cancer prognosis and prediction.

Science.gov (United States)

Kourou, Konstantina; Exarchos, Themis P; Exarchos, Konstantinos P; Karamouzis, Michalis V; Fotiadis, Dimitrios I

2015-01-01

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Academic Globalization: Cultureactive to Ice- the Cross-Cultural, Crossdisciplinary and Cross-Epistemological Transformation

Directory of Open Access Journals (Sweden)

Marta Szabo White

2010-12-01

Full Text Available Commensurate with the concept of Academic Globalization, coupled with the foray of Globalization, this paper underscores the cross-cultural, cross-disciplinary and cross-epistemological transformation from the first-generation Cultureactive to the second-generation InterCultural Edge [ICE]. The former is embedded in the experiential works of cross-cultural consultant. Richard Lewis and the latter is grounded in established theoretical frameworks. Both serve to underscore the impact of the Globalization Phenomenon, as manifested in and enabled by the acceleration of academic and practitioner cross-cultural activities. The contribution of this paper is the celebration of the longawaited arrival of ICE [InterCultural Edge]. While previous research streams have underscored global similarities and differences among cultures, a previous paper [19] established that cross-professional rather than cross-cultural differences are more paramount. Employing Cultureactive and the LMR framework, it was noted that business versus non-business predisposition had a more direct impact on one's individual cultural profile than did nationality. Regardless of culture, persons involved in business are characterized primarily by linear-active modes of communication, and persons involved in non-business activities typically employ more multiactive/hybrid and less linear modes of communication. The pivotal question is this: Now that we have a new and improved tool, are we in a better position to assess and predict leadership, negotiating styles, individual behaviors, etc., which are central to academic globalization and preparing global business leaders?

Machine learning algorithms to classify spinal muscular atrophy subtypes.

Science.gov (United States)

Srivastava, Tuhin; Darras, Basil T; Wu, Jim S; Rutkove, Seward B

2012-07-24

The development of better biomarkers for disease assessment remains an ongoing effort across the spectrum of neurologic illnesses. One approach for refining biomarkers is based on the concept of machine learning, in which individual, unrelated biomarkers are simultaneously evaluated. In this cross-sectional study, we assess the possibility of using machine learning, incorporating both quantitative muscle ultrasound (QMU) and electrical impedance myography (EIM) data, for classification of muscles affected by spinal muscular atrophy (SMA). Twenty-one normal subjects, 15 subjects with SMA type 2, and 10 subjects with SMA type 3 underwent EIM and QMU measurements of unilateral biceps, wrist extensors, quadriceps, and tibialis anterior. EIM and QMU parameters were then applied in combination using a support vector machine (SVM), a type of machine learning, in an attempt to accurately categorize 165 individual muscles. For all 3 classification problems, normal vs SMA, normal vs SMA 3, and SMA 2 vs SMA 3, use of SVM provided the greatest accuracy in discrimination, surpassing both EIM and QMU individually. For example, the accuracy, as measured by the receiver operating characteristic area under the curve (ROC-AUC) for the SVM discriminating SMA 2 muscles from SMA 3 muscles was 0.928; in comparison, the ROC-AUCs for EIM and QMU parameters alone were only 0.877 (p < 0.05) and 0.627 (p < 0.05), respectively. Combining EIM and QMU data categorizes individual SMA-affected muscles with very high accuracy. Further investigation of this approach for classifying and for following the progression of neuromuscular illness is warranted.

Machine Learning of the Reactor Core Loading Pattern Critical Parameters

Directory of Open Access Journals (Sweden)

Krešimir Trontl

2008-01-01

Full Text Available The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR, which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.

Machine Learning of the Reactor Core Loading Pattern Critical Parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2008-01-01

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy

75 FR 15739 - International Business Machines Corporation: Armonk, NY; Notice of Termination of Investigation

Science.gov (United States)

2010-03-30

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-70,580] International Business Machines Corporation: Armonk, NY; Notice of Termination of Investigation Pursuant to Section 223 of the Trade Act of 1974, as amended, an investigation was initiated in response to a petition filed on May 21, 2009 on behalf of workers of International...

Investigations of Calorimeter Clustering in ATLAS using Machine Learning

CERN Document Server

AUTHOR|(CDS)2153685

The Large Hadron Collider (LHC) at CERN is designed to search for new physics by colliding protons with a center-of-mass energy of 13 TeV. The ATLAS detector is a multipurpose particle detector built to record these proton-proton collisions. In order to improve sensitivity to new physics at the LHC, luminosity increases are planned for 2018 and beyond. With this greater luminosity comes an increase in the number of simultaneous proton-proton collisions per bunch crossing (pile-up). This extra pile- up has adverse effects on algorithms for clustering the ATLAS detector's calorimeter cells. These adverse effects stem from overlapping energy deposits originating from distinct particles and could lead to diffculties in accurately reconstructing events. Machine learning algorithms provide a new tool that has potential to clustering per- formance. Recent developments in computer science have given rise to new set of machine learning algorithms that, in many circumstances, out-perform more conven- tional algorithms....

MITS machine operations

International Nuclear Information System (INIS)

Flinchem, J.

1980-01-01

This document contains procedures which apply to operations performed on individual P-1c machines in the Machine Interface Test System (MITS) at AiResearch Manufacturing Company's Torrance, California Facility

Coordinate measuring machines

DEFF Research Database (Denmark)

De Chiffre, Leonardo

This document is used in connection with three exercises of 2 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercises concern three aspects of coordinate measuring: 1) Measuring and verification of tolerances on coordinate measuring machines, 2) Traceabilit...... and uncertainty during coordinate measurements, 3) Digitalisation and Reverse Engineering. This document contains a short description of each step in the exercise and schemes with room for taking notes of the results.......This document is used in connection with three exercises of 2 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercises concern three aspects of coordinate measuring: 1) Measuring and verification of tolerances on coordinate measuring machines, 2) Traceability...

Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine

Science.gov (United States)

Laib dit Leksir, Y.; Mansour, M.; Moussaoui, A.

2018-03-01

Analysis and processing of databases obtained from infrared thermal inspections made on electrical installations require the development of new tools to obtain more information to visual inspections. Consequently, methods based on the capture of thermal images show a great potential and are increasingly employed in this field. However, there is a need for the development of effective techniques to analyse these databases in order to extract significant information relating to the state of the infrastructures. This paper presents a technique explaining how this approach can be implemented and proposes a system that can help to detect faults in thermal images of electrical installations. The proposed method classifies and identifies the region of interest (ROI). The identification is conducted using support vector machine (SVM) algorithm. The aim here is to capture the faults that exist in electrical equipments during an inspection of some machines using A40 FLIR camera. After that, binarization techniques are employed to select the region of interest. Later the comparative analysis of the obtained misclassification errors using the proposed method with Fuzzy c means and Ostu, has also be addressed.

Electric machine

Science.gov (United States)

El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

2012-07-17

An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Directory of Open Access Journals (Sweden)

Zekić-Sušac Marijana

2014-09-01

Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

Using Machine Learning for Sentiment and Social Influence Analysis in Text

OpenAIRE

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

2017-01-01

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

MRR and TWR evaluation on electrical discharge machining of Ti-6Al-4V using tungsten : copper composite electrode

Science.gov (United States)

Prasanna, J.; Rajamanickam, S.; Amith Kumar, O.; Karthick Raj, G.; Sathya Narayanan, P. V. V.

2017-05-01

In this paper Ti-6Al-4V used as workpiece material and it is keenly seen in variety of field including medical, chemical, marine, automotive, aerospace, aviation, electronic industries, nuclear reactor, consumer products etc., The conventional machining of Ti-6Al-4V is very difficult due to its distinctive properties. The Electrical Discharge Machining (EDM) is right choice of machining this material. The tungsten copper composite material is employed as tool material. The gap voltage, peak current, pulse on time and duty factor is considered as the machining parameter to analyze the machining characteristics Material Removal Rate (MRR) and Tool Wear Rate (TWR). The Taguchi method is provided to work for finding the significant parameter of EDM. It is found that for MRR significant parameters rated in the following order Gap Voltage, Pulse On-Time, Peak Current and Duty Factor. On the other hand for TWR significant parameters are listed in line of Gap Voltage, Duty Factor, Peak Current and Pulse On-Time.

Machine Learning and Radiology

Science.gov (United States)

Wang, Shijun; Summers, Ronald M.

2012-01-01

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

Tribology in machine design

CERN Document Server

Stolarski, Tadeusz

1999-01-01

""Tribology in Machine Design is strongly recommended for machine designers, and engineers and scientists interested in tribology. It should be in the engineering library of companies producing mechanical equipment.""Applied Mechanics ReviewTribology in Machine Design explains the role of tribology in the design of machine elements. It shows how algorithms developed from the basic principles of tribology can be used in a range of practical applications within mechanical devices and systems.The computer offers today's designer the possibility of greater stringen

A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

Science.gov (United States)

Lu, Siyuan; Qiu, Xin; Shi, Jianping; Li, Na; Lu, Zhi-Hai; Chen, Peng; Yang, Meng-Meng; Liu, Fang-Yuan; Jia, Wen-Juan; Zhang, Yudong

2017-01-01

It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Quadrilateral Micro-Hole Array Machining on Invar Thin Film: Wet Etching and Electrochemical Fusion Machining

Directory of Open Access Journals (Sweden)

Woong-Kirl Choi

2018-01-01

Full Text Available Ultra-precision products which contain a micro-hole array have recently shown remarkable demand growth in many fields, especially in the semiconductor and display industries. Photoresist etching and electrochemical machining are widely known as precision methods for machining micro-holes with no residual stress and lower surface roughness on the fabricated products. The Invar shadow masks used for organic light-emitting diodes (OLEDs contain numerous micro-holes and are currently machined by a photoresist etching method. However, this method has several problems, such as uncontrollable hole machining accuracy, non-etched areas, and overcutting. To solve these problems, a machining method that combines photoresist etching and electrochemical machining can be applied. In this study, negative photoresist with a quadrilateral hole array pattern was dry coated onto 30-µm-thick Invar thin film, and then exposure and development were carried out. After that, photoresist single-side wet etching and a fusion method of wet etching-electrochemical machining were used to machine micro-holes on the Invar. The hole machining geometry, surface quality, and overcutting characteristics of the methods were studied. Wet etching and electrochemical fusion machining can improve the accuracy and surface quality. The overcutting phenomenon can also be controlled by the fusion machining. Experimental results show that the proposed method is promising for the fabrication of Invar film shadow masks.

A Universal Reactive Machine

DEFF Research Database (Denmark)

Andersen, Henrik Reif; Mørk, Simon; Sørensen, Morten U.

1997-01-01

Turing showed the existence of a model universal for the set of Turing machines in the sense that given an encoding of any Turing machine asinput the universal Turing machine simulates it. We introduce the concept of universality for reactive systems and construct a CCS processuniversal...

