Foreign Energy Company Competitiveness: Background information

Energy Technology Data Exchange (ETDEWEB)

Weimar, M.R.; Freund, K.A.; Roop, J.M.

1994-10-01

This report provides background information to the report Energy Company Competitiveness: Little to Do With Subsidies (DOE 1994). The main body of this publication consists of data uncovered during the course of research on this DOE report. This data pertains to major government energy policies in each country studied. This report also provides a summary of the DOE report. In October 1993, the Office of Energy Intelligence, US Department of Energy (formerly the Office of Foreign Intelligence), requested that Pacific Northwest Laboratory prepare a report addressing policies and actions used by foreign governments to enhance the competitiveness of their energy firms. Pacific Northwest Laboratory prepared the report Energy Company Competitiveness Little to Do With Subsidies (DOE 1994), which provided the analysis requested by DOE. An appendix was also prepared, which provided extensive background documentation to the analysis. Because of the length of the appendix, Pacific Northwest Laboratory decided to publish this information separately, as contained in this report.

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

Science.gov (United States)

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Dolphins Who Blow Bubbles: Anthropological Machines and Native Informants

NARCIS (Netherlands)

Lord, C.

2011-01-01

"Dolphins Who Blow Bubbles: Anthropological Machines and Native Informants" engages a reading between an Oscar winning and now ‘cult’ activist film The Cove (Louise Psihoyos 2009) and classical texts on the human-animal threshold. Giorgio Agamben’s The Open (2002) and Jacques Derrida’s "The Animal

Review of "Conceptual Structures: Information Processing in Mind and Machine."

Science.gov (United States)

Smoliar, Stephen W.

This review of the book, "Conceptual Structures: Information Processing in Mind and Machine," by John F. Sowa, argues that anyone who plans to get involved with issues of knowledge representation should have at least a passing acquaintance with Sowa's conceptual graphs for a database interface. (Used to model the underlying semantics of…

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

OpenAIRE

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

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardize...

Machine 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.

Novel jet observables from machine learning

Science.gov (United States)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

Adaptive Machine Aids to Learning.

Science.gov (United States)

Starkweather, John A.

With emphasis on man-machine relationships and on machine evolution, computer-assisted instruction (CAI) is examined in this paper. The discussion includes the background of machine assistance to learning, the current status of CAI, directions of development, the development of criteria for successful instruction, meeting the needs of users,…

The Institute of American Indian Arts Background Information (Task One of the Transition Evaluation). Background Paper.

Science.gov (United States)

Tippeconnic, John W., Jr.

The paper, prepared as Task One of the Institute of American Indian Arts Transition Evaluation, provides pertinent background information about the Institute of American Indian Arts in Santa Fe, New Mexico. A brief history of the Institute is given, with information about its philosophy and purpose; objectives; organization and administration; the…

Why Machine-Information Metaphors Are Bad for Science and Science Education

Science.gov (United States)

Pigliucci, Massimo; Boudry, Maarten

2011-01-01

Genes are often described by biologists using metaphors derived from computational science: they are thought of as carriers of information, as being the equivalent of "blueprints" for the construction of organisms. Likewise, cells are often characterized as "factories" and organisms themselves become analogous to machines. Accordingly, when the…

Support vector machines applications

CERN Document Server

Guo, Guodong

2014-01-01

Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

Student Modeling and Machine Learning

OpenAIRE

Sison , Raymund; Shimura , Masamichi

1998-01-01

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

Science.gov (United States)

Kase, Sue E.; Vanni, Michelle; Caylor, Justine; Hoye, Jeff

2017-05-01

The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical operational environment. These types of environments are characteristic of intelligence workflow processes conducted during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of crowds to model the interaction of the information consumer and the information required to solve a problem at different levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems (DSS) research represent the experimental conditions in this online single-player against-the-clock game where the player, acting in the role of an intelligence analyst, is tasked with a Commander's Critical Information Requirement (CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks (annotation, relation identification, and link diagram formation) with the assistance of `HAMIE the robot' who offers varying levels of information understanding dependent on question complexity. We provide preliminary results from a pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research platform.

Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

Directory of Open Access Journals (Sweden)

Xin Wang

2017-01-01

Full Text Available In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.

Interactive QR code beautification with full background image embedding

Science.gov (United States)

Lin, Lijian; Wu, Song; Liu, Sijiang; Jiang, Bo

2017-06-01

QR (Quick Response) code is a kind of two dimensional barcode that was first developed in automotive industry. Nowadays, QR code has been widely used in commercial applications like product promotion, mobile payment, product information management, etc. Traditional QR codes in accordance with the international standard are reliable and fast to decode, but are lack of aesthetic appearance to demonstrate visual information to customers. In this work, we present a novel interactive method to generate aesthetic QR code. By given information to be encoded and an image to be decorated as full QR code background, our method accepts interactive user's strokes as hints to remove undesired parts of QR code modules based on the support of QR code error correction mechanism and background color thresholds. Compared to previous approaches, our method follows the intention of the QR code designer, thus can achieve more user pleasant result, while keeping high machine readability.

Book review: A first course in Machine Learning

DEFF Research Database (Denmark)

Ortiz-Arroyo, Daniel

2016-01-01

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background...... to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning....... This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark...

Machine learning a probabilistic perspective

CERN Document Server

Murphy, Kevin P

2012-01-01

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

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

Science.gov (United States)

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

2018-04-01

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

DEVELOPMENT OF INFORMATION-MEASURING SYSTEM OF DIAGNOSTICS OF ONCOLOGICAL DISEASES WITH APPLICATION OF TECHNOLOGIES OF MACHINE TRAINING

Directory of Open Access Journals (Sweden)

Vsevolod Novikov

2017-12-01

Full Text Available The purpose of the work is to create an information and measurement system of cancer with the use of machine learning technology. In this work, the object of research is the information and measuring system for diagnosing cancer, and the subject of the study is the machine learning technology based on Microsoft Azure. They are based on methods of empirical level such as: calculations, measurements, comparisons. Experimental-theoretical level methods: experiment, analysis and modeling.

42 CFR 82.0 - Background information on this part.

Science.gov (United States)

2010-10-01

... 82.0 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES OCCUPATIONAL SAFETY... EMPLOYEES OCCUPATIONAL ILLNESS COMPENSATION PROGRAM ACT OF 2000 Introduction § 82.0 Background information on this part. The Energy Employees Occupational Illness Compensation Program Act (EEOICPA), 42 U.S.C...

26 CFR 7.6041-1 - Return of information as to payments of winnings from bingo, keno, and slot machines.

Science.gov (United States)

2010-04-01

... winnings from bingo, keno, and slot machines. 7.6041-1 Section 7.6041-1 Internal Revenue INTERNAL REVENUE..., keno, and slot machines. (a) In general. On or after May 1, 1977, every person engaged in a trade or... machine play or of $1,500 or more from a keno game shall make an information return with respect to such...

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

Machine Learning.

Science.gov (United States)

Kirrane, Diane E.

1990-01-01

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

Machining of Metal Matrix Composites

CERN Document Server

2012-01-01

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

77 FR 21992 - Proposed Renewal of Information Collection: Applicant Background Survey

Science.gov (United States)

2012-04-12

... customers. By including employees of all backgrounds, all DOI employees gain a measure of knowledge... barriers in our recruitment and selection processes, DOI must track the demographic groups that apply for... need and use of the information: This information is required to obtain the source of recruitment...

Mechanical design of machine components

CERN Document Server

Ugural, Ansel C

2015-01-01

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

The ATLAS Higgs Machine Learning Challenge

CERN Document Server

Cowan, Glen; The ATLAS collaboration; Bourdarios, Claire

2015-01-01

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

Transductive and matched-pair machine learning for difficult target detection problems

Science.gov (United States)

Theiler, James

2014-06-01

This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

The Model of Information Support for Management of Investment Attractiveness of Machine-Building Enterprises

Directory of Open Access Journals (Sweden)

Chernetska Olga V.

2016-11-01

Full Text Available The article discloses the content of the definition of “information support”, identifies basic approaches to the interpretation of this economic category. The main purpose of information support for management of enterprise investment attractiveness is determined. The key components of information support for management of enterprise investment attractiveness are studied. The main types of automated information systems for management of the investment attractiveness of enterprises are identified and characterized. The basic computer programs for assessing the level of investment attractiveness of enterprises are considered. A model of information support for management of investment attractiveness of machine-building enterprises is developed.

Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion

OpenAIRE

Le Cadre , Hélène; Aravena , Ignacio; Papavasiliou , Anthony

2015-01-01

International audience; We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the ag-gregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d'Azur, to evaluate ...

Presenting and processing information in background noise: A combined speaker-listener perspective.

Science.gov (United States)

Bockstael, Annelies; Samyn, Laurie; Corthals, Paul; Botteldooren, Dick

2018-01-01

Transferring information orally in background noise is challenging, for both speaker and listener. Successful transfer depends on complex interaction between characteristics related to listener, speaker, task, background noise, and context. To fully assess the underlying real-life mechanisms, experimental design has to mimic this complex reality. In the current study, the effects of different types of background noise have been studied in an ecologically valid test design. Documentary-style information had to be presented by the speaker and simultaneously acquired by the listener in four conditions: quiet, unintelligible multitalker babble, fluctuating city street noise, and little varying highway noise. For both speaker and listener, the primary task was to focus on the content that had to be transferred. In addition, for the speakers, the occurrence of hesitation phenomena was assessed. The listener had to perform an additional secondary task to address listening effort. For the listener the condition with the most eventful background noise, i.e., fluctuating city street noise, appeared to be the most difficult with markedly longer duration of the secondary task. In the same fluctuating background noise, speech appeared to be less disfluent, suggesting a higher level of concentration from the speaker's side.

A Development of Automatic Audit System for Written Informed Consent using Machine Learning.

Science.gov (United States)

Yamada, Hitomi; Takemura, Tadamasa; Asai, Takahiro; Okamoto, Kazuya; Kuroda, Tomohiro; Kuwata, Shigeki

2015-01-01

In Japan, most of all the university and advanced hospitals have implemented both electronic order entry systems and electronic charting. In addition, all medical records are subjected to inspector audit for quality assurance. The record of informed consent (IC) is very important as this provides evidence of consent from the patient or patient's family and health care provider. Therefore, we developed an automatic audit system for a hospital information system (HIS) that is able to evaluate IC automatically using machine learning.

Machine Learning an algorithmic perspective

CERN Document Server

Marsland, Stephen

2009-01-01

Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

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

1998-03-19

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

Quantum Machine Learning

OpenAIRE

Romero García, Cristian

2017-01-01

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

The information content of cosmic microwave background anisotropies

Science.gov (United States)

Scott, Douglas; Contreras, Dagoberto; Narimani, Ali; Ma, Yin-Zhe

2016-06-01

The cosmic microwave background (CMB) contains perturbations that are close to Gaussian and isotropic. This means that its information content, in the sense of the ability to constrain cosmological models, is closely related to the number of modes probed in CMB power spectra. Rather than making forecasts for specific experimental setups, here we take a more pedagogical approach and ask how much information we can extract from the CMB if we are only limited by sample variance. We show that, compared with temperature measurements, the addition of E-mode polarization doubles the number of modes available out to a fixed maximum multipole, provided that all of the TT, TE, and EE power spectra are measured. However, the situation in terms of constraints on particular parameters is more complicated, as we explain and illustrate graphically. We also discuss the enhancements in information that can come from adding B-mode polarization and gravitational lensing. We show how well one could ever determine the basic cosmological parameters from CMB data compared with what has been achieved with Planck, which has already probed a substantial fraction of the TT information. Lastly, we look at constraints on neutrino mass as a specific example of how lensing information improves future prospects beyond the current 6-parameter model.

The information content of cosmic microwave background anisotropies

International Nuclear Information System (INIS)

Scott, Douglas; Contreras, Dagoberto; Narimani, Ali; Ma, Yin-Zhe

2016-01-01

The cosmic microwave background (CMB) contains perturbations that are close to Gaussian and isotropic. This means that its information content, in the sense of the ability to constrain cosmological models, is closely related to the number of modes probed in CMB power spectra. Rather than making forecasts for specific experimental setups, here we take a more pedagogical approach and ask how much information we can extract from the CMB if we are only limited by sample variance. We show that, compared with temperature measurements, the addition of E -mode polarization doubles the number of modes available out to a fixed maximum multipole, provided that all of the TT , TE , and EE power spectra are measured. However, the situation in terms of constraints on particular parameters is more complicated, as we explain and illustrate graphically. We also discuss the enhancements in information that can come from adding B -mode polarization and gravitational lensing. We show how well one could ever determine the basic cosmological parameters from CMB data compared with what has been achieved with Planck , which has already probed a substantial fraction of the TT information. Lastly, we look at constraints on neutrino mass as a specific example of how lensing information improves future prospects beyond the current 6-parameter model.

Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?

Directory of Open Access Journals (Sweden)

Anjali Goyal

2017-07-01

Full Text Available Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports.

Machine Learning and Data Mining Methods in Diabetes Research.

Science.gov (United States)

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

2017-01-01

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

E-mail Writing: Providing Background Information in the Core of Computer Assisted Instruction

Directory of Open Access Journals (Sweden)

Behzad NAZARI

2015-01-01

Full Text Available The present study highly supported the effective role of providing background information via e-mail by the teacher to write e-mail by the students in learners’ writing ability. A total number of 50 EFL advanced male students aged between 25 and 40 at different branches of Iran Language Institute in Tehran, Tehran. Through the placement test of Oxford English Language Placement Test (OELPT the students' proficiency level seems to be nearly the same. Participants were randomly assign into two groups of experimental and control, each consisting of 25 students. After the administration of the proficiency test, all groups were assigned to write topic 1 as the pre-test. Next, the teacher involved the learners in the new instruction (treatment. During writing topics 2, 3, 4, 5, 6, and 7 experimental group’s background knowledge was activated through e-mail before writing and e-mailing topics while the control group received no background knowledge activation through e-mail. After the treatment was given to the experimental group, the students in both groups were required to write another composition about the last topic, topic 8. Again, in this phase, none of the groups received any background information. The results indicated that providing background information via e-mail by the teacher to write e-mail by the students significantly improved learners’ writing ability.

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.

Study on the background information for the geological disposal concept

International Nuclear Information System (INIS)

Matsui, Kazuaki; Murano, Tohru; Hirusawa, Shigenobu; Komoto, Harumi

2000-03-01

Japan Nuclear Cycle Development Institute (JNC) has published first R and D report in 1992, in which the fruits of the R and D work were compiled. Since then, JNC, has been promoting the second R and D progress report until before 2000, in which the background information on the geological disposal of high level radioactive waste (HLW) was to be presented as well as the technical basis. Recognizing the importance of the social consensus to the geological disposal, understanding and consensus by the society are essential to the development and realization of the geological disposal of HLW. In this fiscal year, studies were divided into 2 phases, considering the time schedule of the second R and D progress report. 1. Phase 1: Analysis of the background information on the geological disposal concept. Based on the recent informations and the research works of last 2 years, final version of the study was made to contribute to the background informations for the second R and D progress report. (This was published in Nov. 1999 as the intermediate report: JNC TJ 1420 2000-006). 2. Phase 2: Following 2 specific items were selected for the candidate issues which need to be studied, considering the present circumstances around the R and D of geological disposal. (1) Educational materials and strategies related to nuclear energy and nuclear waste. Specific strategies and approaches in the area of nuclear energy and nuclear waste educational outreach and curriculum activities by the nuclear industry, government and other entities in 6 countries were surveyed and summarized. (2) Alternatives to geological disposal of HLW: Past national/international consideration and current status. The alternatives for the disposal of HLW have been discussed in the past and the major waste-producing countries have almost all chosen deep geological disposal as preferred method. Here past histories and recent discussions on the variations to geological disposal were studied. (author)

Cybernics fusion of human, machine and information systems

CERN Document Server

Suzuki, Kenji; Hasegawa, Yasuhisa

2014-01-01

Cybernics plays a significant role in coping with an aging society using state-of-the-art technologies from engineering, clinical medicine and humanities. This new interdisciplinary field studies technologies that enhance, strengthen, and support physical and cognitive functions of human beings, based on the fusion of human, machine, and information systems. The design of a seamless interface for interaction between the interior and exterior of the human body is described in this book from diverse aspects such as the physical, neurophysiological, and cognitive levels. It is the first book to cover the many aspects of cybernics, allowing readers to understand the life support robotics technology for the elderly, including remote, in-home, hospital, institutional, community medical welfare, and vital-sensing systems. Serving as a valuable resource, this volume will interest not only graduate students, scientists, and engineers but also newcomers to the field of cybernics.

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

Science.gov (United States)

Dade County Public Schools, Miami, FL.

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

MRTD: man versus machine

Science.gov (United States)

van Rheenen, Arthur D.; Taule, Petter; Thomassen, Jan Brede; Madsen, Eirik Blix

2018-04-01

We present Minimum-Resolvable Temperature Difference (MRTD) curves obtained by letting an ensemble of observers judge how many of the six four-bar patterns they can "see" in a set of images taken with different bar-to-background contrasts. The same images are analyzed using elemental signal analysis algorithms and machine-analysis based MRTD curves are obtained. We show that by adjusting the minimum required signal-to-noise ratio the machine-based MRTDs are very similar to the ones obtained with the help of the human observers.

Identification and summary characterization of materials potentially requiring vitrification: Background information

International Nuclear Information System (INIS)

Croff, A.G.

1996-01-01

This document contains background information for the Workshop in general and the presentation entitled 'Identification and Summary Characterization of Materials Potentially Requiring Vitrification' that was given during the first morning of the workshop. summary characteristics of 9 categories of US materials having some potential to be vitrified are given. This is followed by a 1-2 page elaborations for each of these 9 categories. References to more detailed information are included

The Chainstitch Machine. Module 18.

Science.gov (United States)

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

This module on the chainstitch machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the chainstitch machine. These components are provided: an introduction, directions, an objective, learning activities, student information, a student self-check, and…

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.

