Which, When, and How: Hierarchical Clustering with Human–Machine Cooperation

Directory of Open Access Journals (Sweden)

Huanyang Zheng

2016-12-01

Full Text Available Human–Machine Cooperations (HMCs can balance the advantages and disadvantages of human computation (accurate but costly and machine computation (cheap but inaccurate. This paper studies HMCs in agglomerative hierarchical clusterings, where the machine can ask the human some questions. The human will return the answers to the machine, and the machine will use these answers to correct errors in its current clustering results. We are interested in the machine’s strategy on handling the question operations, in terms of three problems: (1 Which question should the machine ask? (2 When should the machine ask the question (early or late? (3 How does the machine adjust the clustering result, if the machine’s mistake is found by the human? Based on the insights of these problems, an efficient algorithm is proposed with five implementation variations. Experiments on image clusterings show that the proposed algorithm can improve the clustering accuracy with few question operations.

Four Questions

Science.gov (United States)

Hark-Weber, Amara G., Ed.

2013-01-01

The author is pleased to introduce a new section in "TAJ," Four Questions. The structure is simple: four questions are asked to teaching artists working in various media and locations. The questions are always the same, but because each teaching artist's approach is unique, their answers will provide an insight into particular methodologies that…

Machine learning in virtual screening.

Science.gov (United States)

Melville, James L; Burke, Edmund K; Hirst, Jonathan D

2009-05-01

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

Machining dynamics fundamentals, applications and practices

CERN Document Server

Cheng, Kai

2008-01-01

Machining dynamics are vital to the performance of machine tools and machining processes in manufacturing. This book discusses the state-of-the-art applications, practices and research in machining dynamics. It presents basic theory, analysis and control methodology. It is useful for manufacturing engineers, supervisors, engineers and designers.

On The Subject of Thinking Machines

OpenAIRE

Olafenwa , John ,; Olafenwa , Moses

2018-01-01

An investigation of the concepts of thoughts, imagination and consciousness in learning machines.; 68 years ago, Alan Turing proposed the question "Can Machines Think" in his seminal paper [1] titled "Computing Machinery and Intelligence" and he formulated the "Imitation Game" also known as the Turing test as a way to answer this question without referring to a rather ambiguous dictionary definition of the word "Think" We have come a long way to building intelligent machines, in fact, the rat...

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.

Machine Learning and Conflict Prediction: A Use Case

Directory of Open Access Journals (Sweden)

Chris Perry

2013-10-01

Full Text Available For at least the last two decades, the international community in general and the United Nations specifically have attempted to develop robust, accurate and effective conflict early warning system for conflict prevention. One potential and promising component of integrated early warning systems lies in the field of machine learning. This paper aims at giving conflict analysis a basic understanding of machine learning methodology as well as to test the feasibility and added value of such an approach. The paper finds that the selection of appropriate machine learning methodologies can offer substantial improvements in accuracy and performance. It also finds that even at this early stage in testing machine learning on conflict prediction, full models offer more predictive power than simply using a prior outbreak of violence as the leading indicator of current violence. This suggests that a refined data selection methodology combined with strategic use of machine learning algorithms could indeed offer a significant addition to the early warning toolkit. Finally, the paper suggests a number of steps moving forward to improve upon this initial test methodology.

ChargeOut! : discounted cash flow compared with traditional machine-rate analysis

Science.gov (United States)

Ted Bilek

2008-01-01

ChargeOut!, a discounted cash-flow methodology in spreadsheet format for analyzing machine costs, is compared with traditional machine-rate methodologies. Four machine-rate models are compared and a common data set representative of logging skidders’ costs is used to illustrate the differences between ChargeOut! and the machine-rate methods. The study found that the...

Machine cost analysis using the traditional machine-rate method and ChargeOut!

Science.gov (United States)

E. M. (Ted) Bilek

2009-01-01

Forestry operations require ever more use of expensive capital equipment. Mechanization is frequently necessary to perform cost-effective and safe operations. Increased capital should mean more sophisticated capital costing methodologies. However the machine rate method, which is the costing methodology most frequently used, dates back to 1942. CHARGEOUT!, a recently...

Instance-Based Question Answering

Science.gov (United States)

2006-12-01

cluster-based query expan- sion, learning answering strategies, machine learning in NLP To my wife Monica Abstract During recent years, question...process is typically tedious and involves expertise in crafting and implement- ing these models (e.g. rule-based), utilizing NLP resources, and...questions. For languages that use capitalization (e.g. not Chinese or Arabic ) for named entities, IBQA can make use of NE classing (e.g. “Bob Marley

Question popularity analysis and prediction in community question answering services.

Science.gov (United States)

Liu, Ting; Zhang, Wei-Nan; Cao, Liujuan; Zhang, Yu

2014-01-01

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users' interest so as to improve the users' experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository.

Methodology for testing a system for remote monitoring and control on auxiliary machines in electric vehicles

Directory of Open Access Journals (Sweden)

Dimitrov Vasil

2017-01-01

Full Text Available A laboratory system for remote monitoring and control of an asynchronous motor controlled by a soft starter and contemporary measuring and control devices has been developed and built. This laboratory system is used for research and in teaching. A study of the principles of operation, setting up and examination of intelligent energy meters, soft starters and PLC has been made as knowledge of the relevant software products is necessary. This is of great importance because systems for remote monitoring and control of energy consumption, efficiency and proper operation of the controlled objects are very often used in different spheres of industry, in building automation, transport, electricity distribution network, etc. Their implementation in electric vehicles for remote monitoring and control on auxiliary machines is also possible and very useful. In this paper, a methodology of tests is developed and some experiments are presented. Thus, an experimental verification of the developed methodology is made.

Hybrid methodological approach to context-dependent speech recognition

Directory of Open Access Journals (Sweden)

Dragiša Mišković

2017-01-01

Full Text Available Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

A reliability-based preventive maintenance methodology for the projection spot welding machine

Directory of Open Access Journals (Sweden)

Fayzimatov Ulugbek

2018-06-01

Full Text Available An effective operations of a projection spot welding (PSW machine is closely related to the effec-tiveness of the maintenance. Timely maintenance can prevent failures and improve reliability and maintainability of the machine. Therefore, establishing the maintenance frequency for the welding machine is one of the most important tasks for plant engineers. In this regard, reliability analysis of the welding machine can be used to establish preventive maintenance intervals (PMI and to identify the critical parts of the system. In this reliability and maintainability study, analysis of the PSW machine was carried out. The failure and repair data for analysis were obtained from automobile manufacturing company located in Uzbekistan. The machine was divided into three main sub-systems: electrical, pneumatic and hydraulic. Different distributions functions for all sub-systems was tested and their parameters tabulated. Based on estimated parameters of the analyzed distribu-tions, PMI for the PSW machines sub-systems at different reliability levels was calculated. Finally, preventive measures for enhancing the reliability of the PSW machine sub-systems are suggested.

A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

Science.gov (United States)

Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

2017-03-01

Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A fast hybrid methodology based on machine learning, quantum methods, and experimental measurements for evaluating material properties

Science.gov (United States)

Kong, Chang Sun; Haverty, Michael; Simka, Harsono; Shankar, Sadasivan; Rajan, Krishna

2017-09-01

We present a hybrid approach based on both machine learning and targeted ab-initio calculations to determine adhesion energies between dissimilar materials. The goals of this approach are to complement experimental and/or all ab-initio computational efforts, to identify promising materials rapidly and identify in a quantitative manner the relative contributions of the different material attributes affecting adhesion. Applications of the methodology to predict bulk modulus, yield strength, adhesion and wetting properties of copper (Cu) with other materials including metals, nitrides and oxides is discussed in this paper. In the machine learning component of this methodology, the parameters that were chosen can be roughly divided into four types: atomic and crystalline parameters (which are related to specific elements such as electronegativities, electron densities in Wigner-Seitz cells); bulk material properties (e.g. melting point), mechanical properties (e.g. modulus) and those representing atomic characteristics in ab-initio formalisms (e.g. pseudopotentials). The atomic parameters are defined over one dataset to determine property correlation with published experimental data. We then develop a semi-empirical model across multiple datasets to predict adhesion in material interfaces outside the original datasets. Since adhesion is between two materials, we appropriately use parameters which indicate differences between the elements that comprise the materials. These semi-empirical predictions agree reasonably with the trend in chemical work of adhesion predicted using ab-initio techniques and are used for fast materials screening. For the screened candidates, the ab-initio modeling component provides fundamental understanding of the chemical interactions at the interface, and explains the wetting thermodynamics of thin Cu layers on various substrates. Comparison against ultra-high vacuum (UHV) experiments for well-characterized Cu/Ta and Cu/α-Al2O3 interfaces is

Storytelling machines for video search

NARCIS (Netherlands)

Habibian, A.

2016-01-01

We study a fundamental question for developing storytelling machines: what vocabulary is suited for machines to tell the story of a video? We start by manually specifying the vocabulary concepts and their annotations. In order to effectively handcraft the vocabulary, we empirically study what are

Optimization of pocket machining strategy in HSM

OpenAIRE

Msaddek, El Bechir; Bouaziz, Zoubeir; Dessein, Gilles; Baili, Maher

2012-01-01

International audience; Our two major concerns, which should be taken into consideration as soon as we start the selecting the machining parameters, are the minimization of the machining time and the maintaining of the high-speed machining machine in good state. The manufacturing strategy is one of the parameters which practically influences the time of the different geometrical forms manufacturing, as well as the machine itself. In this article, we propose an optimization methodology of the ...

Using PICO Methodology to Answer Questions About Smoking in COPD Patients.

Science.gov (United States)

Jiménez Ruiz, Carlos A; Buljubasich, Daniel; Riesco Miranda, Juan Antonio; Acuña Izcaray, Agustín; de Granda Orive, José Ignacio; Chatkin, José Miguel; Zabert, Gustavo; Guerreros Benavides, Alfredo; Paez Espinel, Nelson; Noé, Valeri; Sánchez-Angarita, Efraín; Núñez-Sánchez, Ingrid; Sansores, Raúl H; Casas, Alejandro; Palomar Lever, Andrés; Alfageme Michavila, Inmaculada

2017-11-01

The ALAT and SEPAR Treatment and Control of Smoking Groups have collaborated in the preparation of this document which attempts to answer, by way of PICO methodology, different questions on health interventions for helping COPD patients to stop smoking. The main recommendations are: (i)moderate-quality evidence and strong recommendation for performing spirometry in COPD patients and in smokers with a high risk of developing the disease, as a motivational tool (particularly for showing evidence of lung age), a diagnostic tool, and for active case-finding; (ii)high-quality evidence and strong recommendation for using intensive dedicated behavioral counselling and drug treatment for helping COPD patients to stop smoking; (iii)high-quality evidence and strong recommendation for initiating interventions for helping COPD patients to stop smoking during hospitalization with improvement when the intervention is prolonged after discharge, and (iv)high-quality evidence and strong recommendation for funding treatment of smoking in COPD patients, in view of the impact on health and health economics. Copyright © 2017 SEPAR. Publicado por Elsevier España, S.L.U. All rights reserved.

Studying depression using imaging and machine learning methods.

Science.gov (United States)

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

2016-01-01

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

Biology Question Generation from a Semantic Network

Science.gov (United States)

Zhang, Lishan

Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students' learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student's current competence so that a suitable question could be selected based on the student's previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from

Machine translation with minimal reliance on parallel resources

CERN Document Server

Tambouratzis, George; Sofianopoulos, Sokratis

2017-01-01

This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.

Clustering Categories in Support Vector Machines

DEFF Research Database (Denmark)

Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

2017-01-01

The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

Studying depression using imaging and machine learning methods

Directory of Open Access Journals (Sweden)

Meenal J. Patel

2016-01-01

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

Application of response surface methodology on investigating flank wear in machining hardened steel using PVD TiN coated mixed ceramic insert

Directory of Open Access Journals (Sweden)

Ashok Kumar Sahoo

2013-10-01

Full Text Available The paper presents the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment. The paper also investigates the effect of process parameter on flank wear (VBc. The experiments have been conducted using three level full factorial design techniques. The machinability model has been developed in terms of cutting speed (v, feed (f and machining time (t as input variable using response surface methodology. The adequacy of model has been checked using correlation coefficients. As the determination coefficient, R2 (98% is higher for the model developed; the better is the response model fits the actual data. In addition, residuals of the normal probability plot lie reasonably close to a straight line showing that the terms mentioned in the model are statistically significant. The predicted flank wear has been found to lie close to the experimental value. This indicates that the developed model can be effectively used to predict the flank wear in the hard turning. Abrasion and diffusion has been found to be the dominant wear mechanism in machining hardened steel from SEM micrographs at highest parametric range. Machining time has been found to be the most significant parameter on flank wear followed by cutting speed and feed as observed from main effect plot and ANOVA study.

A methodology for string resolution

International Nuclear Information System (INIS)

Karonis, N.T.

1992-11-01

In this paper we present a methodology, not a tool. We present this methodology with the intent that it be adopted, on a case by case basis, by each of the existing tools in EPICS. In presenting this methodology, we describe each of its two components in detail and conclude with an example depicting how the methodology can be used across a pair of tools. The task of any control system is to provide access to the various components of the machine being controlled, for example, the Advanced Photon Source (APS). By access, we mean the ability to monitor the machine's status (reading) as well as the ability to explicitly change its status (writing). The Experimental Physics and Industrial Control System (EPICS) is a set of tools, designed to act in concert, that allows one to construct a control system. EPICS provides the ability to construct a control system that allows reading and writing access to the machine. It does this through the notion of databases. Each of the components of the APS that is accessed by the control system is represented in EPICS by a set of named database records. Once this abstraction is made, from physical device to named database records, the process of monitoring and changing the state of that device becomes the simple process of reading and writing information from and to its associated named records

Research on cylindrical indexing cam’s unilateral machining

Directory of Open Access Journals (Sweden)

Junhua Chen

2015-08-01

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

Man-machine dialogue design and challenges

CERN Document Server

Landragin, Frederic

2013-01-01

This book summarizes the main problems posed by the design of a man-machine dialogue system and offers ideas on how to continue along the path towards efficient, realistic and fluid communication between humans and machines. A culmination of ten years of research, it is based on the author's development, investigation and experimentation covering a multitude of fields, including artificial intelligence, automated language processing, man-machine interfaces and notably multimodal or multimedia interfaces. Contents Part 1. Historical and Methodological Landmarks 1. An Assessment of the Evolution

Molecular machines with bio-inspired mechanisms.

Science.gov (United States)

Zhang, Liang; Marcos, Vanesa; Leigh, David A

2018-02-26

The widespread use of molecular-level motion in key natural processes suggests that great rewards could come from bridging the gap between the present generation of synthetic molecular machines-which by and large function as switches-and the machines of the macroscopic world, which utilize the synchronized behavior of integrated components to perform more sophisticated tasks than is possible with any individual switch. Should we try to make molecular machines of greater complexity by trying to mimic machines from the macroscopic world or instead apply unfamiliar (and no doubt have to discover or invent currently unknown) mechanisms utilized by biological machines? Here we try to answer that question by exploring some of the advances made to date using bio-inspired machine mechanisms.

The methodology of man-machine systems

International Nuclear Information System (INIS)

Hollnagel, E.

1981-10-01

This paper provides an elementary discussion of the problems of verification and validation in the context of the empirical evaluation of designs for man-machine systems. After a definition of the basic terms, a breakdown of the major parts of the process of evaluation is given, with the purpose of indicating where problems may occur. This is followed by a discussion of verification and validation, as two distinct concepts. Finally, some of the practical problems of ascertaining validity are discussed. The general conclusion is that rather than rely blindly on a well-established procedure or rule, one should pay attention to the meaningfulness of the aspects which are selected for observation, and the degree of systematism of the methods of observation and analysis. A qualitative approach is thus seen as complementary to a quantitative approach, rather than antithetical to it. (author)

Ergonomics for enhancing detection of machine abnormalities.

Science.gov (United States)

Illankoon, Prasanna; Abeysekera, John; Singh, Sarbjeet

2016-10-17

Detecting abnormal machine conditions is of great importance in an autonomous maintenance environment. Ergonomic aspects can be invaluable when detection of machine abnormalities using human senses is examined. This research outlines the ergonomic issues involved in detecting machine abnormalities and suggests how ergonomics would improve such detections. Cognitive Task Analysis was performed in a plant in Sri Lanka where Total Productive Maintenance is being implemented to identify sensory types that would be used to detect machine abnormalities and relevant Ergonomic characteristics. As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.

Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis.

Science.gov (United States)

Venkatesh, Santosh S; Levenback, Benjamin J; Sultan, Laith R; Bouzghar, Ghizlane; Sehgal, Chandra M

2015-12-01

The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods. AdaBoost also improved the diagnostic performance of the individual human observers and increased the agreement between their analyses. Pairing AdaBoost with selective pruning is a principled methodology for achieving high diagnostic performance without the added cost of an additional reader for differentiating solid breast masses by ultrasound. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Machine shop basics

CERN Document Server

Miller, Rex

2004-01-01

Use the right tool the right wayHere, fully updated to include new machines and electronic/digital controls, is the ultimate guide to basic machine shop equipment and how to use it. Whether you're a professional machinist, an apprentice, a trade student, or a handy homeowner, this fully illustrated volume helps you define tools and use them properly and safely. It's packed with review questions for students, and loaded with answers you need on the job.Mark Richard Miller is a Professor and Chairman of the Industrial Technology Department at Texas A&M University in Kingsville, T

Abstract quantum computing machines and quantum computational logics

Science.gov (United States)

Chiara, Maria Luisa Dalla; Giuntini, Roberto; Sergioli, Giuseppe; Leporini, Roberto

2016-06-01

Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.

USING OF OBJECT-ORIENTED DESIGN PRINCIPLES IN ELECTRIC MACHINES DEVELOPMENT

Directory of Open Access Journals (Sweden)

N.N. Zablodskii

2016-03-01

Full Text Available Purpose. To develop the theoretical basis of electrical machines object-oriented design, mathematical models and software to improve their design synthesis, analysis and optimization. Methodology. We have applied object-oriented design theory in electric machines optimal design and mathematical modelling of electromagnetic transients and electromagnetic field distribution. We have correlated the simulated results with the experimental data obtained by means of the double-stator screw dryer with an external solid rotor, brushless turbo-generator exciter and induction motor with squirrel cage rotor. Results. We have developed object-oriented design methodology, transient mathematical modelling and electromagnetic field equations templates for cylindrical electrical machines, improved and remade Cartesian product and genetic optimization algorithms. This allows to develop electrical machines classifications models, included not only structure development but also parallel synthesis of mathematical models and design software, to improve electric machines efficiency and technical performance. Originality. For the first time, we have applied a new way of design and modelling of electrical machines, which is based on the basic concepts of the object-oriented analysis. For the first time is suggested to use a single class template for structural and system organization of electrical machines, invariant to their specific variety. Practical value. We have manufactured screw dryer for coil dust drying and mixing based on the performed object-oriented theory. We have developed object-oriented software for design and optimization of induction motor with squirrel cage rotor of AIR series and brushless turbo-generator exciter. The experimental studies have confirmed the adequacy of the developed object-oriented design methodology.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

Science.gov (United States)

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

2018-04-01

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

8 Questions About the Conscious Mind

NARCIS (Netherlands)

Dooremalen, A.J.P.W.

Can the mind function separately from the brain? Can machines have conscious minds? Is Google Maps part of the conscious mind? Hans Dooremalen provides answers to these three and five other questions about the conscious mind in an easy to read introduction to the philosophy of mind.

A level set methodology for predicting the effect of mask wear on surface evolution of features in abrasive jet micro-machining

International Nuclear Information System (INIS)

Burzynski, T; Papini, M

2012-01-01

A previous implementation of narrow-band level set methodology developed by the authors was extended to allow for the modelling of mask erosive wear in abrasive jet micro-machining (AJM). The model permits the prediction of the surface evolution of both the mask and the target simultaneously, by representing them as a hybrid and continuous mask–target surface. The model also accounts for the change in abrasive mass flux incident to both the target surface and, for the first time, the eroding mask edge, that is brought about by the presence of the mask edge itself. The predictions of the channel surface and eroded mask profiles were compared with measurements on channels machined in both glass and poly-methyl-methacrylate (PMMA) targets at both normal and oblique incidence, using tempered steel and elastomeric masks. A much better agreement between the predicted and measured profiles was found when mask wear was taken into account. Mask wear generally resulted in wider and deeper glass target profiles and wider PMMA target profiles, respectively, when compared to cases where no mask wear was present. This work has important implications for the AJM of complex MEMS and microfluidic devices that require longer machining times. (paper)

DESIGN METHODOLOGY OF SELF-EXCITED ASYNCHRONOUS GENERATOR

Directory of Open Access Journals (Sweden)

Berzan V.

2012-04-01

Full Text Available The paper sets out the methodology of designing an asynchronous generator with capacitive self-excitation. It is known that its design is possible on the basis of serial synchronous motor with squirrel cage rotor. With this approach, the design reworked only the stator winding of electrical machines, making it cost-effectively implement the creation of the generator. Therefore, the methodology for the design, optimization calculations, the development scheme and the stator winding excitation system gain, not only of practical interest, and may also be useful for specialists in the field of electrical machines in the design of asynchronous generators.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

Science.gov (United States)

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Ontologies and adaptivity in dialogue for question answering

CERN Document Server

Sonntag, D

2010-01-01

Question answering (QA) has become one of the fastest growing topics in computational linguistics and information access. To advance research in the area of dialogue-based question answering, we propose a combination of methods from different scientific fields (i.e., Information Retrieval, Dialogue Systems, Semantic Web, and Machine Learning). This book sheds light on adaptable dialogue-based question answering. We demonstrate the technical and computational feasibility of the proposed ideas, the introspective methods in particular, by beginning with an extensive introduction to the dialogical

Challenges for coexistence of machine to machine and human to human applications in mobile network

DEFF Research Database (Denmark)

Sanyal, R.; Cianca, E.; Prasad, Ramjee

2012-01-01

A key factor for the evolution of the mobile networks towards 4G is to bring to fruition high bandwidth per mobile node. Eventually, due to the advent of a new class of applications, namely, Machine-to-Machine, we foresee new challenges where bandwidth per user is no more the primal driver...... be evolved to address various nuances of the mobile devices used by man and machines. The bigger question is as follows. Is the state-of-the-art mobile network designed optimally to cater both the Human-to-Human and Machine-to-Machine applications? This paper presents the primary challenges....... As an immediate impact of the high penetration of M2M devices, we envisage a surge in the signaling messages for mobility and location management. The cell size will shrivel due to high tele-density resulting in even more signaling messages related to handoff and location updates. The mobile network should...

Survey of methods for integrated sequence analysis with emphasis on man-machine interaction

Energy Technology Data Exchange (ETDEWEB)

Kahlbom, U; Holmgren, P [RELCON, Stockholm (Sweden)

1995-05-01

This report presents a literature study concerning recently developed monotonic methodologies in the human reliability area. The work was performed by RELCON AB on commission by NKS/RAK-1, subproject 3. The topic of subproject 3 is `Integrated Sequence Analysis with Emphasis on Man-Machine Interaction`. The purpose with the study was to compile recently developed methodologies and to propose some of these methodologies for use in the sequence analysis task. The report describes mainly non-dynamic (monotonic) methodologies. One exception is HITLINE, which is a semi-dynamic method. Reference provides a summary of approaches to dynamic analysis of man-machine-interaction, and explains the differences between monotonic and dynamic methodologies. (au) 21 refs.

Survey of methods for integrated sequence analysis with emphasis on man-machine interaction

International Nuclear Information System (INIS)

Kahlbom, U.; Holmgren, P.

1995-05-01

This report presents a literature study concerning recently developed monotonic methodologies in the human reliability area. The work was performed by RELCON AB on commission by NKS/RAK-1, subproject 3. The topic of subproject 3 is 'Integrated Sequence Analysis with Emphasis on Man-Machine Interaction'. The purpose with the study was to compile recently developed methodologies and to propose some of these methodologies for use in the sequence analysis task. The report describes mainly non-dynamic (monotonic) methodologies. One exception is HITLINE, which is a semi-dynamic method. Reference provides a summary of approaches to dynamic analysis of man-machine-interaction, and explains the differences between monotonic and dynamic methodologies. (au) 21 refs

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

Science.gov (United States)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Learning Machines Implemented on Non-Deterministic Hardware

OpenAIRE

Gupta, Suyog; Sindhwani, Vikas; Gopalakrishnan, Kailash

2014-01-01

This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementations. The hardware/software co-design methodology advocated here hinges on the deployment of compute-intensive machine learning kernels onto compute platforms that trade-off deter...

Methodologies for problem identification and solution

International Nuclear Information System (INIS)

Drury, C.G.

This paper describes a methodology for bringing together knowledge on how humans work (ergonomics) and knowledge on a particular system (operating experience), using the concept of the purposive human/machine system. A Task/Operator/Machine/Environment (TOME) system is one in which information from the machine is passed to the operator through displays and the machine receives information from the operator via controls. The human in the system can be described as a set of potentially limiting subsystems. When the muscular-skeletal, physiological, sensory, or attentional limitations of the human are exceeded one has a 'human failure'. The overloading of limiting subsystems may also be described as mismatches between task demands and human capabilities. Detection and analysis of human/system mismatches are central to solving man-machine problems

A methodology for online visualization of the energy flow in a machine tool

DEFF Research Database (Denmark)

Mohammadi, Ali; Züst, Simon; Mayr, Josef

2017-01-01

the machining process and by this increasing its energy efficiency. This study intents to propose a method which has the capability of real-time monitoring of the entire energetic flows in a CNC machine tool including motors, pumps and cooling fluid. The structure of this approach is based on categorizing...

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

Directory of Open Access Journals (Sweden)

Mareike Ließ

Full Text Available Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

A data-based technique for monitoring of wound rotor induction machines: A simulation study

KAUST Repository

Harrou, Fouzi; Ramahaleomiarantsoa, Jacques F.; Nounou, Mohamed N.; Nounou, Hazem N.

2016-01-01

Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM

Comparative analysis of lockout programs and procedures applied to industrial machines

Energy Technology Data Exchange (ETDEWEB)

Chinniah, Y.; Champoux, M.; Burlet-Vienney, D.; Daigle, R. [Institut de recherche Robert-Sauve en sante et en securite du travail, Montreal, PQ (Canada)

2008-09-15

In 2005, approximately 20 workers in Quebec were killed by dangerous machines. Approximately 13,000 accidents in the province were linked to the use of machines. The resulting cost associated with these accidents was estimated to be $70 million to the Quebec Occupational Health and Safety Commission (CSST) in compensation and salary replacement. According to article 185 of the Quebec Occupational Health and Safety Regulation (RSST), workers intervening in hazardous zones of machines and processes during maintenance, repairs, and unjamming activities must apply lockout procedures. Lockout is defined as the placement of a lock or tag on an energy-isolating device in accordance with an established procedure, indicating that the energy-isolating device is not to be operated until removal of the lock or tag in accordance with an established procedure. This report presented a comparative analysis of lockout programs and procedures applied to industrial machines. The study attempted to answer several questions regarding the concept of lockout and its definition in the literature; the differences between legal lockout requirements among provinces and countries; different standards on lockout; the contents of lockout programs as described by different documents; and the compliance of lockout programs in a sample of industries in Quebec in terms of Canadian standard on lockout, the CSA Z460-05 (2005). The report discussed the research objectives, methodology, and results of the study. It was concluded that the concept of lockout has different meanings or definitions in the literature, especially in regulations. However, definitions of lockout which are found in standards have certain similarities. 50 refs., 52 tabs., 2 appendices.