Efficient operation of anisotropic synchronous machines for wind energy systems

International Nuclear Information System (INIS)

Eldeeb, Hisham; Hackl, Christoph M.; Kullick, Julian

2016-01-01

This paper presents an analytical solution for the Maximum-Torque-per-Ampere (MTPA) operation of synchronous machines (SM) with anisotropy and magnetic cross-coupling for the application in wind turbine systems and airborne wind energy systems. For a given reference torque, the analytical MTPA solution provides the optimal stator current references which produce the desired torque while minimizing the stator copper losses. From an implementation point of view, the proposed analytical method is appealing in terms of its fast online computation (compared to classical numerical methods) and its efficiency enhancement of the electrical drive system. The efficiency of the analytical MTPA operation, with and without consideration of cross-coupling, is compared to the conventional method with zero direct current. (paper)

Consequences of heavy machining vis à vis the machine structure – typical applications

International Nuclear Information System (INIS)

Leuch, M

2011-01-01

StarragHeckert has built 5 axis machines since the middle of the 80s for heavy duty milling. The STC-Centres are predominantly utilised in the aerospace industry, especially for milling structural workpieces, casings or Impellers made out of titanium and steel. StarragHeckert has a history of building machines for high performance milling. The machining of these components includes high forces thus spreading the wheat from the chaff. Although FEM calculations and multi-body simulations are carried out in the early stages of development, this paper will illustrate how the real process stability with modal analysis and cutting trials is determined. The experiment observes chatter stability to identify if the machine devices are adequate for the application or if the design has to be improved. Machining parameters of industrial applications are demonstrating the process stability for five axis heavy duties milling of StarragHeckert machine.

Virtual Machine in Automation Projects

OpenAIRE

Xing, Xiaoyuan

2010-01-01

Virtual machine, as an engineering tool, has recently been introduced into automation projects in Tetra Pak Processing System AB. The goal of this paper is to examine how to better utilize virtual machine for the automation projects. This paper designs different project scenarios using virtual machine. It analyzes installability, performance and stability of virtual machine from the test results. Technical solutions concerning virtual machine are discussed such as the conversion with physical...

A Simulation Model for Machine Efficiency Improvement Using Reliability Centered Maintenance: Case Study of Semiconductor Factory

Directory of Open Access Journals (Sweden)

Srisawat Supsomboon

2014-01-01

Full Text Available The purpose of this study was to increase the quality of product by focusing on the machine efficiency improvement. The principle of the reliability centered maintenance (RCM was applied to increase the machine reliability. The objective was to create preventive maintenance plan under reliability centered maintenance method and to reduce defects. The study target was set to reduce the Lead PPM for a test machine by simulating the proposed preventive maintenance plan. The simulation optimization approach based on evolutionary algorithms was employed for the preventive maintenance technique selection process to select the PM interval that gave the best total cost and Lead PPM values. The research methodology includes procedures such as following the priority of critical components in test machine, analyzing the damage and risk level by using Failure Mode and Effects Analysis (FMEA, calculating the suitable replacement period through reliability estimation, and optimizing the preventive maintenance plan. From the result of the study it is shown that the Lead PPM of test machine can be reduced. The cost of preventive maintenance, cost of good product, and cost of lost product were decreased.

Optimization of the Machining parameter of LM6 Alminium alloy in CNC Turning using Taguchi method

Science.gov (United States)

Arunkumar, S.; Muthuraman, V.; Baskaralal, V. P. M.

2017-03-01

Due to widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining process. In machining of parts, surface quality is one of the most specified customer requirements. In order for manufactures to maximize their gains from utilizing CNC turning, accurate predictive models for surface roughness must be constructed. The prediction of optimum machining conditions for good surface finish plays an important role in process planning. This work deals with the study and development of a surface roughness prediction model for machining LM6 aluminum alloy. Two important tools used in parameter design are Taguchi orthogonal arrays and signal to noise ratio (S/N). Speed, feed, depth of cut and coolant are taken as process parameter at three levels. Taguchi’s parameters design is employed here to perform the experiments based on the various level of the chosen parameter. The statistical analysis results in optimum parameter combination of speed, feed, depth of cut and coolant as the best for obtaining good roughness for the cylindrical components. The result obtained through Taguchi is confirmed with real time experimental work.

The Buttonhole Machine. Module 13.

Science.gov (United States)

South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

This module on the bottonhole machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers two topics: performing special operations on the buttonhole machine (parts and purpose) and performing special operations on the buttonhole machine (gauged buttonholes). For each topic these components are…

Applied machining technology

CERN Document Server

Tschätsch, Heinz

2010-01-01

Machining and cutting technologies are still crucial for many manufacturing processes. This reference presents all important machining processes in a comprehensive and coherent way. It includes many examples of concrete calculations, problems and solutions.

Prototype-based models in machine learning.

Science.gov (United States)

Biehl, Michael; Hammer, Barbara; Villmann, Thomas

2016-01-01

An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.

Science.gov (United States)

Li, Guang; Wei, Jie; Huang, Hailiang; Gaebler, Carl Philipp; Yuan, Amy; Deasy, Joseph O

2015-12-01

To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients ( f v ) to account for most of the spectrum energy (Σ f v 2 ). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves ( R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors

Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

2017-04-21

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Machine Protection

International Nuclear Information System (INIS)

Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

2012-01-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

Machine Protection

Energy Technology Data Exchange (ETDEWEB)

Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

2012-07-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

Machine Protection

CERN Document Server

Zerlauth, Markus; Wenninger, Jörg

2012-01-01

The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

Dictionary of machine terms

International Nuclear Information System (INIS)

1990-06-01

This book has introduction of dictionary of machine terms, and a compilation committee and introductory remarks. It gives descriptions of the machine terms in alphabetical order from a to Z and also includes abbreviation of machine terms and symbol table, way to read mathematical symbols and abbreviation and terms of drawings.