Diamond turning machine controller implementation

Energy Technology Data Exchange (ETDEWEB)

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

1988-12-01

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

The ATLAS Higgs machine learning challenge

CERN Document Server

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

2014-01-01

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

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

OpenAIRE

Zekić-Sušac, Marijana; Pfeifer, Sanja; Šarlija, Nataša

2014-01-01

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 ...

Man-machine interactions 3

CERN Document Server

Czachórski, Tadeusz; Kozielski, Stanisław

2014-01-01

Man-Machine Interaction is an interdisciplinary field of research that covers many aspects of science focused on a human and machine in conjunction.  Basic goal of the study is to improve and invent new ways of communication between users and computers, and many different subjects are involved to reach the long-term research objective of an intuitive, natural and multimodal way of interaction with machines.  The rapid evolution of the methods by which humans interact with computers is observed nowadays and new approaches allow using computing technologies to support people on the daily basis, making computers more usable and receptive to the user's needs.   This monograph is the third edition in the series and presents important ideas, current trends and innovations in  the man-machine interactions area.  The aim of this book is to introduce not only hardware and software interfacing concepts, but also to give insights into the related theoretical background. Reader is provided with a compilation of high...

Traditional machining processes research advances

CERN Document Server

2015-01-01

This book collects several examples of research in machining processes. Chapter 1 provides information on polycrystalline diamond tool material and its emerging applications. Chapter 2 is dedicated to the analysis of orthogonal cutting experiments using diamond-coated tools with force and temperature measurements. Chapter 3 describes the estimation of cutting forces and tool wear using modified mechanistic models in high performance turning. Chapter 4 contains information on cutting under gas shields for industrial applications. Chapter 5 is dedicated to the machinability of magnesium and its alloys. Chapter 6 provides information on grinding science. Finally, chapter 7 is dedicated to flexible integration of shape and functional modelling of machine tool spindles in a design framework.    

Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

Science.gov (United States)

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

2011-01-01

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

Beam-gas background calculation for DEAR

International Nuclear Information System (INIS)

Guiducci, S.

1997-01-01

The present paper describes the results obtained from a Monte Carlo simulation of the particles lost in the DAY-ONE Interaction Region (IR) of the DAFNE machine. Coulomb scattering on the nuclei of residual gas and beam-gas Bremsstrahlung, which are the main sources of background in the initial phase of machine operation, were considered. A ray-tracking program (TURTLE) was used to follow the trajectories of the particles in the rings and to evaluate the number of particles that hit the vacuum chamber in the interaction region

Spiritualist Writing Machines: Telegraphy, Typtology, Typewriting

Directory of Open Access Journals (Sweden)

Anthony Enns

2015-09-01

Full Text Available This paper examines how religious concepts both reflected and informed the development of new technologies for encoding, transmitting, and printing written information. While many spiritualist writing machines were based on existing technologies that were repurposed for spirit communication, others prefigured or even inspired more advanced technological innovations. The history of spiritualist writing machines thus not only represents a response to the rise of new media technologies in the nineteenth century, but it also reflects a set of cultural demands that helped to shape the development of new technologies, such as the need to replace handwriting with discrete, uniform lettering, which accelerated the speed of composition; the need to translate written information into codes, which could be transmitted across vast distances; and the need to automate the process of transmitting, translating, and transcribing written information, which seemed to endow the machines themselves with a certain degree of autonomy or even intelligence. While spiritualists and inventors were often (but not always motivated by different goals, the development of spiritualist writing machines and the development of technological writing machines were nevertheless deeply interrelated and interdependent.

Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

OpenAIRE

Wei-Jong Yang; Wei-Hau Du; Pau-Choo Chang; Jar-Ferr Yang; Pi-Hsia Hung

2017-01-01

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an importan...

Quantum cloning machines and the applications

Energy Technology Data Exchange (ETDEWEB)

Fan, Heng, E-mail: hfan@iphy.ac.cn [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Collaborative Innovation Center of Quantum Matter, Beijing 100190 (China); Wang, Yi-Nan; Jing, Li [School of Physics, Peking University, Beijing 100871 (China); Yue, Jie-Dong [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu [School of Physics, Peking University, Beijing 100871 (China)

2014-11-20

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results.

Quantum cloning machines and the applications

International Nuclear Information System (INIS)

Fan, Heng; Wang, Yi-Nan; Jing, Li; Yue, Jie-Dong; Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu

2014-01-01

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

Science.gov (United States)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

[Effects of exposure frequency and background information on preferences for photographs of cars in different locations].

Science.gov (United States)

Matsuda, Ken; Kusumi, Takashi; Hosomi, Naohiro; Osa, Atsushi; Miike, Hidetoshi

2014-08-01

This study examined the influence of familiarity and novelty on the mere exposure effect while manipulating the presentation of background information. We selected presentation stimuli that integrated cars and backgrounds based on the results of pilot studies. During the exposure phase, we displayed the stimuli successively for 3 seconds, manipulating the background information (same or different backgrounds with each presentation) and exposure frequency (3, 6, and 9 times). In the judgment phase, 18 participants judged the cars in terms of preference, familiarity, and novelty on a 7-point scale. As the number of stimulus presentations increased, the preference for the cars increased during the different background condition and decreased during the same background condition. This increased preference may be due to the increase in familiarity caused by the higher exposure frequency and novelty resulting from the background changes per exposure session. The rise in preference judgments was not seen when cars and backgrounds were presented independently. Therefore, the addition of novel features to each exposure session facilitated the mere exposure effect.

The development of management information systems for running machines at full potential

Energy Technology Data Exchange (ETDEWEB)

Steel, W.T.

1988-08-01

Statistics show that in the UK in the month of March 1988, coal face machines ran on average 114 minutes per machine shift. The average for the financial year 1987/88 was 110 minutes, which was 34% of available time. Since January 1986, machine available time has been held at around 320 min. During the same period, however, machine running time has remained static also even though several hundred million pounds has been invested in heavy duty equipment, and the number of retreat faces as a percentage of the total number in operation has increased. Our machines generally are operating at less than 50% of potential, potential being defined as the output produced if the machines operate at the expected rate, and stop only for the expected turn-round time. These statistics are derived from method study standards, which are perhaps open to challenge, but on these norms, one extra minute of machine running time per machine shift throughout the industry in the year 1987/88 would have produced about 26 million pounds of extra revenue. Although the statistics are generalisations, they demonstrate not only the problem to which the industry must address itself if it is to be competitive but also the scope which is available for improvements. 5 figs.

The Machine / Job Features Mechanism

Energy Technology Data Exchange (ETDEWEB)

Alef, M. [KIT, Karlsruhe; Cass, T. [CERN; Keijser, J. J. [NIKHEF, Amsterdam; McNab, A. [Manchester U.; Roiser, S. [CERN; Schwickerath, U. [CERN; Sfiligoi, I. [Fermilab

2017-11-22

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

The machine/job features mechanism

Science.gov (United States)

Alef, M.; Cass, T.; Keijser, J. J.; McNab, A.; Roiser, S.; Schwickerath, U.; Sfiligoi, I.

2017-10-01

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information

Science.gov (United States)

Snyder, Robin M.

2015-01-01

The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and…

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

Science.gov (United States)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

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

The Button Sew Machine. Module 12.

Science.gov (United States)

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

This module on the button sew machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the button sew machine. These components are provided: an introduction, direction, an objective, learning activities, student information, a student self-check, and a…

The Zig Zag Machine. Module 14.

Science.gov (United States)

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

This module on the zig zag machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the zig zag machine. These components are provided: an introduction, directions, an objective, learning activities, student information, a student self-check, and a…

The Bar Tack Machine. Module 16.

Science.gov (United States)

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

This module on the bar tack machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the bar tack machine. These components are provided: an introduction, directions, an objective, learning activities, student information, a student self-check, and a…

An analysis of the Bonn agreement. Background information for evaluating business implications

International Nuclear Information System (INIS)

Torvanger, Asbjoern

2001-01-01

This report has been commissioned by the World Business Council for Sustainable Development and written in August 2001. The aim of the report is to present and analyze the newest developments in the climate negotiations, particularly from part two of the sixth Conference of the Parties to the Climate Convention in Bonn in July 2001, and to provide background information to evaluate what the ''Bonn agreement'' means for business. The report is organized as a collection of slides with supporting text explaining the background and contents of each slide. (author)

Human-Machine Communication

International Nuclear Information System (INIS)

Farbrot, J.E.; Nihlwing, Ch.; Svengren, H.

2005-01-01

New requirements for enhanced safety and design changes in process systems often leads to a step-wise installation of new information and control equipment in the control room of older nuclear power plants, where nowadays modern digital I and C solutions with screen-based human-machine interfaces (HMI) most often are introduced. Human factors (HF) expertise is then required to assist in specifying a unified, integrated HMI, where the entire integration of information is addressed to ensure an optimal and effective interplay between human (operators) and machine (process). Following a controlled design process is the best insurance for ending up with good solutions. This paper addresses the approach taken when introducing modern human-machine communication in the Oskarshamn 1 NPP, the results, and the lessons learned from this work with high operator involvement seen from an HF point of view. Examples of possibilities modern technology might offer for the operators are also addressed. (orig.)

Framework for man-machine interface design evaluation system considering cognitive factor

International Nuclear Information System (INIS)

Itoh, Toru; Sasaki, Kazunori; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko.

1994-01-01

It is necessary to improve human reliability in order to gain a higher reliability of the total plant system taking an account of development of plant automation and improvement of machine reliability. Therefore, the role of the man-machine system will come to be important. Accordingly, the evaluation of the man-machine system design information is desired in order to solve the mismatch problem between plant information presented by the man-machine system and information required by the operator comprehensively. This paper discusses required functions and software framework for the man-machine interface design evaluation system. The man-machine interface design evaluation system has features to extract the potential matters which are inherent on the design information of man-machine system by simulating the operator behavior, the plant system and the man-machine system, considering the operator's cognitive performance and time dependency. (author)

Research on machine learning framework based on random forest algorithm

Science.gov (United States)

Ren, Qiong; Cheng, Hui; Han, Hai

2017-03-01

With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

Man-machine supervision; Supervision homme-machine

Energy Technology Data Exchange (ETDEWEB)

Montmain, J. [CEA Valrho, Dir. de l' Energie Nucleaire (DEN), 30 - Marcoule (France)

2005-05-01

Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

Combining rules, background knowledge and change patterns to maintain semantic annotations.

Science.gov (United States)

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Judicial decision making: order of evidence presentation and availability of background information

NARCIS (Netherlands)

Kerstholt, J.H.; Jackson, J.L.

1998-01-01

An experiment was conducted to investigate both the effect of the order of presentation of defence and prosecution evidence and the prior availability of background information on assessment of guilt. Subjects were required to judge the defendant's probability of guilt either after each witness

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.

Study of the machine background induced by the PEP-II collider with a mini-TPC. Study of the doubly-charmed decay of the B meson with the detector BaBar

International Nuclear Information System (INIS)

Trincaz-Duvoid, S.

2001-01-01

The work presented in this thesis is divided into two parts. The first one deals with the machine background induced by the PEP-II collider. This study has been performed with a mini-TPC before the start of the BaBar experiment. The second part concerns the measurements of the branching ratio of the decay modes B 0 → D *- D(*) 0 K + and of the inclusive branching ratio Br(B 0 → K ± X). These measurements have been obtained with the first BaBar data. During the commissioning of the PEP-II collider, the charged tracks rate close to the interaction point has been measured with the mini-TPC. This study has pointed to the fact that the machine background was much higher than predicted by the simulation. These bad background conditions were due to the poor quality of the vacuum in the rings. This relatively high pressure in the rings produces electro-magnetic showers at the interaction point due to beam gas interactions. The potential risks for the BaBar detector due to the machine backgrounds have been clearly pointed out by the studies performed for this thesis. The addition of some collimators and a deep understanding of the machine have greatly reduced the background. Nevertheless, the radiation level in BaBar is continuously monitored in order to protect the detector. The study of the b → cc-bar channel is an important point for the understanding of the overall picture of the B meson decay. With an integrated luminosity of 17.3 fb -1 recorded by the BaBar detector the following branching ratio using exclusive reconstruction technique have been measured: Br(B 0 → D *- D 0 K + ) = (0.29 ± 0.06 (stat) ± (syst)) % Br(B 0 → D *- D *0 K + ) = (1.16 ± 0.15 (stat) ± 0.16 (syst)) % A partial reconstruction has also been developed. With an integrated luminosity of 8.9 fb -1 , the branching ratio of B 0 into D *- D 0 K + has been measured: Br(B 0 → D *- D 0 K + ) = (0.45 ± 0.12 (stat) ± 0.25 (syst)) % This result is in good agreement with the value obtained

Development of a coppice planting machine to commercial standards

Energy Technology Data Exchange (ETDEWEB)

Turton, J.S.

2000-07-01

This report gives details of the development work carried out on the Turton Engineering Coppice Planting machine in order to commercially market it. The background to the machine which plants single rows of cuttings from rods is traced,, and previous development work, design work, production of sub-assemblies and the assembly of modules, inspection and assembly, static trials, and commercial planting are examined. Further machine developments, proving trials, and recommendations for further work are discussed. Appendices address relationships applicable to vertical planting, the Turton short rotation cultivation machine rod format, estimated prices and charges, and a list of main suppliers. (UK)

Artificial intelligence applications in information and communication technologies

CERN Document Server

Bouguila, Nizar

2015-01-01

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.  .

BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

Study on the background information for the geological disposal concept

International Nuclear Information System (INIS)

Matsui, Kazuaki; Murano, Tohru; Hirusawa, Shigenobu; Komoto, Harumi

1999-11-01

Japan Nuclear Cycle Development Institute (JNC) has published the first R and D progress report in 1992. In which the fruits of the R and D works were compiled. Since then the next step of R and D has been developing progressively in Japan. Now JNC has a plan to make the second R and D progress report until before 2000, in which information on the geological disposal of high level radioactive waste(HLW) will be presented to show the technical reliability and technical basis to contribute for the site selection or the safety-standard developments. Recognizing the importance of the social consensus to the geological disposal of international discussions in 1990's, understanding and consensus by the society are essential to the development and realization of the geological disposal of HLW. For getting social understanding and consensus, it is quite important to present the broad basis background information on the geological disposal of HLW, together with the technical basis and also the international discussion of the issues. In this report, the following studies have been done to help to prepare the background information for the 2nd R and D progress report, based on the recent informations and research and assessment works of last 2 years. These are, (1) As the part of general discussion, characteristics of HLW disposal and several issues to be considered for establishing the measures of the disposal of HLW were identified and analyzed from both practical and logical points of view. Those issues were the concept and image of the long term safety measures, the concept and criteria of geological disposal, and, safety assessment and performance assessment. (2) As the part of specific discussion, questions and concerns frequently raised by the non-specialists were taken up and 10 topics in relation to the geological disposal have been identified based on the discussion. Scientific and technical facts, consensus by the specialists on the issues, and international

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

Science.gov (United States)

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

2017-12-01

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

The Blindstitch Machine. Module 11.

Science.gov (United States)

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

This module on the purpose and use of the blindstitch machine, one in a series on clothing construction for industrial sewing machine operators designed for student self-study, contains three sections. Each section includes the following parts: an introduction, directions, an objective, learning activities, student information, student self-check,…

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.

Machine Vision Handbook

CERN Document Server

2012-01-01

The automation of visual inspection is becoming more and more important in modern industry as a consistent, reliable means of judging the quality of raw materials and manufactured goods . The Machine Vision Handbook  equips the reader with the practical details required to engineer integrated mechanical-optical-electronic-software systems. Machine vision is first set in the context of basic information on light, natural vision, colour sensing and optics. The physical apparatus required for mechanized image capture – lenses, cameras, scanners and light sources – are discussed followed by detailed treatment of various image-processing methods including an introduction to the QT image processing system. QT is unique to this book, and provides an example of a practical machine vision system along with extensive libraries of useful commands, functions and images which can be implemented by the reader. The main text of the book is completed by studies of a wide variety of applications of machine vision in insp...

Machining of Complex Sculptured Surfaces

CERN Document Server

2012-01-01

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

Parallelization of the ROOT Machine Learning Methods

CERN Document Server

Vakilipourtakalou, Pourya

2016-01-01

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

The cognitive approach to conscious machines

CERN Document Server

Haikonen, Pentti O

2003-01-01

Could a machine have an immaterial mind? The author argues that true conscious machines can be built, but rejects artificial intelligence and classical neural networks in favour of the emulation of the cognitive processes of the brain-the flow of inner speech, inner imagery and emotions. This results in a non-numeric meaning-processing machine with distributed information representation and system reactions. It is argued that this machine would be conscious; it would be aware of its own existence and its mental content and perceive this as immaterial. Novel views on consciousness and the mind-

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.

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

Science.gov (United States)

Park, Hanla; Papadaki, Angeliki

2016-01-01

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

LHC machine: Status and plan

International Nuclear Information System (INIS)

Pojer, M.

2013-01-01

The LHC Run I was successfully concluded in March 2012. An incredible amount of data has been collected and the performance continuously improved during these three years. Important information on the limitations of the machine also emerged, which will be used to further increase the potential of the machine in the coming years. (authors)

Classifying cognitive profiles using machine learning with privileged information in Mild Cognitive Impairment

Directory of Open Access Journals (Sweden)

Hanin Hamdan Alahmadi

2016-11-01

Full Text Available Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ classifiers to discriminate patients with Mild Cognitive Impairment (MCI from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1 when overall fMRI signal for structured stimuli is

LHCb: Beam-gas background for LHCb at 3.5 TeV

CERN Multimedia

Brett, D R; Corti, G; Alessio, F; Jacobsson, R; Talanov, V; Lieng, M H

2011-01-01

We consider the machine induced backgrounds for LHCb arising from collisions of the beam with residual gas in the long straight sections of the LHC close to the experiment. We concentrate on the background particle fluxes initiated by inelastic beam-gas interactions with a direct line of sight to the experiment, with the potential impact on the experiment increasing for larger beam currents and changing gas pressures. In this paper we calculate the background rates for parameters foreseen with LHC running in 2011, using realistic residual pressure profiles. We also discuss the effect of using a pressure profile formulated in terms of equivalent hydrogen, through weighting of other residual gases by their cross section, upon the radial fluxes from the machine and the detector response. We present the expected rates and the error introduced through this approximation.