Cutting temperature measurement and material machinability

Directory of Open Access Journals (Sweden)

Nedić Bogdan P.

2014-01-01

Full Text Available Cutting temperature is very important parameter of cutting process. Around 90% of heat generated during cutting process is then away by sawdust, and the rest is transferred to the tool and workpiece. In this research cutting temperature was measured with artificial thermocouples and question of investigation of metal machinability from aspect of cutting temperature was analyzed. For investigation of material machinability during turning artificial thermocouple was placed just below the cutting top of insert, and for drilling thermocouples were placed through screw holes on the face surface. In this way was obtained simple, reliable, economic and accurate method for investigation of cutting machinability.

Characterization of wood dust emission from hand-held woodworking machines.

Science.gov (United States)

Keller, F-X; Chata, F

2018-01-01

This article focuses on the prevention of exposure to wood dust when operating electrical hand-held sawing and sanding machines. A laboratory methodology was developed to measure the dust concentration around machines during operating processes. The main objective was to characterize circular saws and sanders, with the aim of classifying the different power tools tested in terms of dust emission (high dust emitter vs. low dust emitter). A test set-up was developed and is described and a measurement methodology was determined for each of the two operations studied. The robustness of the experimental results is discussed and shows good tendencies. The impact of air-flow extraction rate was assessed and the pressure loss of the system for each machine established. For the circular saws, three machines over the nine tested could be classified in the low dust emitter group. Their mean concentration values measured are between 0.64 and 0.98 mg/m 3 for the low dust emitter group and from 2.55 and 4.37 mg/m 3 for the high dust emitter group. From concentration measurements, a machine classification is possible-one for sanding machines and one for sawing machines-and a ratio from 1-7 is obtained when comparing the results. This classification will be helpful when a choice of high performance power tools, in terms of dust emission, must be made by professionals.

Gradient Boosting Machines, A Tutorial

Directory of Open Access Journals (Sweden)

Alexey eNatekin

2013-12-01

Full Text Available Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods. A theoretical information is complemented with many descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. A set of practical examples of gradient boosting applications are presented and comprehensively analyzed.

Twin support vector machines models, extensions and applications

CERN Document Server

Jayadeva; Chandra, Suresh

2017-01-01

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

Current methodological questions of studying social-political relations of global society

Directory of Open Access Journals (Sweden)

Marković Danilo Ž.

2010-01-01

planetary and cosmic consequences. Dignity of man and his work, regarding the global character of economic life, whose bearers are multinational corporations, and global character of job market, regarding preservation of dignity of work as presumption of preservation of dignity of man. However, the question is if the contemporary level of science development allows this, first of all social sciences and especially sociology (which is generally determined as the most general science on society and its totality. In fact, the question is if the theoretical-cognitive apparatus of sociology allows studying of the complex structure and category of contemporary society and their dynamics of changes that are not accompanied by development of suitable idea-terminological apparatus. As Sorokin points out, many theories have appeared in sociology since Auguste Comte that had their boom, and then they were contested and finally disappeared. Considering the problems of sociological theories, it should be born in mind that a 'universal' sociological theory cannot exist because few theoretical-methodological attitudes are not enough to interpret accelerated complexity of social development dynamics, and the society is changing very fast. However, it is possible to specify the theory parameters in the context for valid interpretation of development tendencies of self-development of society. Tendency of postmodernism to make the society in its construction and dynamics by its subject of study, especially by establishing postmodern sociological terminology, indicates tendency that postmodernism paradigm questions the referred determinacy of sociology itself, which has been developed and survived as science on classical terminology. A danger exists that paradigm arisen from tendency to modernize cognitional apparatus of sociology (the terms questions existence of the science itself within which it has originated, i.e. sociology. Therefore, postmodern sociological paradigm should be also

Model-based machine learning.

Science.gov (United States)

Bishop, Christopher M

2013-02-13

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

Open-ended questions in sensory testing practice

NARCIS (Netherlands)

Piqueras Fiszman, B.

2015-01-01

Why use open-ended questions? This chapter provides an up-to-date overview on the use of open-ended questions in novel rapid sensory methodologies and the potential applications in which they could provide unique benefits. Next, the step-by-step process is described (from task performance to

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

OpenAIRE

Dipnall, Joanna F.; Pasco, Julie A.; Berk, Michael; Williams, Lana J.; Dodd, Seetal; Jacka, Felice N.; Meyer, Denny

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted reg...

Model for Investigation of Operational Wind Power Plant Regimes with Doubly–Fed Asynchronous Machine in Power System

Directory of Open Access Journals (Sweden)

R. I. Mustafayev

2012-01-01

Full Text Available The paper presents methodology for mathematical modeling of power system (its part when jointly operated with wind power plants (stations that contain asynchronous doubly-fed machines used as generators. The essence and advantage of the methodology is that it allows efficiently to mate equations of doubly-fed asynchronous machines, written in the axes that rotate with the machine rotor speed with the equations of external electric power system, written in synchronously rotating axes.

New Methodologies for Development of High Efficient Machining of Difficult to Cut Materials

International Nuclear Information System (INIS)

Durante, S; Comoglio, M; Rostagno, M

2011-01-01

The article focuses on the automotive and aerospace industries. In these industries the need for enhanced materials performance is necessary if they are to remain competitive in global terms. Unfortunately the material properties, which make them so attractive to the aerospace and automotive industry can also make them difficult to machine. This paper will discuss integrated developments in machining techniques and cutting tools, which are emerging to cope with difficult to cut materials.

Design Methodology of a Brushless IPM Machine for a Zero Speed Injection Based Sensorless Control

OpenAIRE

Godbehere, Jonathan; Wrobel, Rafal; Drury, David; Mellor, Phil

2015-01-01

In this paper a design approach for a sensorless controlled, brushless, interior permanent magnet machine is attained. An initial study based on established electrical machine formulas provides the machine’s basic geometrical sizing. The next design stage combines a particle swarm optimisation (PSO) search routine with a magneto-static finite element (FE) solver to provide a more in depth optimisation. The optimisation system has been formulated to derive alternative machine design variants, ...

The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction.

Science.gov (United States)

Schermer, Maartje

2009-12-01

Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have on our 'symbolic order'-on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility.

VEM: Virtual Enterprise Methodology

DEFF Research Database (Denmark)

Tølle, Martin; Vesterager, Johan

2003-01-01

This chapter presents a virtual enterprise methodology (VEM) that outlines activities to consider when setting up and managing virtual enterprises (VEs). As a methodology the VEM helps companies to ask the right questions when preparing for and setting up an enterprise network, which works...

Methodological evolutions in human-machine cooperative problem solving with applications to nuclear plants

International Nuclear Information System (INIS)

Kitamura, Masaharu; Takahashi, Makoto

2002-01-01

A new framework for attaining higher safety of nuclear plants through introducing machine intelligence and robots has been proposed in this paper. The main emphasis of the framework is placed on user-centered human-machine cooperation in solving problems experienced during conducting operation, monitoring and maintenance activities in nuclear plants. In this framework, human operator is supposed to take initiative of actions at any moment of operation. No attempt has been made to replace human experts by machine intelligence and robots. Efforts have been paid to clarify the expertise and behavioral model of human experts so that the developed techniques are consistent with human mental activities in solving highly complicated operational and maintenance problems. Several techniques essential to the functioning of the framework have also been introduced. Modification of environment to provide support information has also been pursued to realize the concept of ubiquitous computing. (author)

Machine Learning for Security

CERN Multimedia

CERN. Geneva

2015-01-01

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

Machine learning: Trends, perspectives, and prospects.

Science.gov (United States)

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Optimal methodology for a machining process scheduling in spot electricity markets

International Nuclear Information System (INIS)

Yusta, J.M.; Torres, F.; Khodr, H.M.

2010-01-01

Electricity spot markets have introduced hourly variations in the price of electricity. These variations allow the increase of the energy efficiency by the appropriate scheduling and adaptation of the industrial production to the hourly cost of electricity in order to obtain the maximum profit for the industry. In this article a mathematical optimization model simulates costs and the electricity demand of a machining process. The resultant problem is solved using the generalized reduced gradient approach, to find the optimum production schedule that maximizes the industry profit considering the hourly variations of the price of electricity in the spot market. Different price scenarios are studied to analyze the impact of the spot market prices for electricity on the optimal scheduling of the machining process and on the industry profit. The convenience of the application of the proposed model is shown especially in cases of very high electricity prices.

Science 101: Q--What Is the Physics behind Simple Machines?

Science.gov (United States)

Robertson, Bill

2013-01-01

Bill Robertson thinks that questioning the physics behind simple machines is a great idea because when he encounters the subject of simple machines in textbooks, activities, and classrooms, he seldom encounters, a scientific explanation of how they work. Instead, what one often sees is a discussion of load, effort, fulcrum, actual mechanical…

Spatial extreme learning machines: An application on prediction of disease counts.

Science.gov (United States)

Prates, Marcos O

2018-01-01

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

Developing Parametric Models for the Assembly of Machine Fixtures for Virtual Multiaxial CNC Machining Centers

Science.gov (United States)

Balaykin, A. V.; Bezsonov, K. A.; Nekhoroshev, M. V.; Shulepov, A. P.

2018-01-01

This paper dwells upon a variance parameterization method. Variance or dimensional parameterization is based on sketching, with various parametric links superimposed on the sketch objects and user-imposed constraints in the form of an equation system that determines the parametric dependencies. This method is fully integrated in a top-down design methodology to enable the creation of multi-variant and flexible fixture assembly models, as all the modeling operations are hierarchically linked in the built tree. In this research the authors consider a parameterization method of machine tooling used for manufacturing parts using multiaxial CNC machining centers in the real manufacturing process. The developed method allows to significantly reduce tooling design time when making changes of a part’s geometric parameters. The method can also reduce time for designing and engineering preproduction, in particular, for development of control programs for CNC equipment and control and measuring machines, automate the release of design and engineering documentation. Variance parameterization helps to optimize construction of parts as well as machine tooling using integrated CAE systems. In the framework of this study, the authors demonstrate a comprehensive approach to parametric modeling of machine tooling in the CAD package used in the real manufacturing process of aircraft engines.

Experience with a clustered parallel reduction machine

NARCIS (Netherlands)

Beemster, M.; Hartel, Pieter H.; Hertzberger, L.O.; Hofman, R.F.H.; Langendoen, K.G.; Li, L.L.; Milikowski, R.; Vree, W.G.; Barendregt, H.P.; Mulder, J.C.

A clustered architecture has been designed to exploit divide and conquer parallelism in functional programs. The programming methodology developed for the machine is based on explicit annotations and program transformations. It has been successfully applied to a number of algorithms resulting in a

Health Informatics via Machine Learning for the Clinical Management of Patients.

Science.gov (United States)

Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

2015-08-13

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Machine learning analysis of binaural rowing sounds

DEFF Research Database (Denmark)

Johard, Leonard; Ruffaldi, Emanuele; Hoffmann, Pablo F.

2011-01-01

Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition metho...... methodology and the evaluation of different machine learning techniques for classifying rowing-sound data. We see that a combination of principal component analysis and shallow networks perform equally well as deep architectures, while being much faster to train.......Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition...

Reverse Engineering Integrated Circuits Using Finite State Machine Analysis

Energy Technology Data Exchange (ETDEWEB)

Oler, Kiri J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Miller, Carl H. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-04-12

In this paper, we present a methodology for reverse engineering integrated circuits, including a mathematical verification of a scalable algorithm used to generate minimal finite state machine representations of integrated circuits.

Active learning machine learns to create new quantum experiments.

Science.gov (United States)

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Data Mining and Machine Learning in Astronomy

Science.gov (United States)

Ball, Nicholas M.; Brunner, Robert J.

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

Dual Numbers Approach in Multiaxis Machines Error Modeling

Directory of Open Access Journals (Sweden)

Jaroslav Hrdina

2014-01-01

Full Text Available Multiaxis machines error modeling is set in the context of modern differential geometry and linear algebra. We apply special classes of matrices over dual numbers and propose a generalization of such concept by means of general Weil algebras. We show that the classification of the geometric errors follows directly from the algebraic properties of the matrices over dual numbers and thus the calculus over the dual numbers is the proper tool for the methodology of multiaxis machines error modeling.

2016 IFToMM Asian Conference on Mechanism and Machine Science (IFToMM Asian MMS 2016) & 2016 International Conference on Mechanism and Machine Science (CCMMS 2016)

CERN Document Server

Wang, Nianfeng; Huang, Yanjiang

2017-01-01

These proceedings collect the latest research results in mechanism and machine science, intended to reinforce and improve the role of mechanical systems in a variety of applications in daily life and industry. Gathering more than 120 academic papers, it addresses topics including: Computational kinematics, Machine elements, Actuators, Gearing and transmissions, Linkages and cams, Mechanism design, Dynamics of machinery, Tribology, Vehicle mechanisms, dynamics and design, Reliability, Experimental methods in mechanisms, Robotics and mechatronics, Biomechanics, Micro/nano mechanisms and machines, Medical/welfare devices, Nature and machines, Design methodology, Reconfigurable mechanisms and reconfigurable manipulators, and Origami mechanisms. This is the fourth installment in the IFToMM Asian conference series on Mechanism and Machine Science (ASIAN MMS 2016). The ASIAN MMS conference initiative was launched to provide a forum mainly for the Asian community working in Mechanism and Machine Science, in order to ...

Chemically intuited, large-scale screening of MOFs by machine learning techniques

Science.gov (United States)

Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

2017-10-01

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

Machine concept optimization for pumped-storage plants through combined dispatch simulation for wholesale and reserve markets

International Nuclear Information System (INIS)

Engels, Klaus; Harasta, Michaela; Braitsch, Werner; Moser, Albert; Schaefer, Andreas

2012-01-01

In Germany's energy markets of today, pumped-storage power plants offer excellent business opportunities due to their outstanding flexibility. However, the energy-economic simulation of pumped-storage plants, which is necessary to base the investment decision on a sound business case, is a highly complex matter since the plant's capacity must be optimized in a given plant portfolio and between two relevant markets: the scheduled wholesale and the reserve market. This mathematical optimization problem becomes even more complex when the question is raised as to which type of machine should be used for a pumped-storage new build option. For the first time, it has been proven possible to simulate the optimum dispatch of different pumped-storage machine concepts within two relevant markets - the scheduled wholesale and the reserve market - thereby greatly supporting the investment decision process. The methodology and findings of a cooperation study between E.ON and RWTH Aachen University in respect of the German pumped-storage extension project 'Waldeck 2+' are described, showing the latest development in dispatch simulation for generation portfolios. (authors)

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

International Nuclear Information System (INIS)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

2017-01-01

Highlights: • An ensemble model is developed to produce both deterministic and probabilistic wind forecasts. • A deep feature selection framework is developed to optimally determine the inputs to the forecasting methodology. • The developed ensemble methodology has improved the forecasting accuracy by up to 30%. - Abstract: With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

A statistical methodology to derive the scaling law for the H-mode power threshold using a large multi-machine database

International Nuclear Information System (INIS)

Murari, A.; Lupelli, I.; Gaudio, P.; Gelfusa, M.; Vega, J.

2012-01-01

In this paper, a refined set of statistical techniques is developed and then applied to the problem of deriving the scaling law for the threshold power to access the H-mode of confinement in tokamaks. This statistical methodology is applied to the 2010 version of the ITPA International Global Threshold Data Base v6b(IGDBTHv6b). To increase the engineering and operative relevance of the results, only macroscopic physical quantities, measured in the vast majority of experiments, have been considered as candidate variables in the models. Different principled methods, such as agglomerative hierarchical variables clustering, without assumption about the functional form of the scaling, and nonlinear regression, are implemented to select the best subset of candidate independent variables and to improve the regression model accuracy. Two independent model selection criteria, based on the classical (Akaike information criterion) and Bayesian formalism (Bayesian information criterion), are then used to identify the most efficient scaling law from candidate models. The results derived from the full multi-machine database confirm the results of previous analysis but emphasize the importance of shaping quantities, elongation and triangularity. On the other hand, the scaling laws for the different machines and at different currents are different from each other at the level of confidence well above 95%, suggesting caution in the use of the global scaling laws for both interpretation and extrapolation purposes. (paper)

Transition towards energy efficient machine tools

Energy Technology Data Exchange (ETDEWEB)

Zein, Andre [Technische Univ. Braunschweig (Germany). Inst. fuer Werkzeugmaschinen und Fertigungstechnik

2012-07-01

Provides unique data about industrial trends affecting the energy demand of machine tools. Presents a comprehensive methodology to assess the energy efficiency of machining processes. Contains an integrated management concept to implement energy performance measures into existing industrial systems. Includes an industrial case study with two exemplary applications. Energy efficiency represents a cost-effective and immediate strategy of a sustainable development. Due to substantial environmental and economic implications, a strong emphasis is put on the electrical energy requirements of machine tools for metalworking processes. The improvement of energy efficiency is however confronted with diverse barriers, which sustain an energy efficiency gap of unexploited potential. The deficiencies lie in the lack of information about the actual energy requirements of machine tools, a minimum energy reference to quantify improvement potential and the possible actions to improve the energy demand. Therefore, a comprehensive concept for energy performance management of machine tools is developed which guides the transition towards energy efficient machine tools. It is structured in four innovative concept modules, which are embedded into step-by-step workflow models. The capability of the performance management concept is demonstrated in an automotive manufacturing environment. The target audience primarily comprises researchers and practitioners challenged to enhance energy efficiency in manufacturing. The book may also be beneficial for graduate students who want to specialize in this field.

Unipolar Electric Machines with Liquid-Metal Current Pickup,

Science.gov (United States)

1984-03-08

A new homopolar motor , e4ournal of the Franklin Institute*. 1954, v. 258, Ne 1. %4 144093, Bjo.1.leTeJb H3o6peTeHxA. 1962,. 14 1. 30. X oao p o a...VIII. Motor Mode of Unipolar Electrical Machine ............... 301 Chapter IX. Bases of Theory and Calculation of Nonpolar Dynamos without...unipolar electric motors . Are examined questions of the classification of acyclic machines, their electromagnetic field, calculation of magnetic circuit

Human and machine perception communication, interaction, and integration

CERN Document Server

Cantoni, Virginio; Setti, Alessandra

2005-01-01

The theme of this book on human and machine perception is communication, interaction, and integration. For each basic topic there are invited lectures, corresponding to approaches in nature and machines, and a panel discussion. The lectures present the state of the art, outlining open questions and stressing synergies among the disciplines related to perception. The panel discussions are forums for open debate. The wide spectrum of topics allows comparison and synergy and can stimulate new approaches.

A linear maglev guide for machine tools

Energy Technology Data Exchange (ETDEWEB)

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

1996-12-31

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

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

Science.gov (United States)

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

The Machine as Art (in the 20th Century: An Introduction

Directory of Open Access Journals (Sweden)

Juliette Bessette

2018-01-01

Full Text Available The machine, over the course of the 20th century, progressively integrated itself into all fields of human activity, including artistic creation; and indeed, with the first decades of that century having established a surprisingly vital and wide-ranging series of perspectives on the relationship between art and the machine, certain artists in the wake of the Second World War no longer felt compelled to treat the machine as a mere theme or source of inspiration: the machine itself becomes art—unless it is art which seeks to become mechanical? The artist mutates into “artist-engineer”; and this transition, resonating within a specific historical context, leads not only to a questioning of the nature of the work itself, but also to a broader questioning which places us within the realm of anthropology: what is this art telling us about the actual conditions of contemporary human society, and what is it telling us about the future to which we aspire? It is the goal of this special issue of Arts to stimulate an historically conscious, protean, and global (rethinking of the cultural relationship between man and machine; and to this end, we welcome contributions falling anywhere within the nearly infinite spectrum represented by the prismatic period during the middle of the last century in which the machine became a legitimate artistic medium.

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

Directory of Open Access Journals (Sweden)

Shekhar C.

2017-01-01

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

Machine learning techniques to examine large patient databases.

Science.gov (United States)

Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

2009-03-01

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

Reflections on Design Methodology Research

DEFF Research Database (Denmark)

2011-01-01

We shall reflect on the results of Design Methodology research and their impact on design practice. In the past 50 years the number of researchers in the field has expanded enormously – as has the number of publications. During the same period design practice and its products have changed...... and produced are also now far more complex and distributed, putting designers under ever increasing pressure. We shall address the question: Are the results of Design Methodology research appropriate and are they delivering the expected results in design practice? In our attempt to answer this question we...

Instrumentation and Control Life Cycle Management Plan Methodology. Volume 1, Manual: Final report

International Nuclear Information System (INIS)

Quick, D.S.; Murray, S.; Florio, F.; Bliss, M.J.

1995-08-01

This methodology manual describes how to develop a Life Cycle Management Plan (LCMP). An LCMP is a long-term strategic plan that can be developed for a nuclear power plant to cost-effectively maintain and upgrade its aging or obsolete Instrumentation and Control (I ampersand C) systems. An LCMP defines the utility's mission and objectives in regards to long range I ampersand C planning, as well as the plant's present configuration (I ampersand C systems, networks, man machine interfaces, etc.), its desired future I ampersand C systems, a long term I ampersand C maintenance strategy, and initial upgrade priorities and schedules to cost-effectively implement system upgrades. This manual is accompanied by a workbook (EPRI TR-105555-V2) which contains various worksheets, outlines, and generic interview questions that aid in the LCNW development process

Risk estimation using probability machines

Science.gov (United States)

2014-01-01

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

Guided Discovery with Socratic Questioning

Directory of Open Access Journals (Sweden)

M. Hakan Türkçapar

2015-04-01

Full Text Available “The Socratic method” is a way of teaching philosophical thinking and knowledge by asking questions. It was first used by in ancient times by the Greek philosopher Socrates who taught his followers by asking questions; these conversations between them are known as “Socratic dialogues”. In this methodology, no new knowledge is taught to the individual; rather, the individual is guided to remember and rediscover what was formerly known through this process. The main method used in cognitive therapy is guided discovery. There are various methods of guided discovery in cognitive therapy. The form of verbal exchange between the therapist and client which is used during the process of cognitive behavioral therapy is known as “socratic questioning”. In this method the goal is to make the client rediscover, with a series of questions, a piece of knowledge which he could otherwise know but is not presently conscious of. The Socratic Questioning consists of several steps, including: identifying the problem by listening to the client and making reflections, finding alternatives by examining and evaluating, reidentification by using the newly rediscovered information and questioning the old distorted belief, and reaching a new conclusion and applying it. Question types used during these procedures are: questions for collecting information, questions revealing meanings, questions revealing beliefs, questions about behaviours during similar past experiences, analytic questions and analytic synthesis questions. In order to make the patient feel understood, it is important to be empathetic and summarize the problem during the interview. In this text, steps of Socratic Questioning-Guided Discovery will be reviewed with sample dialogues provided for each step. [JCBPR 2015; 4(1.000: 47-53

Dynamic Question Ordering in Online Surveys

Directory of Open Access Journals (Sweden)

Early Kirstin

2017-09-01

Full Text Available Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call Dynamic Question Ordering (DQO, where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with two case studies, for the prediction and survey-taking settings. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

Stereoscopic display in a slot machine

Science.gov (United States)

Laakso, M.

2012-03-01

This paper reports the results of a user trial with a slot machine equipped with a stereoscopic display. The main research question was to find out what kind of added value does stereoscopic 3D (S-3D) bring to slot games? After a thorough literature survey, a novel gaming platform was designed and implemented. Existing multi-game slot machine "Nova" was converted to "3DNova" by replacing the monitor with an S-3D display and converting six original games to S-3D format. To evaluate the system, several 3DNova machines were put available for players for four months. Both qualitative and quantitative analysis was carried out from statistical values, questionnaires and observations. According to the results, people find the S-3D concept interesting but the technology is not optimal yet. Young adults and adults were fascinated by the system, older people were more cautious. Especially the need to wear stereoscopic glasses provide a challenge; ultimate system would probably use autostereoscopic technology. Also the games should be designed to utilize its full power. The main contributions of this paper are lessons learned from creating an S-3D slot machine platform and novel information about human factors related to stereoscopic slot machine gaming.

Semi-automated categorization of open-ended questions

Directory of Open Access Journals (Sweden)

Matthias Schonlau

2016-08-01

Full Text Available Text data from open-ended questions in surveys are difficult to analyze and are frequently ignored. Yet open-ended questions are important because they do not constrain respondents’ answer choices. Where open-ended questions are necessary, sometimes multiple human coders hand-code answers into one of several categories. At the same time, computer scientists have made impressive advances in text mining that may allow automation of such coding. Automated algorithms do not achieve an overall accuracy high enough to entirely replace humans. We categorize open-ended questions soliciting narrative responses using text mining for easy-to-categorize answers and humans for the remainder using expected accuracies to guide the choice of the threshold delineating between “easy” and “hard”. Employing multinomial boosting avoids the common practice of converting machine learning “confidence scores” into pseudo-probabilities. This approach is illustrated with examples from open-ended questions related to respondents’ advice to a patient in a hypothetical dilemma, a follow-up probe related to respondents’ perception of disclosure/privacy risk, and from a question on reasons for quitting smoking from a follow-up survey from the Ontario Smoker’s Helpline. Targeting 80% combined accuracy, we found that 54%-80% of the data could be categorized automatically in research surveys.

Can we replace the bodily teacher? The Dutch history of teaching machines (1960s)

NARCIS (Netherlands)

Amsing, Hilda T.A.

2016-01-01

At the beginnings of the 1960s Skinner’s teaching machine reached the Netherlands. This machine used programmed instruction to guide children in small steps through the teaching materials. It provided them with carefully chosen questions and automatic feedback, fitting the principles of

The smallest possible thermal machines and the foundations of thermodynamics

CERN Multimedia

CERN. Geneva

2018-01-01

In my talk I raise the question on the fundamental limits to the size of thermal machines – refrigerators, heat pumps and work producing engines - and I will present the smallest possible ones. I will then discuss the issue of a possible complementarity between size and efficiency and show that even the smallest machines could be maximally efficient and I will also present a new point of view over what is work and what do thermal machines actually do. Finally I will present a completely new approach to the foundations of thermodynamics that follows from these results.

Visual question answering using hierarchical dynamic memory networks

Science.gov (United States)

Shang, Jiayu; Li, Shiren; Duan, Zhikui; Huang, Junwei

2018-04-01

Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What's more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.