HTS machine laboratory prototype

DEFF Research Database (Denmark)

machine. The machine comprises six stationary HTS field windings wound from both YBCO and BiSCOO tape operated at liquid nitrogen temperature and enclosed in a cryostat, and a three phase armature winding spinning at up to 300 rpm. This design has full functionality of HTS synchronous machines. The design...

Mammogram retrieval through machine learning within BI-RADS standards.

Science.gov (United States)

Wei, Chia-Hung; Li, Yue; Huang, Pai Jung

2011-08-01

A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

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

24 CFR 200.1101 - Cross-reference.

Science.gov (United States)

2010-04-01

... URBAN DEVELOPMENT GENERAL INTRODUCTION TO FHA PROGRAMS Social Security Numbers and Employer Identification Numbers; Applicants in Unassisted Programs § 200.1101 Cross-reference. The provisions in subpart B of part 5 of this title apply to Social Security Numbers and Employer Identification Numbers for...

24 CFR 200.1001 - Cross-reference.

Science.gov (United States)

2010-04-01

... URBAN DEVELOPMENT GENERAL INTRODUCTION TO FHA PROGRAMS Social Security Numbers and Employer Identification Numbers; Assistance Applicants and Participants § 200.1001 Cross-reference. The provisions in subpart B of part 5 of this title apply to Social Security Numbers and Employer Identification Numbers for...

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

OpenAIRE

Parker, David L.; 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 standardize...

Machine vision systems using machine learning for industrial product inspection

Science.gov (United States)

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

2002-02-01

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

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.

Machine Directional Register System Modeling for Shaft-Less Drive Gravure Printing Machines

Directory of Open Access Journals (Sweden)

Shanhui Liu

2013-01-01

Full Text Available In the latest type of gravure printing machines referred to as the shaft-less drive system, each gravure printing roller is driven by an individual servo motor, and all motors are electrically synchronized. The register error is regulated by a speed difference between the adjacent printing rollers. In order to improve the control accuracy of register system, an accurate mathematical model of the register system should be investigated for the latest machines. Therefore, the mathematical model of the machine directional register (MDR system is studied for the multicolor gravure printing machines in this paper. According to the definition of the MDR error, the model is derived, and then it is validated by the numerical simulation and experiments carried out in the experimental setup of the four-color gravure printing machines. The results show that the established MDR system model is accurate and reliable.

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.

Machine learning and radiology.

Science.gov (United States)

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Machining with abrasives

CERN Document Server

Jackson, Mark J

2011-01-01

Abrasive machining is key to obtaining the desired geometry and surface quality in manufacturing. This book discusses the fundamentals and advances in the abrasive machining processes. It provides a complete overview of developing areas in the field.

Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

Directory of Open Access Journals (Sweden)

Nurul Arneida Husin

2012-04-01

Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

76 FR 5832 - International Business Machines (IBM), Software Group Business Unit, Optim Data Studio Tools QA...

Science.gov (United States)

2011-02-02

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,554] International Business Machines (IBM), Software Group Business Unit, Optim Data Studio Tools QA, San Jose, CA; Notice of Affirmative Determination Regarding Application for Reconsideration By application dated November 29, 2010, a worker and a state workforce official...

The Knife Machine. Module 15.

Science.gov (United States)

South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

This module on the knife machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the knife machine (a single needle or multi-needle machine which sews and cuts at the same time). These components are provided: an introduction, directions, an objective,…

Restrictions of process machine retooling at machine-building enterprises

Directory of Open Access Journals (Sweden)

Kuznetsova Elena

2017-01-01

Full Text Available The competitiveness of the national economy depends on the technological level of the machine-building enterprises production equipment. Today in Russia there are objective and subjective restrictions for the optimum policy formation of the manufacturing equipment renewal. The analysis of the manufacturing equipment age structure dynamics in the Russian machine-building complex indicates the negative tendencies intensification: increase in the equipment service life, reduction in the share of up-to-date equipment, and drop in its use efficiency. The article investigates and classifies the main restrictions of the manufacturing equipment renewal process, such as regulatory and legislative, financial, organizational, competency-based. The economic consequences of the revealed restrictions influence on the machine-building enterprises activity are shown.

How State Taxes and Policies Targeting Soda Consumption Modify the Association between School Vending Machines and Student Dietary Behaviors: A Cross-Sectional Analysis

OpenAIRE

Taber, Daniel R.; Chriqui, Jamie F.; Vuillaume, Renee; Chaloupka, Frank J.

2014-01-01

Background: Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods: Data on school vending machine access and student diet we...

On the preparation of fire-wood splitting machines equipped with feeder devices

International Nuclear Information System (INIS)

Jaeaeskelaeinen, M.