Machine Translation in Post-Contemporary Era

Science.gov (United States)

Lin, Grace Hui Chin

2010-01-01

This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

MACHINE LEARNING TECHNIQUES USED IN BIG DATA

Directory of Open Access Journals (Sweden)

STEFANIA LOREDANA NITA

2016-07-01

Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

Managing virtual machines with Vac and Vcycle

Science.gov (United States)

McNab, A.; Love, P.; MacMahon, E.

2015-12-01

We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

Criteria of the effectiveness of a liaison center for the machine building industry

International Nuclear Information System (INIS)

Hofer, P.; Wolff, H.; Franzen, D.

1977-06-01

The study aimed at working out a catalogue of criteria for the effective work of a liaison center for the machine building industry within the planned system of information and documentation of the German Government. By selecting this objective, the investigation methodically demanded a continuous change between theoretical analysis (study of literature, analytical deduction of the framework for a user-oriented information system) and empirical observation of information behaviour in the machine building industry (personal interviews with enterprises and important information sources for the machine building industries). An information system keyed to the information needs of the machine building industry must cover three main phases: Collection and documentation of information, selecting and procuring the needed information as well as encouraging and consulting the clients on the use of information. These different tasks complement one another, they correspond to different functions within enterprises: management, staff, and information function. Central point of a liaison center must be the task of selecting the required information (making information available, selling good information, analysing the information needs of clients), completed by fields of activity in documenting information (specifically for the machine building industry) and consulting clients on the use of information (agency for contacts, drawing the clients' attention to the complexity of machine building problems). A concrete catalogue of criteria for an effective conception of a liaison center for the machine building industry has been worked out. (orig.) [de

Criteria of the effectiveness of a liaison center for the machine building industry

International Nuclear Information System (INIS)

Hofer, P.; Wolff, H.; Franzen, D.; Schlichting, J.; Weidig, I.

1978-04-01

The study aimed at working out a catalogue of criteria for the effective work of a liaison center for the machine building industry within the planned system of information and documentation of the German Government. By selecting this objective, the investigation methodically demanded a continuous change between theoretical analysis (study of literature, analytical deduction of the framework for a user-oriented information system) and empirical observation of information behaviour in the machine building industry (personal interviews with enterprises and important information sources for the machine building industries). An information system keyed to the information needs of the machine building industry must cover three main phases: Collection and documentation of information, selecting and procuring the needed information as well as encouraging and consulting the clients on the use of information. These different tasks complement one another, they correspond to different functions within enterprises: management, staff, and information function. Central point of a liaison center must be the task of selecting the required information (making information available, selling good information, analysing the information needs of clients), completed by fields of activity in documenting information (specifically for the machine building industry) and consulting clients on the use of information (agency for contacts, drawing the client's attention to the complexity of machine building problems). A concrete catalogue of criteria for an effective conception of a liaison center for the machine building industry has been worked out. (orig.) [de

Shell Measuring Machine. History and Status Report

Energy Technology Data Exchange (ETDEWEB)

Birchler, Wilbur D. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Fresquez, Philip R. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2000-06-01

Commercialization of the Ring Rotacon Shell Measuring Machine project is a CRADA (NO. LA98C10358) between The University of California (Los Alamos National Laboratory) and Moore Tool Company, Bridgeport, CT. The actual work started on this CRADA in December of 1998. Several meetings were held with the interested parties (Los Alamos, Oak Ridge, Moore Tool, and the University of North Carolina). The results of these meetings were that the original Ring Rotacon did not measure up to the requirements of the Department of Energy and private industry, and a new configuration was investigated. This new configuration (Shell Measuring Machine [SMM]) much better fits the needs of all parties. The work accomplished on the Shell Measuring Machine in FY 99 includes the following; Specifications for size and weight were developed; Performance error budgets were established; Designs were developed; Analyses were performed (stiffness and natural frequency); Existing part designs were compared to the working SMM volume; Peer reviews were conducted; Controller requirements were studied; Fixture requirements were evaluated; and Machine motions were analyzed. The consensus of the Peer Review Committee was that the new configuration has the potential to satisfy the shell inspection needs of Department of Energy as well as several commercial customers. They recommended that more analyses be performed on error budgets, structural stiffness, natural frequency, and thermal effects and that operational processes be developed. Several design issues need to be addressed. They are the type of bearings utilized to support the tables (air bearings or mechanical roller type bearings), the selection of the probes, the design of the probe sliding mechanisms, and the design of the upper table positioning mechanism. Each item has several possible solutions, and more work is required to obtain the best design. This report includes the background and technical objectives; minutes of the working

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.

Selective Exposure to and Acquisition of Information from Educational Television Programs as a Function of Appeal and Tempo of Background Music.

Science.gov (United States)

Wakshlag, Jacob J.; And Others

1982-01-01

The effect of educational television background music on selective exposure and information acquisition was studied. Background music of slow tempo, regardless of its appeal, had negligible effects on attention and information acquisition. Rhythmic, fast-tempo background music, especially when appealing, significantly reduced visual attention to…

Electric machines with axial magnetic flux

Science.gov (United States)

Nuca, I.; Ambros, T.; Burduniuc, M.; Deaconu, S. I.; Turcanu, A.

2018-01-01

The paper contains information on the performance of axial machines compared to cylindrical ones. At the same time, various constructive schemes of synchronous electromechanical converters with permanent magnets and asynchronous with short-circuited rotor are presented. In the developed constructions, the aim is to maximize the usage of the material of the stator windings. The design elements of the axial machine magnetic system are presented. The FEMM application depicted the array of the magnetic field of an axial machine.

PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Directory of Open Access Journals (Sweden)

Baskin Berivan

2003-03-01

Full Text Available Abstract Background The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Results Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Conclusions Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

Machine intelligence and signal processing

CERN Document Server

Vatsa, Mayank; Majumdar, Angshul; Kumar, Ajay

2016-01-01

This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intel...

Next generation of electron-positron colliding beam machines

International Nuclear Information System (INIS)

Richter, B.

1979-03-01

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

The Serger (Overlock) Machine. Module 10.

Science.gov (United States)

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

This module on the purpose and use of the serger machine, one in a series on clothing construction for industrial sewing machine operators designed for student self-study, contains nine sections. Each section includes the following parts: an introduction, directions, an objective, learning activities, student information, student self-check,…

Surface mining machines problems of maintenance and modernization

CERN Document Server

Rusiński, Eugeniusz; Moczko, Przemysław; Pietrusiak, Damian

2017-01-01

This unique volume imparts practical information on the operation, maintenance, and modernization of heavy performance machines such as lignite mine machines, bucket wheel excavators, and spreaders. Problems of large scale machines (mega machines) are highly specific and not well recognized in the common mechanical engineering environment. Prof. Rusiński and his co-authors identify solutions that increase the durability of these machines as well as discuss methods of failure analysis and technical condition assessment procedures. "Surface Mining Machines: Problems in Maintenance and Modernization" stands as a much-needed guidebook for engineers facing the particular challenges of heavy performance machines and offers a distinct and interesting demonstration of scale-up issues for researchers and scientists from across the fields of machine design and mechanical engineering.

An Individual Claims History Simulation Machine

Directory of Open Access Journals (Sweden)

Andrea Gabrielli

2018-03-01

Full Text Available The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

Introducing Machine Learning Concepts with WEKA.

Science.gov (United States)

Smith, Tony C; Frank, Eibe

2016-01-01

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

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...

NetiNeti: discovery of scientific names from text using machine learning methods

Directory of Open Access Journals (Sweden)

Akella Lakshmi

2012-08-01

Full Text Available Abstract Background A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. Results We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing, a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE. NetiNeti performs better (precision = 98.9% and recall = 70.5% compared to a popular dictionary based approach (precision = 97.5% and recall = 54.3% on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central’s full text articles annotated with scientific names, the precision and recall values are 98.5% and 96.2% respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Conclusions We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.

Food labeling; calorie labeling of articles of food in vending machines. Final rule.

Science.gov (United States)

2014-12-01

To implement the vending machine food labeling provisions of the Patient Protection and Affordable Care Act of 2010 (ACA), the Food and Drug Administration (FDA or we) is establishing requirements for providing calorie declarations for food sold from certain vending machines. This final rule will ensure that calorie information is available for certain food sold from a vending machine that does not permit a prospective purchaser to examine the Nutrition Facts Panel before purchasing the article, or does not otherwise provide visible nutrition information at the point of purchase. The declaration of accurate and clear calorie information for food sold from vending machines will make calorie information available to consumers in a direct and accessible manner to enable consumers to make informed and healthful dietary choices. This final rule applies to certain food from vending machines operated by a person engaged in the business of owning or operating 20 or more vending machines. Vending machine operators not subject to the rules may elect to be subject to the Federal requirements by registering with FDA.

Natural background radioactivity of the earth's surface -- essential information for environmental impact studies

International Nuclear Information System (INIS)

Tauchid, M.; Grasty, R.L.

2002-01-01

An environmental impact study is basically a study of change. This change is compared to the preexisting conditions that are usually perceived to be the original one or the 'pristine' stage. Unfortunately reliable information on the 'so called' pristine stage is far from adequate. One of the essential parts of this information is a good knowledge of the earth's chemical make up, or its geochemistry. Presently available data on the geochemistry of the earth's surface, including those related to radioactive elements, are incomplete and inconsistent. The main reason why a number of regulations are judged to be too strict and disproportional to the risks that might be caused by some human activities, is the lack of reliable information on the natural global geochemical background on which environmental regulations should be based. The main objective of this paper is to present a view on the need for complete baseline information on the earth's surface environment and in particular its geochemical character. It is only through the availability of complete information, including reliable baseline information on the natural radioactivity, that an appropriate study on the potential effect of the various naturally occurring elements on human health be carried out. Presented here are a number of examples where the natural radioactivity of an entire country has been mapped, or is in progress. Also described are the ways these undertakings were accomplished. There is a general misconception that elevated radioactivity can be found only around uranium mines, nuclear power reactors and similar nuclear installations. As can be seen from some of these maps, the natural background radioactivity of the earth's surface closely reflects the underlying geological formations and their alteration products. In reality, properly regulated and managed facilities, the levels of radioactivity associated with many of these facilities are generally quite low relative to those associated with

A human-machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)

International Nuclear Information System (INIS)

Ha, Jun Su; Seong, Poong Hyun

2009-01-01

A human-machine interface (HMI) evaluation method, which is named 'difficulty evaluation method in information searching (DEMIS)', is proposed and demonstrated with an experimental study. The DEMIS is based on a human performance model and two measures of attentional-resource effectiveness in monitoring and detection tasks in nuclear power plants (NPPs). Operator competence and HMI design are modeled to be most significant factors to human performance. One of the two effectiveness measures is fixation-to-importance ratio (FIR) which represents attentional resource (eye fixations) spent on an information source compared to importance of the information source. The other measure is selective attention effectiveness (SAE) which incorporates FIRs for all information sources. The underlying principle of the measures is that the information source should be selectively attended to according to its informational importance. In this study, poor performance in information searching tasks is modeled to be coupled with difficulties caused by poor mental models of operators or/and poor HMI design. Human performance in information searching tasks is evaluated by analyzing the FIR and the SAE. Operator mental models are evaluated by a questionnaire-based method. Then difficulties caused by a poor HMI design are evaluated by a focused interview based on the FIR evaluation and then root causes leading to poor performance are identified in a systematic way.

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.

School Vending Machine Purchasing Behavior: Results from the 2005 YouthStyles Survey

Science.gov (United States)

Thompson, Olivia M.; Yaroch, Amy L.; Moser, Richard P.; Rutten, Lila J. Finney; Agurs-Collins, Tanya

2010-01-01

Background: Competitive foods are often available in school vending machines. Providing youth with access to school vending machines, and thus competitive foods, is of concern, considering the continued high prevalence of childhood obesity: competitive foods tend to be energy dense and nutrient poor and can contribute to increased energy intake in…

E-Mail Writing: Providing Background Information in the Core of Computer Assisted Instruction

Science.gov (United States)

Nazari, Behzad; Ninknejad, Sahar

2015-01-01

The present study highly supported the effective role of providing background information via email by the teacher to write e-mail by the students in learners' writing ability. A total number of 50 EFL advanced male students aged between 25 and 40 at different branches of Iran Language Institute in Tehran, Tehran. Through the placement test of…

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

Visualization tool for human-machine interface designers

Science.gov (United States)

Prevost, Michael P.; Banda, Carolyn P.

1991-06-01

As modern human-machine systems continue to grow in capabilities and complexity, system operators are faced with integrating and managing increased quantities of information. Since many information components are highly related to each other, optimizing the spatial and temporal aspects of presenting information to the operator has become a formidable task for the human-machine interface (HMI) designer. The authors describe a tool in an early stage of development, the Information Source Layout Editor (ISLE). This tool is to be used for information presentation design and analysis; it uses human factors guidelines to assist the HMI designer in the spatial layout of the information required by machine operators to perform their tasks effectively. These human factors guidelines address such areas as the functional and physical relatedness of information sources. By representing these relationships with metaphors such as spring tension, attractors, and repellers, the tool can help designers visualize the complex constraint space and interacting effects of moving displays to various alternate locations. The tool contains techniques for visualizing the relative 'goodness' of a configuration, as well as mechanisms such as optimization vectors to provide guidance toward a more optimal design. Also available is a rule-based design checker to determine compliance with selected human factors guidelines.

Solving a Higgs optimization problem with quantum annealing for machine learning.

Science.gov (United States)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Background risk information to assist in risk management decision making

International Nuclear Information System (INIS)

Hammonds, J.S.; Hoffman, F.O.; White, R.K.; Miller, D.B.

1992-10-01

The evaluation of the need for remedial activities at hazardous waste sites requires quantification of risks of adverse health effects to humans and the ecosystem resulting from the presence of chemical and radioactive substances at these sites. The health risks from exposure to these substances are in addition to risks encountered because of the virtually unavoidable exposure to naturally occurring chemicals and radioactive materials that are present in air, water, soil, building materials, and food products. To provide a frame of reference for interpreting risks quantified for hazardous waste sites, it is useful to identify the relative magnitude of risks of both a voluntary and involuntary nature that are ubiquitous throughout east Tennessee. In addition to discussing risks from the ubiquitous presence of background carcinogens in the east Tennessee environment, this report also presents risks resulting from common, everyday activities. Such information should, not be used to discount or trivialize risks from hazardous waste contamination, but rather, to create a sensitivity to general risk issues, thus providing a context for better interpretation of risk information

Background information document to support NESHAPS rulemaking on nuclear power reactors. Draft report

International Nuclear Information System (INIS)

Colli, A.; Conklin, C.; Hoffmeyer, D.

1991-08-01

The purpose of this Background Information Document (BID) is to present information relevant to the Administrator of the Environmental Protection Agency's (EPA) reconsideration of the need for a NESHAP to control radionuclides emitted to the air from commercial nuclear power reactors. The BID presents information on the relevant portions of the regulatory framework that NRC has implemented for nuclear power plant licensees, under the authority of the Atomic Energy Act, as amended, to protect the public's health and safety. To provide context, it summarizes the rulemaking history for Subpart I. It then describes NRC's regulatory program for routine atmospheric emissions of radionuclides and evaluates the doses caused by actual airborne emissions from nuclear power plants, including releases resulting from anticipated operational occurrences

Methods of control the machining process

Directory of Open Access Journals (Sweden)

Yu.V. Petrakov

2017-12-01

Full Text Available Presents control methods, differentiated by the time of receipt of information used: a priori, a posteriori and current. When used a priori information to determine the mode of cutting is carried out by simulation the process of cutting allowance, where the shape of the workpiece and the details are presented in the form of wireframes. The office for current information provides for a system of adaptive control and modernization of CNC machine, where in the input of the unit shall be computed by using established optimization software. For the control by a posteriori information of the proposed method of correction of shape-generating trajectory in the second pass measurement surface of the workpiece formed by the first pass. Developed programs that automatically design the adjusted file for machining.

Virtual Vector Machine for Bayesian Online Classification

OpenAIRE

Minka, Thomas P.; Xiang, Rongjing; Yuan; Qi

2012-01-01

In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Virtual Vector Machine. The virtual vector machine allows you to smoothly trade-off prediction accuracy with memory size. The virtual vector machine summarizes the information con...

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...

The Single Needle Lockstitch Machine. Module 1.

Science.gov (United States)

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

This module on parts of the machine, one in a series on the single needle lockstitch sewing machine for student self-study, contains eight sections. Each section contains the following parts: an introduction, directions, an objective, learning activities, student information, student self-check, check-out activities, and an instructor's final…

An information-theoretic machine learning approach to expression QTL analysis.

Directory of Open Access Journals (Sweden)

Tao Huang

Full Text Available Expression Quantitative Trait Locus (eQTL analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL, and on other chromosomes (trans-eQTL. Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What's more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR and Incremental Feature Selection (IFS, were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA. In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.

Game-powered machine learning.

Science.gov (United States)

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

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

Gene selection and classification for cancer microarray data based on machine learning and similarity measures

Directory of Open Access Journals (Sweden)

Liu Qingzhong

2011-12-01

Full Text Available Abstract Background Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. Results To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. Conclusions On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.

Using Machine Learning to Search for MSSM Higgs Bosons

CERN Document Server

Diesing, Rebecca

2016-01-01

This paper examines the performance of machine learning in the identification of Minimally Su- persymmetric Standard Model (MSSM) Higgs Bosons, and compares this performance to that of traditional cut strategies. Two boosted decision tree algorithms were tested, scikit-learn and XGBoost. These tests indicated that machine learning can perform significantly better than traditional cuts. However, since machine learning in this form cannot be directly implemented in a real MSSM Higgs analysis, this performance information was instead used to better understand the relationships between training variables. Further studies might use this information to construct an improved cut strategy.

Man-machine supervision

International Nuclear Information System (INIS)

Montmain, J.

2005-01-01

Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

[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.

What is the machine learning.