Questioning the Universe concepts in physics

CERN Document Server

Sadoff, Ahren

2008-01-01

UNITS AND POWERS OF TEN PHYSICS AND ITS METHODOLOGY  What Is Physics? Methodology The First Scientist Why Do You Believe? Back to the Questions How Do We Answer theQuestions? The Need to BeQuantitative Theories Models AestheticJudgments  MOTION Relating the Variables of Motion Graphs of One-Dimensional Motion Constant Speed Constant Acceleration Two-Dimensional Motion FORCES The Fundamental Forces A Specific Force Law: Newtonian Gravity Weight How Does Force Affect Motion? Newton's SecondLaw Newton, the Apple, and the Moon Combining Two Laws The Mass of the Earth Newton's Firs

Dictionary Based Machine Translation from Kannada to Telugu

Science.gov (United States)

Sindhu, D. V.; Sagar, B. M.

2017-08-01

Machine Translation is a task of translating from one language to another language. For the languages with less linguistic resources like Kannada and Telugu Dictionary based approach is the best approach. This paper mainly focuses on Dictionary based machine translation for Kannada to Telugu. The proposed methodology uses dictionary for translating word by word without much correlation of semantics between them. The dictionary based machine translation process has the following sub process: Morph analyzer, dictionary, transliteration, transfer grammar and the morph generator. As a part of this work bilingual dictionary with 8000 entries is developed and the suffix mapping table at the tag level is built. This system is tested for the children stories. In near future this system can be further improved by defining transfer grammar rules.

Proceedings of IEEE Machine Learning for Signal Processing Workshop XVI

DEFF Research Database (Denmark)

Larsen, Jan

These proceedings contains refereed papers presented at the sixteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP'2006), held in Maynooth, Co. Kildare, Ireland, September 6-8, 2006. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP......). The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized by the Machine Learning for Signal Processing Technical Committee...... the same standard as the printed version and facilitates the reading and searching of the papers. The field of machine learning has matured considerably in both methodology and real-world application domains and has become particularly important for solution of problems in signal processing. As reflected...

Proceedings of the IEEE Machine Learning for Signal Processing XVII

DEFF Research Database (Denmark)

The seventeenth of a series of workshops sponsored by the IEEE Signal Processing Society and organized by the Machine Learning for Signal Processing Technical Committee (MLSP-TC). The field of machine learning has matured considerably in both methodology and real-world application domains and has...... become particularly important for solution of problems in signal processing. As reflected in this collection, machine learning for signal processing combines many ideas from adaptive signal/image processing, learning theory and models, and statistics in order to solve complex real-world signal processing......, and two papers from the winners of the Data Analysis Competition. The program included papers in the following areas: genomic signal processing, pattern recognition and classification, image and video processing, blind signal processing, models, learning algorithms, and applications of machine learning...

Who Should Decide How Machines Make Morally Laden Decisions?

Science.gov (United States)

Martin, Dominic

2017-08-01

Who should decide how a machine will decide what to do when it is driving a car, performing a medical procedure, or, more generally, when it is facing any kind of morally laden decision? More and more, machines are making complex decisions with a considerable level of autonomy. We should be much more preoccupied by this problem than we currently are. After a series of preliminary remarks, this paper will go over four possible answers to the question raised above. First, we may claim that it is the maker of a machine that gets to decide how it will behave in morally laden scenarios. Second, we may claim that the users of a machine should decide. Third, that decision may have to be made collectively or, fourth, by other machines built for this special purpose. The paper argues that each of these approaches suffers from its own shortcomings, and it concludes by showing, among other things, which approaches should be emphasized for different types of machines, situations, and/or morally laden decisions.

"Hypothetical machines": the science fiction dreams of Cold War social science.

Science.gov (United States)

Lemov, Rebecca

2010-06-01

The introspectometer was a "hypothetical machine" Robert K. Merton introduced in the course of a 1956 how-to manual describing an actual research technique, the focused interview. This technique, in turn, formed the basis of wartime morale research and consumer behavior studies as well as perhaps the most ubiquitous social science tool, the focus group. This essay explores a new perspective on Cold War social science made possible by comparing two kinds of apparatuses: one real, the other imaginary. Even as Merton explored the nightmare potential of such machines, he suggested that the clear aim of social science was to build them or their functional equivalent: recording machines to access a person's experiential stream of reality, with the ability to turn this stream into real-time data. In this way, the introspectometer marks and symbolizes a broader entry during the Cold War of science-fiction-style aspirations into methodological prescriptions and procedural manuals. This essay considers the growth of the genre of methodological visions and revisions, painstakingly argued and absorbed, but punctuated by sci-fi aims to transform "the human" and build newly penetrating machines. It also considers the place of the nearly real-, and the artificial "near-substitute" as part of an experimental urge that animated these sciences.

Multiphysics simulation by design for electrical machines, power electronics and drives

CERN Document Server

Rosu, Marius; Lin, Dingsheng; Ionel, Dan M; Popescu, Mircea; Blaabjerg, Frede; Rallabandi, Vandana; Staton, David

2018-01-01

This book combines the knowledge of experts from both academia and the software industry to present theories of multiphysics simulation by design for electrical machines, power electronics, and drives. The comprehensive design approach described within supports new applications required by technologies sustaining high drive efficiency. The highlighted framework considers the electric machine at the heart of the entire electric drive. The book also emphasizes the simulation by design concept--a concept that frames the entire highlighted design methodology, which is described and illustrated by various advanced simulation technologies. Multiphysics Simulation by Design for Electrical Machines, Power Electronics and Drives begins with the basics of electrical machine design and manufacturing tolerances. It also discusses fundamental aspects of the state of the art design process and includes examples from industrial practice. It explains FEM-based analysis techniques for electrical machine design--providing deta...

Computer and machine vision theory, algorithms, practicalities

CERN Document Server

Davies, E R

2012-01-01

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-il...

Machine speech and speaking about machines

Energy Technology Data Exchange (ETDEWEB)

Nye, A. [Univ. of Wisconsin, Whitewater, WI (United States)

1996-12-31

Current philosophy of language prides itself on scientific status. It boasts of being no longer contaminated with queer mental entities or idealist essences. It theorizes language as programmable variants of formal semantic systems, reimaginable either as the properly epiphenomenal machine functions of computer science or the properly material neural networks of physiology. Whether or not such models properly capture the physical workings of a living human brain is a question that scientists will have to answer. I, as a philosopher, come at the problem from another direction. Does contemporary philosophical semantics, in its dominant truth-theoretic and related versions, capture actual living human thought as it is experienced, or does it instead reflect, regardless of (perhaps dubious) scientific credentials, pathology of thought, a pathology with a disturbing social history.

Recent developments in man-machine systems

International Nuclear Information System (INIS)

Johannsen, G.

1987-01-01

The field of man-machine systems is introduced with its subareas and a short outline of its history of 45 years. Three current lines of development in university and industrial research are emphasized. Today, the human problem solving activities are experimentally investigated and analytically described more vigorously than the control activities. Further, improved information presentations and decision support are made possible through new technologies of computer graphics and expert systems. At last, work on a general design methodology for man-machine systems is in progress. The aim is to better support human operators of dynamic technological systems as well as designers of graphics for visual display units and of dialogue styles. Thereby, safety and availability of the complete system can be increased. (orig.) [de

ENERGY EFFICIENCY DETERMINATION OF LOADING-BACK SYSTEM OF ELECTRIC TRACTION MACHINES

Directory of Open Access Journals (Sweden)

A. M. Afanasov

2014-03-01

Full Text Available Purpose.Acceptance post-repair testsof electric traction machinesare conducted onloading-backstandsthat reducethe overall power costsfor the tests.Currentlya numberof possiblecircuit designs of loading-backsystems of electric machines are known, but there is nomethod of determiningtheir energy efficiency. This in turn makes difficult the choiceof rationaloptions. The purpose of the article is the development of the corresponding methodo-logy to make easier this process. Methodology. Expressions for determining theenergy efficiency ofa stand for testingof electric traction machineswere obtained using the generalizedscheme analysisof energy transformationsin the loading-backsystems of universal structure. Findings.Thetechnique wasoffered and the analytical expressions for determining the energy efficiency of loading-backsystemsof electric traction machines wereobtained. Energy efficiency coefficientofloading-backsystemisproposed to consider as the ratio of the total actionenergy of the mechanical and electromotive forces, providing anchors rotation and flowof currents in electric machines, which are being tested,to the total energy, consumed during the test from the external network. Originality. The concept was introduced and the analytical determination method of the energy efficiency of loading-backsystem in electric traction machines was offered. It differs by efficiency availability of power sources and converters, as well as energy efficiency factors of indirect methods of loss compensation. Practical value. The proposed technique of energy efficiency estimation of a loading-backsystemcan be used in solving the problem of rational options choice of schematics stands decisions for electric traction machines acceptance tests of main line and industrial transport.

New trends in educational activity in the field of mechanism and machine theory

CERN Document Server

Castejon, Cristina

2014-01-01

The First International Symposium on the Education in Mechanism and Machine Science (ISEMMS 2013) aimed to create a stable platform for the interchange of experience among researches of mechanism and machine science. Topics treated include contributions on subjects such as new trends and experiences in mechanical engineering education; mechanism and machine science in mechanical engineering curricula; MMS in engineering programs, such as, for example, methodology, virtual labs and new laws. All papers have been rigorously reviewed and represent the state of the art in their field.

Gendering China studies: peripheral perspectives, central questions

NARCIS (Netherlands)

de Kloet, J.

2008-01-01

This article explores the connections between the field of China studies and the field of gender and sexuality studies. It engages with three questions. First, why is it that theoretical, conceptual and methodological cross-fertilization between China studies and cultural studies remains quite

You Know Arnold Schwarzenegger? On Doing Questioning in Second Language Dyadic Tutorials

Science.gov (United States)

Belhiah, Hassan

2012-01-01

This study analyses question-answer (QA) sequences in second language tutorial interaction. Using conversation analysis methodology as an analytical tool, the study demonstrates how the act of questioning is a dominant form of interaction in tutoring discourse. The doing of questioning is accomplished through a myriad of forms other than…

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...

Machine learning paradigms applications in recommender systems

CERN Document Server

Lampropoulos, Aristomenis S

2015-01-01

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

A review of machine learning in obesity.

Science.gov (United States)

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

2018-05-01

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

Comparison Of Irms Delhi Methodology With Who Methodology On Immunization Coverage

Directory of Open Access Journals (Sweden)

Singh Padam

1996-01-01

Full Text Available Research question: What are the merits of IRMS Model over WHO Model for Coverage Evaluation Survey? Which method is superior and appropriate for coverage evolution survey of immunization in our setting? Objective: To compare IRMS Delhi methodology with WHO methodology on Immunization Coverage. Study Design: Cross-Sectional Setting: Urban and Rural both. Participants: Mothers& Children Sample Size: 300 children between 1-2 years and 300 mothers in rural areas and 75 children and 75 mothers in urban areas. Study Variables: Rural, Urban, Cast-Group, Size of the stratum, Literacy, Sex and Cost effectiveness. Outcome Variables: Coverage level of immunization. Analysis: Routine Statistical Analysis. Results: IRMS developed methodology scores better rating over WHO methodology, especially when coverage evolution is attempted in medium size villages with existence of socio-economic seggregation-which remains the main characteristic of the Indian villages.

Methodology of Education and R&D in Mechatronics.

Science.gov (United States)

Yamazaki, K.; And Others

1985-01-01

Describes the concept and methodology of "mechatronics" (application of microelectronics to mechanism control) and research and development (R&D) projects through the activities initiated at the Precision Machining Laboratory of the Department of Production Systems Engineering of the new Toyohashi University of Technology. (JN)

Do User (Browse and Click) Sessions Relate to Their Questions in a Domain-specific Collection?

DEFF Research Database (Denmark)

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

2013-01-01

relate to the question that they are answering. The contribution of this paper is the evalua-tion of the suitability of common machine learning metrics (measuring the dis-tance between two sessions) to distinguish sessions of users searching for the answer to same or different questions. We found...... that sessions for people an-swering the same question are significantly different that those answering dif-ferent questions, but results are dependent on the distance metric used. We ex-plain why some distance metrics performed better than others....

Simulation-driven machine learning: Bearing fault classification

Science.gov (United States)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

Science.gov (United States)

Binder, Harald

2014-07-01

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

Directory of Open Access Journals (Sweden)

Shun Jia

2017-04-01

Full Text Available Energy demand modeling of machining processes is the foundation of energy optimization. Energy demand of machining state transition is integral to the energy requirements of the machining process. However, research focus on energy modeling of state transition is scarce. To fill this gap, an energy demand modeling methodology of key state transitions of the turning process is proposed. The establishment of an energy demand model of state transition could improve the accuracy of the energy model of the machining process, which also provides an accurate model and reliable data for energy optimization of the machining process. Finally, case studies were conducted on a CK6153i CNC lathe, the results demonstrating that predictive accuracy with the proposed method is generally above 90% for the state transition cases.

PSA methodology

International Nuclear Information System (INIS)

Magne, L.

1996-01-01

The purpose of this text is first to ask a certain number of questions on the methods related to PSAs. Notably we will explore the positioning of the French methodological approach - as applied in the EPS 1300 1 and EPS 900 2 PSAs - compared to other approaches (Part One). This reflection leads to more general reflection: what contents, for what PSA? This is why, in Part Two, we will try to offer a framework for definition of the criteria a PSA should satisfy to meet the clearly identified needs. Finally, Part Three will quickly summarize the questions approached in the first two parts, as an introduction to the debate. 15 refs

Improvement of the auto wire feeder machine in a de-soldering process

Directory of Open Access Journals (Sweden)

Niramon Nonkhukhetkhong

2016-10-01

Full Text Available This paper presents the methodology of the de-soldering process for rework of disk drive Head Stack Assembly (HSA units. The auto wire feeder is a machine that generates Tin (Sn on the product. This machine was determined to be one of the major sources of excess Sn on the HSA. The defect rate due to excess Sn is more than 30%, which leads to increased processing time and cost to perform additional cleaning steps. From process analysis, the major causes of excess Sn are as follows: 1 The machine cannot cut the wire all the way into the flux core area; 2 The sizes and types of soldering irons are not appropriate for the unit parts; and, 3 There are variations introduced into the de-soldering process by the workforce. This paper proposes a methodology to address all three of these causes. First, the auto wire feeder machine in the de-solder process will be adjusted in order to cut wires into flux core. Second, the types of equipment and material used in de-soldering will be optimized. Finally, a new standard method for operators, which can be controlled more easily, will be developed in order to reduce defects due to workforce related variation. After these process controls and machine adjustments were implemented, the overall Sn related problems were significantly improved. Sn contamination was reduced by 41% and cycle time was reduced by an average of 15 seconds.

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

Directory of Open Access Journals (Sweden)

Longjun Dong

2014-01-01

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

The machines maintenance conditions assessment in the plastic industry

Directory of Open Access Journals (Sweden)

Stanisław Borkowski

2015-12-01

Full Text Available The purpose, methodology, main findings, the originality of the subject area (research, way of using. The main research analysis purpose is a presentation, an assessment and an interpretation of the research findings on the machines maintenance conditions in the plastic industry. The research analysis was carried out with applying Technology ABC method and TPM coefficients calculations connected with Techno pak machine components maintenance. The research was carried out in the chosen manufacturing enterprise of the plastic industry. Research findings interpretation results have been introduced in the analyzed enterprise in the form of the manufacturing processes improvement.

Methodology for dimensional variation analysis of ITER integrated systems

International Nuclear Information System (INIS)

Fuentes, F. Javier; Trouvé, Vincent; Cordier, Jean-Jacques; Reich, Jens

2016-01-01

Highlights: • Tokamak dimensional management methodology, based on 3D variation analysis, is presented. • Dimensional Variation Model implementation workflow is described. • Methodology phases are described in detail. The application of this methodology to the tolerance analysis of ITER Vacuum Vessel is presented. • Dimensional studies are a valuable tool for the assessment of Tokamak PCR (Project Change Requests), DR (Deviation Requests) and NCR (Non-Conformance Reports). - Abstract: The ITER machine consists of a large number of complex systems highly integrated, with critical functional requirements and reduced design clearances to minimize the impact in cost and performances. Tolerances and assembly accuracies in critical areas could have a serious impact in the final performances, compromising the machine assembly and plasma operation. The management of tolerances allocated to part manufacture and assembly processes, as well as the control of potential deviations and early mitigation of non-compliances with the technical requirements, is a critical activity on the project life cycle. A 3D tolerance simulation analysis of ITER Tokamak machine has been developed based on 3DCS dedicated software. This integrated dimensional variation model is representative of Tokamak manufacturing functional tolerances and assembly processes, predicting accurate values for the amount of variation on critical areas. This paper describes the detailed methodology to implement and update the Tokamak Dimensional Variation Model. The model is managed at system level. The methodology phases are illustrated by its application to the Vacuum Vessel (VV), considering the status of maturity of VV dimensional variation model. The following topics are described in this paper: • Model description and constraints. • Model implementation workflow. • Management of input and output data. • Statistical analysis and risk assessment. The management of the integration studies based on

Methodology for dimensional variation analysis of ITER integrated systems

Energy Technology Data Exchange (ETDEWEB)

Fuentes, F. Javier, E-mail: FranciscoJavier.Fuentes@iter.org [ITER Organization, Route de Vinon-sur-Verdon—CS 90046, 13067 St Paul-lez-Durance (France); Trouvé, Vincent [Assystem Engineering & Operation Services, rue J-M Jacquard CS 60117, 84120 Pertuis (France); Cordier, Jean-Jacques; Reich, Jens [ITER Organization, Route de Vinon-sur-Verdon—CS 90046, 13067 St Paul-lez-Durance (France)

2016-11-01

Highlights: • Tokamak dimensional management methodology, based on 3D variation analysis, is presented. • Dimensional Variation Model implementation workflow is described. • Methodology phases are described in detail. The application of this methodology to the tolerance analysis of ITER Vacuum Vessel is presented. • Dimensional studies are a valuable tool for the assessment of Tokamak PCR (Project Change Requests), DR (Deviation Requests) and NCR (Non-Conformance Reports). - Abstract: The ITER machine consists of a large number of complex systems highly integrated, with critical functional requirements and reduced design clearances to minimize the impact in cost and performances. Tolerances and assembly accuracies in critical areas could have a serious impact in the final performances, compromising the machine assembly and plasma operation. The management of tolerances allocated to part manufacture and assembly processes, as well as the control of potential deviations and early mitigation of non-compliances with the technical requirements, is a critical activity on the project life cycle. A 3D tolerance simulation analysis of ITER Tokamak machine has been developed based on 3DCS dedicated software. This integrated dimensional variation model is representative of Tokamak manufacturing functional tolerances and assembly processes, predicting accurate values for the amount of variation on critical areas. This paper describes the detailed methodology to implement and update the Tokamak Dimensional Variation Model. The model is managed at system level. The methodology phases are illustrated by its application to the Vacuum Vessel (VV), considering the status of maturity of VV dimensional variation model. The following topics are described in this paper: • Model description and constraints. • Model implementation workflow. • Management of input and output data. • Statistical analysis and risk assessment. The management of the integration studies based on

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

Science.gov (United States)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption

Science.gov (United States)

Kant Garg, Girish; Garg, Suman; Sangwan, K. S.

2018-04-01

The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.

Advanced Machine Learning Emulators of Radiative Transfer Models

Science.gov (United States)

Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.

2017-12-01

Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

Probability and sensitivity analysis of machine foundation and soil interaction

Directory of Open Access Journals (Sweden)

Králik J., jr.

2009-06-01

Full Text Available This paper deals with the possibility of the sensitivity and probabilistic analysis of the reliability of the machine foundation depending on variability of the soil stiffness, structure geometry and compressor operation. The requirements to design of the foundation under rotating machines increased due to development of calculation method and computer tools. During the structural design process, an engineer has to consider problems of the soil-foundation and foundation-machine interaction from the safety, reliability and durability of structure point of view. The advantages and disadvantages of the deterministic and probabilistic analysis of the machine foundation resistance are discussed. The sensitivity of the machine foundation to the uncertainties of the soil properties due to longtime rotating movement of machine is not negligible for design engineers. On the example of compressor foundation and turbine fy. SIEMENS AG the affectivity of the probabilistic design methodology was presented. The Latin Hypercube Sampling (LHS simulation method for the analysis of the compressor foundation reliability was used on program ANSYS. The 200 simulations for five load cases were calculated in the real time on PC. The probabilistic analysis gives us more complex information about the soil-foundation-machine interaction as the deterministic analysis.

Introduction to the theory of machines and languages

Energy Technology Data Exchange (ETDEWEB)

Weidhaas, P. P.

1976-04-01

This text is intended to be an elementary ''guided tour'' through some basic concepts of modern computer science. Various models of computing machines and formal languages are studied in detail. Discussions center around questions such as, ''What is the scope of problems that can or cannot be solved by computers.''

COMPONENTS PROVISION MANAGEMENT FOR MACHINE BUILDING MANUFACTURER

Directory of Open Access Journals (Sweden)

Ekaterina P. Bochkareva

2014-01-01

Full Text Available In the paper is given an approach to themanagement of components provision formachine building manufacturer based uponinternational standards and best practicesof leading international companies. Thecomplex expertise methods are used forthe development of the proposed machinebuilding manufacturer suppliers’ operational management method. At a strategic level is proposed a tool for planning the suppliers’portfolio and a machine building manufacturer supplier development methodology.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

Science.gov (United States)

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

Directory of Open Access Journals (Sweden)

Joanna F Dipnall

Full Text Available Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010. Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30, serum glucose (OR 1.01; 95% CI 1.00, 1.01 and total bilirubin (OR 0.12; 95% CI 0.05, 0.28. Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016, and current smokers (p<0.001.The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling

Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation

Directory of Open Access Journals (Sweden)

Takuya Goto

2010-09-01

Full Text Available Since English expressions vary according to the genres, it is important for students to study questions that are generated from sentences of the target genre. Although various questions are prepared, it is still not enough to satisfy various genres which students want to learn. On the other hand, when producing English questions, sufficient grammatical knowledge and vocabulary are needed, so it is difficult for non-expert to prepare English questions by themselves. In this paper, we propose an automatic generation system of multiple-choice cloze questions from English texts. Empirical knowledge is necessary to produce appropriate questions, so machine learning is introduced to acquire knowledge from existing questions. To generate the questions from texts automatically, the system (1 extracts appropriate sentences for questions from texts based on Preference Learning, (2 estimates a blank part based on Conditional Random Field, and (3 generates distracters based on statistical patterns of existing questions. Experimental results show our method is workable for selecting appropriate sentences and blank part. Moreover, our method is appropriate to generate the available distracters, especially for the sentence that does not contain the proper noun.

Composite Material Testing Data Reduction to Adjust for the Systematic 6-DOF Testing Machine Aberrations

Science.gov (United States)

Athanasios lliopoulos; John G. Michopoulos; John G. C. Hermanson

2012-01-01

This paper describes a data reduction methodology for eliminating the systematic aberrations introduced by the unwanted behavior of a multiaxial testing machine, into the massive amounts of experimental data collected from testing of composite material coupons. The machine in reference is a custom made 6-DoF system called NRL66.3 and developed at the NAval...

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

Directory of Open Access Journals (Sweden)

C. V. Subbulakshmi

2015-01-01

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

Mind, Machine, and Creativity: An Artist's Perspective.

Science.gov (United States)

Sundararajan, Louise

2014-06-01

Harold Cohen is a renowned painter who has developed a computer program, AARON, to create art. While AARON has been hailed as one of the most creative AI programs, Cohen consistently rejects the claims of machine creativity. Questioning the possibility for AI to model human creativity, Cohen suggests in so many words that the human mind takes a different route to creativity, a route that privileges the relational, rather than the computational, dimension of cognition. This unique perspective on the tangled web of mind, machine, and creativity is explored by an application of three relational models of the mind to an analysis of Cohen's talks and writings, which are available on his website: www.aaronshome.com.

Machining variability impacts on the strength of a 'chair-side' CAD-CAM ceramic.

LENUS (Irish Health Repository)

Addison, Owen

2012-08-01

To develop a novel methodology to generate specimens for bi-axial flexure strength (BFS) determination from a \\'chair-side\\' CAD-CAM feldspathic ceramic with surface defect integrals analogous to the clinical state. The hypotheses tested were: BFS and surface roughness (R(a)) are independent of machining variability introduced by the renewal or deterioration of form-grinding tools and that a post-machining annealing cycle would significantly modify BFS.

Application of machine learning for the evaluation of turfgrass plots using aerial images

Science.gov (United States)

Ding, Ke; Raheja, Amar; Bhandari, Subodh; Green, Robert L.

2016-05-01

Historically, investigation of turfgrass characteristics have been limited to visual ratings. Although relevant information may result from such evaluations, final inferences may be questionable because of the subjective nature in which the data is collected. Recent advances in computer vision techniques allow researchers to objectively measure turfgrass characteristics such as percent ground cover, turf color, and turf quality from the digital images. This paper focuses on developing a methodology for automated assessment of turfgrass quality from aerial images. Images of several turfgrass plots of varying quality were gathered using a camera mounted on an unmanned aerial vehicle. The quality of these plots were also evaluated based on visual ratings. The goal was to use the aerial images to generate quality evaluations on a regular basis for the optimization of water treatment. Aerial images are used to train a neural network so that appropriate features such as intensity, color, and texture of the turfgrass are extracted from these images. Neural network is a nonlinear classifier commonly used in machine learning. The output of the neural network trained model is the ratings of the grass, which is compared to the visual ratings. Currently, the quality and the color of turfgrass, measured as the greenness of the grass, are evaluated. The textures are calculated using the Gabor filter and co-occurrence matrix. Other classifiers such as support vector machines and simpler linear regression models such as Ridge regression and LARS regression are also used. The performance of each model is compared. The results show encouraging potential for using machine learning techniques for the evaluation of turfgrass quality and color.

The Five Key Questions of Human Performance Modeling.

Science.gov (United States)

Wu, Changxu

2018-01-01

Via building computational (typically mathematical and computer simulation) models, human performance modeling (HPM) quantifies, predicts, and maximizes human performance, human-machine system productivity and safety. This paper describes and summarizes the five key questions of human performance modeling: 1) Why we build models of human performance; 2) What the expectations of a good human performance model are; 3) What the procedures and requirements in building and verifying a human performance model are; 4) How we integrate a human performance model with system design; and 5) What the possible future directions of human performance modeling research are. Recent and classic HPM findings are addressed in the five questions to provide new thinking in HPM's motivations, expectations, procedures, system integration and future directions.

Technical and Symbolic Knowledge in CNC Machining: A Study of Technical Workers of Different Backgrounds.

Science.gov (United States)

Martin, Laura M. W.; Beach, King

Performances of 45 individuals with varying degrees of formal and informal training in machining and programming were compared on tasks designed to tap intellectual changes that may occur with the introduction of computer numerical control (CNC). Participants--30 machinists, 8 machine operators, and 7 engineers--were asked background questions and…

Practical aspects of the use of three-phase alternating current electric machines in electricity storage system

Science.gov (United States)

Ciucur, Violeta

2015-02-01

Of three-phase alternating current electric machines, it brings into question which of them is more advantageous to be used in electrical energy storage system by pumping water. The two major categories among which are given dispute are synchronous and the asynchronous machine. To consider the synchronous machine with permanent magnet configuration because it brings advantages compared with conventional synchronous machine, first by removing the necessary additional excitation winding. From the point of view of loss of the two types of machines, the optimal adjustment of the magnetic flux density is obtained to minimize the copper loss by hysteresis and eddy currents.