1995-01-01

As part of the Bionergy research programme, the TTS Institute's Department of Forestry conducted a study on firewood splitting machines manufactured by Bilke Oy and Peurala Oy. The said machines are the first of their kind in Finland to be equipped with a feeder device. The Bilke unit is fed by a conveyor belt arrangement while the Peurala unit is equipped with a feeder roll. In addition, both machine types are equipped with a conveyor belt discharging device (e.g. for discharging split wood directly into a trailer). The Bilke unit can also be operated on extraction racks in the woods as the stems due to be split are fed into it from the side and the conveyor discharges the split wood rearwards into the trailer. The trailer can be hooked onto the splitting unit's draw-hook. At the landing, the Bilke unit was used to split a total of 14.2 m 3 and the Peurala unit 8.0 m 3 of approx. 3-metrelong stems averaging 9 cm in diameter. In addition to this, the Bilke unit was used to split ca. 17 m 3 of 3.5 metre-long stems with on average diameter of ca. 6 cm; the stems were in bunches alongside the extraction racks. The person operating the machines for the purposes of this study was a 27-year- old forest worker. The mean productivity achieved at the landing when using the Bilke unit was 7.7 m 3 while that of the Peurala unit was 5.8 m 3 per effective hour. When operated in the woods, the productivity achieved with the Bilke unit (excluding felling-cross cutting-delimbing-bunching and forest haulage work) was 2.8 m 3 /h. When applying a productivity of 1.7 m 3 for felling-cross cutting- delimbing-bunching work and 5.5. m 3 for forest haulage, the entire harvesting chain's productivity was 0.9 m 3 per effective hour

Mechanical design of machine components

CERN Document Server

Ugural, Ansel C

2015-01-01

Mechanical Design of Machine Components, Second Edition strikes a balance between theory and application, and prepares students for more advanced study or professional practice. It outlines the basic concepts in the design and analysis of machine elements using traditional methods, based on the principles of mechanics of materials. The text combines the theory needed to gain insight into mechanics with numerical methods in design. It presents real-world engineering applications, and reveals the link between basic mechanics and the specific design of machine components and machines. Divided into three parts, this revised text presents basic background topics, deals with failure prevention in a variety of machine elements and covers applications in design of machine components as well as entire machines. Optional sections treating special and advanced topics are also included.Key Features of the Second Edition:Incorporates material that has been completely updated with new chapters, problems, practical examples...

Soft computing in machine learning

CERN Document Server

Park, Jooyoung; Inoue, Atsushi

2014-01-01

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It...

Applicability of Machine-Learning Enabled LIBS in Post Irradiation Nuclear Forensic Analysis of High Level Waste

International Nuclear Information System (INIS)

Onkongi, J.; Maina, D.; Angeyo, H.K.

2017-01-01

Nuclear Forensics seeks Information to determine; Chemical Composition, Routes of transit, Origin (Provenance) and Intended use. Post Irradiation/Post detonation NF In a post-detonation event could you get clues/signatures from glass debris, minute sample sizes? Nuclear Forensic Technique Should be State-of -the art that is Rapid, Non-invasive, Remote ability and Non-destructive. Laser Induced Breakdown Spectroscopy (LIBS) unlike other Analytic Techniques that require tedious sample preparations such as Dissolution, digestion & matrix removal, which generate additional nuclear wastes that require proper Procedures for handling, storage & ultimate disposal, LIBS overcomes these limitations. Utility of Machine Learning Techniques employed include; Artificial Neural Networks, ANN (Regression/Modelling), Principal component Analysis, PCA (Classification) and Support Vector Machine SVM (Comparative study/Classification Machine Learning coupled with LIBS gives a state of the art analytic method. Utility of the technic in safeguards security and non-proliferation

Exploring example models of cross-sector, sessional employment of pharmacists to improve medication management and pharmacy support in rural hospitals.

Science.gov (United States)

Tan, Amy Cw; Emmerton, Lynne M; Hattingh, Laetitia; La Caze, Adam

2015-01-01

Many rural hospitals in Australia are not large enough to sustain employment of a full-time pharmacist, or are unable to recruit or retain a full-time pharmacist. The absence of a pharmacist may result in hospital nurses undertaking medication-related roles outside their scope of practice. A potential solution to address rural hospitals' medication management needs is contracted part-time ('sessional') employment of a local pharmacist external to the hospital ('cross-sector'). The aim of this study was to explore the roles and experiences of pharmacists in their provision of sessional services to rural hospitals with no on-site pharmacist and explore how these roles could potentially address shortfalls in medication management in rural hospitals. A qualitative study was conducted to explore models with pharmacists who had provided sessional services to a rural hospital. A semi-structured interview guide was informed by a literature review, preliminary research and stakeholder consultation. Participants were recruited via advertisement and personal contacts. Consenting pharmacists were interviewed between August 2012 and January 2013 via telephone or Skype for 40-55 minutes. Thirteen pharmacists with previous or ongoing hospital sessional contracts in rural communities across Australia and New Zealand participated. Most commonly, the pharmacists provided weekly services to rural hospitals. All believed the sessional model was a practical solution to increase hospital access to pharmacist-mediated support and to address medication management gaps. Roles perceived to promote quality use of medicines were inpatient consultation services, medicines information/education to hospital staff, assistance with accreditation matters and system reviews, and input into pharmaceutical distribution activities. This study is the first to explore the concept of sessional rural hospital employment undertaken by pharmacists in Australia and New Zealand. Insights from participants

Mankind, machines and people

Energy Technology Data Exchange (ETDEWEB)

Hugli, A

1984-01-01

The following questions are addressed: is there a difference between machines and men, between human communication and communication with machines. Will we ever reach the point when the dream of artificial intelligence becomes a reality. Will thinking machines be able to replace the human spirit in all its aspects. Social consequences and philosophical aspects are addressed. 8 references.