CERN Multimedia

CERN. Geneva

2018-01-01

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. In this talk, I explore a procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus non-linear nature of the boundaries between signal and background. I will demonstrate these features in both an easy to understand toy model and an idealized LHC resonance scenario.

Italian: Area Background Information.

Science.gov (United States)

Defense Language Inst., Washington, DC.

This booklet has been assembled in order to provide students of Italian with a compact source of cultural information on their target area. Chapters include discussion of: (1) introduction to Italian; (2) origins of the Italian population; (3) geography; (4) history including the Roman Era, the Middle Ages, the Renaissance, the "Risorgimento," and…

Learning with incomplete information in the committee machine.

Science.gov (United States)

Bergmann, Urs M; Kühn, Reimer; Stamatescu, Ion-Olimpiu

2009-12-01

We study the problem of learning with incomplete information in a student-teacher setup for the committee machine. The learning algorithm combines unsupervised Hebbian learning of a series of associations with a delayed reinforcement step, in which the set of previously learnt associations is partly and indiscriminately unlearnt, to an extent that depends on the success rate of the student on these previously learnt associations. The relevant learning parameter lambda represents the strength of Hebbian learning. A coarse-grained analysis of the system yields a set of differential equations for overlaps of student and teacher weight vectors, whose solutions provide a complete description of the learning behavior. It reveals complicated dynamics showing that perfect generalization can be obtained if the learning parameter exceeds a threshold lambda ( c ), and if the initial value of the overlap between student and teacher weights is non-zero. In case of convergence, the generalization error exhibits a power law decay as a function of the number of examples used in training, with an exponent that depends on the parameter lambda. An investigation of the system flow in a subspace with broken permutation symmetry between hidden units reveals a bifurcation point lambda* above which perfect generalization does not depend on initial conditions. Finally, we demonstrate that cases of a complexity mismatch between student and teacher are optimally resolved in the sense that an over-complex student can emulate a less complex teacher rule, while an under-complex student reaches a state which realizes the minimal generalization error compatible with the complexity mismatch.

Quantum Machine Learning

Science.gov (United States)

Biswas, Rupak

2018-01-01

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Empirical Studies On Machine Learning Based Text Classification Algorithms

OpenAIRE

Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni

2011-01-01

Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...

Research on Key Technologies of Unit-Based CNC Machine Tool Assembly Design

OpenAIRE

Zhongqi Sheng; Lei Zhang; Hualong Xie; Changchun Liu

2014-01-01

Assembly is the part that produces the maximum workload and consumed time during product design and manufacturing process. CNC machine tool is the key basic equipment in manufacturing industry and research on assembly design technologies of CNC machine tool has theoretical significance and practical value. This study established a simplified ASRG for CNC machine tool. The connection between parts, semantic information of transmission, and geometric constraint information were quantified to as...

An information theory based approach for quantitative evaluation of man-machine interface complexity

International Nuclear Information System (INIS)

Kang, Hyun Gook

1999-02-01

In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

An information theory based approach for quantitative evaluation of man-machine interface complexity

Energy Technology Data Exchange (ETDEWEB)

Kang, Hyun Gook

1999-02-15

In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

Exploring cluster Monte Carlo updates with Boltzmann machines.

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Exploring cluster Monte Carlo updates with Boltzmann machines

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Transition Towards Energy Efficient Machine Tools

CERN Document Server

Zein, André

2012-01-01

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 ...

Automatic optical detection and classification of marine animals around MHK converters using machine vision

Energy Technology Data Exchange (ETDEWEB)

Brunton, Steven [Univ. of Washington, Seattle, WA (United States)

2018-01-15

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

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 ...

Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques.

Science.gov (United States)

Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

2017-06-10

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver's intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver's intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver's intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver's intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

Graphic man-machine interface applied to nuclear reactor designs

International Nuclear Information System (INIS)

Pereira, Claudio M.N.A; Mol, Antonio Carlos A.

1999-01-01

The Man-Machine Interfaces have been of interest of many researchers in the area of nuclear human factors engineering, principally applied to monitoring systems. The clarity of information provides best adaptation of the men to the machine. This work proposes the development of a Graphic Man-Machine Interface applied to nuclear reactor designs as a tool to optimize them. Here is present a prototype of a graphic man-machine interface for the Hammer code developed for PC under the Windows environment. The results of its application are commented. (author)

A monitored retrievable storage facility: Technical background information

International Nuclear Information System (INIS)

1991-07-01

The US government is seeking a site for a monitored retrievable storage facility (MRS). Employing proven technologies used in this country and abroad, the MRS will be an integral part of the federal system for safe and permanent disposal of the nation's high-level radioactive wastes. The MRS will accept shipments of spent fuel from commercial nuclear power plants, temporarily store the spent fuel above ground, and stage shipments of it to a geologic repository for permanent disposal. The law authorizing the MRS provides an opportunity for a state or an Indian tribe to volunteer to host the MRS. The law establishes the Office of the Nuclear Waste Negotiator, who is to seek a state or an Indian tribe willing to host an MRS at a technically-qualified site on reasonable terms, and is to negotiate a proposed agreement specifying the terms and conditions under which the MRS would be developed and operated at that site. This agreement can ensure that the MRS is acceptable to -- and benefits -- the host community. The proposed agreement must be submitted to Congress and enacted into law to become effective. This technical background information presents an overview of various aspects of a monitored retrievable storage facility, including the process by which it will be developed

Handbook of machine soldering SMT and TH

CERN Document Server

Woodgate, Ralph W

1996-01-01

A shop-floor guide to the machine soldering of electronics Sound electrical connections are the operational backbone of every piece of electronic equipment-and the key to success in electronics manufacturing. The Handbook of Machine Soldering is dedicated to excellence in the machine soldering of electrical connections. Self-contained, comprehensive, and down-to-earth, it cuts through jargon, peels away outdated notions, and presents all the information needed to select, install, and operate machine soldering equipment. This fully updated and revised volume covers all of the new technologies and processes that have emerged in recent years, most notably the use of surface mount technology (SMT). Supplemented with 200 illustrations, this thoroughly accessible text Describes reflow and wave soldering in detail, including reflow soldering of SMT boards and the use of nitrogen blankets * Explains the setup, operation, and maintenance of a variety of soldering machines * Discusses theory, selection, and control met...

An object-oriented extension for debugging the virtual machine

Energy Technology Data Exchange (ETDEWEB)

Pizzi, Jr, Robert G. [Univ. of California, Davis, CA (United States)

1994-12-01

A computer is nothing more then a virtual machine programmed by source code to perform a task. The program`s source code expresses abstract constructs which are compiled into some lower level target language. When a virtual machine breaks, it can be very difficult to debug because typical debuggers provide only low-level target implementation information to the software engineer. We believe that the debugging task can be simplified by introducing aspects of the abstract design and data into the source code. We introduce OODIE, an object-oriented extension to programming languages that allows programmers to specify a virtual environment by describing the meaning of the design and data of a virtual machine. This specification is translated into symbolic information such that an augmented debugger can present engineers with a programmable debugging environment specifically tailored for the virtual machine that is to be debugged.

Massively collaborative machine learning

NARCIS (Netherlands)

Rijn, van J.N.

2016-01-01

Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

A study of wear in refrigerating machines using thin layer activation

International Nuclear Information System (INIS)

Hammer, P.; Eichhorn, K.; Eifrig, C.

1986-01-01

Wear is studied in a ball-and-socket joint of a newly developed refrigerating machine. Using deuteron activation a 15 μm deep Co-57 layer is generated at the ring-shaped friction area in the steel socket of the joint. The measurement of the Co-57 intensity of the wear particles held back on an oil filter provides information about the wear rate of the socket during machine operation. The measurement of the Co-57 contaminations occuring in the individual parts of the machine at the end of the test gives information about the distribution of the wear particles in the machine and about the material transfer in the ball-and-socket joint. (author)

Machine Perfusion or Cold Storage in Deceased-Donor Kidney Transplantation

NARCIS (Netherlands)

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

2009-01-01

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

Machine Learning Based Localization and Classification with Atomic Magnetometers

Science.gov (United States)

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

2018-01-01

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

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)

Background information to the installers guide for small scale mains connected PV

Energy Technology Data Exchange (ETDEWEB)

NONE

2002-07-01

This report contains background information used by BRE, EA Technology, Halcrows and Sundog when compiling guidance for the UK's New and Renewable Energy Programme on the installation of small-scale photovoltaics (PV) in buildings. The report considers: relevant standards; general safety issues; fire and safety issues, including the fire resistance of PV modules; PV module ratings such as maximum voltage and maximum current; DC cabling; the DC disconnect; the DC junction box; fault analysis; general and AC side earthing; DC earthing; lightning and surge suppression; inverters; AC modules; AC systems; getting connection; mounting options; and installation issues.

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

NARCIS (Netherlands)

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

2012-01-01

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

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

Science.gov (United States)

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

Man machine interface and its implementation

International Nuclear Information System (INIS)

Hills, B.G.; Boettcher, D.B.; Reed, R.

1992-01-01

Sizewell B is the latest nuclear power station to be constructed in the United Kingdom: its Man-Machine Interfaces are therefore, by definition, the state-of-the-art. This paper discusses the principal Man-Machine Interfaces used in the operation of the station, and the systems that implement them. The Man-Machine Interface facilities discussed are: in the Main Control Room, which is used for normal operation and shutdown of the plant: in the Auxiliary Shutdown Room, which allows shutdown of the reactor if evacuation of the main Control Room is necessary: and in the Technical Support Centre, which is used for remote monitoring of the plant. The Man-Machine Interfaces that are described are parts of a station-wide group of interlinked computer systems called the Data Processing and Control System. This system collects data from the plant and displays it to the operators via discrete devices and on graphical computer displays. It also acquires control inputs from the operators via switches, which are then used to provide remote manual control, modulating control and sequence control. The computer system that handles the plant process data and alarm information displays uses a windowing interface with keyboard and trackerball navigation to allow easy retrieval and viewing of information. It is this system that is the main topic of this paper. (author)

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.

Chemical Source Localization Fusing Concentration Information in the Presence of Chemical Background Noise.

Science.gov (United States)

Pomareda, Víctor; Magrans, Rudys; Jiménez-Soto, Juan M; Martínez, Dani; Tresánchez, Marcel; Burgués, Javier; Palacín, Jordi; Marco, Santiago

2017-04-20

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented.

Ship localization in Santa Barbara Channel using machine learning classifiers.

Science.gov (United States)

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

Cosmic String Detection with Tree-Based Machine Learning

Science.gov (United States)

Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

2018-05-01

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

Improvement in luminosity, background and chamber protection with beam scrapers in the ISR

CERN Document Server

Bryant, P; Johnsen, Kjell; Laeger, H; Montague, Brian William St. Leger; Neet, D; Schneider, F W; Turner, S

1973-01-01

The Intersecting Storage Rings (ISR) are equipped with beam scrapers used for various purposes such as improving luminosity, reducing background, beam diagnostics and for protection of machine components. A description is given of the different types of scrapers and of the results in the various applications obtained during the last year. In particular, the substantial improvements in luminosity and background by scraping are described. (3 refs).

Accessible engineering drawings for visually impaired machine operators.

Science.gov (United States)

Ramteke, Deepak; Kansal, Gayatri; Madhab, Benu

2014-01-01

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

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.

Modeling and simulation of five-axis virtual machine based on NX

Science.gov (United States)

Li, Xiaoda; Zhan, Xianghui

2018-04-01

Virtual technology in the machinery manufacturing industry has shown the role of growing. In this paper, the Siemens NX software is used to model the virtual CNC machine tool, and the parameters of the virtual machine are defined according to the actual parameters of the machine tool so that the virtual simulation can be carried out without loss of the accuracy of the simulation. How to use the machine builder of the CAM module to define the kinematic chain and machine components of the machine is described. The simulation of virtual machine can provide alarm information of tool collision and over cutting during the process to users, and can evaluate and forecast the rationality of the technological process.

76 FR 30200 - Forging Machines; Extension of the Office of Management and Budget's (OMB) Approval of...

Science.gov (United States)

2011-05-24

...] Forging Machines; Extension of the Office of Management and Budget's (OMB) Approval of Information... extend OMB approval of the information collection requirements contained in the Forging Machines Standard... to reduce employees' risk of death or serious injury by ensuring that forging machines used by them...

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.

Giro form reading machine

Science.gov (United States)

Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

1995-08-01

Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

A SURVEY OF AUTOMATED TELLER MACHINE USAGE IN NORTH INDIA

OpenAIRE

Shweta Sankhwar*1 & Dhirendra Pandey2

2017-01-01

A machine that hand out cash or executes several banking services at a swipe of Automated Teller Machine (ATM) card in it. An ATM is a machine is use to withdraw cash using a debit card issued by a bank to the consumer(s). An ATM card is issued by a financial organization that permits user to access an Automated Teller Machine (ATM) such as to deposit amount, cash withdrawals, to check account information, etc. It is now preferred to cash by a large majority of the urban population in many ci...

An active role for machine learning in drug development

Science.gov (United States)

Murphy, Robert F.

2014-01-01

Due to the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development. PMID:21587249

GeoDeepDive: Towards a Machine Reading-Ready Digital Library and Information Integration Resource

Science.gov (United States)

Husson, J. M.; Peters, S. E.; Livny, M.; Ross, I.

2015-12-01

Recent developments in machine reading and learning approaches to text and data mining hold considerable promise for accelerating the pace and quality of literature-based data synthesis, but these advances have outpaced even basic levels of access to the published literature. For many geoscience domains, particularly those based on physical samples and field-based descriptions, this limitation is significant. Here we describe a general infrastructure to support published literature-based machine reading and learning approaches to information integration and knowledge base creation. This infrastructure supports rate-controlled automated fetching of original documents, along with full bibliographic citation metadata, from remote servers, the secure storage of original documents, and the utilization of considerable high-throughput computing resources for the pre-processing of these documents by optical character recognition, natural language parsing, and other document annotation and parsing software tools. New tools and versions of existing tools can be automatically deployed against original documents when they are made available. The products of these tools (text/XML files) are managed by MongoDB and are available for use in data extraction applications. Basic search and discovery functionality is provided by ElasticSearch, which is used to identify documents of potential relevance to a given data extraction task. Relevant files derived from the original documents are then combined into basic starting points for application building; these starting points are kept up-to-date as new relevant documents are incorporated into the digital library. Currently, our digital library stores contains more than 360K documents supplied by Elsevier and the USGS and we are actively seeking additional content providers. By focusing on building a dependable infrastructure to support the retrieval, storage, and pre-processing of published content, we are establishing a foundation for

Foreign Developments in Information Processing and Machine Translation, No. 1

Science.gov (United States)

1960-09-29

technicians] (Sestier (A.) -- La Traduction automatfguT"" des textes ecrits scJQntifiqaes ej^J^chplc^es dxun langage~ dans__un’"*""* ’’^t^’T^^i...are more and more comprehensible to others than machine translation technicians will result. Sketch of a proaram. This outline of work xtfiich will

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.

Zambia Country Background Report

DEFF Research Database (Denmark)

Hampwaye, Godfrey; Jeppesen, Søren; Kragelund, Peter

This paper provides background data and general information for the Zambia studies focusing on local food processing sub­‐sector; and the local suppliers to the mines as part of the SAFIC project (Successful African Firms and Institutional Change).......This paper provides background data and general information for the Zambia studies focusing on local food processing sub­‐sector; and the local suppliers to the mines as part of the SAFIC project (Successful African Firms and Institutional Change)....

An Overall Perspective of Machine Translation with its Shortcomings

Directory of Open Access Journals (Sweden)

Alireza Akbari

2014-01-01

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

Experiences with nutrition-related information during antenatal care of pregnant women of different ethnic backgrounds residing in the area of Oslo, Norway.

Science.gov (United States)

Garnweidner, Lisa M; Sverre Pettersen, Kjell; Mosdøl, Annhild

2013-12-01

to explore experiences with nutrition-related information during routine antenatal care among women of different ethnical backgrounds. individual interviews with seventeen participants were conducted twice during pregnancy. Data collection and analysis were inspired by an interpretative phenomenological approach. participants were purposively recruited at eight Mother and Child Health Centres in the area of Oslo, Norway, where they received antenatal care. participants had either immigrant backgrounds from African and Asian countries (n=12) or were ethnic Norwegian (n=5). Participants were pregnant with their first child and had a pre-pregnancy Body Mass Index above 25 kg/m(2). participants experienced that they were provided with little nutrition-related information in antenatal care. The information was perceived as presented in very general terms and focused on food safety. Weight management and the long-term prevention of diet-related chronic diseases had hardly been discussed. Participants with immigrant backgrounds appeared to be confused about information given by the midwife which was incongruent with their original food culture. The participants were actively seeking for nutrition-related information and had to navigate between various sources of information. the midwife is considered a trustworthy source of nutrition-related information. Therefore, antenatal care may have considerable potential to promote a healthy diet to pregnant women. Findings suggest that nutrition communication in antenatal care should be more tailored towards women's dietary habits and cultural background, nutritional knowledge as well as level of nutrition literacy. Copyright © 2012 Elsevier Ltd. All rights reserved.

Do warning signs on electronic gaming machines influence irrational cognitions?

Science.gov (United States)

Monaghan, Sally; Blaszczynski, Alex; Nower, Lia

2009-08-01

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

The Machinic Temporality of Metadata

Directory of Open Access Journals (Sweden)

Claudio Celis

2015-03-01

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

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

Science.gov (United States)

Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

2017-01-01

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

Directory of Open Access Journals (Sweden)

Il-Hwan Kim

2017-06-01

Full Text Available Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN models, and the augmented information is fed to a support vector machine (SVM to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

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.

Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored f or software bug prediction on public available data sets. Results showed most of the mac ...

A computer architecture for intelligent machines

Science.gov (United States)

Lefebvre, D. R.; Saridis, G. N.