Machine tool metrology an industrial handbook

CERN Document Server

Smith, Graham T

2016-01-01

Maximizing reader insights into the key scientific disciplines of Machine Tool Metrology, this text will prove useful for the industrial-practitioner and those interested in the operation of machine tools. Within this current level of industrial-content, this book incorporates significant usage of the existing published literature and valid information obtained from a wide-spectrum of manufacturers of plant, equipment and instrumentation before putting forward novel ideas and methodologies. Providing easy to understand bullet points and lucid descriptions of metrological and calibration subjects, this book aids reader understanding of the topics discussed whilst adding a voluminous-amount of footnotes utilised throughout all of the chapters, which adds some additional detail to the subject. Featuring an extensive amount of photographic-support, this book will serve as a key reference text for all those involved in the field. .

Micro-machined resonator oscillator

Science.gov (United States)

Koehler, Dale R.; Sniegowski, Jeffry J.; Bivens, Hugh M.; Wessendorf, Kurt O.

1994-01-01

A micro-miniature resonator-oscillator is disclosed. Due to the miniaturization of the resonator-oscillator, oscillation frequencies of one MHz and higher are utilized. A thickness-mode quartz resonator housed in a micro-machined silicon package and operated as a "telemetered sensor beacon" that is, a digital, self-powered, remote, parameter measuring-transmitter in the FM-band. The resonator design uses trapped energy principles and temperature dependence methodology through crystal orientation control, with operation in the 20-100 MHz range. High volume batch-processing manufacturing is utilized, with package and resonator assembly at the wafer level. Unique design features include squeeze-film damping for robust vibration and shock performance, capacitive coupling through micro-machined diaphragms allowing resonator excitation at the package exterior, circuit integration and extremely small (0.1 in. square) dimensioning. A family of micro-miniature sensor beacons is also disclosed with widespread applications as bio-medical sensors, vehicle status monitors and high-volume animal identification and health sensors. The sensor family allows measurement of temperatures, chemicals, acceleration and pressure. A microphone and clock realization is also available.

PSA methodology

Energy Technology Data Exchange (ETDEWEB)

Magne, L

1997-12-31

The purpose of this text is first to ask a certain number of questions on the methods related to PSAs. Notably we will explore the positioning of the French methodological approach - as applied in the EPS 1300{sup 1} and EPS 900{sup 2} PSAs - compared to other approaches (Part One). This reflection leads to more general reflection: what contents, for what PSA? This is why, in Part Two, we will try to offer a framework for definition of the criteria a PSA should satisfy to meet the clearly identified needs. Finally, Part Three will quickly summarize the questions approached in the first two parts, as an introduction to the debate. 15 refs.

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...

An Evaluative Study of Machine Translation in the EFL Scenario of Saudi Arabia

Science.gov (United States)

Al-Tuwayrish, Raneem Khalid

2016-01-01

Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT) have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in…

Attitude strength as an explanation for wording effects in political opinion questions

NARCIS (Netherlands)

Holleman, Bregje; Kamoen, Naomi

2017-01-01

Survey methodological research shows over and again that contrastive wordings in attitude questions affect the answers obtained. Rugg (1940) was the first to establish that a question about freedom of speech phrased with the verb ‘allow’ elicited more ‘no’-answers compared to the number of

Matrix stochastic analysis of the maintainability of a machine under shocks

International Nuclear Information System (INIS)

Montoro-Cazorla, Delia; Pérez-Ocón, Rafael

2014-01-01

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

Operating point resolved loss computation in electrical machines

Directory of Open Access Journals (Sweden)

Pfingsten Georg Von

2016-03-01

Full Text Available Magnetic circuits of electromagnetic energy converters, such as electrical machines, are nowadays highly utilized. This proposition is intrinsic for the magnetic as well as the electric circuit and depicts that significant enhancements of electrical machines are difficult to achieve in the absence of a detailed understanding of underlying effects. In order to improve the properties of electrical machines the accurate determination of the locally distributed iron losses based on idealized model assumptions solely is not sufficient. Other loss generating effects have to be considered and the possibility being able to distinguish between the causes of particular loss components is indispensable. Parasitic loss mechanisms additionally contributing to the total losses originating from field harmonics, non-linear material behaviour, rotational magnetizations, and detrimental effects caused by the manufacturing process or temperature, are not explicitly considered in the common iron-loss models, probably even not specifically contained in commonly used calibration factors. This paper presents a methodology being able to distinguish between different loss mechanisms and enables to individually consider particular loss mechanisms in the model of the electric machine. A sensitivity analysis of the model parameters can be performed to obtain information about which decisive loss origin for which working point has to be manipulated by the electromagnetic design or the control of the machine.

Complexity of preemptive minsum scheduling on unrelated parallel machines

NARCIS (Netherlands)

Sitters, R.A.

2005-01-01

We show that the problems of minimizing total completion time and of minimizing the number of late jobs on unrelated parallel machines, when preemption is allowed, are both NP-hard in the strong sense. The former result settles a long-standing open question and is remarkable since the non-preemptive

Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

Directory of Open Access Journals (Sweden)

Thangam Chinnadurai

2016-12-01

Full Text Available This study focuses on investigating the effects of process parameters, namely, Peak current (Ip, Pulse on time (Ton, Pulse off time (Toff, Water pressure (Wp, Wire feed rate (Wf, Wire tension (Wt, Servo voltage (Sv and Servo feed setting (Sfs, on the Material Removal Rate (MRR and Surface Roughness (SR for Wire electrical discharge machining (Wire-EDM of nickel using Taguchi method. Response Surface Methodology (RSM is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used.

Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

International Nuclear Information System (INIS)

Chinnadurai, T.; Vendan, S.A.

2016-01-01

This study focuses on investigating the effects of process parameters, namely, Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff), Water pressure (Wp), Wire feed rate (Wf), Wire tension (Wt), Servo voltage (Sv) and Servo feed setting (Sfs), on the Material Removal Rate (MRR) and Surface Roughness (SR) for Wire electrical discharge machining (Wire-EDM) of nickel using Taguchi method. Response Surface Methodology (RSM) is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA) method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used. (Author)

Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

Energy Technology Data Exchange (ETDEWEB)

Chinnadurai, T.; Vendan, S.A.

2016-07-01

This study focuses on investigating the effects of process parameters, namely, Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff), Water pressure (Wp), Wire feed rate (Wf), Wire tension (Wt), Servo voltage (Sv) and Servo feed setting (Sfs), on the Material Removal Rate (MRR) and Surface Roughness (SR) for Wire electrical discharge machining (Wire-EDM) of nickel using Taguchi method. Response Surface Methodology (RSM) is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA) method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used. (Author)

[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.

Imaginative methodologies in the social sciences

DEFF Research Database (Denmark)

Imaginative Methodologies develops, expands and challenges conventional social scientific methodology and language by way of literary, poetic and other alternative sources of inspiration. Sociologists, social workers, anthropologists, criminologists and psychologists all try to rethink, provoke...... and reignite social scientific methodology. Imaginative Methodologies challenges the mainstream social science methodological orthodoxy closely guarding the boundaries between the social sciences and the arts and humanities, pointing out that authors and artists are often engaged in projects parallel to those...... of the social sciences and vice versa, and that artistic and cultural productions today do not constitute a specialist field, but are integral to our social reality. The book will be of interest to scholars and students in the social sciences and across the arts and humanities working with questions...

Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology

Science.gov (United States)

Kumar, Amit; Soota, Tarun; Kumar, Jitendra

2018-03-01

Wire electric discharge machining (WEDM) is one of the advanced machining processes. Response surface methodology coupled with Grey relation analysis method has been proposed and used to optimise the machining parameters of WEDM. A face centred cubic design is used for conducting experiments on high speed steel (HSS) M2 grade workpiece material. The regression model of significant factors such as pulse-on time, pulse-off time, peak current, and wire feed is considered for optimising the responses variables material removal rate (MRR), surface roughness and Kerf width. The optimal condition of the machining parameter was obtained using the Grey relation grade. ANOVA is applied to determine significance of the input parameters for optimising the Grey relation grade.

Dream machine hopes for a giant collider lie in a worldwide appeal

CERN Multimedia

Appell, D

2004-01-01

"High-energy physicists have a new machine in mind: an unprecedented accelerator 30 kilometers long that would offer a precise tool to explore some of the most imprtant unanswered questions in physics" (1 page)

ChargeOut! : determining machine and capital equipment charge-out rates using discounted cash-flow analysis

Science.gov (United States)

E.M. (Ted) Bilek

2007-01-01

The model ChargeOut! was developed to determine charge-out rates or rates of return for machines and capital equipment. This paper introduces a costing methodology and applies it to a piece of capital equipment. Although designed for the forest industry, the methodology is readily transferable to other sectors. Based on discounted cash-flow analysis, ChargeOut!...

Single-molecule imaging and manipulation of biomolecular machines and systems.

Science.gov (United States)

Iino, Ryota; Iida, Tatsuya; Nakamura, Akihiko; Saita, Ei-Ichiro; You, Huijuan; Sako, Yasushi

2018-02-01

Biological molecular machines support various activities and behaviors of cells, such as energy production, signal transduction, growth, differentiation, and migration. We provide an overview of single-molecule imaging methods involving both small and large probes used to monitor the dynamic motions of molecular machines in vitro (purified proteins) and in living cells, and single-molecule manipulation methods used to measure the forces, mechanical properties and responses of biomolecules. We also introduce several examples of single-molecule analysis, focusing primarily on motor proteins and signal transduction systems. Single-molecule analysis is a powerful approach to unveil the operational mechanisms both of individual molecular machines and of systems consisting of many molecular machines. Quantitative, high-resolution single-molecule analyses of biomolecular systems at the various hierarchies of life will help to answer our fundamental question: "What is life?" This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato. Copyright © 2017 Elsevier B.V. All rights reserved.

Knowledge-based support for design and operational use of human-machine interfaces

International Nuclear Information System (INIS)

Johannsen, G.

1994-01-01

The possibilities for knowledge support of different human user classes, namely operators, operational engineers and designers of human-machine interfaces, are discussed. Several human-machine interface functionalities are briefly explained. The paper deals with such questions as which type of knowledge is needed for design and operation, how to represent it, where to get it from, how to process it, and how to consider and use it. The relationships between design and operational use are thereby emphasised. (author)

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

Science.gov (United States)

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

2013-01-01

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

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

Directory of Open Access Journals (Sweden)

Ching Lee Koo

2013-01-01

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

Numerical modeling and optimization of machining duplex stainless steels

Directory of Open Access Journals (Sweden)

Rastee D. Koyee

2015-01-01

Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also

Questioning Mathematical Knowledge in Different Didactic Paradigms: The Case of Group Theory

Science.gov (United States)

Bosch, Marianna; Gascón, Josep; Nicolás, Pedro

2018-01-01

What is questioned and what is taken for granted when carrying out research into the teaching of a given mathematical topic such as Group Theory? This paper presents two different questioning procedures using the methodological tools provided by the Anthropological Theory of the Didactic (ATD). The first one, leading to an undergraduate…

CAGE IIIA Distributed Simulation Design Methodology

Science.gov (United States)

2014-05-01

2 VHF Very High Frequency VLC Video LAN Codec – an Open-source cross-platform multimedia player and framework VM Virtual Machine VOIP Voice Over...Implementing Defence Experimentation (GUIDEx). The key challenges for this methodology are with understanding how to: • design it o define the...operation and to be available in the other nation’s simulations. The challenge for the CAGE campaign of experiments is to continue to build upon this

Can Big Data Machines Analyze Stock Market Sentiment?

Science.gov (United States)

Dhar, Vasant

2014-12-01

Do the massive amounts of social and professionally curated data on the Internet contain useful sentiment about the market that "big data machines" can extract systematically? If so, what are the important challenges in creating economic value from these diffuse sources? In this commentary, I delve into these questions and frame the challenges involved using recent market developments as an illustrative backdrop.

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

NARCIS (Netherlands)

Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D G; Cordeiro, Maria Natália D S; Gümüş, Zeynep H; Costa, Joaquim; Bonvin, Alexandre M J J; de Sousa Moreira, Irina

2016-01-01

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model

Identification of Nigerian English idioms: A methodological perspective

African Journals Online (AJOL)

The task becomes more challenging when the feature of interest involves figurative meaning such as idioms and idiomatic expressions. In a recently completed project that sought to examine idioms in Nigerian English, questions and issues about methodology were raised. Among these were the questions of how idioms in ...

Applications of mixed-methods methodology in clinical pharmacy research.

Science.gov (United States)

Hadi, Muhammad Abdul; Closs, S José

2016-06-01

Introduction Mixed-methods methodology, as the name suggests refers to mixing of elements of both qualitative and quantitative methodologies in a single study. In the past decade, mixed-methods methodology has gained popularity among healthcare researchers as it promises to bring together the strengths of both qualitative and quantitative approaches. Methodology A number of mixed-methods designs are available in the literature and the four most commonly used designs in healthcare research are: the convergent parallel design, the embedded design, the exploratory design, and the explanatory design. Each has its own unique advantages, challenges and procedures and selection of a particular design should be guided by the research question. Guidance on designing, conducting and reporting mixed-methods research is available in the literature, so it is advisable to adhere to this to ensure methodological rigour. When to use it is best suited when the research questions require: triangulating findings from different methodologies to explain a single phenomenon; clarifying the results of one method using another method; informing the design of one method based on the findings of another method, development of a scale/questionnaire and answering different research questions within a single study. Two case studies have been presented to illustrate possible applications of mixed-methods methodology. Limitations Possessing the necessary knowledge and skills to undertake qualitative and quantitative data collection, analysis, interpretation and integration remains the biggest challenge for researchers conducting mixed-methods studies. Sequential study designs are often time consuming, being in two (or more) phases whereas concurrent study designs may require more than one data collector to collect both qualitative and quantitative data at the same time.

Inverse analysis of turbidites by machine learning

Science.gov (United States)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small

Consumer support for healthy food and drink vending machines in public places.

Science.gov (United States)

Carrad, Amy M; Louie, Jimmy Chun-Yu; Milosavljevic, Marianna; Kelly, Bridget; Flood, Victoria M

2015-08-01

To investigate the feasibility of introducing vending machines for healthier food into public places, and to examine the effectiveness of two front-of-pack labelling systems in the vending machine context. A survey was conducted with 120 students from a university and 120 employees, patients and visitors of a hospital in regional NSW, Australia. Questions explored vending machine use, attitudes towards healthier snack products and price, and the performance of front-of-pack labelling formats for vending machine products. Most participants viewed the current range of snacks and drinks as "too unhealthy" (snacks 87.5%; drinks 56.7%). Nuts and muesli bars were the most liked healthier vending machine snack. Higher proportions of participants were able to identify the healthier snack in three of the five product comparisons when products were accompanied with any type of front-of-pack label (all pvending machines. Front-of-pack label formats on vending machines may assist consumers to identify healthier products. Public settings, such as universities and hospitals, should support consumers to make healthy dietary choices by improving food environments. © 2015 Public Health Association of Australia.

Questioning the Questions

Science.gov (United States)

Tienken, Christopher H.; Goldberg, Stephanie; DiRocco, Dominic

2010-01-01

Historical accounts of questioning used in the education process trace back to Socrates. One of the best examples of his use of questioning is found in Plato's "The Republic." Socrates used a series of strategic questions to help his student Glaucon come to understand the concept of justice. Socrates purposefully posed a series of…

Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

Directory of Open Access Journals (Sweden)

Satoshi Tsutsui

2017-12-01

Full Text Available Purpose: This paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD and related diseases using the machine reading approach. Design/methodology/approach: The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level. Findings: Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV/Acquired Immune Deficiency Syndrome (AIDS, which can help answer the question of how AIDS/HIV and AD are very different yet related diseases. Research limitations: Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction. Practical implications: This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV. Originality/value: Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

Technology of high-speed combined machining with brush electrode

Science.gov (United States)

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

2018-03-01

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

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 ...

Crawl and crowd to bring machine translation to under-resourced languages

NARCIS (Netherlands)

Toral Ruiz, Antonio

2017-01-01

We present a widely applicable methodology to bring machine translation (MT) to under-resourced languages in a cost-effective and rapid manner. Our proposal relies on web crawling to automatically acquire parallel data to train statistical MT systems if any such data can be found for the language

A survey on Barrett's esophagus analysis using machine learning.

Science.gov (United States)

de Souza, Luis A; Palm, Christoph; Mendel, Robert; Hook, Christian; Ebigbo, Alanna; Probst, Andreas; Messmann, Helmut; Weber, Silke; Papa, João P

2018-05-01

This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimization of the man-machine interface for LMFBRs

International Nuclear Information System (INIS)

Seeman, S.E.; Colley, R.W.; Stratton, R.C.

1982-01-01

An effort is underway to optimize the roles of man and machine in control of Liquid Metal Fast Breeder Reactors. The work reported on here describes two systems that have been developed. The first of these, MIDAS, is a large data base system developed for use at FFTF as an aid to operators in determining how to proceed with maintenance and repairs to be carried out on plant components. This system is presently in use at FFTF. The second system, the Procedure Prompting System, is a system being developed to demonstrate a new methodology for automatically generating off-normal plant recovery instructions. The methodology for this system has been demonstrated on a model of a small subsystem of FFTF

“Investigations on the machinability of Waspaloy under dry environment”

Science.gov (United States)

Deepu, J.; Kuppan, P.; SBalan, A. S.; Oyyaravelu, R.

2016-09-01

Nickel based superalloy, Waspaloy is extensively used in gas turbine, aerospace and automobile industries because of their unique combination of properties like high strength at elevated temperatures, resistance to chemical degradation and excellent wear resistance in many hostile environments. It is considered as one of the difficult to machine superalloy due to excessive tool wear and poor surface finish. The present paper is an attempt for removing cutting fluids from turning process of Waspaloy and to make the processes environmentally safe. For this purpose, the effect of machining parameters such as cutting speed and feed rate on the cutting force, cutting temperature, surface finish and tool wear were investigated barrier. Consequently, the strength and tool wear resistance and tool life increased significantly. Response Surface Methodology (RSM) has been used for developing and analyzing a mathematical model which describes the relationship between machining parameters and output variables. Subsequently ANOVA was used to check the adequacy of the regression model as well as each machining variables. The optimal cutting parameters were determined based on multi-response optimizations by composite desirability approach in order to minimize cutting force, average surface roughness and maximum flank wear. The results obtained from the experiments shown that machining of Waspaloy using coated carbide tool with special ranges of parameters, cutting fluid could be completely removed from machining process

Recent Educational Experiences in Electric Machine Maintenance Teaching

Directory of Open Access Journals (Sweden)

Jose Alfonso Antonino-Daviu

2013-05-01

Full Text Available Maintenance of electric machines and installations is a particularly important area; eventual faults in these devices may lead to significant losses in terms of time and money. The investment and concern in developing proper maintenance protocols have been gradually increasing over recent decades. As a consequence, there is a need to instruct future engineers in the electric machines and installations maintenance area. The subject "Maintenance of Electric Machines and Installations" has been designed under this idea. It is taught within an official master degree in Maintenance Engineering. This work describes the educational experiences reached during the initial years of the teaching of the subject. Aspects such as student profiles, subject approaches, design of the syllabus, methodology and structure of the laboratory sessions are remarked in the work. In addition, the paper discusses other educational strategies which are being introduced to increase the interest in the subject, such as integration of Information and Communication Technologies (ICT, promotion of the collaborative work, inclusion of the possibility of remote learning or development of new assessment systems.

Political Transformation and Research Methodology in Doctoral Education

Science.gov (United States)

Herman, Chaya

2010-01-01

This paper examines the relationship between political change and epistemologies and methodologies employed at doctorate level. It does so by analysing the range of topics, questions and methodologies used by doctoral students at the University of Pretoria's Faculty of Education between 1985 and 2005--a time-frame that covers the decade before and…

MACHINE-TRANSFORMER UNITS FOR WIND TURBINES

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V.I. Panchenko

2016-03-01

Full Text Available Background. Electric generators of wind turbines must meet the following requirements: they must be multi-pole; to have a minimum size and weight; to be non-contact, but controlled; to ensure the maximum possible output voltage when working on the power supply system. Multipole and contactless are relatively simply realized in the synchronous generator with permanent magnet excitation and synchronous inductor generator with electromagnetic excitation; moreover the first one has a disadvantage that there is no possibility to control the output voltage, and the second one has a low magnetic leakage coefficient with the appropriate consequences. Purpose. To compare machine dimensions and weight of the transformer unit with induction generators and is an opportunity to prove their application for systems with low RMS-growth rotation. Methodology. A new design of the electric inductor machine called in technical literature as machine-transformer unit (MTU is presented. A ratio for estimated capacity determination of such units is obtained. Results. In a specific example it is shown that estimated power of MTU may exceed the same one for traditional synchronous machines at the same dimensions. The MTU design allows placement of stator coil at some distance from the rotating parts of the machine, namely, in a closed container filled with insulating liquid. This will increase capacity by means of more efficient cooling of coil, as well as to increase the output voltage of the MTU as a generator to a level of 35 kV or more. The recommendations on the certain parameters selection of the MTU stator winding are presented. The formulas for copper cost calculating on the MTU field winding and synchronous salient-pole generator are developed. In a specific example it is shown that such costs in synchronous generator exceed 2.5 times the similar ones in the MTU.

Big universe, big data

DEFF Research Database (Denmark)

Kremer, Jan; Stensbo-Smidt, Kristoffer; Gieseke, Fabian Cristian

2017-01-01

, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing......, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications....

Application of Machine Learning for Dragline Failure Prediction

Directory of Open Access Journals (Sweden)

Taghizadeh Amir

2017-01-01

Full Text Available Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Draglines’ unavailability and unexpected failures result in delayed productions and increased maintenance and operating costs. Therefore, achieving high availability of draglines plays a crucial role for increasing economic feasibility of mining projects. Applications of methodologies which can forecast the failure type of dragline based on the available failure data not only help to reduce the maintenance and operating costs but also increase the availability and the production rate. In this study, Machine Learning approaches have been applied for data which has been gathered from an operating coal mine in Turkey. The study methodology consists of three algorithms as: i implementation of K-Nearest Neighbors, ii implementation of Multi-Layer Perceptron, and iii implementation of Radial Basis Function. The algorithms have been utilized for predicting the draglines’ failure types. In this sense, the input data, which are mean time-to-failure, and the output data, failure types, have been fed to the algorithms. The regression analysis of methodologies have been compared and showed the K- Nearest Neighbors has a higher rate of regression which is around 70 percent. Thus, the K-Nearest Neighbor algorithm can be applied in order to preventive components replacement which causes to minimized preventive and corrective cost parameters. The accurate prediction of failure type, indeed, causes to optimized number of inspections. The novelty of this study is application of machine learning approaches in draglines’ reliability subject for first time.

ANALYSIS OF PARAMETERS AFFECTING THE QUALITY OF A CUTTING MACHINE

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Iveta Onderová

2014-02-01

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

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)

Analysis and prediction of dimensions and cost of laser micro-machining internal channel fabrication process

Directory of Open Access Journals (Sweden)

Brabazon D.

2010-06-01

Full Text Available This paper presents the utilisation of Response Surface Methodology (RSM as the prediction tool for the laser micro-machining process. Laser internal microchannels machined using pulsed Nd:YVO4 laser in polycarbonate were investigated. The experiments were carried out according to 33 factorial Design of Experiment (DoE. In this work the three input process set as control parameters were laser power, P; pulse repetition frequency, PRF; and sample translation speed, U. Measured responses were the channel width and the micro-machining operating cost per metre of produced microchannels. The responses were sufficiently predicted within the set micro-machining parameters limits. Two factorial interaction (2FI and quadratic polynomial regression equations for both responses were constructed. It is proposed that the developed prediction equations can be used to find locally optimal micro-machining process parameters under experimental and operational conditions.

Trim cut machining and surface integrity analysis of Nimonic 80A alloy using wire cut EDM

Directory of Open Access Journals (Sweden)

Amitesh Goswami

2017-02-01

Full Text Available This present work deals with the features of trim cut wire EDM machining of Nimonic 80A in terms of machining parameters, to predict material removal rate (MRR, surface roughness (Ra, wire wear ratio (WWR and microstructure analysis. Trim cut is performed after rough cut to remove the rough layer deposited after machining due to melting and re-solidification of the eroded metal from workpiece as well as from wire electrode. Taguchi’s design of experiments methodology has been used for planning and designing the experiments. The relative significance of various factors has also been evaluated and analyzed using ANOVA. The results clearly indicate trim cut potential for high surface finish compared to rough cut machining.

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.

The use of machine learning methodologies to analyse antibiotic and biocide susceptibility in Staphylococcus aureus.

Directory of Open Access Journals (Sweden)

Joana Rosado Coelho

Full Text Available BACKGROUND: The rise of antibiotic resistance in pathogenic bacteria is a significant problem for the treatment of infectious diseases. Resistance is usually selected by the antibiotic itself; however, biocides might also co-select for resistance to antibiotics. Although resistance to biocides is poorly defined, different in vitro studies have shown that mutants presenting low susceptibility to biocides also have reduced susceptibility to antibiotics. However, studies with natural bacterial isolates are more limited and there are no clear conclusions as to whether the use of biocides results in the development of multidrug resistant bacteria. METHODS: The main goal is to perform an unbiased blind-based evaluation of the relationship between antibiotic and biocide reduced susceptibility in natural isolates of Staphylococcus aureus. One of the largest data sets ever studied comprising 1632 human clinical isolates of S. aureus originated worldwide was analysed. The phenotypic characterization of 13 antibiotics and 4 biocides was performed for all the strains. Complex links between reduced susceptibility to biocides and antibiotics are difficult to elucidate using the standard statistical approaches in phenotypic data. Therefore, machine learning techniques were applied to explore the data. RESULTS: In this pioneer study, we demonstrated that reduced susceptibility to two common biocides, chlorhexidine and benzalkonium chloride, which belong to different structural families, is associated to multidrug resistance. We have consistently found that a minimum inhibitory concentration greater than 2 mg/L for both biocides is related to antibiotic non-susceptibility in S. aureus. CONCLUSIONS: Two important results emerged from our work, one methodological and one other with relevance in the field of antibiotic resistance. We could not conclude on whether the use of antibiotics selects for biocide resistance or vice versa. However, the observation of

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

Science.gov (United States)

Plagianakos, V P; Magoulas, G D; Vrahatis, M N

2006-03-01

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

Qualitative and quantitative research in Sociolinguistics: methodological dadaism?