Ergonomic requirements for the operation of machines and technical equipment

Directory of Open Access Journals (Sweden)

Górny Adam

2017-01-01

Full Text Available In order to operate machinery and equipment safely, it is critical for the solutions in place to conform to design-related and operating requirements. Design-related requirements are primarily the responsibility of machine designers/developers and manufacturers. Operating requirements should be satisfied by employers, who are responsible for ensuring safe working conditions for their employees. Under applicable laws, machinery and equipment should be designed, produced and then operated without placing excessive loads on workers and in keeping with machine functionality and intended use. One should also ensure that machinery and equipment can be maintained and adjusted without exposing their operators to hazards. Ergonomic criteria are an integral part of such requirements. They ensure that human users and operators of technical equipment are enabled to function properly. Design-related requirements are viewed as a priority safety consideration. While they facilitate the use of technical tools, the actual safety of employees ultimately depends on the satisfaction of specific requirements during operation.

A Concrete Framework for Environment Machines

DEFF Research Database (Denmark)

Biernacka, Malgorzata; Danvy, Olivier

2007-01-01

calculus with explicit substitutions), we extend it minimally so that it can also express one-step reduction strategies, and we methodically derive a series of environment machines from the specification of two one-step reduction strategies for the lambda-calculus: normal order and applicative order....... The derivation extends Danvy and Nielsen’s refocusing-based construction of abstract machines with two new steps: one for coalescing two successive transitions into one, and the other for unfolding a closure into a term and an environment in the resulting abstract machine. The resulting environment machines...... include both the Krivine machine and the original version of Krivine’s machine, Felleisen et al.’s CEK machine, and Leroy’s Zinc abstract machine....

Graphene-based bimorphs for micron-sized, autonomous origami machines.

Science.gov (United States)

Miskin, Marc Z; Dorsey, Kyle J; Bircan, Baris; Han, Yimo; Muller, David A; McEuen, Paul L; Cohen, Itai

2018-01-16

Origami-inspired fabrication presents an attractive platform for miniaturizing machines: thinner layers of folding material lead to smaller devices, provided that key functional aspects, such as conductivity, stiffness, and flexibility, are persevered. Here, we show origami fabrication at its ultimate limit by using 2D atomic membranes as a folding material. As a prototype, we bond graphene sheets to nanometer-thick layers of glass to make ultrathin bimorph actuators that bend to micrometer radii of curvature in response to small strain differentials. These strains are two orders of magnitude lower than the fracture threshold for the device, thus maintaining conductivity across the structure. By patterning 2-[Formula: see text]m-thick rigid panels on top of bimorphs, we localize bending to the unpatterned regions to produce folds. Although the graphene bimorphs are only nanometers thick, they can lift these panels, the weight equivalent of a 500-nm-thick silicon chip. Using panels and bimorphs, we can scale down existing origami patterns to produce a wide range of machines. These machines change shape in fractions of a second when crossing a tunable pH threshold, showing that they sense their environments, respond, and perform useful functions on time and length scales comparable with microscale biological organisms. With the incorporation of electronic, photonic, and chemical payloads, these basic elements will become a powerful platform for robotics at the micrometer scale.

Application of a Fault Detection and Isolation System on a Rotary Machine

Directory of Open Access Journals (Sweden)

Silvia M. Zanoli

2013-01-01

Full Text Available The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA, has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

Findings From the National Machine Guarding Program-A Small Business Intervention: Machine Safety.

Science.gov (United States)

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

2016-09-01

The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 to 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of Occupational Safety and Health Administration (OSHA) citations and may result in serious traumatic injury. Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits, and a 12-month follow-up evaluation. The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10% point increase in business-level machine scores (P increase in LOTO program scores (P < 0.0001). Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees.

Variation in access to sugar-sweetened beverages in vending machines across rural, town and urban high schools.

Science.gov (United States)

Adachi-Mejia, A M; Longacre, M R; Skatrud-Mickelson, M; Li, Z; Purvis, L A; Titus, L J; Beach, M L; Dalton, M A

2013-05-01

The 2010 Dietary Guidelines for Americans include reducing consumption of sugar-sweetened beverages. Among the many possible routes of access for youth, school vending machines provide ready availability of sugar-sweetened beverages. The purpose of this study was to determine variation in high school student access to sugar-sweetened beverages through vending machines by geographic location - urban, town or rural - and to offer an approach for analysing school vending machine content. Cross-sectional observational study. Between October 2007 and May 2008, trained coders recorded beverage vending machine content and machine-front advertising in 113 machines across 26 schools in New Hampshire and Vermont, USA. Compared with town schools, urban schools were significantly less likely to offer sugar-sweetened beverages (P = 0.002). Rural schools also offered more sugar-sweetened beverages than urban schools, but this difference was not significant. Advertisements for sugar-sweetened beverages were highly prevalent in town schools. High school students have ready access to sugar-sweetened beverages through their school vending machines. Town schools offer the highest risk of exposure; school vending machines located in towns offer up to twice as much access to sugar-sweetened beverages in both content and advertising compared with urban locations. Variation by geographic region suggests that healthier environments are possible and some schools can lead as inspirational role models. Copyright © 2013 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Quantitative forecasting of PTSD from early trauma responses: A Machine Learning application

DEFF Research Database (Denmark)

Galatzer-Levy, I. R.; Karstoft, K. I.; Statnikov, A.

2014-01-01

-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics...... algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder......). The feature selection algorithm identified 16 predictors, present in >= 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC...

Research on the proficient machine system. Theoretical part; Jukutatsu machine system no chosa kenkyu. Rironhen

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-03-01

The basic theory of the proficient machine system to be developed was studied. Important proficient techniques in manufacturing industries are becoming extinct because of insufficient succession to next generation. The proficient machine system was proposed to cope with such situation. This machine system includes the mechanism for progress and evolution of techniques and sensibilities to be adaptable to environmental changes by learning and recognizing various motions such as work and process. Consequently, the basic research fields are composed of thought, learning, perception and action. This machine requires not only deigned fixed functions but also introduction of the same proficient concept as human being to be adaptable to changes in situation, purpose, time and machine`s complexity. This report explains in detail the basic concept, system principle, approaching procedure and practical elemental technologies of the proficient machine system, and also describes the future prospect. 133 refs., 110 figs., 7 tabs.