1992-01-01

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Reinforcement and Systemic Machine Learning for Decision Making

CERN Document Server

Kulkarni, Parag

2012-01-01

Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available-or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm-creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new an

Human-machine interaction in nuclear power plants

International Nuclear Information System (INIS)

Yoshikawa, Hidekazu

2005-01-01

Advanced nuclear power plants are generally large complex systems automated by computers. Whenever a rate plant emergency occurs the plant operators must cope with the emergency under severe mental stress without committing any fatal errors. Furthermore, the operators must train to improve and maintain their ability to cope with every conceivable situation, though it is almost impossible to be fully prepared for an infinite variety of situations. In view of the limited capability of operators in emergency situations, there has been a new approach to preventing the human error caused by improper human-machine interaction. The new approach has been triggered by the introduction of advanced information systems that help operators recognize and counteract plant emergencies. In this paper, the adverse effect of automation in human-machine systems is explained. The discussion then focuses on how to configure a joint human-machine system for ideal human-machine interaction. Finally, there is a new proposal on how to organize technologies that recognize the different states of such a joint human-machine system

A nanoplasmonic switch based on molecular machines

KAUST Repository

Zheng, Yue Bing

2009-06-01

We aim to develop a molecular-machine-driven nanoplasmonic switch for its use in future nanophotonic integrated circuits (ICs) that have applications in optical communication, information processing, biological and chemical sensing. Experimental data show that an Au nanodisk array, coated with rotaxane molecular machines, switches its localized surface plasmon resonances (LSPR) reversibly when it is exposed to chemical oxidants and reductants. Conversely, bare Au nanodisks and disks coated with mechanically inert control compounds, do not display the same switching behavior. Along with calculations based on time-dependent density functional theory (TDDFT), these observations suggest that the nanoscale movements within surface-bound "molecular machines" can be used as the active components in plasmonic devices. ©2009 IEEE.

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

Science.gov (United States)

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

2012-01-01

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

Quantum cloning machines and their implementation in physical systems

International Nuclear Information System (INIS)

Wu Tao; Ye Liu; Fang Bao-Long

2013-01-01

We review the basic theory of approximate quantum cloning for discrete variables and some schemes for implementing quantum cloning machines. Several types of approximate quantum clones and their expansive quantum clones are introduced. As for the implementation of quantum cloning machines, we review some design methods and recent experimental results. (topical review - quantum information)

Homopolar machine design

International Nuclear Information System (INIS)

Thullen, P.

1978-01-01

A general conceptual design for a disc-type homopolar machine is presented. This machine uses a superconducting, air-core, solenoidal field winding with a peak field of 8 T. A total energy of 500 MJ is stored in two counter-rotating disc rotors that operate at a surface speed of 200 m/s. Terminal voltages of 500 to 2000 V are obtained over the range of designs studied. Brush systems to collect 3 MA are investigated. Various brush materials are discussed to determine their usefulness in this application. Sufficient information on operating characteristics in high-power applications is only available for copper-graphite brushes. The use of sliding brushes for terminal voltage regulation is discussed. This feature cannot provide a great deal of flexibility in this particular application although it may be useful during start-up. The brush system is the most demanding feature of this design. Few systems in the million ampere range have been constructed, consequently, it is not possible to predict the behavior of this brush system with great certainty. A detailed design of the brushes should be undertaken. It is estimated that the cost of such a machine will range from 0.5 to 1.5 cents per joule

Collaborative human-machine analysis using a controlled natural language

Science.gov (United States)

Mott, David H.; Shemanski, Donald R.; Giammanco, Cheryl; Braines, Dave

2015-05-01

A key aspect of an analyst's task in providing relevant information from data is the reasoning about the implications of that data, in order to build a picture of the real world situation. This requires human cognition, based upon domain knowledge about individuals, events and environmental conditions. For a computer system to collaborate with an analyst, it must be capable of following a similar reasoning process to that of the analyst. We describe ITA Controlled English (CE), a subset of English to represent analyst's domain knowledge and reasoning, in a form that it is understandable by both analyst and machine. CE can be used to express domain rules, background data, assumptions and inferred conclusions, thus supporting human-machine interaction. A CE reasoning and modeling system can perform inferences from the data and provide the user with conclusions together with their rationale. We present a logical problem called the "Analysis Game", used for training analysts, which presents "analytic pitfalls" inherent in many problems. We explore an iterative approach to its representation in CE, where a person can develop an understanding of the problem solution by incremental construction of relevant concepts and rules. We discuss how such interactions might occur, and propose that such techniques could lead to better collaborative tools to assist the analyst and avoid the "pitfalls".

Improved machine learning method for analysis of gas phase chemistry of peptides

Directory of Open Access Journals (Sweden)

Ahn Natalie

2008-12-01

Full Text Available Abstract Background Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. Results We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. Conclusion The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future.

Ergonomic principles for the design of combined drilling and loading machines

Energy Technology Data Exchange (ETDEWEB)

Mason, S.; Simpson, G.C.

1990-08-08

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

An artificial molecular machine that builds an asymmetric catalyst

Science.gov (United States)

De Bo, Guillaume; Gall, Malcolm A. Y.; Kuschel, Sonja; De Winter, Julien; Gerbaux, Pascal; Leigh, David A.

2018-05-01

Biomolecular machines perform types of complex molecular-level tasks that artificial molecular machines can aspire to. The ribosome, for example, translates information from the polymer track it traverses (messenger RNA) to the new polymer it constructs (a polypeptide)1. The sequence and number of codons read determines the sequence and number of building blocks incorporated into the biomachine-synthesized polymer. However, neither control of sequence2,3 nor the transfer of length information from one polymer to another (which to date has only been accomplished in man-made systems through template synthesis)4 is easily achieved in the synthesis of artificial macromolecules. Rotaxane-based molecular machines5-7 have been developed that successively add amino acids8-10 (including β-amino acids10) to a growing peptide chain by the action of a macrocycle moving along a mono-dispersed oligomeric track derivatized with amino-acid phenol esters. The threaded macrocycle picks up groups that block its path and links them through successive native chemical ligation reactions11 to form a peptide sequence corresponding to the order of the building blocks on the track. Here, we show that as an alternative to translating sequence information, a rotaxane molecular machine can transfer the narrow polydispersity of a leucine-ester-derivatized polystyrene chain synthesized by atom transfer radical polymerization12 to a molecular-machine-made homo-leucine oligomer. The resulting narrow-molecular-weight oligomer folds to an α-helical secondary structure13 that acts as an asymmetric catalyst for the Juliá-Colonna epoxidation14,15 of chalcones.

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

Science.gov (United States)

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

2016-01-01

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

Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings

International Nuclear Information System (INIS)

Kiluk, Sebastian

2017-01-01

Highlights: • Energy efficiency classification sustainability benefits from knowledge prediction. • Diagnostic classification can be validated with its dynamics and current data. • Diagnostic classification dynamics provides novelty extraction for knowledge update. • Data mining comparison can be performed with knowledge dynamics and uncertainty. • Diagnostic information refinement benefits form comparing classifiers dynamics. - Abstract: Modern ways of exploring the diagnostic knowledge provided by data mining and machine learning raise some concern about the ways of evaluating the quality of output knowledge, usually represented by information systems. Especially in district heating, the stationarity of efficiency models, and thus the relevance of diagnostic classification system, cannot be ensured due to the impact of social, economic or technological changes, which are hard to identify or predict. Therefore, data mining and machine learning have become an attractive strategy for automatically and continuously absorbing such dynamics. This paper presents a new method of evaluation and comparison of diagnostic information systems gathered algorithmically in district heating efficiency supervision based on exploring the evolution of information system and analyzing its dynamic features. The process of data mining and knowledge discovery was applied to the data acquired from district heating substations’ energy meters to provide the automated discovery of diagnostic knowledge base necessary for the efficiency supervision of district heating-supplied buildings. The implemented algorithm consists of several steps of processing the billing data, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract models representing energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operating under similar climate conditions and

Predicting genome-wide redundancy using machine learning

Directory of Open Access Journals (Sweden)

Shasha Dennis E

2010-11-01

Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

4th International Conference on Man–Machine Interactions

CERN Document Server

Brachman, Agnieszka; Kozielski, Stanisław; Czachórski, Tadeusz

2016-01-01

This book provides an overview of the current state of research on development and application of methods, algorithms, tools and systems associated with the studies on man-machine interaction. Modern machines and computer systems are designed not only to process information, but also to work in dynamic environment, supporting or even replacing human activities in areas such as business, industry, medicine or military. The interdisciplinary field of research on man-machine interactions focuses on broad range of aspects related to the ways in which human make or use computational artifacts, systems and infrastructure.   This monograph is the fourth edition in the series and presents new concepts concerning analysis, design and evaluation of man-machine systems. The selection of high-quality, original papers covers a wide scope of research topics focused on the main problems and challenges encountered within rapidly evolving new forms of human-machine relationships. The presented material is structured into fol...

A Modified Method Combined with a Support Vector Machine and Bayesian Algorithms in Biological Information

Directory of Open Access Journals (Sweden)

Wen-Gang Zhou

2015-06-01

Full Text Available With the deep research of genomics and proteomics, the number of new protein sequences has expanded rapidly. With the obvious shortcomings of high cost and low efficiency of the traditional experimental method, the calculation method for protein localization prediction has attracted a lot of attention due to its convenience and low cost. In the machine learning techniques, neural network and support vector machine (SVM are often used as learning tools. Due to its complete theoretical framework, SVM has been widely applied. In this paper, we make an improvement on the existing machine learning algorithm of the support vector machine algorithm, and a new improved algorithm has been developed, combined with Bayesian algorithms. The proposed algorithm can improve calculation efficiency, and defects of the original algorithm are eliminated. According to the verification, the method has proved to be valid. At the same time, it can reduce calculation time and improve prediction efficiency.

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.

Trust metrics in information fusion

Science.gov (United States)

Blasch, Erik

2014-05-01

Trust is an important concept for machine intelligence and is not consistent across many applications. In this paper, we seek to understand trust from a variety of factors: humans, sensors, communications, intelligence processing algorithms and human-machine displays of information. In modeling the various aspects of trust, we provide an example from machine intelligence that supports the various attributes of measuring trust such as sensor accuracy, communication timeliness, machine processing confidence, and display throughput to convey the various attributes that support user acceptance of machine intelligence results. The example used is fusing video and text whereby an analyst needs trust information in the identified imagery track. We use the proportional conflict redistribution rule as an information fusion technique that handles conflicting data from trusted and mistrusted sources. The discussion of the many forms of trust explored in the paper seeks to provide a systems-level design perspective for information fusion trust quantification.

Background Information on Crimes against Children Study. Information Memorandum 86-20.

Science.gov (United States)

Haas, Shaun

This document was prepared to assist the Wisconsin Legislative Council's Special Committee on Crimes Against Children in its study of current laws relating to crimes against children. It provides the background of the origin of the study and describes the characteristics of the Criminal Code, upon which much of the committee review will center.…

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

Science.gov (United States)

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

2017-11-01

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

Revisit of Machine Learning Supported Biological and Biomedical Studies.

Science.gov (United States)

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Background Information for the Nevada National Security Site Integrated Sampling Plan, Revision 0

Energy Technology Data Exchange (ETDEWEB)

Farnham, Irene; Marutzky, Sam

2014-12-01

This document describes the process followed to develop the Nevada National Security Site (NNSS) Integrated Sampling Plan (referred to herein as the Plan). It provides the Plan’s purpose and objectives, and briefly describes the Underground Test Area (UGTA) Activity, including the conceptual model and regulatory requirements as they pertain to groundwater sampling. Background information on other NNSS groundwater monitoring programs—the Routine Radiological Environmental Monitoring Plan (RREMP) and Community Environmental Monitoring Program (CEMP)—and their integration with the Plan are presented. Descriptions of the evaluations, comments, and responses of two Sampling Plan topical committees are also included.

MACHINE-TRANSFORMER UNITS FOR WIND TURBINES

Directory of Open Access Journals (Sweden)

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.

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.

Background information and technical basis for assessment of environmental implications of magnetic fusion energy

International Nuclear Information System (INIS)

Cannon, J.B.

1983-08-01

This report contains background information for assessing the potential environmental implications of fusion-based central electric power stations. It was developed as part of an environmental review of the Magnetic Fusion Energy Program. Transition of the program from demonstration of purely scientific feasibility (breakeven conditions) to exploration of engineering feasibility suggests that formal program environmental review under the National Environmental Policy Act is timely. This report is the principal reference upon which an environmental impact statement on magnetic fusion will be based

LHC Orbit Correction Reproducibility and Related Machine Protection

CERN Document Server

Baer, T; Schmidt, R; Wenninger, J

2012-01-01

The Large Hadron Collider (LHC) has an unprecedented nominal stored beam energy of up to 362 MJ per beam. In order to ensure an adequate machine protection by the collimation system, a high reproducibility of the beam position at collimators and special elements like the final focus quadrupoles is essential. This is realized by a combination of manual orbit corrections, feed forward and real time feedback. In order to protect the LHC against inconsistent orbit corrections, which could put the machine in a vulnerable state, a novel software-based interlock system for orbit corrector currents was developed. In this paper, the principle of the new interlock system is described and the reproducibility of the LHC orbit correction is discussed against the background of this system.

The APS intranet as a man-machine interface

International Nuclear Information System (INIS)

Ciarlette, D.; Gerig, R.; McDowell, W.

1997-01-01

The Advanced Photon Source at Argonne National Laboratory has implemented a number of methods for people to interact with the accelerator systems. The accelerator operators use Sun workstations running MEDM and WCL to interface interactively with the accelerator, however, many people need to view information rather than interact with the machine. One of the most common interfaces for viewing information at the Advanced Photon Source is the World Wide Web. Information such as operations logbook entries, machine status updates, and displays of archived and current data are easily available to APS personnel. This interface between people and the accelerator has proven to be quite useful. Because the Intranet is operating-system independent and inherently unidirectional, ensuring the prevention of unauthorized or accidental control of the accelerators is straightforward

Availability of Vending Machines and School Stores in California Schools

Science.gov (United States)

Cisse-Egbuonye, Nafissatou; Liles, Sandy; Schmitz, Katharine E.; Kassem, Nada; Irvin, Veronica L.; Hovell, Melbourne F.

2016-01-01

Background: This study examined the availability of foods sold in vending machines and school stores in United States public and private schools, and associations of availability with students' food purchases and consumption. Methods: Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected…

Man machine interaction for operator information systems : a general purpose display package on PC/AT

International Nuclear Information System (INIS)

Chandra, A.K.; Dubey, B.P.; Deshpande, S.V.; Vaidya, U.W.; Khandekar, A.B.

1991-01-01

Several operator information systems for nuclear plants have been developed at Reactor Control Division of BARC and these have involved extensive operator interaction to extract the maximum information from the systems. Each of these systems used a different scheme for operator interaction. A composite package has now been developed on PC/AT with EGA/VGA for use with any system to obviate the necessity to develop new software for each project. This permits information to be displayed in various formats viz. trend and history curves, tabular data, bar graphs and core matrix (both for 235 and 500 MWe cores). It also allows data to be printed and plotted using multi colour plotter. This package thus integrates all the features of the earlier systems. It also integrates the operator interaction scheme. It uses window based pull down menus to select parameters to be fed into a particular display format. Within any display format the operator has significant flexibility to modify the selected parameters using context dependent soft keys. The package also allows data to be retrieved in machine readable form. This report describes the various user friendly functions implemented and also the design of the system software. (author). 1 tab., 10 fig., 3 refs

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.

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...

Parental attitudes towards soft drink vending machines in high schools.

Science.gov (United States)

Hendel-Paterson, Maia; French, Simone A; Story, Mary

2004-10-01

Soft drink vending machines are available in 98% of US high schools. However, few data are available about parents' opinions regarding the availability of soft drink vending machines in schools. Six focus groups with 33 parents at three suburban high schools were conducted to describe the perspectives of parents regarding soft drink vending machines in their children's high school. Parents viewed the issue of soft drink vending machines as a matter of their children's personal choice more than as an issue of a healthful school environment. However, parents were unaware of many important details about the soft drink vending machines in their children's school, such as the number and location of machines, hours of operation, types of beverages available, or whether the school had contracts with soft drink companies. Parents need more information about the number of soft drink vending machines at their children's school, the beverages available, the revenue generated by soft drink vending machine sales, and the terms of any contracts between the school and soft drink companies.

Proactive condition monitoring of low-speed machines

CERN Document Server

Stamboliska, Zhaklina; Moczko, Przemyslaw

2015-01-01

This book broadens readers’ understanding of proactive condition monitoring of low-speed machines in heavy industries. It focuses on why low-speed machines are different than others and how maintenance of these machines should be implemented with particular attention. The authors explain the best available monitoring techniques for various equipment and the principle of how to get proactive information from each technique. They further put forward possible strategies for application of FEM for detection of faults and technical assessment of machinery. Implementation phases are described and industrial case-studies of proactive condition monitoring are included. Proactive Condition Monitoring of Low-Speed Machines is an essential resource for engineers and technical managers across a range of industries as well as design engineers working in industrial product development. This book also: ·         Explains the practice of proactive condition monitoring and illustrates implementation phases ·   ...

Evolution of Replication Machines

Science.gov (United States)

Yao, Nina Y.; O'Donnell, Mike E.

2016-01-01

The machines that decode and regulate genetic information require the translation, transcription and replication pathways essential to all living cells. Thus, it might be expected that all cells share the same basic machinery for these pathways that were inherited from the primordial ancestor cell from which they evolved. A clear example of this is found in the translation machinery that converts RNA sequence to protein. The translation process requires numerous structural and catalytic RNAs and proteins, the central factors of which are homologous in all three domains of life, bacteria, archaea and eukarya. Likewise, the central actor in transcription, RNA polymerase, shows homology among the catalytic subunits in bacteria, archaea and eukarya. In contrast, while some “gears” of the genome replication machinery are homologous in all domains of life, most components of the replication machine appear to be unrelated between bacteria and those of archaea and eukarya. This review will compare and contrast the central proteins of the “replisome” machines that duplicate DNA in bacteria, archaea and eukarya, with an eye to understanding the issues surrounding the evolution of the DNA replication apparatus. PMID:27160337

Brain-machine and brain-computer interfaces.