Directory of Open Access Journals (Sweden)

Caroline Rodrigues Cardoso

2013-07-01

Full Text Available The confluence between quantitative and qualitative research in Sociolinguistics is a methodological Dadaism? The issue here is not epistemology, because I assume that the Sociolinguistics studies the language linked to social. I want to demonstrate that the methodological approach depends on the research question, ie, the subject about which a thesis is developed.

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Science.gov (United States)

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

2015-07-01

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

INTELLIGENT QUESTION-ANSWERING SYSTEM “MIVAR VIRTUAL CONSULTANT”

Directory of Open Access Journals (Sweden)

Larisa Evgen’evna Adamova

2017-05-01

Full Text Available The paper deals with the process of designing question-answering system “Mivar Virtual Consultant” using specialized information-technology platform for understanding meaning of text in the natural Russian language. The system is capable of accumulating knowledge from texts in the natural Russian language and managing this knowledge. The methodology for training virtual consultant is described.

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.

Study of Environmental Data Complexity using Extreme Learning Machine

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

The Lattice-Valued Turing Machines and the Lattice-Valued Type 0 Grammars

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Juan Tang

2014-01-01

Full Text Available Purpose. The purpose of this paper is to study a class of the natural languages called the lattice-valued phrase structure languages, which can be generated by the lattice-valued type 0 grammars and recognized by the lattice-valued Turing machines. Design/Methodology/Approach. From the characteristic of natural language, this paper puts forward a new concept of the l-valued Turing machine. It can be used to characterize recognition, natural language processing, and dynamic characteristics. Findings. The mechanisms of both the generation of grammars for the lattice-valued type 0 grammar and the dynamic transformation of the lattice-valued Turing machines were given. Originality/Value. This paper gives a new approach to study a class of natural languages by using lattice-valued logic theory.

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.

Towards efficient 5-axis flank CNC machining of free-form surfaces via fitting envelopes of surfaces of revolution

OpenAIRE

Bo P.; Bartoň M.; Plakhotnik D.; Pottmann H.

2016-01-01

We introduce a new method that approximates free-form surfaces by envelopes of one-parameter motions of surfaces of revolution. In the context of 5-axis computer numerically controlled (CNC) machining, we propose a flank machining methodology which is a preferable scallop-free scenario when the milling tool and the machined free-form surface meet tangentially along a smooth curve. We seek both an optimal shape of the milling tool as well as its optimal path in 3D space and propose an optimiza...

Two NP-hardness results for preemptive minsum scheduling of unrelated parallel machines

NARCIS (Netherlands)

Sitters, R.A.; Aardal, K.; Gerards, B.

2001-01-01

We show that the problems of minimizing total completion time and of minimizing the number of late jobs on unrelated parallel machines, when preemption is allowed, are both NP-hard in the strong sense. The former result settles a long-standing open question.

Missing Things and Methodological Swerves: Unsettling the It-Ness of VET

Science.gov (United States)

Shore, Sue; Butler, Elaine

2012-01-01

This paper argues for approaches to research methodologies that interrupt the machinic metaphors and relationships for living circulating in so much VET research. Using the schematic of "cyborg as a figuration" and Wilson's (2009) four epistemological interventions (witnessing, situating, diffracting, acquiring) the authors practice a form of…

Modification and Performance Evaluation of a Low Cost Electro-Mechanically Operated Creep Testing Machine

Directory of Open Access Journals (Sweden)

John J. MOMOH

2010-12-01

Full Text Available Existing mechanically operated tensile and creep testing machine was modified to a low cost, electro-mechanically operated creep testing machine capable of determining the creep properties of aluminum, lead and thermoplastic materials as a function of applied stress, time and temperature. The modification of the testing machine was necessitated by having an electro-mechanically operated creep testing machine as a demonstration model ideal for use and laboratory demonstrations, which will provide an economical means of performing standard creep experiments. The experimental result is a more comprehensive understanding of the laboratory experience, as the technology behind the creep testing machine, the test methodology and the response of materials loaded during experiment are explored. The machine provides a low cost solution for Mechanics of Materials laboratories interested in creep testing experiment and demonstration but not capable of funding the acquisition of commercially available creep testing machines. Creep curves of strain versus time on a thermoplastic material were plotted at a stress level of 1.95MPa, 3.25MPa and 4.55MPa and temperature of 20oC, 40oC and 60oC respectively. The machine is satisfactory since it is always ready for operation at any given time.

Machine learning \\& artificial intelligence in the quantum domain

OpenAIRE

Dunjko, Vedran; Briegel, Hans J.

2017-01-01

Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent th...

Twin Support Vector Machine: A review from 2007 to 2014

Directory of Open Access Journals (Sweden)

Divya Tomar

2015-03-01

Full Text Available Twin Support Vector Machine (TWSVM is an emerging machine learning method suitable for both classification and regression problems. It utilizes the concept of Generalized Eigen-values Proximal Support Vector Machine (GEPSVM and finds two non-parallel planes for each class by solving a pair of Quadratic Programming Problems. It enhances the computational speed as compared to the traditional Support Vector Machine (SVM. TWSVM was initially constructed to solve binary classification problems; later researchers successfully extended it for multi-class problem domain. TWSVM always gives promising empirical results, due to which it has many attractive features which enhance its applicability. This paper presents the research development of TWSVM in recent years. This study is divided into two main broad categories - variant based and multi-class based TWSVM methods. The paper primarily discusses the basic concept of TWSVM and highlights its applications in recent years. A comparative analysis of various research contributions based on TWSVM is also presented. This is helpful for researchers to effectively utilize the TWSVM as an emergent research methodology and encourage them to work further in the performance enhancement of TWSVM.

Exploring undergraduates' understanding of photosynthesis using diagnostic question clusters.

Science.gov (United States)

Parker, Joyce M; Anderson, Charles W; Heidemann, Merle; Merrill, John; Merritt, Brett; Richmond, Gail; Urban-Lurain, Mark

2012-01-01

We present a diagnostic question cluster (DQC) that assesses undergraduates' thinking about photosynthesis. This assessment tool is not designed to identify individual misconceptions. Rather, it is focused on students' abilities to apply basic concepts about photosynthesis by reasoning with a coordinated set of practices based on a few scientific principles: conservation of matter, conservation of energy, and the hierarchical nature of biological systems. Data on students' responses to the cluster items and uses of some of the questions in multiple-choice, multiple-true/false, and essay formats are compared. A cross-over study indicates that the multiple-true/false format shows promise as a machine-gradable format that identifies students who have a mixture of accurate and inaccurate ideas. In addition, interviews with students about their choices on three multiple-choice questions reveal the fragility of students' understanding. Collectively, the data show that many undergraduates lack both a basic understanding of the role of photosynthesis in plant metabolism and the ability to reason with scientific principles when learning new content. Implications for instruction are discussed.

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

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

Assessing the Ability of Vegetation Indices to Identify Shallow Subsurface Water Flow Pathways from Hyperspectral Imagery Using Machine Learning: Methodology

Science.gov (United States)

Byers, J. M.; Doctor, K.

2017-12-01

A common application of the satellite and airborne acquired hyperspectral imagery in the visible and NIR spectrum is the assessment of vegetation. Various absorption features of plants related to both water and chlorophyll content can be used to measure the vigor and access to underlying water sources of the vegetation. The typical strategy is to form hand-crafted features from the hyperspectral data cube by selecting two wavelengths to form difference or ratio images in the pixel space. The new image attempts to provide greater contrast for some feature of the vegetation. The Normalized Difference Vegetation Index (NDVI) is a widely used example formed from the ratio of differences and sums at two different wavelengths. There are dozens of these indices that are ostensibly formed using insights about the underlying physics of the spectral absorption with claims to efficacy in representing various properties of vegetation. In the language of machine learning these vegetation indices are features that can be used as a useful data representation within an algorithm. In this work we use a powerful approach from machine learning, probabilistic graphical models (PGM), to balance the competing needs of using existing hydrological classifications of terrain while finding statistically reliable features within hyperspectral data for identifying the generative process of the data. The algorithm in its simplest form is called a Naïve Bayes (NB) classifier and can be constructed in a data-driven estimation procedure of the conditional probability distributions that form the PGM. The Naïve Bayes model assumes that all vegetation indices (VI) are independent of one another given the hydrological class label. We seek to test its validity in a pilot study of detecting subsurface water flow pathways from VI. A more sophisticated PGM will also be explored called a tree-augmented NB that accounts for the probabilistic dependence between VI features. This methodology provides a

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

Science.gov (United States)

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (pmachine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future

A review of supervised machine learning applied to ageing research.

Science.gov (United States)

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

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

Science.gov (United States)

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

2017-01-01

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

OPTIMIZATION OF MAGNETIZATION AND MAGNATION REGIMES OF STOPPED THREE-PHASE SYNCHRONOUS MACHINE

Directory of Open Access Journals (Sweden)

V. A. VOLKOV

2018-05-01

Full Text Available Purpose. Investigation and optimization (minimization of electric energy losses in a stopped synchronous machine with a thyristor exciter under conditions of its magnetization and demagnetization. Methodology. Operator and variational calculus, mathematical analysis and simulation computer simulation. Findings. The mathematical description of the system under study is developed: "thyristor exciter – stopped synchronous machine", which represents the analytical dependencies for electromagnetic processes, as well as the total power and energy losses in the system under magnetization and demagnetization regimes of the synchronous machine. The optimal time functions for changing the flux linkages of the damper winding and the excitation current of the stopped synchronous machine, in which they are minimized by energy in the system under investigation when the machine is magnetized and demagnetized. The dependences of the total energy losses in the system under study on the durations of the magnetization and demagnetization times of the machine are calculated, and their comparison is compared for different types (linear, parabolic and proposed optimal of the trajectories of the change of the linkage, as well as for a linear and exponential change in the excitation current of the machine. Analytic dependencies are obtained using the calculations of electromagnetic and energy transient processes in the "thyristor exciter – stopped synchronous machine" system under the considered types of variation of flux linkage and excitation current of the machine. Originality. It consists in finding the optimal trajectories of the time variation of the excitation current of a stopped synchronous machine and the optimal durations of its magnetization and demagnetization times, which ensure minimization of energy losses in the system "thyristor exciter – stopped synchronous machine". Practical value. It consists in reducing unproductive energy losses in

FAULT DIAGNOSIS IN ROTATING MACHINE USING FULL SPECTRUM OF VIBRATION AND FUZZY LOGIC

Directory of Open Access Journals (Sweden)

ROGER R. DA SILVA

2017-11-01

Full Text Available Industries are always looking for more efficient maintenance systems to minimize machine downtime and productivity liabilities. Among several approaches, artificial intelligence techniques have been increasingly applied to machine diagnosis. Current paper forwards the development of a system for the diagnosis of mechanical faults in the rotating structures of machines, based on fuzzy logic, using rules foregrounded on the full spectrum of the machine´s complex vibration signal. The diagnostic system was developed in Matlab and it was applied to a rotor test rig where different faults were introduced. Results showed that the diagnostic system based on full spectra and fuzzy logic is capable of identifying with precision different types of faults, which have similar half spectrum. The methodology has a great potential to be implemented in predictive maintenance programs in industries and may be expanded to include the identification of other types of faults not covered in the case study under analysis.

An LWR design decision Methodology

International Nuclear Information System (INIS)

Leahy, T.J.; Rees, D.C.; Young, J.

1982-01-01

While all parties involved in nuclear plant regulation endeavor to make decisions which optimize the considerations of plant safety and financial impacts, these decisions are generally made without the benefit of a systematic and rigorous approach to the questions confronting the decision makers. A Design Decision Methodology has been developed which provides such a systematic approach. By employing this methodology, which makes use of currently accepted probabilistic risk assessment techniques and cost estimation, informed decisions may be made against a background of comparisons between the relative levels of safety and costs associated with various design alternatives

Mining software specifications methodologies and applications

CERN Document Server

Lo, David

2011-01-01

An emerging topic in software engineering and data mining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes recent approaches for mining specifications of software systems. Experts in the field illustrate how to apply state-of-the-art data mining and machine learning techniques to address software engineering concerns. In the first set of chapters, the book introduces a number of studies on mining finite

Tooth-coil permanent magnet synchronous machine design for special applications

Energy Technology Data Exchange (ETDEWEB)

Ponomarev, P.

2013-11-01

This doctoral thesis presents a study on the design of tooth-coil permanent magnet synchronous machines. The electromagnetic properties of concentrated non-overlapping winding permanent magnet synchronous machines, or simply tooth-coil permanent magnet synchronous machines (TC-PMSMs), are studied in details. It is shown that current linkage harmonics play the deterministic role in the behavior of this type of machines. Important contributions are presented as regards of calculation of parameters of TC-PMSMs,particularly the estimation of inductances. The current linkage harmonics essentially define the air-gap harmonic leakage inductance, rotor losses and localized temporal inductance variation. It is proven by FEM analysis that inductance variation caused by the local temporal harmonic saturation results in considerable torque ripple, and can influence on sensorless control capabilities. Example case studies an integrated application of TC-IPMSMs in hybrid off-highway working vehicles. A methodology for increasing the efficiency of working vehicles is introduced. It comprises several approaches - hybridization, working operations optimization, component optimization and integration. As a result of component optimization and integration, a novel integrated electro-hydraulic energy converter (IEHEC) for off-highway working vehicles is designed. The IEHEC can considerably increase the operational efficiency of a hybrid working vehicle. The energy converter consists of an axial-piston hydraulic machine and an integrated TCIPMSM being built on the same shaft. The compact assembly of the electrical and hydraulic machines enhances the ability to find applications for such a device in the mobile environment of working vehicles.Usage of hydraulic fluid, typically used in working actuators, enables direct-immersion oil cooling of designed electrical machine, and further increases the torque- and power- densities of the whole device. (orig.)

USAGE OF CONSTRUCTION-ORIENTED SOFTWARE SCAD FOR ANALYSIS OF WORK OF MACHINE-BUILDING STRUCTURES

Directory of Open Access Journals (Sweden)

D. О. Bannikov

2018-02-01

Full Text Available Purpose. In the case of analysis of work of the machine-building structures, the direct usage of construction-oriented software developments is impossible, since ideology and methodology for solving various tasks in construction and machine-building are different. Therefore, in the conducting of practical calculations, there is a need for a certain adjustment of the approaches put in the program complexes and their adaptation to the engineering industry. The presentation of the author's experience of the construction-oriented software SCAD usage for Windows for analyzing the work of various machine-building structures, their components and assemblies is the immediate purpose of the publication. Methodology. During a long period of time the author was engaged in analyzing the work of building, mainly thin-walled, steel structures using the Finite Element Method based on the SCAD for Windows software package. At the same time, a considerable number of machine-building structures were considered, including railroad rolling stock units. Most of these tasks grew into a scientific and research problem that needed to be thoroughly researched and analyzed before giving design recommendations. Findings. The publication presents more than a dozen different tasks, typical for the machine-building industry, which the author had to deal with. Static and quasi-static problems, the problem of motion in time, the contact problem, the problem of the cracks deve-lopment, the physical and geometric non-linearity are among them. Accordingly, for each of these problems the main challenges, features and practical techniques developed during the work are presented, as well as the constructed finite element models are presented as an illustration. Originality. The experience of construction-oriented software product usage on the basis of the Finite Element Method for analyzing of the work of machine-building structures is generalized. A number of practical methods and

A Critique for the Methodology of Scientific Research Programmes

Directory of Open Access Journals (Sweden)

Saeed Naji

2009-01-01

Full Text Available The purpose of the paper is to evaluate of Imre Lakatos' MSRP (Methodology of Scientific Research Programs. Presenting the methodology which is based on Popperian Refutationism, Lakatos intended to overcome Pluralism (, Relativism and Skepticism and distinguishes the best theory (/program in science. The question is that did the lakatos' secondary change in the form and content of MSRP -against some historical facts and criticisms- make some serious deficiencies in his methodology? The answer to this question is positive. One of Lakatos' changes in MSRP is to resort to a new concept of "rationality". Presenting a logical analysis, the paper shows that this change causes MSRP to be unable to distinguish the best program among others. Furthermore he gives a new definition of the term 'methodology'. This definition, in its turn, makes MSRP main task to be inactive.Showing the irreparable harms Lakatos' changes produce in MSRP, the paper shows that these changes not only cannot get rid of the deficiencies therein, but it is also unable to meet lakatos' original purpose for MSRP.

Question-asking behavior as a form of cognitive activity

Directory of Open Access Journals (Sweden)

Elvira A. Baranova

2017-03-01

Full Text Available Children’s questions are an indicator of active cognitive perception of reality. Questions but not answers are relevant in revealing a child’s mental life, consciousness and thinking. The lack of question-asking skills can hinder learning, searching and exploration in children. To determine in 7- and 8-year-old school children the common and variable peculiarities of designing a search process for necessary information concerning an unknown object by volitionally formulated questions, as well as the dynamics of the questioning process throughout a school year. The study was based on an experimental methodology, codenamed Guess what there is in the box, and was conducted in four schools in Cheboksary. The sample comprised 158 primary school first-graders who took part in a confirmatory experiment twice, once in September and once in May. The research showed that 96.3% of the questions asked were search questions. Only 30% of the first-graders initiated their searching activities of their own will without having to resort to the given search algorithm, while 70% did not begin asking questions without outside stimulation. The analysis of the dynamics of children’s question-asking behavior exhibited a tendency to decrease in a number of questions asked over the course of the school year. Primary school children need psychological and pedagogical scaffolding aimed at developing a question-asking behavior as a form of cognitive activity to achieve a possible age potential in development.

Machining of bone: Analysis of cutting force and surface roughness by turning process.

Science.gov (United States)

Noordin, M Y; Jiawkok, N; Ndaruhadi, P Y M W; Kurniawan, D

2015-11-01

There are millions of orthopedic surgeries and dental implantation procedures performed every year globally. Most of them involve machining of bones and cartilage. However, theoretical and analytical study on bone machining is lagging behind its practice and implementation. This study views bone machining as a machining process with bovine bone as the workpiece material. Turning process which makes the basis of the actually used drilling process was experimented. The focus is on evaluating the effects of three machining parameters, that is, cutting speed, feed, and depth of cut, to machining responses, that is, cutting forces and surface roughness resulted by the turning process. Response surface methodology was used to quantify the relation between the machining parameters and the machining responses. The turning process was done at various cutting speeds (29-156 m/min), depths of cut (0.03 -0.37 mm), and feeds (0.023-0.11 mm/rev). Empirical models of the resulted cutting force and surface roughness as the functions of cutting speed, depth of cut, and feed were developed. Observation using the developed empirical models found that within the range of machining parameters evaluated, the most influential machining parameter to the cutting force is depth of cut, followed by feed and cutting speed. The lowest cutting force was obtained at the lowest cutting speed, lowest depth of cut, and highest feed setting. For surface roughness, feed is the most significant machining condition, followed by cutting speed, and with depth of cut showed no effect. The finest surface finish was obtained at the lowest cutting speed and feed setting. © IMechE 2015.

Unintended consequences of machine learning in medicine?

Science.gov (United States)

McDonald, Laura; Ramagopalan, Sreeram V; Cox, Andrew P; Oguz, Mustafa

2017-01-01

Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care.

VQABQ: Visual Question Answering by Basic Questions

KAUST Repository

Huang, Jia-Hong

2017-03-19

Taking an image and question as the input of our method, it can output the text-based answer of the query question about the given image, so called Visual Question Answering (VQA). There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question. We formulate the basic questions generation problem as a LASSO optimization problem, and also propose a criterion about how to exploit these basic questions to help answer main question. Our method is evaluated on the challenging VQA dataset and yields state-of-the-art accuracy, 60.34% in open-ended task.

VQABQ: Visual Question Answering by Basic Questions

KAUST Repository

Huang, Jia-Hong; Alfadly, Modar; Ghanem, Bernard

2017-01-01

Taking an image and question as the input of our method, it can output the text-based answer of the query question about the given image, so called Visual Question Answering (VQA). There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question. We formulate the basic questions generation problem as a LASSO optimization problem, and also propose a criterion about how to exploit these basic questions to help answer main question. Our method is evaluated on the challenging VQA dataset and yields state-of-the-art accuracy, 60.34% in open-ended task.

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.

On the Development of Episodic Memory: Two Basic Questions

DEFF Research Database (Denmark)

Dahl, Jonna Jelsbak; Sonne, Trine; Kingo, Osman Skjold

2013-01-01

In this focused review we present and discuss two basic questions related to the early development of episodic memory in children: (1) “What is an episode?”, and (2) “How do preverbal children recall a specific episode of a recurring event?” First, a brief introduction to episodic memory...... is outlined. We argue in favor of employing a definition of episodic memory allowing us to investigate the development of episodic memory by purely behavioral measures. Second, research related to each of the two questions are presented and discussed, at first separately, and subsequently together. We argue...... and attempt to demonstrate, that pursuing answers to both questions is of crucial importance – both conceptually and methodologically - if we are ever to understand the early development of episodic memory. ...

A methodology for on-line calculation of temperature and thermal stress under non-linear boundary conditions

International Nuclear Information System (INIS)

Botto, D.; Zucca, S.; Gola, M.M.

2003-01-01

In the literature many works have been written dealing with the task of on-line calculation of temperature and thermal stress for machine components and structures, in order to evaluate fatigue damage accumulation and estimate residual life. One of the most widespread methodologies is the Green's function technique (GFT), by which machine parameters such as fluid temperatures, pressures and flow rates are converted into metal temperature transients and thermal stresses. However, since the GFT is based upon the linear superposition principle, it cannot be directly used in the case of varying heat transfer coefficients. In the present work, a different methodology is proposed, based upon CMS for temperature transient calculation and upon the GFT for the related thermal stress evaluation. This new approach allows variable heat transfer coefficients to be accounted for. The methodology is applied for two different case studies, taken from the literature: a thick pipe and a nozzle connected to a spherical head, both subjected to multiple convective boundary conditions

Quantification of human error and common-mode failures in man-machine systems

International Nuclear Information System (INIS)

Lisboa, J.J.

1988-01-01

Quantification of human performance, particularly the determination of human error, is essential for realistic assessment of overall system performance of man-machine systems. This paper presents an analysis of human errors in nuclear power plant systems when measured against common-mode failures (CMF). Human errors evaluated are improper testing, inadequate maintenance strategy, and miscalibration. The methodology presented in the paper represents a positive contribution to power plant systems availability by identifying sources of common-mode failure when operational functions are involved. It is also applicable to other complex systems such as chemical plants, aircraft and motor industries; in fact, any large man-created, man-machine system could be included

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.

Use of IT platform in determination of efficiency of mining machines

Science.gov (United States)

Brodny, Jarosław; Tutak, Magdalena

2018-01-01

Determination of effective use of mining devices has very significant meaning for mining enterprises. High costs of their purchase and tenancy cause that these enterprises tend to the best use of possessed technical potential. However, specifics of mining production causes that this process not always proceeds without interferences. Practical experiences show that determination of objective measure of utilization of machine in mining enterprise is not simple. In the paper a proposition for solution of this problem is presented. For this purpose an IT platform and overall efficiency model OEE were used. This model enables to evaluate the machine in a range of its availability performance and quality of product, and constitutes a quantitative tool of TPM strategy. Adapted to the specificity of mining branch the OEE model together with acquired data from industrial automatic system enabled to determine the partial indicators and overall efficiency of tested machines. Studies were performed for a set of machines directly use in coal exploitation process. They were: longwall-shearer and armoured face conveyor, and beam stage loader. Obtained results clearly indicate that degree of use of machines by mining enterprises are unsatisfactory. Use of IT platforms will significantly facilitate the process of registration, archiving and analytical processing of the acquired data. In the paper there is presented methodology of determination of partial indices and total OEE together with a practical example of its application for investigated machines set. Also IT platform was characterized for its construction, function and application.

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.

TEA CO2 laser machining of CFRP composite

Science.gov (United States)

Salama, A.; Li, L.; Mativenga, P.; Whitehead, D.

2016-05-01

Carbon fibre-reinforced polymer (CFRP) composites have found wide applications in the aerospace, marine, sports and automotive industries owing to their lightweight and acceptable mechanical properties compared to the commonly used metallic materials. Machining of CFRP composites using lasers can be challenging due to inhomogeneity in the material properties and structures, which can lead to thermal damages during laser processing. In the previous studies, Nd:YAG, diode-pumped solid-state, CO2 (continuous wave), disc and fibre lasers were used in cutting CFRP composites and the control of damages such as the size of heat-affected zones (HAZs) remains a challenge. In this paper, a short-pulsed (8 μs) transversely excited atmospheric pressure CO2 laser was used, for the first time, to machine CFRP composites. The laser has high peak powers (up to 250 kW) and excellent absorption by both the carbon fibre and the epoxy binder. Design of experiment and statistical modelling, based on response surface methodology, was used to understand the interactions between the process parameters such as laser fluence, repetition rate and cutting speed and their effects on the cut quality characteristics including size of HAZ, machining depth and material removal rate (MRR). Based on this study, process parameter optimization was carried out to minimize the HAZ and maximize the MRR. A discussion is given on the potential applications and comparisons to other lasers in machining CFRP.

Toward methodological emancipation in applied health research.

Science.gov (United States)

Thorne, Sally

2011-04-01

In this article, I trace the historical groundings of what have become methodological conventions in the use of qualitative approaches to answer questions arising from the applied health disciplines and advocate an alternative logic more strategically grounded in the epistemological orientations of the professional health disciplines. I argue for an increasing emphasis on the modification of conventional qualitative approaches to the particular knowledge demands of the applied practice domain, challenging the merits of what may have become unwarranted attachment to theorizing. Reorienting our methodological toolkits toward the questions arising within an evidence-dominated policy agenda, I encourage my applied health disciplinary colleagues to make themselves useful to that larger project by illuminating that which quantitative research renders invisible, problematizing the assumptions on which it generates conclusions, and filling in the gaps in knowledge needed to make decisions on behalf of people and populations.

Aliens and time in the machine age

Science.gov (United States)

Brake, Mark; Hook, Neil

2006-12-01

The 19th century saw sweeping changes for the development of astrobiology, both in the constituency of empirical science encroaching upon all aspects of life and in the evolution of ideas, with Lyell's Principles of Geology radically raising expectation of the true age of the Earth and the drama of Darwinism questioning biblically literalist accounts of natural history. This paper considers the popular culture spun on the crackling loom of the emergent aspects of astrobiology of the day: Edward Bulwer-Lytton's The Coming Race (1871), which foretold the race of the future, and satirist Samuel Butler's anticipation of machine intelligence, `Darwin Among the Machines', in his Erewhon (1872). Finally, we look at the way Darwin, Huxley and natural selection travelled into space with French astronomer Camille Flammarion's immensely popular Récits de l'infini (Stories of Infinity, 1872), and the social Darwinism of H.G. Wells' The Time Machine (1895) and The War of the Worlds (1898). These works of popular culture presented an effective and inspiring communication of science; their crucial discourse was the reducible gap between the new worlds uncovered by science and exploration and the fantastic strange worlds of the imagination. As such they exemplify a way in which the culture and science of popular astrobiology can be fused.

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

Science.gov (United States)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

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

Machine learning methods without tears: a primer for ecologists.