Machining of uranium and uranium alloys

International Nuclear Information System (INIS)

Morris, T.O.

1981-01-01

Uranium and uranium alloys can be readily machined by conventional methods in the standard machine shop when proper safety and operating techniques are used. Material properties that affect machining processes and recommended machining parameters are discussed. Safety procedures and precautions necessary in machining uranium and uranium alloys are also covered. 30 figures

Transient characteristics of current lead losses for the large scale high-temperature superconducting rotating machine

International Nuclear Information System (INIS)

Le, T. D.; Kim, J. H.; Park, S. I.; Kim, D. J.; Kim, H. M.; Lee, H. G.; Yoon, Y. S.; Jo, Y. S.; Yoon, K. Y.

2014-01-01

To minimize most heat loss of current lead for high-temperature superconducting (HTS) rotating machine, the choice of conductor properties and lead geometry - such as length, cross section, and cooling surface area - are one of the various significant factors must be selected. Therefore, an optimal lead for large scale of HTS rotating machine has presented before. Not let up with these trends, this paper continues to improve of diminishing heat loss for HTS part according to different model. It also determines the simplification conditions for an evaluation of the main flux flow loss and eddy current loss transient characteristics during charging and discharging period.

[Comparison of machinability of two types of dental machinable ceramic].

Science.gov (United States)

Fu, Qiang; Zhao, Yunfeng; Li, Yong; Fan, Xinping; Li, Yan; Lin, Xuefeng

2002-11-01

In terms of the problems of now available dental machinable ceramics, a new type of calcium-mica glass-ceramic, PMC-I ceramic, was developed, and its machinability was compared with that of Vita MKII quantitatively. Moreover, the relationship between the strength and the machinability of PMC-I ceramic was studied. Samples of PMC-I ceramic were divided into four groups according to their nucleation procedures. 600-seconds drilling tests were conducted with high-speed steel tools (Phi = 2.3 mm) to measure the drilling depths of Vita MKII ceramic and PMC-I ceramic, while constant drilling speed of 600 rpm and constant axial load of 39.2 N were used. And the 3-point bending strength of the four groups of PMC-I ceramic were recorded. Drilling depth of Vita MKII was 0.71 mm, while the depths of the four groups of PMC-I ceramic were 0.88 mm, 1.40 mm, 0.40 mm and 0.90 mm, respectively. Group B of PMC-I ceramic showed the largest depth of 1.40 mm and was statistically different from other groups and Vita MKII. And the strength of the four groups of PMC-I ceramic were 137.7, 210.2, 118.0 and 106.0 MPa, respectively. The machinability of the new developed dental machinable ceramic of PMC-I could meet the need of the clinic.

Social class, employment status and inequality in psychological well-being in the UK: Cross-sectional and fixed effects analyses over two decades.

Science.gov (United States)

Richards, Lindsay; Paskov, Marii

2016-10-01

A body of academic research has shown a social class gradient in psychological well-being. Some recent work has also suggested that the gradient is worsening over time, though the evidence is mixed. We focus on two straightforward research questions: Is there a class gradient in mental health? Has this gradient changed over time? We answer these questions with attention to two specific causal pathways: employment status and unobserved heterogeneity. We use two data sources: repeated cross-sections from the Health Survey of England (HSE) and longitudinal data from the British Household Panel Survey (BHPS). The combination of pooled OLS regression (with HSE) and fixed effects analysis (with BHPS) allows for a robust analysis of the relationship between class and psychological well-being. We argue that employment status is a confounder in the analysis of class inequalities and show that, along with unobserved heterogeneity, these two pathways go a long way to explain the class gradient. The effects of employment status are substantive and, unlike social class, cannot be explained away by unobserved heterogeneity. We conclude that employment status deserves greater prominence in the debate as both a pathway by which the class gradient transpires, and as another 'dimension' of inequality in its own right. Our overtime analysis suggests that skilled and unskilled manual workers had higher psychological well-being in the 1990s but by 2008 were closer to the average. Class inequalities do not appear to be widening. Copyright © 2016 Elsevier Ltd. All rights reserved.

High productivity machining of holes in Inconel 718 with SiAlON tools

Science.gov (United States)

Agirreurreta, Aitor Arruti; Pelegay, Jose Angel; Arrazola, Pedro Jose; Ørskov, Klaus Bonde

2016-10-01

Inconel 718 is often employed in aerospace engines and power generation turbines. Numerous researches have proven the enhanced productivity when turning with ceramic tools compared to carbide ones, however there is considerably less information with regard to milling. Moreover, no knowledge has been published about machining holes with this type of tools. Additional research on different machining techniques, like for instance circular ramping, is critical to expand the productivity improvements that ceramics can offer. In this a 3D model of the machining and a number of experiments with SiAlON round inserts have been carried out in order to evaluate the effect of the cutting speed and pitch on the tool wear and chip generation. The results of this analysis show that three different types of chips are generated and also that there are three potential wear zones. Top slice wear is identified as the most critical wear type followed by the notch wear as a secondary wear mechanism. Flank wear and adhesion are also found in most of the tests.

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

OpenAIRE

Ming Cheng; Le Sun; Giuseppe Buja; Lihua Song

2015-01-01

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 perm...