Science.gov (United States)

Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

2004-11-01

The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

A nanoplasmonic switch based on molecular machines

KAUST Repository

Zheng, Yue Bing; Yang, Ying-Wei; Jensen, Lasse; Fang, Lei; Juluri, Bala Krishna; Weiss, Paul S.; Stoddart, J. Fraser; Huang, Tony Jun

2009-01-01

We aim to develop a molecular-machine-driven nanoplasmonic switch for its use in future nanophotonic integrated circuits (ICs) that have applications in optical communication, information processing, biological and chemical sensing. Experimental

Reliability analysis in intelligent machines

Science.gov (United States)

Mcinroy, John E.; Saridis, George N.

1990-01-01

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

Some Considerations about Modern Database Machines

Directory of Open Access Journals (Sweden)

Manole VELICANU

2010-01-01

Full Text Available Optimizing the two computing resources of any computing system - time and space - has al-ways been one of the priority objectives of any database. A current and effective solution in this respect is the computer database. Optimizing computer applications by means of database machines has been a steady preoccupation of researchers since the late seventies. Several information technologies have revolutionized the present information framework. Out of these, those which have brought a major contribution to the optimization of the databases are: efficient handling of large volumes of data (Data Warehouse, Data Mining, OLAP – On Line Analytical Processing, the improvement of DBMS – Database Management Systems facilities through the integration of the new technologies, the dramatic increase in computing power and the efficient use of it (computer networks, massive parallel computing, Grid Computing and so on. All these information technologies, and others, have favored the resumption of the research on database machines and the obtaining in the last few years of some very good practical results, as far as the optimization of the computing resources is concerned.

Ion torrent personal genome machine sequencing for genomic typing of Neisseria meningitidis for rapid determination of multiple layers of typing information.

Science.gov (United States)

Vogel, Ulrich; Szczepanowski, Rafael; Claus, Heike; Jünemann, Sebastian; Prior, Karola; Harmsen, Dag

2012-06-01

Neisseria meningitidis causes invasive meningococcal disease in infants, toddlers, and adolescents worldwide. DNA sequence-based typing, including multilocus sequence typing, analysis of genetic determinants of antibiotic resistance, and sequence typing of vaccine antigens, has become the standard for molecular epidemiology of the organism. However, PCR of multiple targets and consecutive Sanger sequencing provide logistic constraints to reference laboratories. Taking advantage of the recent development of benchtop next-generation sequencers (NGSs) and of BIGSdb, a database accommodating and analyzing genome sequence data, we therefore explored the feasibility and accuracy of Ion Torrent Personal Genome Machine (PGM) sequencing for genomic typing of meningococci. Three strains from a previous meningococcus serogroup B community outbreak were selected to compare conventional typing results with data generated by semiconductor chip-based sequencing. In addition, sequencing of the meningococcal type strain MC58 provided information about the general performance of the technology. The PGM technology generated sequence information for all target genes addressed. The results were 100% concordant with conventional typing results, with no further editing being necessary. In addition, the amount of typing information, i.e., nucleotides and target genes analyzed, could be substantially increased by the combined use of genome sequencing and BIGSdb compared to conventional methods. In the near future, affordable and fast benchtop NGS machines like the PGM might enable reference laboratories to switch to genomic typing on a routine basis. This will reduce workloads and rapidly provide information for laboratory surveillance, outbreak investigation, assessment of vaccine preventability, and antibiotic resistance gene monitoring.

Efficient forced vibration reanalysis method for rotating electric machines

Science.gov (United States)

Saito, Akira; Suzuki, Hiromitsu; Kuroishi, Masakatsu; Nakai, Hideo

2015-01-01

Rotating electric machines are subject to forced vibration by magnetic force excitation with wide-band frequency spectrum that are dependent on the operating conditions. Therefore, when designing the electric machines, it is inevitable to compute the vibration response of the machines at various operating conditions efficiently and accurately. This paper presents an efficient frequency-domain vibration analysis method for the electric machines. The method enables the efficient re-analysis of the vibration response of electric machines at various operating conditions without the necessity to re-compute the harmonic response by finite element analyses. Theoretical background of the proposed method is provided, which is based on the modal reduction of the magnetic force excitation by a set of amplitude-modulated standing-waves. The method is applied to the forced response vibration of the interior permanent magnet motor at a fixed operating condition. The results computed by the proposed method agree very well with those computed by the conventional harmonic response analysis by the FEA. The proposed method is then applied to the spin-up test condition to demonstrate its applicability to various operating conditions. It is observed that the proposed method can successfully be applied to the spin-up test conditions, and the measured dominant frequency peaks in the frequency response can be well captured by the proposed approach.

Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach

OpenAIRE

Weng, Wei-Hung; Wagholikar, Kavishwar B.; McCray, Alexa T.; Szolovits, Peter; Chueh, Henry C.

2017-01-01

Background The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. Methods We constructed the pipeline using the clinical ...

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...

PLASTIC DEFORMATION ON THE MACHINED SURFACE OF STEEL Cr20Ni10MoTi AT DRILLING

Directory of Open Access Journals (Sweden)

Jozef Jurko

2009-07-01

Full Text Available Information about material machinability is very important for the machining technology. Precise and reliable information on the machinability of a material before it enters the machining process is a necessity, and this brings the verification of technological methods in practice. This article presents the conclusions of machinability tests on austenitic stainless steel according to EN-EU (ISO: steel Cr20Ni10MoTi. This article presents the conclusions of VEGA grant agency at the Ministry of Education SR for supporting research work and co-financing the projects: Grant work #01/3173/2006 with the title „Experimental investigation of cutting zones in drilled and milled stainless steels

An implementation of support vector machine on sentiment classification of movie reviews

Science.gov (United States)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Research on intelligent machine self-perception method based on LSTM

Science.gov (United States)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

An improved machine learning protocol for the identification of correct Sequest search results

Directory of Open Access Journals (Sweden)

Lu Hui

2010-12-01

Full Text Available Abstract Background Mass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols. Results The developed model improves on published machine learning classification procedures by 6% as measured by the area under the ROC curve. Further, we show how the developed model can be presented as an interpretable tree of additive rules, thereby effectively removing the 'black-box' notion often associated with machine learning classifiers, allowing for comparison with expert rule-of-thumb. Finally, a method for extending the developed peptide identification protocol to give probabilistic estimates of the presence of a given protein is proposed and tested. Conclusions We demonstrate the construction of a high accuracy classification model for Sequest search results from MS/MS spectra obtained by using the MALDI ionization. The developed model performs well in identifying correct peptide-spectrum matches and is easily extendable to the protein identification problem. The relative ease with which additional experimental parameters can be incorporated into the classification framework, to give additional discriminatory power, allows for future tailoring of the model to take advantage of information from specific instrument set-ups.

Machine Translation for Academic Purposes

Science.gov (United States)

Lin, Grace Hui-chin; Chien, Paul Shih Chieh

2009-01-01

Due to the globalization trend and knowledge boost in the second millennium, multi-lingual translation has become a noteworthy issue. For the purposes of learning knowledge in academic fields, Machine Translation (MT) should be noticed not only academically but also practically. MT should be informed to the translating learners because it is a…

Machine implications for detectors and physics

International Nuclear Information System (INIS)

Tauchi, Toshiaki

2001-01-01

Future linear colliders are very different at many aspects because of low repetition rate (5∼200 Hz) and high accelerating gradient (22∼150 MeV/m). For high luminosity, the beam sizes must be squeezed in extremely small region at interaction point (IP). We briefly describe new phenomena at the IP, i.e. beamstrahlung process, creations of e + e - pairs and minijets. We also report machine implications related to the energy spread, beamstrahlung, bunch-train structure, beam polarizations and backgrounds for detectors and physics

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

Technical background information for the environmental and safety report, Volume 4: White Oak Lake and Dam

International Nuclear Information System (INIS)

Oakes, T.W.; Kelly, B.A.; Ohnesorge, W.F.; Eldridge, J.S.; Bird, J.C.; Shank, K.E.; Tsakeres, F.S.

1982-03-01

This report has been prepared to provide background information on White Oak Lake for the Oak Ridge National Laboratory Environmental and Safety Report. The paper presents the history of White Oak Dam and Lake and describes the hydrological conditions of the White Oak Creek watershed. Past and present sediment and water data are included; pathway analyses are described in detail

Technical background information for the environmental and safety report, Volume 4: White Oak Lake and Dam

Energy Technology Data Exchange (ETDEWEB)

Oakes, T.W.; Kelly, B.A.; Ohnesorge, W.F.; Eldridge, J.S.; Bird, J.C.; Shank, K.E.; Tsakeres, F.S.

1982-03-01

This report has been prepared to provide background information on White Oak Lake for the Oak Ridge National Laboratory Environmental and Safety Report. The paper presents the history of White Oak Dam and Lake and describes the hydrological conditions of the White Oak Creek watershed. Past and present sediment and water data are included; pathway analyses are described in detail.

Indonesian Stock Prediction using Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Santoso Murtiyanto

2018-01-01

Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

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.

Background information on a multimedia nitrogen emission reduction strategy; Hintergrundpapier zu einer multimedialen Stickstoffemissionsminderungsstrategie

Energy Technology Data Exchange (ETDEWEB)

Geupel; Jering; Frey (and others)

2009-04-15

The background information report on a multimedia nitrogen reduction strategy covers the following chapters: 1. Introduction: the nitrogen cascade and the anthropogenic influence, environmental impact of increased nitrogen emissions and effects on human health. 2. Sources and balancing of anthropogenic nitrogen emissions in Germany. 3. Environmental quality targets, activity goals of environmental measures and instruments of an integrated nitrogen reduction strategy. 4. Conclusions and perspectives. The attachments include emission sources, nitrogen release and nitrogen transport in Germany; catalogue of measures and instruments according the criteria efficiency and cost-efficacy.

Hierarchical State Machines as Modular Horn Clauses

Directory of Open Access Journals (Sweden)

Pierre-Loïc Garoche

2016-07-01

Full Text Available In model based development, embedded systems are modeled using a mix of dataflow formalism, that capture the flow of computation, and hierarchical state machines, that capture the modal behavior of the system. For safety analysis, existing approaches rely on a compilation scheme that transform the original model (dataflow and state machines into a pure dataflow formalism. Such compilation often result in loss of important structural information that capture the modal behaviour of the system. In previous work we have developed a compilation technique from a dataflow formalism into modular Horn clauses. In this paper, we present a novel technique that faithfully compile hierarchical state machines into modular Horn clauses. Our compilation technique preserves the structural and modal behavior of the system, making the safety analysis of such models more tractable.

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).

Full simulation of the beam-related backgrounds at the ILC

Energy Technology Data Exchange (ETDEWEB)

Schuetz, Anne [DESY (Germany); KIT (Germany)

2016-07-01

The ILC has been proposed as the next machine at the energy frontier and a Technical Design Report was presented in 2012. As part of the site-specific studies to prepare the hosting of the ILC in Japan, the final focus region of the ILC had to be adapted. In this contribution, updated results for the beam-related background as well as new results for the backgrounds originating from the beam dump are presented. The beam-related backgrounds are simulated using GuineaPig and are then propagated through the full simulation of the SiD detector. The impact of various modifications in the final-focus region on the detector occupancies are then evaluated. For the neutron background from the beam dump, the FLUKA simulation suite is used, which is well established for dosimetry and shielding studies. With this program, the effect of the neutrons from the ILC beam dumps on the ILC detectors are studied.

Application of machine learning methods in bioinformatics

Science.gov (United States)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

Monitor Clean and Efficient. Background information. Methods and references as applied in the Monitor in April 2009

International Nuclear Information System (INIS)

Gerdes, J.; De Ligt, T.

2010-01-01

This report contains background information about the Monitor Clean and Efficient that was published in April 2009. The goal and approach of the Monitor are clarified, as well as the methods and data that are used. The structure of this report resembles the structure of the Monitor. Sources and dates of availability are mentioned along with the data, as are the parties collecting and processing the information. The results that were found using this methodology have been published in the Monitor Clean and Efficient. [nl

Early failure analysis of machining centers: a case study

International Nuclear Information System (INIS)

Wang Yiqiang; Jia Yazhou; Jiang Weiwei

2001-01-01

To eliminate the early failures and improve the reliability, nine ex-factory machining centers are traced under field conditions in workshops. Their early failure information throughout the ex-factory run-in test is collected. The field early failure database is constructed based on the collection of field early failure data and the codification of data. Early failure mode and effects analysis is performed to indicate the weak subsystem of a machining center or the troublemaker. The distribution of the time between early failures is analyzed and the optimal ex-factory run-in test time for machining center that may expose sufficiently the early failures and cost minimum is discussed. Suggestions how to arrange ex-factory run-in test and how to take actions to reduce early failures for machining center is proposed

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...

16 CFR 1404.2 - Background.

Science.gov (United States)

2010-01-01

... 16 Commercial Practices 2 2010-01-01 2010-01-01 false Background. 1404.2 Section 1404.2 Commercial Practices CONSUMER PRODUCT SAFETY COMMISSION CONSUMER PRODUCT SAFETY ACT REGULATIONS CELLULOSE INSULATION § 1404.2 Background. Based on available fire incident information, engineering analysis of the probable...

Hand Gesture Recognition Using Modified 1$ and Background Subtraction Algorithms

Directory of Open Access Journals (Sweden)

Hazem Khaled

2015-01-01

Full Text Available Computers and computerized machines have tremendously penetrated all aspects of our lives. This raises the importance of Human-Computer Interface (HCI. The common HCI techniques still rely on simple devices such as keyboard, mice, and joysticks, which are not enough to convoy the latest technology. Hand gesture has become one of the most important attractive alternatives to existing traditional HCI techniques. This paper proposes a new hand gesture detection system for Human-Computer Interaction using real-time video streaming. This is achieved by removing the background using average background algorithm and the 1$ algorithm for hand’s template matching. Then every hand gesture is translated to commands that can be used to control robot movements. The simulation results show that the proposed algorithm can achieve high detection rate and small recognition time under different light changes, scales, rotation, and background.

The effects of alcohol expectancy and intake on slot machine gambling behavior.

Science.gov (United States)

Sagoe, Dominic; Mentzoni, Rune Aune; Leino, Tony; Molde, Helge; Haga, Sondre; Gjernes, Mikjel Fredericson; Hanss, Daniel; Pallesen, Ståle

2017-06-01

Background and aims Although alcohol intake and gambling often co-occur in related venues, there is conflicting evidence regarding the effects of alcohol expectancy and intake on gambling behavior. We therefore conducted an experimental investigation of the effects of alcohol expectancy and intake on slot machine gambling behavior. Methods Participants were 184 (females = 94) individuals [age range: 18-40 (mean = 21.9) years] randomized to four independent conditions differing in information/expectancy about beverage (told they received either alcohol or placebo) and beverage intake [actually ingesting low (target blood alcohol concentration [BAC]  0.40 mg/L; ≈0.80 mg/L) amounts of alcohol]. All participants completed self-report questionnaires assessing demographic variables, subjective intoxication, alcohol effects (stimulant and sedative), and gambling factors (behavior and problems, evaluation, and beliefs). Participants also gambled on a simulated slot machine. Results A significant main effect of beverage intake on subjective intoxication and alcohol effects was detected as expected. No significant main or interaction effects were detected for number of gambling sessions, bet size and variation, remaining credits at termination, reaction time, and game evaluation. Conclusion Alcohol expectancy and intake do not affect gambling persistence, dissipation of funds, reaction time, or gambling enjoyment.

Interactive Algorithms for Unsupervised Machine Learning

Science.gov (United States)

2015-06-01

in Neural Information Processing Systems, 2013. 14 [3] Louigi Addario-Berry, Nicolas Broutin, Luc Devroye, and Gábor Lugosi. On combinato- rial...Myung Jin Choi, Vincent Y F Tan , Animashree Anandkumar, and Alan S Willsky. Learn- ing Latent Tree Graphical Models. Journal of Machine Learning

A Survey of Statistical Machine Translation

Science.gov (United States)

2007-04-01

methods are notoriously sen- sitive to domain differences, however, so the move to informal text is likely to present many interesting challenges ...Och, Christoph Tillman, and Hermann Ney. Improved alignment models for statistical machine translation. In Proc. of EMNLP- VLC , pages 20–28, Jun 1999

Assessing Implicit Knowledge in BIM Models with Machine Learning

DEFF Research Database (Denmark)

Krijnen, Thomas; Tamke, Martin

2015-01-01

architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models....

Development of Mathematical Model for Lifecycle Management Process of New Type of Multirip Saw Machine

Directory of Open Access Journals (Sweden)

B. V. Phung

2017-01-01

Full Text Available The subject of research is a new type of the multirip saw machine with circular reciprocating saw blades. This machine has a number of advantages in comparison with other machines of similar purpose. The paper presents an overview of different types of saw equipment and describes basic characteristics of the machine under investigation.Using the concept of lifecycle management of the considered machine in a unified information space is necessary to improve quality and competitiveness in the current production environment. In this lifecycle all the members, namely designers, technologists, customers, etc., have a philosophy to tend to optimize the overall machine design as much as possible. However, it is not always possible to achieve. Conversely, at the boundary between the phases there are several mismatching situations, if not even conflicting inconsistencies. For example, improvement of mass characteristics can lead to poor stability and rigidity of the saw blade. Machine output improvement through increasing frequency of the machine motor rotation, on the other side, results in reducing stable ability of the saw blades and so on.In order to provide a coherent framework for the collaborative environment between the members of the life cycle, the article presents a technique to construct a mathematical model that allows combining all different members’ requirements in the unified information model. The article also gives analysis of kinematic and dynamic behavior and technological characteristics of the machine. Describes in detail all the controlled parameters, functional constraints, and quality criteria of the machine under consideration. Depending on the controlled parameters, the analytical relationships formulate functional constraints and quality criteria of the machine. The proposed algorithm allows fast and exact calculation of all the functional constraints and quality criteria of the machine for a given vector of the control

The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

DEFF Research Database (Denmark)

Sabuncu, Mert R.; Van Leemput, Koen

2012-01-01

This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially...

Crowdsourcing Salient Information from News and Tweets

NARCIS (Netherlands)

Inel, O.A.; Caselli, T.; Aroyo, L.M.