Science.gov (United States)

Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy

2008-06-01

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

Graduating the age-specific fertility pattern using Support Vector Machines

Directory of Open Access Journals (Sweden)

Anastasia Kostaki

2009-06-01

Full Text Available A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.

Artificial Intelligence in Medical Practice: The Question to the Answer?

Science.gov (United States)

Miller, D Douglas; Brown, Eric W

2018-02-01

Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials. Copyright © 2018 Elsevier Inc. All rights reserved.

Ten questions concerning the large-eddy simulation of turbulent flows

International Nuclear Information System (INIS)

Pope, Stephen B

2004-01-01

In the past 30 years, there has been considerable progress in the development of large-eddy simulation (LES) for turbulent flows, which has been greatly facilitated by the substantial increase in computer power. In this paper, we raise some fundamental questions concerning the conceptual foundations of LES and about the methodologies and protocols used in its application. The 10 questions addressed are stated at the end of the introduction. Several of these questions highlight the importance of recognizing the dependence of LES calculations on the artificial parameter Δ (i.e. the filter width or, more generally, the turbulence resolution length scale). The principle that LES predictions of turbulence statistics should depend minimally on Δ provides an alternative justification for the dynamic procedure

Utilizing the Human, Machine, and Environment Matrix in investigations

International Nuclear Information System (INIS)

Curry, David; McKinney, John M.

2006-01-01

'How did we get into this situation?' How many times has this question been asked at the outset of an investigation, or more importantly, at the completion of an investigation? If the answer is not readily and thoroughly apparent, the investigation is not complete. Subsequently, those who will have the responsibility for correction of the conditions leading to the incident will not have all the information necessary to properly complete their task. For many years, in many writings, the Human/Machine interaction and its impact on process design has been discussed. The same impact should be examined when performing incident investigations. Consideration of the interaction of human and machine along with the environment in which they are used has long been recommended by the National Safety Council, in both design and investigation

Vibration Prediction Method of Electric Machines by using Experimental Transfer Function and Magnetostatic Finite Element Analysis

International Nuclear Information System (INIS)

Saito, A; Kuroishi, M; Nakai, H

2016-01-01

This paper concerns the noise and structural vibration caused by rotating electric machines. Special attention is given to the magnetic-force induced vibration response of interior-permanent magnet machines. In general, to accurately predict and control the vibration response caused by the electric machines, it is inevitable to model not only the magnetic force induced by the fluctuation of magnetic fields, but also the structural dynamic characteristics of the electric machines and surrounding structural components. However, due to complicated boundary conditions and material properties of the components, such as laminated magnetic cores and varnished windings, it has been a challenge to compute accurate vibration response caused by the electric machines even after their physical models are available. In this paper, we propose a highly-accurate vibration prediction method that couples experimentally-obtained discrete structural transfer functions and numerically-obtained distributed magnetic-forces. The proposed vibration synthesis methodology has been applied to predict vibration responses of an interior permanent magnet machine. The results show that the predicted vibration response of the electric machine agrees very well with the measured vibration response for several load conditions, for wide frequency ranges. (paper)

The Value Simulation-Based Learning Added to Machining Technology in Singapore

Science.gov (United States)

Fang, Linda; Tan, Hock Soon; Thwin, Mya Mya; Tan, Kim Cheng; Koh, Caroline

2011-01-01

This study seeks to understand the value simulation-based learning (SBL) added to the learning of Machining Technology in a 15-week core subject course offered to university students. The research questions were: (1) How did SBL enhance classroom learning? (2) How did SBL help participants in their test? (3) How did SBL prepare participants for…

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...

Questioning Questions: Elementary Teachers' Adaptations of Investigation Questions Across the Inquiry Continuum

Science.gov (United States)

Biggers, Mandy

2018-02-01

Questioning is a central practice in science classrooms. However, not every question translates into a "good" science investigation. Questions that drive science investigations can be provided by many sources including the teacher, the curriculum, or the student. The variations in the source of investigation questions were explored in this study. A dataset of 120 elementary science classroom videos and associated lesson plans from 40 elementary teachers (K-5) across 21 elementary school campuses were scored on an instrument measuring the amount of teacher-direction or student-direction of the lessons' investigation questions. Results indicated that the investigation questions were overwhelmingly teacher directed in nature, with no opportunities for students to develop their own questions for investigation. This study has implications for researchers and practitioners alike, calling attention to the teacher-directed nature of investigation questions in existing science curriculum materials, and the need for teacher training in instructional strategies to adapt their existing curriculum materials across the continuum of teacher-directed and student-directed investigation questions. Teachers need strategies for adapting the teacher-directed questions provided in their existing curriculum materials in order to allow students the opportunity to engage in this essential scientific practice.

Metrological Aspects of Surface Topographies Produced by Different Machining Operations Regarding Their Potential Functionality

Directory of Open Access Journals (Sweden)

Żak Krzysztof

2017-06-01

Full Text Available This paper presents a comprehensive methodology for measuring and characterizing the surface topographies on machined steel parts produced by precision machining operations. The performed case studies concern a wide spectrum of topographic features of surfaces with different geometrical structures but the same values of the arithmetic mean height Sa. The tested machining operations included hard turning operations performed with CBN tools, grinding operations with Al2O3 ceramic and CBN wheels and superfinish using ceramic stones. As a result, several characteristic surface textures with the Sa roughness parameter value of about 0.2 μm were thoroughly characterized and compared regarding their potential functional capabilities. Apart from the standard 2D and 3D roughness parameters, the fractal, motif and frequency parameters were taken in the consideration.

Using questions sent to an Ask-A-Scientist site to identify children's interests in science

Science.gov (United States)

Baram-Tsabari, Ayelet; Sethi, Ricky J.; Bry, Lynn; Yarden, Anat

2006-11-01

Interest is a powerful motivator; nonetheless, science educators often lack the necessary information to make use of the power of student-specific interests in the reform process of science curricula. This study suggests a novel methodology, which might be helpful in identifying such interests - using children's self-generated questions as an indication of their scientific interests. In this research, children's interests were measured by analyzing 1555 science-related questions submitted to an international Ask-A-Scientist Internet site. The analysis indicated that the popularity of certain topics varies with age and gender. Significant differences were found between children's spontaneous (intrinsically motivated) and school-related (extrinsically motivated) interests. Surprisingly, girls contributed most of the questions to the sample; however, the number of American girls dropped upon entering senior high school. We also found significant differences between girls' and boys' interests, with girls generally preferring biological topics. The two genders kept to their stereotypic fields of interest, in both their school-related and spontaneous questions. Children's science interests, as inferred from questions to Web sites, could ultimately inform classroom science teaching. This methodology extends the context in which children's interests can be investigated.

Development of a machine learning potential for graphene

Science.gov (United States)

Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

2018-02-01

We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

Evaluating the assessment of essay type questions in the basic ...

African Journals Online (AJOL)

Methodology: We examined the merits and demerits of the closed and open systems of assessment of essay type questions and viva voce in professional exams in the Basic Medical Sciences together with the challenges of present day Medical Education. Result: The result showed that the closed system of marking in its ...

Multi criteria decision making of machining parameters for Die Sinking EDM Process

Directory of Open Access Journals (Sweden)

G. K. Bose

2015-04-01

Full Text Available Electrical Discharge Machining (EDM is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI, pulse on time (POT, pulse off time (POF and spark gap (SG on performance response like Material removal rate (MRR, Surface Roughness (Ra & Overcut (OC using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA. It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control using Machine Learning

DEFF Research Database (Denmark)

Andersen, Thomas Timm; Amor, Heni Ben; Andersen, Nils Axel

2015-01-01

and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. The resulting models can be used to predict the delays as well...

Hidden physics models: Machine learning of nonlinear partial differential equations

Science.gov (United States)

Raissi, Maziar; Karniadakis, George Em

2018-03-01

While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

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

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Muselli, Marco

2005-01-01

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

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

Research Methodology in Business: A Starter’s Guide

OpenAIRE

Ragab, Mohamed AF; Arisha, Amr

2018-01-01

A cardinal requisite of successful research lies in the proper selection of the research methodology applied to achieve research objectives using the available resources. In addition to acquiring sufficient knowledge of their specific research topic, researchers are urged to develop good understanding of alternative research methodologies at their disposal to be able to identify the best-suited methods to address the research question. This, however, often poses a challenge for novice researc...

Multivariate Mapping of Environmental Data Using Extreme Learning Machines

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2014-05-01

Lake Geneva. The basic idea is to model several pollutants together taking into account complex dependencies between them. The original data set consists of 200 measurements on 8 pollutants. Pairwise analysis of the variables using correlation matrix shows different relationships between them: linear correlations, nonlinear correlations, no correlations. The use of different combination of pollutants helps to understand the behavior of ELM and to perform detailed methodological analysis. Besides this real data case study, simulated patterns (generated by adding shuffled data) were analyzed as well. The methodology proposed and preliminary results obtained are very promising and establish a solid basis for more challenging case studies on high dimensional and multivariate data relating to environmental risks and natural hazards. References Huang G.-B., Zhu Q.-Y., and Siew C.-K. 2006, Extreme learning machine: theory and applications, Neurocomputing, vol. 70: 489-501. Kanevski M., Pozdnoukhov A., and Timonin V. 2009, Machine Learning for Spatial Environmental Data. Theory, Applications and Software, EPLF Press, Lausanne.

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.

HUMAN DECISIONS AND MACHINE PREDICTIONS.

Science.gov (United States)

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

2018-02-01

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

Machine 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).

Towards the compression of parton densities through machine learning algorithms

CERN Document Server

Carrazza, Stefano

2016-01-01

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

Human friendly man-machine system with advanced media technology

International Nuclear Information System (INIS)

Ogino, Takamichi; Sasaki, Kazunori

1993-01-01

This paper deals with the methodology to implement the man-machine system (MMS) with enhanced human friendliness for nuclear power plants. The relevant technologies are investigated from the two view points: One is integrated multi-media usage for user-computer interface and the other cognitive engineering for user-task interaction. Promising MMS design methodologies, concepts, and their limitations are discussed. To overcome uncertain factors found in human behaviors or individual differences in performance and preference of operators, a design appproach to natural and flexible man-computer interactive environment is proposed by intergrated use of not only cognitive and psychological knowledge but also advanced media technology. Multi-media operator support system under development is shown as an example to evaluate the effectiveness of the new approach and future advancement is prospected. (orig.)

Electric-Discharge Machining Techniques for Evaluating Tritium Effects on Materials

International Nuclear Information System (INIS)

Morgan, M.J.

2003-01-01

In this investigation, new ways to evaluate the long-term effects of tritium on the structural properties of components were developed. Electric-discharge machining (EDM) techniques for cutting tensile and fracture toughness samples from tritium exposed regions of returned reservoirs were demonstrated. An existing electric discharge machine was used to cut sub-size tensile and fracture toughness samples from the inside surfaces of reservoir mockups. Tensile properties from the EDM tensile samples were similar to those measured using full-size samples cut from similar stock. Although the existing equipment could not be used for machining tritium-exposed hardware, off-the shelf EDM units are available that could. With the right equipment and the required radiological controls in place, similar machining and testing techniques could be used to directly measure the effects of tritium on the properties of material cut from reservoir returns. Stress-strain properties from tritium-exposed reservoirs would improve finite element modeling of reservoir performance because the data would be representative of the true state of the reservoir material in the field. Tensile data from samples cut directly from reservoirs would also complement existing shelf storage and burst test data of the Life Storage Program and help answer questions about a specific reservoir's processing history and properties

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...

Machinability of titanium metal matrix composites (Ti-MMCs)

Science.gov (United States)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in

Questioning the Homogenization of Irregular Migrants in Educational Policy: From (Il)Legal Residence to Inclusive Education

Science.gov (United States)

Hemelsoet, Elias

2011-01-01

In this article Elias Hemelsoet questions the way irregular migrants are approached in educational policymaking. In most cases, estimations of the number of irregular migrants serve--despite large methodological problems--as a starting point for policymaking. Given the very diverse composition of this group of people, the question is whether…

Does the sequence of data collection influence participants' responses to closed and open-ended questions? A methodological study.

Science.gov (United States)

Covell, Christine L; Sidani, Souraya; Ritchie, Judith A

2012-06-01

The sequence used for collecting quantitative and qualitative data in concurrent mixed-methods research may influence participants' responses. Empirical evidence is needed to determine if the order of data collection in concurrent mixed methods research biases participants' responses to closed and open-ended questions. To examine the influence of the quantitative-qualitative sequence on responses to closed and open-ended questions when assessing the same variables or aspects of a phenomenon simultaneously within the same study phase. A descriptive cross-sectional, concurrent mixed-methods design was used to collect quantitative (survey) and qualitative (interview) data. The setting was a large multi-site health care centre in Canada. A convenience sample of 50 registered nurses was selected and participated in the study. Participants were randomly assigned to one of two sequences for data collection, quantitative-qualitative or qualitative-quantitative. Independent t-tests were performed to compare the two groups' responses to the survey items. Directed content analysis was used to compare the participants' responses to the interview questions. The sequence of data collection did not greatly affect the participants' responses to the closed-ended questions (survey items) or the open-ended questions (interview questions). The sequencing of data collection, when using both survey and semi-structured interviews, may not bias participants' responses to closed or open-ended questions. Additional research is required to confirm these findings. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modeling and Analysis of The Pressure Die Casting Using Response Surface Methodology

International Nuclear Information System (INIS)

Kittur, Jayant K.; Herwadkar, T. V.; Parappagoudar, M. B.

2010-01-01

Pressure die casting is successfully used in the manufacture of Aluminum alloys components for automobile and many other industries. Die casting is a process involving many process parameters having complex relationship with the quality of the cast product. Though various process parameters have influence on the quality of die cast component, major influence is seen by the die casting machine parameters and their proper settings. In the present work, non-linear regression models have been developed for making predictions and analyzing the effect of die casting machine parameters on the performance characteristics of die casting process. Design of Experiments (DOE) with Response Surface Methodology (RSM) has been used to analyze the effect of effect of input parameters and their interaction on the response and further used to develop nonlinear input-output relationships. Die casting machine parameters, namely, fast shot velocity, slow shot to fast shot change over point, intensification pressure and holding time have been considered as the input variables. The quality characteristics of the cast product were determined by porosity, hardness and surface rough roughness (output/responses). Design of experiments has been used to plan the experiments and analyze the impact of variables on the quality of casting. On the other-hand Response Surface Methodology (Central Composite Design) is utilized to develop non-linear input-output relationships (regression models). The developed regression models have been tested for their statistical adequacy through ANOVA test. The practical usefulness of these models has been tested with some test cases. These models can be used to make the predictions about different quality characteristics, for the known set of die casting machine parameters, without conducting the experiments.

The simulation of man-machine interaction in NPPs: the system response analyser project

International Nuclear Information System (INIS)

Cacciabue, P.C.

1990-01-01

In this paper, the ongoing research at Joint Research Centre-Ispra on the simulation of man-machine interaction is reviewed with reference to the past experience of system modelling and to the advances of the technological world. These require the coalescence of mixed disciplines covering the fields of engineering, psychology and sociology. In particular, the complexity of man-machine systems with respect to safety analysis is depicted. The developments and issues in modelling humans and machines are discussed: the possibility of combining them through the System Response Analyser methodology is presented as a balanced to be applied when the objective is the study of safety of systems during abnormal sequences. The three analytical tools which constitute the body of system response analysis namely a quasi-classical simulation of the actual plant, a cognitive model of the operator activities and a driver model, are described. (author)

Present stage evaluation of Furnas calculus methodology qualification

International Nuclear Information System (INIS)

1987-07-01

This technical note is about the present stage evaluation of FURNAS Calculus Methodology Qualification related to reload licensing process and licensing support of operation questions of Angra 1 NPP concerning transient and Core ThermalHydraulic areas. (Author) [pt

Implementation of Total Productive Maintenance (TPM to Improve Sheeter Machine Performance

Directory of Open Access Journals (Sweden)

Candra Nofri Eka

2017-01-01

Full Text Available This paper purpose is an evaluation of TPM implementation, as a case study at sheeter machine cut size line 5 finishing department, PT RAPP, Indonesia. Research methodology collected the Overall Equipment Effectiveness (OEE data of sheeter machine and computed its scores. Then, OEE analysis big losses, statistical analysis using SPSS 20 and focused maintenance evaluation of TPM were performed. The data collected to machine sheeter’s production for 10 months (January-October 2016. The data analyses was resulted the OEE average score of 82.75%. This score was still below the world class OEE (85% and the company target (90%. Based the big losses of OEE analysis was obtained the reduce speed losses, which most significant losses of OEE scores. The reduce speed losses value was 44.79% of total losses during the research period. The high score of these losses due to decreasing of machine production speed by operators, which intended to improve the quality of resulting products. The OEE scores statistical analysis was found breakdown losses and reduces speed losses, which significantly affected to OEE scores. Implementations of focused maintenance of TPM in the case study may need to improve because there were still occurred un-expecting losses during the research period.

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

Science.gov (United States)

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

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

Design and construction of automatic sorting station with machine vision

Directory of Open Access Journals (Sweden)

Oscar D. Velasco-Delgado

2014-01-01

Full Text Available This article presents the design, construction and testing of an automatic product sorting system in belt conveyor with machine vision that integrates Free and Open Source Software technology and Allen Bradley commercial equipment. Requirements are defined to determine features such as: mechanics of manufacturing station, an app of product sorting with machine vision and for automation system. For the app of machine vision a library is used for optical digital image processing Open CV, for the mechanical design of the manufacturing station is used the CAD tool Solid Edge and for the design and implementation of automation ISA standards are used along with an automation engineering project methodology integrating a PLC, an inverter, a Panel View and a DeviceNet Network. Performance tests are shown by classifying bottles and PVC pieces in four established types, the behavior of the integrated system is checked so as the efficiency of the same. The processing time on machine vision is 0.290 s on average for a piece of PVC, a capacity of 206 accessories per minute, for bottles was obtained a processing time of 0.267 s, a capacity of 224 bottles per minute. A maximum mechanical performance is obtained with 32 products per minute (1920 products/hour with the conveyor to 22 cm/s and 40 cm of distance between products obtaining an average error of 0.8%.

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

Constructive Analysis : A Study in Epistemological Methodology

DEFF Research Database (Denmark)

Ahlström, Kristoffer

, and develops a framework for a kind of analysis that is more in keeping with recent psychological research on categorization. Finally, it is shown that this kind of analysis can be applied to the concept of justification in a manner that furthers the epistemological goal of providing intellectual guidance.......The present study is concerned the viability of the primary method in contemporary philosophy, i.e., conceptual analysis. Starting out by tracing the roots of this methodology to Platonic philosophy, the study questions whether such a methodology makes sense when divorced from Platonic philosophy...

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

Process Damping and Cutting Tool Geometry in Machining

Science.gov (United States)

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

2011-12-01

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

Process Damping and Cutting Tool Geometry in Machining

International Nuclear Information System (INIS)

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

2011-01-01

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

Investigating the Human Computer Interaction Problems with Automated Teller Machine Navigation Menus

Science.gov (United States)

Curran, Kevin; King, David

2008-01-01

Purpose: The automated teller machine (ATM) has become an integral part of our society. However, using the ATM can often be a frustrating experience as people frequently reinsert cards to conduct multiple transactions. This has led to the research question of whether ATM menus are designed in an optimal manner. This paper aims to address the…

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

Science.gov (United States)

Marek, Lukáš; Pászto, Vít

2017-11-08

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

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

Directory of Open Access Journals (Sweden)

Lukáš Marek

2017-11-01

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

Law machines: scale models, forensic materiality and the making of modern patent law.

Science.gov (United States)

Pottage, Alain

2011-10-01

Early US patent law was machine made. Before the Patent Office took on the function of examining patent applications in 1836, questions of novelty and priority were determined in court, within the forum of the infringement action. And at all levels of litigation, from the circuit courts up to the Supreme Court, working models were the media through which doctrine, evidence and argument were made legible, communicated and interpreted. A model could be set on a table, pointed at, picked up, rotated or upended so as to display a point of interest to a particular audience within the courtroom, and, crucially, set in motion to reveal the 'mode of operation' of a machine. The immediate object of demonstration was to distinguish the intangible invention from its tangible embodiment, but models also'machined' patent law itself. Demonstrations of patent claims with models articulated and resolved a set of conceptual tensions that still make the definition and apprehension of the invention difficult, even today, but they resolved these tensions in the register of materiality, performativity and visibility, rather than the register of conceptuality. The story of models tells us something about how inventions emerge and subsist within the context of patent litigation and patent doctrine, and it offers a starting point for renewed reflection on the question of how technology becomes property.

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...

Data preparation for municipal virtual assistant using machine learning

OpenAIRE

Jovan, Leon Noe

2016-01-01

The main goal of this master’s thesis was to develop a procedure that will automate the construction of the knowledge base for a virtual assistant that answers questions about municipalities in Slovenia. The aim of the procedure is to replace or facilitate manual preparation of the virtual assistant's knowledge base. Theoretical backgrounds of different machine learning fields, such as multilabel classification, text mining and learning from weakly labeled data were examined to gain a better ...

Standardized spacecraft: a methodology for decision making. AMS report No. 1199

International Nuclear Information System (INIS)

Greenberg, J.S.; Nichols, R.A.

1974-01-01

As the space program matures, more and more attention is being focused on ways to reduce the costs of performing space missions. Standardization has been suggested as a way of providing cost reductions. The question of standardization at the system level, in particular, the question of the desirability of spacecraft standardization for geocentric space missions is addressed. The spacecraft is considered to be a bus upon which mission oriented equipment, the payload, is mounted. Three basic questions are considered: (1) is spacecraft standardization economically desirable; (2) if spacecraft standardization is economically desirable, what standardized spacecraft configuration or mix of configurations and technologies should be developed; and (3) if standardized spacecraft are to be developed, what power levels should they be designed for. A methodology which has been developed and which is necessary to follow if the above questions are to be answered and informed decisions made relative to spacecraft standardization is described. To illustrate the decision making problems and the need for the developed methodology and the data requirements, typical standardized spacecraft have been considered. Both standardized solar and nuclear-powered spacecraft and mission specialized spacecraft have been conceptualized and performance and cost estimates have been made. These estimates are not considered to be of sufficient accuracy to allow decisions regarding spacecraft mix and power levels to be made at this time. The estimates are deemed of sufficient accuracy so as to demonstrate the desirability of spacecraft standardization and the methodology (as well as the need for the methodology) which is necessary to decide upon the best mix of standardized spacecraft and their design power levels. (U.S.)

Literacy research methodologies

CERN Document Server

Duke, Nell K

2012-01-01

The definitive reference on literacy research methods, this book serves as a key resource for researchers and as a text in graduate-level courses. Distinguished scholars clearly describe established and emerging methodologies, discuss the types of questions and claims for which each is best suited, identify standards of quality, and present exemplary studies that illustrate the approaches at their best. The book demonstrates how each mode of inquiry can yield unique insights into literacy learning and teaching and how the methods can work together to move the field forward.   New to This Editi

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.

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Directory of Open Access Journals (Sweden)

Hao Li

2017-01-01

Full Text Available Predicting the performance of solar water heater (SWH is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

Combining discrepancy analysis with sensorless signal resampling for condition monitoring of rotating machines under fluctuating operations

CSIR Research Space (South Africa)

Heyns, T

2012-12-01

Full Text Available This paper proposes a novel framework for monitoring the condition of a rotating machine (for example a gearbox or a bearing) that may be subject to load and speed fluctuations. The methodology is especially relevant in situations where no (or only...

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

The Double Dance of Agency: a socio-theoretic account of how machines and humans interact

DEFF Research Database (Denmark)

Rose, Jeremy; Jones, Matthew

2005-01-01

The nature of the relationship between information technology (IT) and organizations has been a long-standing debate in the Information Systems literature. Does IT shape organiza-tions, or do people in organisations control how IT is used? To formulate the question a little differently: does agency...... that ma-chines may also be agents. Drawing on critiques of both structuration theory and actor net-work theory, this paper develops a theoretical account of the interaction between human and machine agency: the double dance of agency. The account seeks to contribute to theorisation of the relationship...

Questioning Questions: Elementary Teachers' Adaptations of Investigation Questions across the Inquiry Continuum

Science.gov (United States)

Biggers, Mandy

2018-01-01

Questioning is a central practice in science classrooms. However, not every question translates into a "good" science investigation. Questions that drive science investigations can be provided by many sources including the teacher, the curriculum, or the student. The variations in the source of investigation questions were explored in…

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…

Effects of fluid flow on heat transfer in large rotating electrical machines

International Nuclear Information System (INIS)

Lancial, Nicolas

2014-01-01

EDF operates a large number of electrical rotating machines in its electricity generation capacity. Thermal stresses which affect them can cause local heating, sufficient to damage their integrity. The present work contributes to provide methodologies for detecting hot spots in these machines, better understanding the topology of rotating flows and identifying their effects on heat transfer. Several experimental scale model were used by increasing their complexity to understand and validate the numerical simulations. A first study on a turbulent wall jet over a non-confined backward-facing step (half-pole hydro-generator) notes significant differences compared to results from confined case: both of them are present in an hydro-generator. A second study was done on a small confined rotating scale model to determinate the effects of a Taylor-Couette-Poiseuille on temperature distribution and position of hot spots on the heated rotor, by studying the overall flow regimes flow. These studies have helped to obtain a reliable method based on conjugate heat transfer (CHT) simulations. Another method, based on FEM coupled with the use of an inverse method, has been studied on a large model of hydraulic generator so as to solve the computation time issue of the first methodology. It numerically calculates the convective heat transfer from temperature measurements, but depends on the availability of experimental data. This work has also developed new no-contact measurement techniques as the use of a high-frequency pyrometer which can be applied on rotating machines for monitoring temperature. (author)

Implementation of a classifier didactical machine for learning mechatronic processes

Directory of Open Access Journals (Sweden)

Alex De La Cruz

2017-06-01

Full Text Available The present article shows the design and construction of a classifier didactical machine through artificial vision. The implementation of the machine is to be used as a learning module of mechatronic processes. In the project, it is described the theoretical aspects that relate concepts of mechanical design, electronic design and software management which constitute popular field in science and technology, which is mechatronics. The design of the machine was developed based on the requirements of the user, through the concurrent design methodology to define and materialize the appropriate hardware and software solutions. LabVIEW 2015 was implemented for high-speed image acquisition and analysis, as well as for the establishment of data communication with a programmable logic controller (PLC via Ethernet and an open communications platform known as Open Platform Communications - OPC. In addition, the Arduino MEGA 2560 platform was used to control the movement of the step motor and the servo motors of the module. Also, is used the Arduino MEGA 2560 to control the movement of the stepper motor and servo motors in the module. Finally, we assessed whether the equipment meets the technical specifications raised by running specific test protocols.

Investigating Mesoscale Convective Systems and their Predictability Using Machine Learning

Science.gov (United States)

Daher, H.; Duffy, D.; Bowen, M. K.