Nutritional Status and Effect of Maternal Employment among Children Aged 6-59 Months in Wolayta Sodo Town, Southern Ethiopia: A Cross-sectional Study.

Science.gov (United States)

Eshete, Hiwot; Abebe, Yewelsew; Loha, Eskindir; Gebru, Teklemichael; Tesheme, Tesfalem

2017-03-01

Childhood malnutrition remains common in many parts of the world; the magnitude of worldwide stunting, underweight and wasting in children under five years of age were 24.7 %, 15.1 % and 7.8 %, respectively. More than 150 million children under the age of five years in the developing world are malnourished. Ethiopia is one of the countries in sub-Saharan Africa with the highest rates of malnutrition. In Ethiopia, 44.4% and 9.7% of children under-five years old were stunted and wasted, respectively. This study was aimed to assess nutritional status and effect of maternal employment among children aged 6-59 months. A cross-sectional study was conducted in Wolayta Sodo Town, Southern Ethiopia. Socio-demographic characteristics, child feeding and healthcare seeking practice of mothers, and child's anthropometric status were assessed. Probability proportional to size sampling approach was used to select a sample of 316 mothers having children aged 6-59 months. The study was ethically approved by Institutional Review Board of Health Science College, Hawasa University. The overall result revealed that the prevalence of stunting was 22.2%, of which 21.8% and 22.6% were in children of employed and unemployed mothers, respectively. Low-weight-for age was 10.8% for children of employed mothers and 13.4% for children of unemployed mothers. Wasting was 8.8% and 10.8% for children of employed and unemployed mothers, respectively. There was no statistically significant association between maternal employment and nutritional status of their children. However, chronic malnutrition (stunting) was influenced by being educated mother (OR: 0.37) child age group of 24-59 months (OR: 0.36) and households' fifth wealth quintile (OR: 0.28). Low prevalence of stunting was observed. Stunting is a public health concern in the study area. Furthermore, stunting is significantly influenced by mothers' education, household wealth and child age. However, maternal employment was not statistically

Normal mammogram detection based on local probability difference transforms and support vector machines

International Nuclear Information System (INIS)

Chiracharit, W.; Kumhom, P.; Chamnongthai, K.; Sun, Y.; Delp, E.J.; Babbs, C.F

2007-01-01

Automatic detection of normal mammograms, as a ''first look'' for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable ''crossed-distributions'' in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity. (author)

Experiments with the Dragon Machine

International Nuclear Information System (INIS)

Malenfant, R.E.

2005-01-01

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

Non linear seismic analysis of charge/discharge machine

International Nuclear Information System (INIS)

Dostal, M.; Trbojevic, V.M.; Nobile, M.

1987-01-01

The main conclusions of the seismic analysis of the Latina CDM are: i. The charge machine has been demonstrated to be capable of withstanding the effects of a 0.1 g earthquake. Stresses and displacements were all within allowable limits and the stability criteria were fully satisfied for all positions of the cross-travel bogie on the gantry. ii. Movements due to loss of friction between the cross-travel bogie wheels and the rail was found to be small, i.e. less than 2 mm for all cases considered. The modes of rocking of the fixed and hinged legs preclude any possibility of excessive movement between the long travel bogie wheels and the rail. iii. The non-linear analysis incorporating contact and friction has given more realistic results than any of the linear verification analyses. The method of analysis indicates that even the larger structures can be efficiently solved on a mini computer for a long forcing input (16 s). (orig.)

Findings from the National Machine Guarding Program–A Small Business Intervention: Machine Safety

Science.gov (United States)

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

2016-01-01

Objectives The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 – 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of OSHA citations and may result in serious traumatic injury. Methods Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits and a twelve-month follow-up evaluation. Results The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10-percentage point increase in business-level machine scores (p< 0.0001) and a 33-percentage point increase in LOTO program scores (p <0.0001). Conclusions Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees. PMID:26716850

Job Market Polarization and Employment Protection in Europe

DEFF Research Database (Denmark)

Pertold-Gebicka, Barbara

Although much attention has been paid to the polarization of national labor markets, with employment and wage growth occurring in both low- and high- but not middle-skill occupations, there is little consistent evidence on cross-country dierences in this process. I analyze job polarization in 12...

Electronic gaming machines and gambling disorder: A cross-cultural comparison between treatment-seeking subjects from Brazil and the United States.

Science.gov (United States)

Medeiros, Gustavo C; Leppink, Eric W; Yaemi, Ana; Mariani, Mirella; Tavares, Hermano; Grant, Jon E

2015-12-15

The objective of this paper is to perform a cross-cultural comparison of gambling disorder (GD) due to electronic gaming machines (EGM), a form of gambling that may have a high addictive potential. Our goal is to investigate two treatment-seeking samples of adults collected in Brazil and the United States, countries with different socio-cultural backgrounds. This comparison may lead to a better understanding of cultural influences on GD. The total studied sample involved 733 treatment-seeking subjects: 353 men and 380 women (average age=45.80, standard deviation ±10.9). The Brazilian sample had 517 individuals and the American sample 216. Subjects were recruited by analogous strategies. We found that the Brazilian sample was younger, predominantly male, less likely to be Caucasian, more likely to be partnered, tended to have a faster progression from recreational gambling to GD, and were more likely to endorse chasing losses. This study demonstrated that there are significant differences between treatment-seeking samples of adults presenting GD due to EGM in Brazil and in the United States. These findings suggest that cultural aspects may have a relevant role in GD due to EGM. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Machine Tool Software

Science.gov (United States)

1988-01-01

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

Machine Ethics: Creating an Ethical Intelligent Agent