2016-01-01

The increasing streams of information pose challenges to both humans and machines. On the one hand, humans need to identify relevant information and consume only the information that lies at their interests. On the other hand, machines need to understand the information that is published in online

Design of man-machine-communication-systems

International Nuclear Information System (INIS)

Zimmermann, R.

1975-04-01

This paper shows some fundamentals of man-machine-communication and deduces demands and recommendations for the design of communication systems. The main points are the directives for the design of optic display systems with details for visual perception and resolution, luminance and contrast, as well as discernibility and coding of displayed information. The most important rules are recommendations for acoustic information systems, control devices and for design of consoles are also given. (orig.) [de

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

Electrical discharge machining for vessel sample removal

International Nuclear Information System (INIS)

Litka, T.J.

1993-01-01

Due to aging-related problems or essential metallurgy information (plant-life extension or decommissioning) of nuclear plants, sample removal from vessels may be required as part of an examination. Vessel or cladding samples with cracks may be removed to determine the cause of cracking. Vessel weld samples may be removed to determine the weld metallurgy. In all cases, an engineering analysis must be done prior to sample removal to determine the vessel's integrity upon sample removal. Electrical discharge machining (EDM) is being used for in-vessel nuclear power plant vessel sampling. Machining operations in reactor coolant system (RCS) components must be accomplished while collecting machining chips that could cause damage if they become part of the flow stream. The debris from EDM is a fine talclike particulate (no chips), which can be collected by flushing and filtration

Machine learning for Big Data analytics in plants.

Science.gov (United States)

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

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

Landslide susceptibility mapping using support vector machine and ...

Indian Academy of Sciences (India)

the prediction rate methods, the validation process was performed by ... support vector machine (SVM); geographical information systems (GIS); ... 2012a), decision tree methods (Akgun .... gence or divergence of water during downhill flow.

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…

Molecular computing: paths to chemical Turing machines.

Science.gov (United States)

Varghese, Shaji; Elemans, Johannes A A W; Rowan, Alan E; Nolte, Roeland J M

2015-11-13

To comply with the rapidly increasing demand of information storage and processing, new strategies for computing are needed. The idea of molecular computing, where basic computations occur through molecular, supramolecular, or biomolecular approaches, rather than electronically, has long captivated researchers. The prospects of using molecules and (bio)macromolecules for computing is not without precedent. Nature is replete with examples where the handling and storing of data occurs with high efficiencies, low energy costs, and high-density information encoding. The design and assembly of computers that function according to the universal approaches of computing, such as those in a Turing machine, might be realized in a chemical way in the future; this is both fascinating and extremely challenging. In this perspective, we highlight molecular and (bio)macromolecular systems that have been designed and synthesized so far with the objective of using them for computing purposes. We also present a blueprint of a molecular Turing machine, which is based on a catalytic device that glides along a polymer tape and, while moving, prints binary information on this tape in the form of oxygen atoms.

Prediction of chaos in non-salient permanent-magnet synchronous machines

Energy Technology Data Exchange (ETDEWEB)

Rasoolzadeh, Arsalan [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Tavazoei, Mohammad Saleh, E-mail: tavazoei@sharif.edu [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)

2012-12-03

This Letter tries to find the area in parameter space of a non-salient Permanent-Magnet Synchronous Machine (PMSM) in which chaos can occur. This area is briefly named as chaotic area. The predicted chaotic area is obtained by checking some conditions which are necessary for existence of chaos in a dynamical system. In this Letter, it is assumed that this machine is in the generator mode, and its model is based on direct and quadrature axis of stator voltages and currents. The information of the predicted area is used in non-chaotic maximum power control of torque in the machine.

Identification of boosted top quarks and W bosons with Machine learning in ATLAS

CERN Document Server

Majersky, Oliver; The ATLAS collaboration

2017-01-01

We present techniques for the identification of hadronically-decaying W bosons and top quarks using high-level features as inputs to boosted decision trees and deep neural networks in the ATLAS experiment at sqrt(s)=13 TeV. The performance of these machine learning based taggers is compared in Monte Carlo simulation with various different tagging algorithms. An improvement in background rejection with respect to different taggers is observed. In addition, the performance of the machine learning taggers is examined in full Run-II data set in top quark pair, dijet and photon+jet topologies.

Background information on the SSC project

International Nuclear Information System (INIS)

Warren, J.

1991-10-01

This report discusses the following information about the Superconducting Super Collider: Goals and milestones; civil construction; ring components; cryogenics; vacuum and cooling water systems; electrical power; instrumentation and control systems; and installation planning

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

Energy Technology Data Exchange (ETDEWEB)

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

2010-01-15

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

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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.

Resonances and background: A decomposition of scattering information

International Nuclear Information System (INIS)

Engdahl, E.; Braendas, E.; Rittby, M.; Elander, N.

1988-01-01

An analytic representation of the full Green's function including bound states, resonances, and remaining contributions has been obtained for a class of dilatation analytic potentials, including the superimposed Coulomb potential. It is demonstrated how to obtain the locations and residues of the poles of the Green's function as well as the associated generalized spectral density. For a model potential which has a barrier and decreases exponentially at infinity we have found a certain deflation property of the generalized spectral density. A qualitative explanation of this phenomenon is suggested. This constitutes the motivation for an approximation that explicitly shows a decomposition of the (real) continuum, corresponding to scattering data, into resonances and background contributions. The present representation is also shown to incorporate the appropriate pole-background interferences. Numerical residue strings are computed and analyzed. Results for the Coulomb potential plus the above-mentioned model potential are reported and compared with the previous non-Coulomb case. A similar deflation effect is seen to occur, as well as basically the same pole- and residue-string behavior. The relevance of the present analysis in relation to recently planned experiments with electron-cooled beams of highly charged ions is briefly discussed

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.

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.

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.)

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

Directory of Open Access Journals (Sweden)

Chuck J. Biddle

2018-01-01

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

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.

Support vector machines in analysis of top quark production

International Nuclear Information System (INIS)

Vaiciulis, A.

2003-01-01

The Support Vector Machine (SVM) learning algorithm is a new alternative to multivariate methods such as neural networks. Potential applications of SVMs in high energy physics include the common classification problem of signal/background discrimination as well as particle identification. A comparison of a conventional method and an SVM algorithm is presented here for the case of identifying top quark events in Run II physics at the CDF experiment

Machine Composition - Between Lisp and Max : Between AI and Music. Lisp, Max, maxlisp and other recombinations

OpenAIRE

Marschall, Olav-Andreas

2007-01-01

This paper consists of three complementary parts. First, there are the fundamental problems and challenges about the foundations [1,2,9] of Machine Composition (MC), especially in relation to human composition. Second, there is the background of existing theory and experimental results from main areas of Machine Compositional Research and their constructions of prototypes [4,5,7]. And finally, the most practical part introduces two important environments or tools [3,6,8] for the construction ...

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)

Increasing Power by Sharing Information from Genetic Background and Treatment in Clustering of Gene Expression Time Series

Directory of Open Access Journals (Sweden)

Sura Zaki Alrashid

2018-02-01

Full Text Available Clustering of gene expression time series gives insight into which genes may be co-regulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different conditions or genetic background. This paper develops
a new clustering method that allows each cluster to be parameterised according to whether the behaviour of the genes across conditions is correlated or anti-correlated. By specifying correlation between such genes,more information is gain within the cluster about how the genes interrelate. Amyotrophic lateral sclerosis (ALS is an irreversible neurodegenerative disorder that kills the motor neurons and results in death within 2 to 3 years from the symptom onset. Speed of progression for different patients are heterogeneous with significant variability. The SOD1G93A transgenic mice from different backgrounds (129Sv and C57 showed consistent phenotypic differences for disease progression. A hierarchy of Gaussian isused processes to model condition-specific and gene-specific temporal co-variances. This study demonstrated about finding some significant gene expression profiles and clusters of associated or co-regulated gene expressions together from four groups of data (SOD1G93A and Ntg from 129Sv and C57 backgrounds. Our study shows the effectiveness of sharing information between replicates and different model conditions when modelling gene expression time series. Further gene enrichment score analysis and ontology pathway analysis of some specified clusters for a particular group may lead toward identifying features underlying the differential speed of disease progression.

Pricing Power of Agricultural Products under the Background of Small Peasant Management and Information Asymmetry

Institute of Scientific and Technical Information of China (English)

Dexuan LI

2016-01-01

From the background of small peasant management and information asymmetry,this paper introduced the middle profit sharing model and discussed influence factors and ownership of pricing power of agricultural products. It obtained following results:( i) the transaction scale has positive effect on farmer’s pricing power of agricultural products,while the competitor’s transaction scale has negative effect on it,so does the cost for information search;( ii) under the condition of small peasant management system,farmer is in a relatively weak position in the distribution of pricing power of agricultural products,due to factors such as small transaction scale,information asymmetry and farmer’s weak negotiation ability;( iii) through cooperative game,farmer and buyers can share cooperative surplus at the agreed ratio;( iv) the introduction of self-organizing specialized farmers cooperatives is favorable for solving the problem of pricing power of agricultural products,and possible problems,such as " collective action dilemma" and " fake cooperatives" in the cooperative development process can be solved by internal and external division of labor and specialization of cooperatives.

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

A distributed algorithm for machine learning

Science.gov (United States)

Chen, Shihong

2018-04-01

This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

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.

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.

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.

Cutting force model for high speed machining process

International Nuclear Information System (INIS)

Haber, R. E.; Jimenez, J. E.; Jimenez, A.; Lopez-Coronado, J.

2004-01-01

This paper presents cutting force-based models able to describe a high speed machining process. The model considers the cutting force as output variable, essential for the physical processes that are taking place in high speed machining. Moreover, this paper shows the mathematical development to derive the integral-differential equations, and the algorithms implemented in MATLAB to predict the cutting force in real time MATLAB is a software tool for doing numerical computations with matrices and vectors. It can also display information graphically and includes many toolboxes for several research and applications areas. Two end mill shapes are considered (i. e. cylindrical and ball end mill) for real-time implementation of the developed algorithms. the developed models are validated in slot milling operations. The results corroborate the importance of the cutting force variable for predicting tool wear in high speed machining operations. The developed models are the starting point for future work related with vibration analysis, process stability and dimensional surface finish in high speed machining processes. (Author) 19 refs

Looking for Cosmic Neutrino Background

Directory of Open Access Journals (Sweden)

Chiaki eYanagisawa

2014-06-01

Full Text Available Since the discovery of neutrino oscillation in atmospheric neutrinos by the Super-Kamiokande experiment in 1998, study of neutrinos has been one of exciting fields in high-energy physics. All the mixing angles were measured. Quests for 1 measurements of the remaining parameters, the lightest neutrino mass, the CP violating phase(s, and the sign of mass splitting between the mass eigenstates m3 and m1, and 2 better measurements to determine whether the mixing angle theta23 is less than pi/4, are in progress in a well-controlled manner. Determining the nature of neutrinos, whether they are Dirac or Majorana particles is also in progress with continuous improvement. On the other hand, although the ideas of detecting cosmic neutrino background have been discussed since 1960s, there has not been a serious concerted effort to achieve this goal. One of the reasons is that it is extremely difficult to detect such low energy neutrinos from the Big Bang. While there has been tremendous accumulation of information on Cosmic Microwave Background since its discovery in 1965, there is no direct evidence for Cosmic Neutrino Background. The importance of detecting Cosmic Neutrino Background is that, although detailed studies of Big Bang Nucleosynthesis and Cosmic Microwave Background give information of the early Universe at ~a few minutes old and ~300 k years old, respectively, observation of Cosmic Neutrino Background allows us to study the early Universe at $sim$ 1 sec old. This article reviews progress made in the past 50 years on detection methods of Cosmic Neutrino Background.

European public deliberation on brain machine interface technology: five convergence seminars.

Science.gov (United States)

Jebari, Karim; Hansson, Sven-Ove

2013-09-01

We present a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of five seminars are presented.

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

Study of the machine background induced by the PEP-II collider with a mini-TPC. Study of the doubly-charmed decay of the B meson with the detector BaBar; Etude du bruit de fond engendre par la machine PEP-2 a l'aide d'une mini-TPC. Etude de la desintegration doublement charmee du meson B avec le detecteur BaBar

Energy Technology Data Exchange (ETDEWEB)

Trincaz-Duvoid, S

2001-01-01

The work presented in this thesis is divided into two parts. The first one deals with the machine background induced by the PEP-II collider. This study has been performed with a mini-TPC before the start of the BaBar experiment. The second part concerns the measurements of the branching ratio of the decay modes B{sup 0} {yields} D{sup *-}D(*){sup 0}K{sup +} and of the inclusive branching ratio Br(B{sup 0} {yields} K{sup {+-}}X). These measurements have been obtained with the first BaBar data. During the commissioning of the PEP-II collider, the charged tracks rate close to the interaction point has been measured with the mini-TPC. This study has pointed to the fact that the machine background was much higher than predicted by the simulation. These bad background conditions were due to the poor quality of the vacuum in the rings. This relatively high pressure in the rings produces electro-magnetic showers at the interaction point due to beam gas interactions. The potential risks for the BaBar detector due to the machine backgrounds have been clearly pointed out by the studies performed for this thesis. The addition of some collimators and a deep understanding of the machine have greatly reduced the background. Nevertheless, the radiation level in BaBar is continuously monitored in order to protect the detector. The study of the b {yields} cc-bar channel is an important point for the understanding of the overall picture of the B meson decay. With an integrated luminosity of 17.3 fb{sup -1} recorded by the BaBar detector the following branching ratio using exclusive reconstruction technique have been measured: Br(B{sup 0} {yields} D{sup *-}D{sup 0}K{sup +}) = (0.29 {+-} 0.06 (stat) {+-} (syst)) % Br(B{sup 0} {yields} D{sup *-}D{sup *0}K{sup +}) = (1.16 {+-} 0.15 (stat) {+-} 0.16 (syst)) % A partial reconstruction has also been developed. With an integrated luminosity of 8.9 fb{sup -1}, the branching ratio of B{sup 0} into D{sup *-}D{sup 0}K{sup +} has been measured

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

Introduction to machine learning for brain imaging.

Science.gov (United States)

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

2011-05-15

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.

Quantum neural network based machine translator for Hindi to English.

Science.gov (United States)

Narayan, Ravi; Singh, V P; Chakraverty, S

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

Acceleration of saddle-point searches with machine learning

Energy Technology Data Exchange (ETDEWEB)

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu [School of Engineering, Brown University, Providence, Rhode Island 02912 (United States)

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning

International Nuclear Information System (INIS)

Peterson, Andrew A.

2016-01-01

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning.

Science.gov (United States)

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

A two-level real-time vision machine combining coarse and fine grained parallelism

DEFF Research Database (Denmark)

Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Pauwels, Karl

2010-01-01

In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas...... a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU--core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications....

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.

The CANDU man-machine interface and simulator training

International Nuclear Information System (INIS)

Hinchley, E.M.; Yanofsky, N.

1982-09-01

The most significant features of the man-machine interface for CANDU power stations are the extensive use of computer-driven colour graphics displays and the small number of manual controls. The man-machine interface in CANDU stations is designed to present the operator with concise, easy-to-understand information. Future developments in the use of computers in safety shutdown systems, and the use of data highway technologies in plant regulating systems will present special requirements and new opportunities in the application of human factors engineering to the control room. Good man-machine interaction depends on operator training as much as on control room design. In Canada computerized training simulators, which indicate plant response to operator action, are being introducted for operator training. Such simulators support training in normal operation of all plant systems and also in the fault management tasks following malfunctions

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

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

Science.gov (United States)

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

2014-07-01

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

Radiation levels at the boundaries of the Frascati center and natural background

International Nuclear Information System (INIS)

Lucci, F.; Pelliccioni, M.

1975-01-01

The results of dosimetric controls carried out with thermoluminescent dosimeters along the boundaries of the Frascati Center since 1969 are presented. The average yearly exposure observed during the period 1969-1974 is 206mR/y, while no significant tendency is evident. In particular neither effects of accelerator shutdowns nor seasonal ones are noticed. The measurements at the boundary are also compared with measurements of natural background carried out near Frascati and in Rome, Thus we can estimate that the machine contribution to the total dose along the boundaries is equal to a small fraction of either the natural background or the general population dose limit [fr

Research on Key Technologies of Unit-Based CNC Machine Tool Assembly Design

Directory of Open Access Journals (Sweden)

Zhongqi Sheng

2014-01-01

Full Text Available Assembly is the part that produces the maximum workload and consumed time during product design and manufacturing process. CNC machine tool is the key basic equipment in manufacturing industry and research on assembly design technologies of CNC machine tool has theoretical significance and practical value. This study established a simplified ASRG for CNC machine tool. The connection between parts, semantic information of transmission, and geometric constraint information were quantified to assembly connection strength to depict the assembling difficulty level. The transmissibility based on trust relationship was applied on the assembly connection strength. Assembly unit partition based on assembly connection strength was conducted, and interferential assembly units were identified and revised. The assembly sequence planning and optimization of parts in each assembly unit and between assembly units was conducted using genetic algorithm. With certain type of high speed CNC turning center, as an example, this paper explored into the assembly modeling, assembly unit partition, and assembly sequence planning and optimization and realized the optimized assembly sequence of headstock of CNC machine tool.

Recent results relevant to ignition physics and machine design issues

International Nuclear Information System (INIS)

Coppi, B.; Airoldi, A.; Bombarda, F.

2001-01-01

The plasma regimes under which ignition can be achieved involve a characteristic range of parameters and issues on which information has been provided by recent experiments. In particular, these results have motivated a new, in-depth analysis of the expected performance of the Ignitor machine as well as of the plasma processes that it can investigate. The main results and recent advances in the design of key systems of the machine are reported. (author)

Recent results relevant to ignition physics and machine design issues

International Nuclear Information System (INIS)

Coppi, B.; Airoldi, A.; Bombarda, F.

1999-01-01

The plasma regimes under which ignition can be achieved involve a characteristic range of parameters and issues on which information has been provided by recent experiments. In particular, these results have motivated a new, in-depth analysis of the expected performance of the Ignitor machine as well as of the plasma processes that it can investigate. The main results and recent advances in the design of key systems of the machine are reported. (author)

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.