2016-12-01

A mesoscale convective system (MCS) is a thunderstorm region that lasts several hours long and forms near weather fronts and can often develop into tornadoes. Here we seek to answer the question of whether these tornadoes are "predictable" by looking for a defining characteristic(s) separating MCSs that evolve into tornadoes versus those that do not. Using NASA's Modern Era Retrospective-analysis for Research and Applications 2 reanalysis data (M2R12K), we apply several state of the art machine learning techniques to investigate this question. The spatial region examined in this experiment is Tornado Alley in the United States over the peak tornado months. A database containing select variables from M2R12K is created using PostgreSQL. This database is then analyzed using machine learning methods such as Symbolic Aggregate approXimation (SAX) and DBSCAN (an unsupervised density-based data clustering algorithm). The incentive behind using these methods is to mathematically define a MCS so that association rule mining techniques can be used to uncover some sort of signal or teleconnection that will help us forecast which MCSs will result in tornadoes and therefore give society more time to prepare and in turn reduce casualties and destruction.

Predicting human miRNA target genes using a novel evolutionary methodology

KAUST Repository

Aigli, Korfiati; Kleftogiannis, Dimitrios A.; Konstantinos, Theofilatos; Spiros, Likothanassis; Athanasios, Tsakalidis; Seferina, Mavroudi

2012-01-01

The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset. © 2012 Springer-Verlag.

Predicting human miRNA target genes using a novel evolutionary methodology

KAUST Repository

Aigli, Korfiati

2012-01-01

The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3\\'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset. © 2012 Springer-Verlag.

A diagnostic methodology for refrigerating systems; Methodologie de diagnostic des installations frigorifiques

Energy Technology Data Exchange (ETDEWEB)

Vrinat, G. [Association Francaise du Froid (AFF), 75 - Paris (France)

1997-12-31

A diagnostic methodology for refrigerating machines, equipment and plants has been defined and evaluated for EDF, the French national power utility and ADEME, the French Agency for Energy Conservation, in the framework of energy conservation objectives: the diagnostic method should enable to identify malfunctions, assess the cost efficiency of the equipment, identify limiting factors, and consider corrective measures

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.

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.

Passivity-Based Control of Electric Machines

Energy Technology Data Exchange (ETDEWEB)

Nicklasson, P.J.

1996-12-31

This doctoral thesis presents new results on the design and analysis of controllers for a class of electric machines. Nonlinear controllers are derived from a Lagrangian model representation using passivity techniques, and previous results on induction motors are improved and extended to Blondel-Park transformable machines. The relation to conventional techniques is discussed, and it is shown that the formalism introduced in this work facilitates analysis of conventional methods, so that open questions concerning these methods may be resolved. In addition, the thesis contains the following improvements of previously published results on the control of induction motors: (1) Improvement of a passivity-based speed/position controller, (2) Extension of passivity-based (observer-less and observer-based) controllers from regulation to tracking of rotor flux norm, (3) An extension of the classical indirect FOC (Field-Oriented Control) scheme to also include global rotor flux norm tracking, instead of only torque tracking and rotor flux norm regulation. The design is illustrated experimentally by applying the proposed control schemes to a squirrel-cage induction motor. The results show that the proposed methods have advantages over previous designs with respect to controller tuning, performance and robustness. 145 refs., 21 figs.

Is It Necessary to Articulate a Research Methodology When Reporting on Theoretical Research?

Directory of Open Access Journals (Sweden)

Juliana Smith

2017-05-01

Full Text Available In this paper the authors share their insights on whether it is necessary to articulate a research methodology when reporting on theoretical research. Initially the authors, one being a supervisor and the other, a PhD student and a colleague, were confronted with the question during supervision and writing of a thesis on theoretical research. Reflection on the external examiners’ reports about whether a research methodology for theoretical research is necessary prompted the writing of this paper. In order to answer the question, the characteristics of theoretical research are clarified and contrasting views regarding the necessity or not of including a research methodology section in such a thesis, are examined. The paper also highlights the justification for including a research methodology in a thesis that reports on theoretical research, investigates the soundness of such justification and finally draws conclusions on the matter.

A COMPUTERIZED DIAGNOSTIC COMPLEX FOR RELIABILITY TESTING OF ELECTRIC MACHINES

Directory of Open Access Journals (Sweden)

O.О. Somka

2015-06-01

Full Text Available Purpose. To develop a diagnostic complex meeting the criteria and requirements for carrying out accelerated reliability test and realizing the basic modes of electric machines operation and performance of the posed problems necessary in the process of such test. Methodology. To determine and forecast the indices of electric machines reliability in accordance with the statistic data of repair plants we have conditionally divided them into structural parts that are most likely to fail. We have preliminarily assessed the state of each of these parts, which includes revelation of faults and deviations of technical and geometric parameters. We have determined the analyzed electric machine controlled parameters used for assessment of quantitative characteristics of reliability of these parts and electric machines on the whole. Results. As a result of the research, we have substantiated the structure of a computerized complex for electric machines reliability test. It allows us to change thermal and vibration actions without violation of the physics of the processes of aging and wearing of the basic structural parts and elements material. The above mentioned makes it possible to considerably reduce time spent on carrying out electric machines reliability tests and improve trustworthiness of the data obtained as a result of their performance. Originality. A special feature of determination of the controlled parameters consists in removal of vibration components in the idle mode and after disconnection of the analyzed electric machine from the power supply with the aim of singling out the vibration electromagnetic component, fixing the degree of sparking and bend of the shaft by means of phototechnique and local determination of structural parts temperature provided by corresponding location of thermal sensors. Practical value. We have offered a scheme of location of thermal and vibration sensors, which allows improvement of parameters measuring accuracy

Current breathomics-a review on data pre-processing techniques and machine learning in metabolomics breath analysis

DEFF Research Database (Denmark)

Smolinska, A.; Hauschild, A. C.; Fijten, R. R. R.

2014-01-01

been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start...... different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus...

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.

Method for strength calculating of structural elements of mobile machines for flash butt welding of rails

Directory of Open Access Journals (Sweden)

Andriy Valeriy Moltasov

2017-12-01

Full Text Available Purpose. The subject of this study is the strength of the loaded units of mobile machines for flash butt welding by refining high-strength rails. The theme of the work is related to the development of a technique for strength calculating of the insulation of the central axis of these machines. The aim of the paper is to establish the mathematical dependence of the pressure on the insulation on the magnitude of deflections of the central axis under the action of the upset force. Design/methodology/approach. Using the Mohr’s method, the displacements of the investigated sections of the central axis under the action of the upset force and the equivalent load distributed along the length of the insulation were calculated. The magnitude of the load distributed along the length of the insulation equivalent to the draft force was determined from the condition that the displacements of the same cross sections are equal under the action of this load and under the action of the upset force. Results. An analytical expression for establishing the relationship between the pressure acting on the insulation and the magnitude of the upset force and the geometric dimensions of the structural elements of the machine was obtained. Based on the condition of the strength of the insulation for crushing, an analytical expression for establishing the relationship between the length of insulation and the size of the upset force, the geometric dimensions of the structural elements of the machine, and the physical and mechanical properties of the insulation material was obtained. Originality/cost. The proposed methodology was tested in the calculation and design of the K1045 mobile rail welding machine, 4 of which is currently successfully used in the USA for welding rails in hard-to-reach places.

Rhetorical questions or rhetorical uses of questions?

Directory of Open Access Journals (Sweden)

Špago Džemal

2016-12-01

Full Text Available This paper aims to explore whether some rhetorical questions contain certain linguistic elements or forms which would differentiate them from answer-eliciting and action-eliciting questions, and thereby hint at their rhetorical nature even outside the context. Namely, despite the fact that the same questions can be rhetorical in one context, and answer-eliciting in another, some of them are more likely to be associated with rhetorical or non-rhetorical use. The analysis is based on extensive data (over 1200 examples of rhetorical questions taken from 30 plays by two British and two American writers, and the results are expected to give an insight into whether we can talk about rhetorical questions or just a rhetorical use of questions.

Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities

Science.gov (United States)

Crippa, Alessandro; Salvatore, Christian; Perego, Paolo; Forti, Sara; Nobile, Maria; Molteni, Massimo; Castiglioni, Isabella

2015-01-01

In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children…

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

Science.gov (United States)

Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent

2016-08-01

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

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

Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

Directory of Open Access Journals (Sweden)

Mauricio Holguín-Londoño

2016-01-01

Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

Support Vector Machines as tools for mortality graduation

Directory of Open Access Journals (Sweden)

Alberto Olivares

2011-01-01

Full Text Available A topic of interest in demographic and biostatistical analysis as well as in actuarial practice,is the graduation of the age-specific mortality pattern. A classical graduation technique is to fit parametric models. Recently, particular emphasis has been given to graduation using nonparametric techniques. Support Vector Machines (SVM is an innovative methodology that could be utilized for mortality graduation purposes. This paper evaluates SVM techniques as tools for graduating mortality rates. We apply SVM to empirical death rates from a variety of populations and time periods. For comparison, we also apply standard graduation techniques to the same data.

Machine learning classifiers and fMRI: a tutorial overview.

Science.gov (United States)

Pereira, Francisco; Mitchell, Tom; Botvinick, Matthew

2009-03-01

Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).

Towards Modern Inclusive Factories: A Methodology for the Development of Smart Adaptive Human-Machine Interfaces

OpenAIRE

Villani, Valeria; Sabattini, Lorenzo; Czerniak, Julia N.; Mertens, Alexander; Vogel-Heuser, Birgit; Fantuzzi, Cesare

2017-01-01

Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally imp...

Analysis of students’ generated questions in laboratory learning environments

Directory of Open Access Journals (Sweden)

Juan Antonio Llorens-Molina

2012-03-01

Full Text Available In order to attain a reliable laboratory work assessment, we argue taking the Learning Environment as a core concept and a research paradigm that considers the factors affecting the laboratory as a particularly complex educational context. With regard to Laboratory Learning Environments (LLEs, a well known approach is the SLEI (Science Laboratory Environment Inventory. The aim of this research is to design and apply an alternative and qualitative assessment tool to characterize Laboratory Learning Environments in an introductory course of organic chemistry. An alternative and qualitative assessment tool would be useful for providing feed-back for experimental learning improvement; serving as a complementary triangulation tool in educational research on LLEs; and generating meaningful categories in order to design quantitative research instruments. Toward this end, spontaneous questions by students have been chosen as a reliable source of information. To process these questions, a methodology based on the Grounded Theory has been developed to provide a framework for characterizing LLEs. This methodology has been applied in two case studies. The conclusions lead us to argue for using more holistic assessment tools in both everyday practice and research. Likewise, a greater attention should be paid to metacognition to achieve suitable self-perception concerning students’ previous knowledge and manipulative skills.

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.

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.

Beyond "on" or "with": Questioning Power Dynamics and Knowledge Production in "Child-Oriented" Research Methodology

Science.gov (United States)

Hunleth, Jean

2011-01-01

By taking a reflexive approach to research methodology, this article contributes to discussions on power dynamics and knowledge production in the social studies of children. The author describes and analyzes three research methods that she used with children--drawing, child-led tape-recording and focus group discussions. These methods were carried…

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

Science.gov (United States)

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

2010-01-01

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

Application of artificial neural network with extreme learning machine for economic growth estimation

Science.gov (United States)

Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

2017-01-01

The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

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.

Testing audiovisual comprehension tasks with questions embedded in videos as subtitles: a pilot multimethod study

Directory of Open Access Journals (Sweden)

Juan Carlos Casañ Núñez

2017-06-01

Full Text Available Listening, watching, reading and writing simultaneously in a foreign language is very complex. This paper is part of wider research which explores the use of audiovisual comprehension questions imprinted in the video image in the form of subtitles and synchronized with the relevant fragments for the purpose of language learning and testing. Compared to viewings where the comprehension activity is available only on paper, this innovative methodology may provide some benefits. Among them, it could reduce the conflict in visual attention between watching the video and completing the task, by spatially and temporally approximating the questions and the relevant fragments. The technique is seen as especially beneficial for students with a low proficiency language level. The main objectives of this study were to investigate if embedded questions had an impact on SFL students’ audiovisual comprehension test performance and to find out what examinees thought about them. A multimethod design (Morse, 2003 involving the sequential collection of three quantitative datasets was employed. A total of 41 learners of Spanish as a foreign language (SFL participated in the study (22 in the control group and 19 in the experimental one. Informants were selected by non-probabilistic sampling. The results showed that imprinted questions did not have any effect on test performance. Test-takers’ attitudes towards this methodology were positive. Globally, students in the experimental group agreed that the embedded questions helped them to complete the tasks. Furthermore, most of them were in favour of having the questions imprinted in the video in the audiovisual comprehension test of the final exam. These opinions are in line with those obtained in previous studies that looked into experts’, SFL students’ and SFL teachers’ views about this methodology (Casañ Núñez, 2015a, 2016a, in press-b. On the whole, these studies suggest that this technique has

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.

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)

Allocation of functions to man and machine in the automated control room

International Nuclear Information System (INIS)

Pulliam, R.; Price, H.E.

1983-01-01

A practical framework and set of methodologic tools are discussed which could be used by a design team in allocating nuclear power plant control functions to either man or machine control. It is concluded that allocations of functions must eventually become a formal step in control system design, i.e., it will become increasingly necessary to invest in human factors analysis as an integral part of the design process

A Narrative in Search of a Methodology.

Science.gov (United States)

Treloar, Anna; Stone, Teresa Elizabeth; McMillan, Margaret; Flakus, Kirstin

2015-07-01

Research papers present us with the summaries of scholars' work; what we readers do not see are the struggles behind the decision to choose one methodology over another. A student's mental health portfolio contained a narrative that led to an exploration of the most appropriate methodology for a projected study of clinical anecdotes told by nurses who work in mental health settings to undergraduates and new recruits about mental health nursing. This paper describes the process of struggle, beginning with the student's account, before posing a number of questions needing answers before the choice of the most appropriate methodology. We argue, after discussing the case for the use of literary analysis, discourse analysis, symbolic interactionism, hermeneutics, and narrative research, that case study research is the methodology of choice. Case study is frequently used in educational research and is sufficiently flexible to allow for an exploration of the phenomenon. © 2014 Wiley Periodicals, Inc.

Methodological remarks on studying prehistoric Greek religion

Directory of Open Access Journals (Sweden)

Petra Pakkanen

1999-01-01

Full Text Available This paper presents a methodological approach to the study of Greek religion of the period which lacks written documents, i.e. prehistory. The assumptions and interpretations of religion of that time have to be based on archaeological material. How do we define religion and cultic activity on the basis of primary archaeological material from this period, and which are the methodological tools for this difficult task? By asking questions on the nature and definition of religion and culture scholars of religion have provided us with some methodological apparatus to approach religion of the past in general, but there are models developed by archaeologists as well. Critical combination of these methodological tools leads to the best possible result. Archaeology studies the material culture of the past. History of religion studies the spiritual culture of the past. In the background the two have important theoretical and even philosophical speculations since they both deal with meanings (of things or practices and with interpretation.

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

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

Science.gov (United States)

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

2015-11-01

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

Non-conventional electrical machines

CERN Document Server

Rezzoug, Abderrezak

2013-01-01

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

Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

Directory of Open Access Journals (Sweden)

Mata-Cabrera Francisco

2013-10-01

Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

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

Innovation and Integrity in Intervention Research: Conceptual Issues, Methodology, and Knowledge Translation.

Science.gov (United States)

Malti, Tina; Beelmann, Andreas; Noam, Gil G; Sommer, Simon

2018-04-01

In this article, we introduce the special issue entitled Innovation and Integrity in Intervention Science. Its focus is on essential problems and prospects for intervention research examining two related topics, i.e., methodological issues and research integrity, and challenges in the transfer of research knowledge into practice and policy. The main aims are to identify how to advance methodology in order to improve research quality, examine scientific integrity in the field of intervention science, and discuss future steps to enhance the transfer of knowledge about evidence-based intervention principles into sustained practice, routine activities, and policy decisions. Themes of the special issue are twofold. The first includes questions about research methodology in intervention science, both in terms of research design and methods, as well as data analyses and the reporting of findings. Second, the issue tackles questions surrounding the types of knowledge translation frameworks that might be beneficial to mobilize the transfer of research-based knowledge into practice and public policies. The issue argues that innovations in methodology and thoughtful approaches to knowledge translation can enable transparency, quality, and sustainability of intervention research.

Bisociation of artistic and academic methodologies

DEFF Research Database (Denmark)

Heinrich, Falk

This paper elaborates on the integration of academic and artistic methodologies within the field of art and technology. The term art and technology refers to a recognized research field and to higher education programmes, such as the BA program Art and Technology at Aalborg University. Art...... with and design of teaching designs aims at the description of a methodology and a heuristic for drafting concrete teaching designs....... and discovery. Koestler proposes the concept of bisociation for academic discoveries and artistic revealings alike by looking at the results of this creation (work of art, scientific discovery). However, my question is, whether the blending of academic and artistic discourses and methodologies––being a second...

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.

Investigation of Carbon Fiber Reinforced Plastics Machining Using 355 nm Picosecond Pulsed Laser

Science.gov (United States)

Hu, Jun; Zhu, Dezhi

2018-06-01

Carbon fiber reinforced plastics (CFRP) has been widely used in the aircraft industry and automobile industry owing to its superior properties. In this paper, a Nd:YVO4 picosecond pulsed system emitting at 355 nm has been used for CFRP machining experiments to determine optimum milling conditions. Milling parameters including laser power, milling speed and hatch distance were optimized by using box-behnken design of response surface methodology (RSM). Material removal rate was influenced by laser beam overlap ratio which affects mechanical denudation. The results in heat affected zones (HAZ) and milling quality were discussed through the machined surface observed with scanning electron microscope. A re-focusing technique based on the experiment with different focal planes was proposed and milling mechanism was also analyzed in details.

Investigation of Carbon Fiber Reinforced Plastics Machining Using 355 nm Picosecond Pulsed Laser

Science.gov (United States)

Hu, Jun; Zhu, Dezhi

2017-08-01

Carbon fiber reinforced plastics (CFRP) has been widely used in the aircraft industry and automobile industry owing to its superior properties. In this paper, a Nd:YVO4 picosecond pulsed system emitting at 355 nm has been used for CFRP machining experiments to determine optimum milling conditions. Milling parameters including laser power, milling speed and hatch distance were optimized by using box-behnken design of response surface methodology (RSM). Material removal rate was influenced by laser beam overlap ratio which affects mechanical denudation. The results in heat affected zones (HAZ) and milling quality were discussed through the machined surface observed with scanning electron microscope. A re-focusing technique based on the experiment with different focal planes was proposed and milling mechanism was also analyzed in details.

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

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

Science.gov (United States)

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

2015-08-25

personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

Robustness and prediction accuracy of machine learning for objective visual quality assessment

OpenAIRE

HINES, ANDREW

2014-01-01

PUBLISHED Lisbon, Portugal Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reli- ability of ML-based techniques within objective quality as- sessment metrics is often questioned. In this study, the ro- bustness of ML in supporting objective quality assessment is investigated, specific...

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.

Methodological Issues in HIV-Related Social Research in Nigeria

African Journals Online (AJOL)

AJRH Managing Editor

Methodological Issues in HIV/AIDS Social Research in Nigeria ... convaincue au commencement de l'étude qu'une étude sur l'interaction entre le VIH/sida et les questions sensibles comme les ..... One of the vexed issues was the requirement.

A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

Directory of Open Access Journals (Sweden)

Francis Markham

2017-05-01

Full Text Available Abstract Background Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. Methods A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Results Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by

A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines.

Science.gov (United States)

Markham, Francis; Young, Martin; Doran, Bruce; Sugden, Mark

2017-05-23

Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2  ≥ 0.97; R 2  ≤ 0.01). The

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.

Design methodology of Dutch banknotes

Science.gov (United States)

de Heij, Hans A. M.

2000-04-01

Since the introduction of a design methodology for Dutch banknotes, the quality of Dutch paper currency has improved in more than one way. The methodology is question provides for (i) a design policy, which helps fix clear objectives; (ii) design management, to ensure a smooth cooperation between the graphic designer, printer, papermaker an central bank, (iii) a program of requirements, a banknote development guideline for all parties involved. This systematic approach enables an objective selection of design proposals, including security features. Furthermore, the project manager obtains regular feedback from the public by conducting market surveys. Each new design of a Netherlands Guilder banknote issued by the Nederlandsche Bank of the past 50 years has been an improvement on its predecessor in terms of value recognition, security and durability.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

Science.gov (United States)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Love-hate for man-machine metaphors in Soviet physiology: from Pavlov to "physiological cybernetics".

Science.gov (United States)

Gerovitch, Slava

2002-06-01

This article reinterprets the debate between orthodox followers of the Pavlovian reflex theory and Soviet "cybernetic physiologists" in the 1950s and 60s as a clash of opposing man-machine metaphors. While both sides accused each other of "mechanistic," reductionist methodology, they did not see anything "mechanistic" about their own central metaphors: the telephone switchboard metaphor for nervous activity (the Pavlovians), and the analogies between the human brain and a computer (the cyberneticians). I argue that the scientific utility of machine analogies was closely intertwined with their philosophical and political meanings and that new interpretations of these metaphors emerged as a result of political conflicts and a realignment of forces within the scientific community and in society at large. I suggest that the constant travel of man-machine analogies, back and forth between physiology and technology has blurred the traditional categories of the "mechanistic" and the "organic" in Soviet neurophysiology, as perhaps in the history of physiology in general.

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

Directory of Open Access Journals (Sweden)

Senyue Zhang

2016-01-01

Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

Can we share questions? Performance of questions from different question banks in a single medical school.

Science.gov (United States)

Freeman, Adrian; Nicholls, Anthony; Ricketts, Chris; Coombes, Lee

2010-01-01

To use progress testing, a large bank of questions is required, particularly when planning to deliver tests over a long period of time. The questions need not only to be of good quality but also balanced in subject coverage across the curriculum to allow appropriate sampling. Hence as well as creating its own questions, an institution could share questions. Both methods allow ownership and structuring of the test appropriate to the educational requirements of the institution. Peninsula Medical School (PMS) has developed a mechanism to validate questions written in house. That mechanism can be adapted to utilise questions from an International question bank International Digital Electronic Access Library (IDEAL) and another UK-based question bank Universities Medical Assessment Partnership (UMAP). These questions have been used in our progress tests and analysed for relative performance. Data are presented to show that questions from differing sources can have comparable performance in a progress testing format. There are difficulties in transferring questions from one institution to another. These include problems of curricula and cultural differences. Whilst many of these difficulties exist, our experience suggests that it only requires a relatively small amount of work to adapt questions from external question banks for effective use. The longitudinal aspect of progress testing (albeit summatively) may allow more flexibility in question usage than single high stakes exams.

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

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

Science.gov (United States)

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

2018-02-01

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

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.

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

Science.gov (United States)

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

2011-01-01

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

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 * ...

MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES

Directory of Open Access Journals (Sweden)

Aleksandr Vasilyevich Koshkarov

2018-05-01

Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.

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...

[Methodological aspects of a study of medical service satisfaction in patients with borderline mental disorders].

Science.gov (United States)

Malygin, Ya V; Tsygankov, B D

The authors discussed a methodology of the study of medical service satisfaction and it's factors: moment of assessment, methodology of data collection, format of data, bench-marking, principles of inclusion of questions into a questionnaire, organizing and frequency of conducting studies.

EXPERIMENTAL STUDY OF THE DYNAMICS OF CENTRIFUGAL CASTING MACHINES FOR PRODUCTION OF MILL ROLLS

Directory of Open Access Journals (Sweden)

P. G. Anofriev

2017-06-01

Full Text Available Purpose. The main purpose of experimental studies is to establish the adequacy of the developed mathematical models of machine fluctuations and the actual parameters of machine vibration. Almost all casting machines for the production of mill rolls have a unique design and performances. The additional aim of this work is to compare the vibration level of the casting machine with the requirements of the current vibration standards for new technological machines. Frequency analysis of the oscillations allows establishing defects in workmanship, errors of rotating parts installation and their influence on the dynamics of the machine. Methodology. Measurement of vibration parameters was performed on the moving parts of roller bearings of the machine. To measure the amplitudes of accelerations in three mutually perpendicular directions piezoelectric sensors with magnetic mount were used. Electrical signals from the sensors were recorded on magnetic tape. Further analysis of the oscillations was carried out and visualized using specialized frequency analyzer. The frequency analyzer implements the algorithm of fast Fourier transformation and/or integration of sensor input signal. After the first integration the data for plotting the vibration velocity spectrogram were obtained and as a result of the second integration there are the data of vibration displacements spectrogram of the machine supports. Findings. The results of experimental studies of centrifugal casting machine vibrations for the production of two-layer rolls were presented. There were obtained and analyzed the spectrograms of accelerations, velocities and displacements of moving parts of the upper and lower roller supports. The work of the machine is associated with the calculated values passing of critical frequencies and the short-term development of resonance oscillations of the rotor and roller bearings. Originality. For the first time the author obtained the frequency spectra of

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

Energy Technology Data Exchange (ETDEWEB)

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

2003-10-01

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

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

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Andres Wanner

2014-12-01

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

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Science.gov (United States)

Chen, Chi-Jim; Pai, Tun-Wen; Cheng, Mox

2015-01-01

A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates. PMID:25835186

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Directory of Open Access Journals (Sweden)

Chi-Jim Chen

2015-03-01

Full Text Available A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS, successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM, based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates.

A Function-Behavior-State Approach to Designing Human Machine Interface for Nuclear Power Plant Operators

Science.gov (United States)

Lin, Y.; Zhang, W. J.

2005-02-01

This paper presents an approach to human-machine interface design for control room operators of nuclear power plants. The first step in designing an interface for a particular application is to determine information content that needs to be displayed. The design methodology for this step is called the interface design framework (called framework ). Several frameworks have been proposed for applications at varying levels, including process plants. However, none is based on the design and manufacture of a plant system for which the interface is designed. This paper presents an interface design framework which originates from design theory and methodology for general technical systems. Specifically, the framework is based on a set of core concepts of a function-behavior-state model originally proposed by the artificial intelligence research community and widely applied in the design research community. Benefits of this new framework include the provision of a model-based fault diagnosis facility, and the seamless integration of the design (manufacture, maintenance) of plants and the design of human-machine interfaces. The missing linkage between design and operation of a plant was one of the causes of the Three Mile Island nuclear reactor incident. A simulated plant system is presented to explain how to apply this framework in designing an interface. The resulting human-machine interface is discussed; specifically, several fault diagnosis examples are elaborated to demonstrate how this interface could support operators' fault diagnosis in an unanticipated situation.

An efficient methodology for the analysis of primary frequency control of electric power systems

Energy Technology Data Exchange (ETDEWEB)

Popovic, D.P. [Nikola Tesla Institute, Belgrade (Yugoslavia); Mijailovic, S.V. [Electricity Coordinating Center, Belgrade (Yugoslavia)

2000-06-01

The paper presents an efficient methodology for the analysis of primary frequency control of electric power systems. This methodology continuously monitors the electromechanical transient processes with durations that last up to 30 s, occurring after the characteristic disturbances. It covers the period of short-term dynamic processes, appearing immediately after the disturbance, in which the dynamics of the individual synchronous machines is dominant, as well as the period with the uniform movement of all generators and restoration of their voltages. The characteristics of the developed methodology were determined based on the example of real electric power interconnection formed by the electric power systems of Yugoslavia, a part of Republic of Srpska, Romania, Bulgaria, former Yugoslav Republic of Macedonia, Greece and Albania (the second UCPTE synchronous zone). (author)

A heuristic for the inventory management of smart vending machine systems

Directory of Open Access Journals (Sweden)

Yang-Byung Park

2012-12-01

Full Text Available Purpose: The purpose of this paper is to propose a heuristic for the inventory management of smart vending machine systems with product substitution under the replenishment point, order-up-to level policy and to evaluate its performance.Design/methodology/approach: The heuristic is developed on the basis of the decoupled approach. An integer linear mathematical model is built to determine the number of product storage compartments and replenishment threshold for each smart vending machine in the system and the Clarke and Wright’s savings algorithm is applied to route vehicles for inventory replenishments of smart vending machines that share the same delivery days. Computational experiments are conducted on several small-size test problems to compare the proposed heuristic with the integrated optimization mathematical model with respect to system profit. Furthermore, a sensitivity analysis is carried out on a medium-size test problem to evaluate the effect of the customer service level on system profit using a computer simulation.Findings: The results show that the proposed heuristic yielded pretty good solutions with 5.7% error rate on average compared to the optimal solutions. The proposed heuristic took about 3 CPU minutes on average in the test problems being consisted of 10 five-product smart vending machines. It was confirmed that the system profit is significantly affected by the customer service level.Originality/value: The inventory management of smart vending machine systems is newly treated. Product substitutions are explicitly considered in the model. The proposed heuristic is effective as well as efficient. It can be easily modified for application to various retail vending settings under a vendor-managed inventory scheme with POS system.

Intensification of the Students' Self-Development Process When Performing Design and Settlement Works on the "Machine Parts" Course

Science.gov (United States)

Timerbaev, Rais Mingalievich; Muhutdinov, Rafis Habreevich; Danilov, Valeriy Fedorovich

2015-01-01

The article addresses issues related to the methodology of intensifying self-development process when performing design and settlement works on the "Machine Parts" course for the students studying in such areas of training as "Technology" and "Vocational Education" with the use of computer technologies. At the same…

Predictive modeling for corrective maintenance of imaging devices from machine logs.

Science.gov (United States)

Patil, Ravindra B; Patil, Meru A; Ravi, Vidya; Naik, Sarif

2017-07-01

In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers (OEMs). In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it. We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. One of the use-case of predicting component failure of PHILIPS iXR system is explained in this article.

When is a research question not a research question?

Science.gov (United States)

Mayo, Nancy E; Asano, Miho; Barbic, Skye Pamela

2013-06-01

Research is undertaken to answer important questions yet often the question is poorly expressed and lacks information on the population, the exposure or intervention, the comparison, and the outcome. An optimal research question sets out what the investigator wants to know, not what the investigator might do, nor what the results of the study might ultimately contribute. The purpose of this paper is to estimate the extent to which rehabilitation scientists optimally define their research questions. A cross-sectional survey of the rehabilitation research articles published during 2008. Two raters independently rated each question according to pre-specified criteria; a third rater adjudicated all discrepant ratings. The proportion of the 258 articles with a question formulated as methods or expected contribution and not as what knowledge was being sought was 65%; 30% of questions required reworking. The designs which most often had poorly formulated research questions were randomized trials, cross-sectional and measurement studies. Formulating the research question is not purely a semantic concern. When the question is poorly formulated, the design, analysis, sample size calculations, and presentation of results may not be optimal. The gap between research and clinical practice could be bridged by a clear, complete, and informative research question.

Design and Assessment of a Machine Vision System for Automatic Vehicle Wheel Alignment

Directory of Open Access Journals (Sweden)

Rocco Furferi

2013-05-01

Full Text Available Abstract Wheel alignment, consisting of properly checking the wheel characteristic angles against vehicle manufacturers' specifications, is a crucial task in the automotive field since it prevents irregular tyre wear and affects vehicle handling and safety. In recent years, systems based on Machine Vision have been widely studied in order to automatically detect wheels' characteristic angles. In order to overcome the limitations of existing methodologies, due to measurement equipment being mounted onto the wheels, the present work deals with design and assessment of a 3D machine vision-based system for the contactless reconstruction of vehicle wheel geometry, with particular reference to characteristic planes. Such planes, properly referred to as a global coordinate system, are used for determining wheel angles. The effectiveness of the proposed method was tested against a set of measurements carried out using a commercial 3D scanner; the absolute average error in measuring toe and camber angles with the machine vision system resulted in full compatibility with the expected accuracy of wheel alignment systems.

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)

ROBUSTNESS AND PREDICTION ACCURACY OF MACHINE LEARNING FOR OBJECTIVE VISUAL QUALITY ASSESSMENT

OpenAIRE

Hines, Andrew; Kendrick, Paul; Barri, Adriaan; Narwaria, Manish; Redi, Judith A.

2014-01-01

Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reliability of ML-based techniques within objective quality assessment metrics is often questioned. In this study, the robustness of ML in supporting objective quality assessment is investigated, specifically when the feature set adopted for prediction is suboptim...

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...

An Evaluative Study of Machine Translation in the EFL Scenario of Saudi Arabia

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Raneem Khalid Al-Tuwayrish

2016-02-01

Full Text Available Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in the current paradigm because new translation technologies, such as, translation memories, data-based machine translation, and collaborative translation, far from being just additional tools, are changing the very nature of the translators’ cognitive activity, social relations, and professional standing. In fact, in some translation situations such as when translating technical materials or subject matter that are not a specialization with human translators, one potentially needs technology.  The purview of this paper, however, is limited to the role of MT in day to day situations where the generic MT tools like Google Translate or Bing Translator are encouraged. Further, it endeavours to weigh and empirically demonstrate the pros and cons of MT with a view to recommending measures for better communication training in the EFL set up of Saudi Arabia. Keywords: AI, MT, translation, technology, necessity, communication

Regulation of unbalanced electromagnetic moment in mutual loading systems of electric machines of traction rolling stock and multiple unit of mainline and industrial transport

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A. M. Afanasov

2014-12-01

Full Text Available Purpose. The research data are aimed to identify the regulatory principles of unbalanced electromagnetic moment of mutually loaded electric machines of traction rolling stock and multiple unit of main and industrial transport. The purpose of this study is energy efficiency increase of the testing of traction electric machines of direct and pulse current using the improvement methods of their mutual loading, including the principles of automatic regulation of mutual loading system. Methodology. The general theoretical provisions and principles of system approach to the theoretical electric engineering, the theory of electric machines and theoretical mechanics are the methodological basis of this research. The known methods of analysis of electromagnetic and electromechanical processes in electrical machines of direct and pulse current are used in the study. Methods analysis of loading modes regulation of traction electric machines was conducted using the generalized scheme of mutual loading. It is universal for all known methods to cover the losses of idling using the electric power. Findings. The general management principles of mutual loading modes of the traction electric machines of direct and pulse current by regulating their unbalanced electric magnetic moment were developed. Regulatory options of unbalanced electromagnetic moment are examined by changing the difference of the magnetic fluxes of mutually loaded electric machines, the current difference of electric machines anchors, the difference of the angular velocities of electric machines shafts. Originality. It was obtained the scientific basis development to improve the energy efficiency test methods of traction electric machines of direct and pulse current. The management principles of mutual loading modes of traction electric machines were formulated. For the first time it is introduced the concept and developed the principles of regulation of unbalanced electromagnetic moment in

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

Parametric and non-parametric models for lifespan modeling of insulation systems in electrical machines

OpenAIRE

Salameh , Farah; Picot , Antoine; Chabert , Marie; Maussion , Pascal

2017-01-01

International audience; This paper describes an original statistical approach for the lifespan modeling of electric machine insulation materials. The presented models aim to study the effect of three main stress factors (voltage, frequency and temperature) and their interactions on the insulation lifespan. The proposed methodology is applied to two different insulation materials tested in partial discharge regime. Accelerated ageing tests are organized according to experimental optimization m...

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

Directory of Open Access Journals (Sweden)

R. Gholami

2012-01-01

Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

Object-Oriented Support for Adaptive Methods on Paranel Machines

Directory of Open Access Journals (Sweden)

Sandeep Bhatt

1993-01-01

Full Text Available This article reports on experiments from our ongoing project whose goal is to develop a C++ library which supports adaptive and irregular data structures on distributed memory supercomputers. We demonstrate the use of our abstractions in implementing "tree codes" for large-scale N-body simulations. These algorithms require dynamically evolving treelike data structures, as well as load-balancing, both of which are widely believed to make the application difficult and cumbersome to program for distributed-memory machines. The ease of writing the application code on top of our C++ library abstractions (which themselves are application independent, and the low overhead of the resulting C++ code (over hand-crafted C code supports our belief that object-oriented approaches are eminently suited to programming distributed-memory machines in a manner that (to the applications programmer is architecture-independent. Our contribution in parallel programming methodology is to identify and encapsulate general classes of communication and load-balancing strategies useful across applications and MIMD architectures. This article reports experimental results from simulations of half a million particles using multiple methods.

Questions and Questioning Techniques: A View of Indonesian Students’ Preferences

Directory of Open Access Journals (Sweden)

Debora Tri Ragawanti

2009-01-01

Full Text Available This study investigated students’ preference on teacher’s questions and questionings techniques and more importantly on how they could facilitate or impede their learning. The results on teacher’s questioning techniques showed that random nomination was more preferred than pre-arranged format nomination. In addition, techniques of nominating volunteering students and of giving wait-time were disliked by most student-respondents. As for types of question, the yes/no question was favored by most of the respondents. Different from the yes/no question, the number of respondents leaning forward to the analysis question, questions about fact of life, and questions to state opinion did not show a significant difference from the number of those leaning against the same questions.

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.

Monitoring machining conditions by infrared images

Science.gov (United States)

Borelli, Joao E.; Gonzaga Trabasso, Luis; Gonzaga, Adilson; Coelho, Reginaldo T.

2001-03-01

During machining process the knowledge of the temperature is the most important factor in tool analysis. It allows to control main factors that influence tool use, life time and waste. The temperature in the contact area between the piece and the tool is resulting from the material removal in cutting operation and it is too difficult to be obtained because the tool and the work piece are in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements in the process: tool, work piece and chips. Each gray tone in the image corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is gotten by the previous of infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed of three modules. The first module makes the image acquisition and processing. The second module makes the feature image extraction and performs the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and supplies the tool state diagnostic as output.

Issues in the global applications of methodology in forensic anthropology.

Science.gov (United States)

Ubelaker, Douglas H

2008-05-01

The project and research reported in this collection of articles follows a long-term historical pattern in forensic anthropology in which new case work and applications reveal methodological issues that need to be addressed. Forensic anthropological analysis in the area of the former Yugoslavia led to questions raised regarding the applicability of methods developed from samples in other regions. The subsequently organized project reveals that such differences exist and new methodology and data are presented to facilitate applications in the Balkan area. The effort illustrates how case applications and court testimony can stimulate research advances. The articles also serve as a model for the improvement of methodology available for global applications.

A methodology for the characterization and diagnosis of cognitive impairments-Application to specific language impairment.

Science.gov (United States)

Oliva, Jesús; Serrano, J Ignacio; del Castillo, M Dolores; Iglesias, Angel

2014-06-01

The diagnosis of mental disorders is in most cases very difficult because of the high heterogeneity and overlap between associated cognitive impairments. Furthermore, early and individualized diagnosis is crucial. In this paper, we propose a methodology to support the individualized characterization and diagnosis of cognitive impairments. The methodology can also be used as a test platform for existing theories on the causes of the impairments. We use computational cognitive modeling to gather information on the cognitive mechanisms underlying normal and impaired behavior. We then use this information to feed machine-learning algorithms to individually characterize the impairment and to differentiate between normal and impaired behavior. We apply the methodology to the particular case of specific language impairment (SLI) in Spanish-speaking children. The proposed methodology begins by defining a task in which normal and individuals with impairment present behavioral differences. Next we build a computational cognitive model of that task and individualize it: we build a cognitive model for each participant and optimize its parameter values to fit the behavior of each participant. Finally, we use the optimized parameter values to feed different machine learning algorithms. The methodology was applied to an existing database of 48 Spanish-speaking children (24 normal and 24 SLI children) using clustering techniques for the characterization, and different classifier techniques for the diagnosis. The characterization results show three well-differentiated groups that can be associated with the three main theories on SLI. Using a leave-one-subject-out testing methodology, all the classifiers except the DT produced sensitivity, specificity and area under curve values above 90%, reaching 100% in some cases. The results show that our methodology is able to find relevant information on the underlying cognitive mechanisms and to use it appropriately to provide better

Interstitial Lung Disease due to Siderosis in a Lathe Machine Worker.

Science.gov (United States)

Gothi, D; Satija, B; Kumar, S; Kaur, Omkar

2015-01-01

Since its first description in 1936, siderosis of lung has been considered a benign pneumoconiosis due to absence of significant clinical symptoms or respiratory impairment. Subsequently, authors have questioned the non-fibrogenic property of iron. However, siderosis causing interstitial lung disease with usual interstitial pneumonia (UIP) pattern has not been described in the past. We report a case of UIP on high resolution computed tomography, proven to be siderosis on transbronchial lung biopsy in a lathe machine worker.

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 Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

Science.gov (United States)

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (pmachine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

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.

Methodology for developing new test methods

Directory of Open Access Journals (Sweden)

A. I. Korobko

2017-06-01

Full Text Available The paper describes the methodology for developing new test methods and forming solutions for the development of new test methods. The basis of the methodology for developing new test methods is the individual elements of the system and process approaches. They contribute to the development of an effective research strategy for the object, the study of interrelations, the synthesis of an adequate model of the test method. The effectiveness of the developed test method is determined by the correct choice of the set of concepts, their interrelations and mutual influence. This allows you to solve the tasks assigned to achieve the goal. The methodology is based on the use of fuzzy cognitive maps. The question of the choice of the method on the basis of which the model for the formation of solutions is based is considered. The methodology provides for recording a model for a new test method in the form of a finite set of objects. These objects are significant for the test method characteristics. Then a causal relationship is established between the objects. Further, the values of fitness indicators and the observability of the method and metrological tolerance for the indicator are established. The work is aimed at the overall goal of ensuring the quality of tests by improving the methodology for developing the test method.

Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines

Science.gov (United States)

Delahaye, Jean-Paul; Gauvrit, Nicolas

2014-01-01

Drawing on various notions from theoretical computer science, we present a novel numerical approach, motivated by the notion of algorithmic probability, to the problem of approximating the Kolmogorov-Chaitin complexity of short strings. The method is an alternative to the traditional lossless compression algorithms, which it may complement, the two being serviceable for different string lengths. We provide a thorough analysis for all binary strings of length and for most strings of length by running all Turing machines with 5 states and 2 symbols ( with reduction techniques) using the most standard formalism of Turing machines, used in for example the Busy Beaver problem. We address the question of stability and error estimation, the sensitivity of the continued application of the method for wider coverage and better accuracy, and provide statistical evidence suggesting robustness. As with compression algorithms, this work promises to deliver a range of applications, and to provide insight into the question of complexity calculation of finite (and short) strings. Additional material can be found at the Algorithmic Nature Group website at http://www.algorithmicnature.org. An Online Algorithmic Complexity Calculator implementing this technique and making the data available to the research community is accessible at http://www.complexitycalculator.com. PMID:24809449

A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

Directory of Open Access Journals (Sweden)

M. Fera

2018-09-01

Full Text Available Additive Manufacturing (AM is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®, is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

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

Generation of daily global solar irradiation with support vector machines for regression

International Nuclear Information System (INIS)

Antonanzas-Torres, F.; Urraca, R.; Antonanzas, J.; Fernandez-Ceniceros, J.; Martinez-de-Pison, F.J.

2015-01-01

Highlights: • New methodology for estimation of daily solar irradiation with SVR. • Automatic procedure for training models and selecting meteorological features. • This methodology outperforms other well-known parametric and numeric techniques. - Abstract: Solar global irradiation is barely recorded in isolated rural areas around the world. Traditionally, solar resource estimation has been performed using parametric-empirical models based on the relationship of solar irradiation with other atmospheric and commonly measured variables, such as temperatures, rainfall, and sunshine duration, achieving a relatively high level of certainty. Considerable improvement in soft-computing techniques, which have been applied extensively in many research fields, has lead to improvements in solar global irradiation modeling, although most of these techniques lack spatial generalization. This new methodology proposes support vector machines for regression with optimized variable selection via genetic algorithms to generate non-locally dependent and accurate models. A case of study in Spain has demonstrated the value of this methodology. It achieved a striking reduction in the mean absolute error (MAE) – 41.4% and 19.9% – as compared to classic parametric models; Bristow & Campbell and Antonanzas-Torres et al., respectively

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.

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

CERN Document Server

Zielesny, Achim

2016-01-01

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

Unattended Monitoring System Design Methodology

International Nuclear Information System (INIS)

Drayer, D.D.; DeLand, S.M.; Harmon, C.D.; Matter, J.C.; Martinez, R.L.; Smith, J.D.

1999-01-01

A methodology for designing Unattended Monitoring Systems starting at a systems level has been developed at Sandia National Laboratories. This proven methodology provides a template that describes the process for selecting and applying appropriate technologies to meet unattended system requirements, as well as providing a framework for development of both training courses and workshops associated with unattended monitoring. The design and implementation of unattended monitoring systems is generally intended to respond to some form of policy based requirements resulting from international agreements or domestic regulations. Once the monitoring requirements are established, a review of the associated process and its related facilities enables identification of strategic monitoring locations and development of a conceptual system design. The detailed design effort results in the definition of detection components as well as the supporting communications network and data management scheme. The data analyses then enables a coherent display of the knowledge generated during the monitoring effort. The resultant knowledge is then compared to the original system objectives to ensure that the design adequately addresses the fundamental principles stated in the policy agreements. Implementation of this design methodology will ensure that comprehensive unattended monitoring system designs provide appropriate answers to those critical questions imposed by specific agreements or regulations. This paper describes the main features of the methodology and discusses how it can be applied in real world situations

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

Science.gov (United States)

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach.

Science.gov (United States)

Aromataris, Edoardo; Fernandez, Ritin; Godfrey, Christina M; Holly, Cheryl; Khalil, Hanan; Tungpunkom, Patraporn

2015-09-01

With the increase in the number of systematic reviews available, a logical next step to provide decision makers in healthcare with the evidence they require has been the conduct of reviews of existing systematic reviews. Syntheses of existing systematic reviews are referred to by many different names, one of which is an umbrella review. An umbrella review allows the findings of reviews relevant to a review question to be compared and contrasted. An umbrella review's most characteristic feature is that this type of evidence synthesis only considers for inclusion the highest level of evidence, namely other systematic reviews and meta-analyses. A methodology working group was formed by the Joanna Briggs Institute to develop methodological guidance for the conduct of an umbrella review, including diverse types of evidence, both quantitative and qualitative. The aim of this study is to describe the development and guidance for the conduct of an umbrella review. Discussion and testing of the elements of methods for the conduct of an umbrella review were held over a 6-month period by members of a methodology working group. The working group comprised six participants who corresponded via teleconference, e-mail and face-to-face meeting during this development period. In October 2013, the methodology was presented in a workshop at the Joanna Briggs Institute Convention. Workshop participants, review authors and methodologists provided further testing, critique and feedback on the proposed methodology. This study describes the methodology and methods developed for the conduct of an umbrella review that includes published systematic reviews and meta-analyses as the analytical unit of the review. Details are provided regarding the essential elements of an umbrella review, including presentation of the review question in a Population, Intervention, Comparator, Outcome format, nuances of the inclusion criteria and search strategy. A critical appraisal tool with 10 questions to

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...

The specific aspects for the ASSET methodology implementation in Romania

Energy Technology Data Exchange (ETDEWEB)

Serbanescu, D [National Commission for Nuclear Activities Control of Romania (Romania)

1997-10-01

The main aspects of the implementation of a root cause analysis methodology are as follows: The Test Operating Licence requires that a systematical root cause analysis method for the event analysis to clarify the three questions from the ASSET methodology has to be implemented; A Training seminar on the ASSET methodology for the plant staff was performed at Cernavoda 1 NPP in April 1997, with the IAEA support; The self assessment process for the events which occurred during commissioning phases has to be performed by the plant up to the end of this year; An ASSET Peer Review of the Plant Self Assessment is planned in 1998; The Regulatory Authority has the task to evaluated independently the plant conclusions on various events. The tool used by CNCAN is the ASSET methodology.

The specific aspects for the ASSET methodology implementation in Romania

International Nuclear Information System (INIS)

Serbanescu, D.

1997-01-01

The main aspects of the implementation of a root cause analysis methodology are as follows: The Test Operating Licence requires that a systematical root cause analysis method for the event analysis to clarify the three questions from the ASSET methodology has to be implemented; A Training seminar on the ASSET methodology for the plant staff was performed at Cernavoda 1 NPP in April 1997, with the IAEA support; The self assessment process for the events which occurred during commissioning phases has to be performed by the plant up to the end of this year; An ASSET Peer Review of the Plant Self Assessment is planned in 1998; The Regulatory Authority has the task to evaluated independently the plant conclusions on various events. The tool used by CNCAN is the ASSET methodology

Textbook Publishers' Website Objective Question Banks: Does Their Use Improve Students' Examination Performance?

Science.gov (United States)

Johnston, Scott Paul; Huczynski, Andrzej

2006-01-01

This article presents the findings of a survey of students' usage of the objective question bank section of an academic publisher's textbook website. The findings are based on a survey of 239 business and management undergraduates conducted using a quantitative research methodology. The results suggest that increased use of the objective question…

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.

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.

Methodology for astronaut reconditioning research.

Science.gov (United States)

Beard, David J; Cook, Jonathan A

2017-01-01

Space medicine offers some unique challenges, especially in terms of research methodology. A specific challenge for astronaut reconditioning involves identification of what aspects of terrestrial research methodology hold and which require modification. This paper reviews this area and presents appropriate solutions where possible. It is concluded that spaceflight rehabilitation research should remain question/problem driven and is broadly similar to the terrestrial equivalent on small populations, such as rare diseases and various sports. Astronauts and Medical Operations personnel should be involved at all levels to ensure feasibility of research protocols. There is room for creative and hybrid methodology but careful systematic observation is likely to be more achievable and fruitful than complex trial based comparisons. Multi-space agency collaboration will be critical to pool data from small groups of astronauts with the accepted use of standardised outcome measures across all agencies. Systematic reviews will be an essential component. Most limitations relate to the inherent small sample size available for human spaceflight research. Early adoption of a co-operative model for spaceflight rehabilitation research is therefore advised. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Directory of Open Access Journals (Sweden)

Wang Lily

2008-07-01

Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

Systematic Methodology for Design of Tailor-Made Blended Products: Fuels and Other Blended Products

DEFF Research Database (Denmark)

Yunus, Nor Alafiza Binti

property values are verified by means of rigorous models for the properties and the mixtures. Besides the methodology, as the main contribution, specific supporting tools that were developed to perform each task are also important contributions of this research work. The applicability of the developed...... important in daily life, since they not only keep people moving around, but also guarantee that machines and equipment work smoothly. The objective of this work is to tackle the blending problems using computer-aided tools for the initial stage of the product design. A systematic methodology for design...... methodology and tools was tested through two case studies. In the first case study, two different gasoline blend problems have been solved. In the second case study, four different lubricant design problems have been solved....

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.

Scholar-Craftsmanship: Question-Type, Epistemology, Culture of Inquiry, and Personality-Type in Dissertation Research Design

Science.gov (United States)

Werner, Thomas P.; Rogers, Katrina S.

2013-01-01

"Scholar-Craftsmanship" (SC) is a quadrant methodological framework created to help social science doctoral students construct first-time dissertation research. The framework brackets and predicts how epistemological domains, cultures of inquiries, personality indicators, and research question--types can be correlated in dissertation…

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Science.gov (United States)

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian

2015-01-01

like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. PMID:26307512

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

Robustness Analysis of Visual Question Answering Models by Basic Questions

KAUST Repository

Huang, Jia-Hong

2017-11-01

Visual Question Answering (VQA) models should have both high robustness and accuracy. Unfortunately, most of the current VQA research only focuses on accuracy because there is a lack of proper methods to measure the robustness of VQA models. There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the ranked basic questions, with similarity scores, of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question about the given image. We claim that a robust VQA model is one, whose performance is not changed much when related basic questions as also made available to it as input. We formulate the basic questions generation problem as a LASSO optimization, and also propose a large scale Basic Question Dataset (BQD) and Rscore (novel robustness measure), for analyzing the robustness of VQA models. We hope our BQD will be used as a benchmark for to evaluate the robustness of VQA models, so as to help the community build more robust and accurate VQA models.

Robustness Analysis of Visual Question Answering Models by Basic Questions

KAUST Repository

Huang, Jia-Hong

2017-01-01

Visual Question Answering (VQA) models should have both high robustness and accuracy. Unfortunately, most of the current VQA research only focuses on accuracy because there is a lack of proper methods to measure the robustness of VQA models. There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the ranked basic questions, with similarity scores, of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question about the given image. We claim that a robust VQA model is one, whose performance is not changed much when related basic questions as also made available to it as input. We formulate the basic questions generation problem as a LASSO optimization, and also propose a large scale Basic Question Dataset (BQD) and Rscore (novel robustness measure), for analyzing the robustness of VQA models. We hope our BQD will be used as a benchmark for to evaluate the robustness of VQA models, so as to help the community build more robust and accurate VQA models.

Two-Year-Old Children Differentiate Test Questions from Genuine Questions

Science.gov (United States)

Grosse, Gerlind; Tomasello, Michael

2012-01-01

Children are frequently confronted with so-called "test questions". While genuine questions are requests for missing information, test questions ask for information obviously already known to the questioner. In this study we explored whether two-year-old children respond differentially to one and the same question used as either a genuine question…

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...

Automated Biometric Voice-Based Access Control in Automatic Teller Machine (ATM)

OpenAIRE

Yekini N.A.; Itegboje A.O.; Oyeyinka I.K.; Akinwole A.K.

2012-01-01

An automatic teller machine requires a user to pass an identity test before any transaction can be granted. The current method available for access control in ATM is based on smartcard. Efforts were made to conduct an interview with structured questions among the ATM users and the result proofed that a lot of problems was associated with ATM smartcard for access control. Among the problems are; it is very difficult to prevent another person from attaining and using a legitimate persons card, ...

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

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.

Nuclear questions; Le nucleaire en questions

Energy Technology Data Exchange (ETDEWEB)

Berg, Eugene

2012-02-15

Civilian and military nuclear questions fill a multitude of publications these days, especially after the Japanese tsunami and the Fukushima disaster. The author analyses some of them and highlights the links between civil and military nuclear industries, the realities of the nuclear cycle and related industrial questions before concluding on the controversial issue of weapons and their proliferation potential

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