Opportunities for renewable biomass in the Dutch province of Zeeland. Background information

International Nuclear Information System (INIS)

De Buck, A.; Croezen, H.

2009-04-01

The Dutch province of Zeeland is organizing three bio-debates to map economically attractive and renewable biomass opportunities. Participants included industrial businesses, ZLTO, ZMF, Zeeland Seaports, Impuls Zeeland, Hogeschool Zeeland and the University of Ghent. CE Delft is organizing the debates and provides the expertise in this field. In the first debate (Goes, 22 January 2009) the main lines for deployment of biomass in Zeeland were established. One of the conclusions was that there are opportunities for existing industry to implement new technology for large-scale use of (imported) biomass. As for agriculture, there may be opportunities for high-quality chemicals from agricultural crops. Agriculture and industry have opportunities in the short term for better and more high-quality utilization of existing residual flows of biomass. The second and third debate should address concrete opportunities for the industry and agriculture in Zeeland. This report is background information to support the debates. [nl

A modern approach for the realisation of the man-machine information system

International Nuclear Information System (INIS)

Martin, D.; Grensemann, D.

1980-01-01

The tool for man-machine communication has been given the name VISCOMP-VISual COmmunication Man-Process. Dependent on the project implementation phase VISCOMP functions either autonomously (planning and definition phases) or in conjunction with a data acquisition system during the operational phase on the plant. The relationship of the required components to the project phases is illustrated. (orig./HP)

Automatic microseismic event picking via unsupervised machine learning

Science.gov (United States)

Chen, Yangkang

2018-01-01

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

Bionic machines and systems

Energy Technology Data Exchange (ETDEWEB)

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

2004-07-01

of bio-structures. Today's robotics research is directed towards solving the problems of the third generation intelligent robots. Most of them are not any more intended for working in production lines, as their second generation predecessors do, but for serving in different tasks related to natural environment or urban structures. Many of them are supposed to work in close cooperation with humans as a member of their community. One of the basic features needed is mobility, capability to go to the work, because works are not any more coming to the machine - as they do in factories - but the machines have to move. This, in turn, implies need for other primary functions, such as localization and navigation. Further, because the environment and details of the task are usually not known beforehand, the control system of the robot has to relay on perceptive information through sensors and senses in order to complete satisfactorily the task. Biological species have developed a large variety of solutions for all these primary functions. The variety in motion control methods provides also many interesting solutions, like walking, swimming and flying, which all are worth of mimicking in robotics. Learning is still in its infancy in intelligent robotics, especially regarding skilled tasks done with hands or tools. Biological research offers many interesting results on animal learning, which, while being a complex process in its own right, is still simpler than the corresponding human learning and thus easier to mimic. The report is based on the presentations given by the participants. The material has been collected from published references in literature and Web. Besides written material, also a video file archive has been collected and is available as an appendix to this report. The presentation order follows in a way bottom up hierarchy of subsystems in biological machines. Chapter 2 introduces the background of biological energy. Chapter 3 deals with motions, motion

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

Experimental research of kinetic and dynamic characteristics of temperature movements of machines

Science.gov (United States)

Parfenov, I. V.; Polyakov, A. N.

2018-03-01

Nowadays, the urgency of informational support of machines at different stages of their life cycle is increasing in the form of various experimental characteristics that determine the criteria for working capacity. The effectiveness of forming the base of experimental characteristics of machines is related directly to the duration of their field tests. In this research, the authors consider a new technique that allows reducing the duration of full-scale testing of machines by 30%. To this end, three new indicator coefficients were calculated in real time to determine the moments corresponding to the characteristic points. In the work, new terms for thermal characteristics of machine tools are introduced: kinetic and dynamic characteristics of the temperature movements of the machine. This allow taking into account not only the experimental values for the temperature displacements of the elements of the carrier system of the machine, but also their derivatives up to the third order, inclusively. The work is based on experimental data obtained in the course of full-scale thermal tests of a drilling-milling and boring CNC machine.

Your Sewing Machine.

Science.gov (United States)

Peacock, Marion E.

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

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Jui-Pin Hung

2016-03-01

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

Introduction to AC machine design

CERN Document Server

Lipo, Thomas A

2018-01-01

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

Precision machining commercialization

International Nuclear Information System (INIS)

1978-01-01

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

The Cryogenic Dark Matter Search and Background Rejection with Event Position Information

International Nuclear Information System (INIS)

Wang, Gen-sheng

2005-01-01

Evidence from observational cosmology and astrophysics indicates that about one third of the universe is matter, but that the known baryonic matter only contributes to the universe at 4%. A large fraction of the universe is cold and non-baryonic matter, which has important role in the universe structure formation and its evolution. The leading candidate for the non-baryonic dark matter is Weakly Interacting Massive Particles (WIMPs), which naturally occurs in the supersymmetry theory in particle physics. The Cryogenic Dark Matter Search (CDMS) experiment is searching for evidence of a WIMP interaction off an atomic nucleus in crystals of Ge and Si by measuring simultaneously the phonon energy and ionization energy of the interaction in the CDMS detectors. The WIMP interaction energy is from a few keV to tens of keV with a rate less than 0.1 events/kg/day. To reach the goal of WIMP detection, the CDMS experiment has been conducted in the Soudan mine with an active muon veto and multistage passive background shields. The CDMS detectors have a low energy threshold and background rejection capabilities based on ionization yield. However, betas from contamination and other radioactive sources produce surface interactions, which have low ionization yield, comparable to that of bulk nuclear interactions. The low-ionization surface electron recoils must be removed in the WIMP search data analysis. An emphasis of this thesis is on developing the method of the surface-interaction rejection using location information of the interactions, phonon energy distributions and phonon timing parameters. The result of the CDMS Soudan run118 92.3 live day WIMP search data analysis is presented, and represents the most sensitive search yet performed

The Cryogenic Dark Matter Search and Background Rejection with Event Position Information

Energy Technology Data Exchange (ETDEWEB)

Wang, Gensheng [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Physics

2005-01-01

Evidence from observational cosmology and astrophysics indicates that about one third of the universe is matter, but that the known baryonic matter only contributes to the universe at 4%. A large fraction of the universe is cold and non-baryonic matter, which has important role in the universe structure formation and its evolution. The leading candidate for the non-baryonic dark matter is Weakly Interacting Massive Particles (WIMPs), which naturally occurs in the supersymmetry theory in particle physics. The Cryogenic Dark Matter Search (CDMS) experiment is searching for evidence of a WIMP interaction off an atomic nucleus in crystals of Ge and Si by measuring simultaneously the phonon energy and ionization energy of the interaction in the CDMS detectors. The WIMP interaction energy is from a few keV to tens of keV with a rate less than 0.1 events/kg/day. To reach the goal of WIMP detection, the CDMS experiment has been conducted in the Soudan mine with an active muon veto and multistage passive background shields. The CDMS detectors have a low energy threshold and background rejection capabilities based on ionization yield. However, betas from contamination and other radioactive sources produce surface interactions, which have low ionization yield, comparable to that of bulk nuclear interactions. The low-ionization surface electron recoils must be removed in the WIMP search data analysis. An emphasis of this thesis is on developing the method of the surface-interaction rejection using location information of the interactions, phonon energy distributions and phonon timing parameters. The result of the CDMS Soudan run118 92.3 live day WIMP search data analysis is presented, and represents the most sensitive search yet performed.

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...

Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain-machine interfaces

Science.gov (United States)

Abbott, W. W.; Faisal, A. A.

2012-08-01

Eye movements are highly correlated with motor intentions and are often retained by patients with serious motor deficiencies. Despite this, eye tracking is not widely used as control interface for movement in impaired patients due to poor signal interpretation and lack of control flexibility. We propose that tracking the gaze position in 3D rather than 2D provides a considerably richer signal for human machine interfaces by allowing direct interaction with the environment rather than via computer displays. We demonstrate here that by using mass-produced video-game hardware, it is possible to produce an ultra-low-cost binocular eye-tracker with comparable performance to commercial systems, yet 800 times cheaper. Our head-mounted system has 30 USD material costs and operates at over 120 Hz sampling rate with a 0.5-1 degree of visual angle resolution. We perform 2D and 3D gaze estimation, controlling a real-time volumetric cursor essential for driving complex user interfaces. Our approach yields an information throughput of 43 bits s-1, more than ten times that of invasive and semi-invasive brain-machine interfaces (BMIs) that are vastly more expensive. Unlike many BMIs our system yields effective real-time closed loop control of devices (10 ms latency), after just ten minutes of training, which we demonstrate through a novel BMI benchmark—the control of the video arcade game ‘Pong’.

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.)

‘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. 

Health-promoting vending machines: evaluation of a pediatric hospital intervention.

Science.gov (United States)

Van Hulst, Andraea; Barnett, Tracie A; Déry, Véronique; Côté, Geneviève; Colin, Christine

2013-01-01

Taking advantage of a natural experiment made possible by the placement of health-promoting vending machines (HPVMs), we evaluated the impact of the intervention on consumers' attitudes toward and practices with vending machines in a pediatric hospital. Vending machines offering healthy snacks, meals, and beverages were developed to replace four vending machines offering the usual high-energy, low-nutrition fare. A pre- and post-intervention evaluation design was used; data were collected through exit surveys and six-week follow-up telephone surveys among potential vending machine users before (n=293) and after (n=226) placement of HPVMs. Chi-2 statistics were used to compare pre- and post-intervention participants' responses. More than 90% of pre- and post-intervention participants were satisfied with their purchase. Post-intervention participants were more likely to state that nutritional content and appropriateness of portion size were elements that influenced their purchase. Overall, post-intervention participants were more likely than pre-intervention participants to perceive as healthy the options offered by the hospital vending machines. Thirty-three percent of post-intervention participants recalled two or more sources of information integrated in the HPVM concept. No differences were found between pre- and post-intervention participants' readiness to adopt healthy diets. While the HPVM project had challenges as well as strengths, vending machines offering healthy snacks are feasible in hospital settings.

Study on intelligent processing system of man-machine interactive garment frame model

Science.gov (United States)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

Study of a variable mass Atwood's machine using a smartphone

Science.gov (United States)

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-03-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses that compose the variable Atwood's machine, laboratories typically use a smart pulley. Now, the first work that introduced a smartphone as data acquisition equipment to study the acceleration in the Atwood's machine was the one by M. Monteiro et al. Since then, there has been no further information available on the usage of smartphones in variable mass systems. This prompted us to do a study of this kind of system by means of data obtained with a smartphone and to show the practicality of using smartphones in complex experimental situations.

AFM surface imaging of AISI D2 tool steel machined by the EDM process

International Nuclear Information System (INIS)

Guu, Y.H.

2005-01-01

The surface morphology, surface roughness and micro-crack of AISI D2 tool steel machined by the electrical discharge machining (EDM) process were analyzed by means of the atomic force microscopy (AFM) technique. Experimental results indicate that the surface texture after EDM is determined by the discharge energy during processing. An excellent machined finish can be obtained by setting the machine parameters at a low pulse energy. The surface roughness and the depth of the micro-cracks were proportional to the power input. Furthermore, the AFM application yielded information about the depth of the micro-cracks is particularly important in the post treatment of AISI D2 tool steel machined by EDM

AFM surface imaging of AISI D2 tool steel machined by the EDM process

Science.gov (United States)

Guu, Y. H.

2005-04-01

The surface morphology, surface roughness and micro-crack of AISI D2 tool steel machined by the electrical discharge machining (EDM) process were analyzed by means of the atomic force microscopy (AFM) technique. Experimental results indicate that the surface texture after EDM is determined by the discharge energy during processing. An excellent machined finish can be obtained by setting the machine parameters at a low pulse energy. The surface roughness and the depth of the micro-cracks were proportional to the power input. Furthermore, the AFM application yielded information about the depth of the micro-cracks is particularly important in the post treatment of AISI D2 tool steel machined by EDM.

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.

Estimating extinction using unsupervised machine learning

Science.gov (United States)

Meingast, Stefan; Lombardi, Marco; Alves, João

2017-05-01

Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, Pnicer, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction toward single sources is determined by fitting Gaussian mixture models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities, rather than a single value. Pnicer effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, Pnicer offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. The Pnicer software package also offers access to the well-established Nicer technique in a simple unified interface and is capable of building extinction maps including the Nicest correction for cloud substructure. Pnicer is offered to the community as an open-source software solution and is entirely written in Python.

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...

Dreaming Machines : On multimodal fusion and information retrieval using neural-symbolic cognitive agents

NARCIS (Netherlands)

Penning, H.L.H. de; Avila Garcez, A. d; Meyer, J.J.C.

2013-01-01

Deep Boltzmann Machines (DBM) have been used as a computational cognitive model in various AI-related research and applications, notably in computational vision and multimodal fusion. Being regarded as a biological plausible model of the human brain, the DBM is also becoming a popular instrument to

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 ...

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

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

Science.gov (United States)

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

2016-12-01

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

Machine learning of the reactor core loading pattern critical parameters

International Nuclear Information System (INIS)

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

2007-01-01

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

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

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

Science.gov (United States)

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

2012-09-01

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

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

OpenAIRE

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

2014-01-01

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

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

International Nuclear Information System (INIS)

Miao, Qiang; Huang, Hong Zhong; Fan, Xianfeng

2007-01-01

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area

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...

Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Zhiming Gui

2014-01-01

Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

Directory of Open Access Journals (Sweden)

Li Deng

2016-01-01

Full Text Available In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

Science.gov (United States)

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

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.

Differential Privacy and Machine Learning: a Survey and Review

OpenAIRE

Ji, Zhanglong; Lipton, Zachary C.; Elkan, Charles

2014-01-01

The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information. Thus it seems hard to reconcile these competing interests. However, they frequently must be balanced when mining sensitive data. For example, medical research represents an important application where it is necessary both to extract useful information and protect patient privacy. One way to resolve the conflict is to extract general characteristics of whole popul...

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.

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.

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

Science.gov (United States)

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

2017-01-01

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

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

Science.gov (United States)

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

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

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

Science.gov (United States)

Heikamp, Kathrin; Bajorath, Jürgen

2013-07-22

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

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

47 CFR 215.1 - Background.

Science.gov (United States)

2010-10-01

... POINT FOR ELECTROMAGNETIC PULSE (EMP) INFORMATION § 215.1 Background. (a) The nuclear electromagnetic pulse (EMP) is part of the complex environment produced by nuclear explosions. It consists of transient...

Thin-shell bubbles and information loss problem in anti de Sitter background

Energy Technology Data Exchange (ETDEWEB)

Sasaki, Misao [Yukawa Institute for Theoretical Physics,Kyoto University, Kyoto 606-8502 (Japan); Tomsk State Pedagogical University,634050 Tomsk (Russian Federation); Yeom, Dong-han [Yukawa Institute for Theoretical Physics,Kyoto University, Kyoto 606-8502 (Japan); Leung Center for Cosmology and Particle Astrophysics, National Taiwan University,Taipei 10617, Taiwan (China)

2014-12-24

We study the motion of thin-shell bubbles and their tunneling in anti de Sitter (AdS) background. We are interested in the case when the outside of a shell is a Schwarzschild-AdS space (false vacuum) and the inside of it is an AdS space with a lower vacuum energy (true vacuum). If a collapsing true vacuum bubble is created, classically it will form a Schwarzschild-AdS black hole. However, this collapsing bubble can tunnel to a bouncing bubble that moves out to spatial infinity. Then, although the classical causal structure of a collapsing true vacuum bubble has the singularity and the event horizon, quantum mechanically the wavefunction has support for a history without any singularity nor event horizon which is mediated by the non-perturbative, quantum tunneling effect. This may be regarded an explicit example that shows the unitarity of an asymptotic observer in AdS, while a classical observer who only follows the most probable history effectively lose information due to the formation of an event horizon.

Thin-shell bubbles and information loss problem in anti de Sitter background

International Nuclear Information System (INIS)

Sasaki, Misao; Yeom, Dong-han

2014-01-01

We study the motion of thin-shell bubbles and their tunneling in anti de Sitter (AdS) background. We are interested in the case when the outside of a shell is a Schwarzschild-AdS space (false vacuum) and the inside of it is an AdS space with a lower vacuum energy (true vacuum). If a collapsing true vacuum bubble is created, classically it will form a Schwarzschild-AdS black hole. However, this collapsing bubble can tunnel to a bouncing bubble that moves out to spatial infinity. Then, although the classical causal structure of a collapsing true vacuum bubble has the singularity and the event horizon, quantum mechanically the wavefunction has support for a history without any singularity nor event horizon which is mediated by the non-perturbative, quantum tunneling effect. This may be regarded an explicit example that shows the unitarity of an asymptotic observer in AdS, while a classical observer who only follows the most probable history effectively lose information due to the formation of an event horizon.

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.

Using a vision cognitive algorithm to schedule virtual machines

Directory of Open Access Journals (Sweden)

Zhao Jiaqi

2014-09-01

Full Text Available Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

Description of background data in the SKB database GEOTAB

International Nuclear Information System (INIS)

Eriksson, E.; Sehlstedt, S.

1989-02-01

During the research and development program performed by SKB for the final disposal of spent nuclear fuel, a large quantity of geoscientific data was collected. Most of this data was stored in a database called GEOTAB. This report describes data within the background data group. This data provides information on the location of areas studied, borehole positions and also some drilling information. The background data group (subject), called BGR, is divided into several subgroups (methods): BGAREA area background data; BGDRILL drilling information; BGDRILLP drill penetration data; BGHOLE borehole information; BGTABLES number of rows in a table, and BGTOLR data table tolerance. A method consists of one or several data tables. In each chapter a method and its data tables are described. (orig./HP)

Automated mapping of building facades by machine learning

DEFF Research Database (Denmark)

Höhle, Joachim

2014-01-01

Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds...

Power quality in power systems and electrical machines

CERN Document Server

Fuchs, Ewald

2015-01-01

The second edition of this must-have reference covers power quality issues in four parts, including new discussions related to renewable energy systems. The first part of the book provides background on causes, effects, standards, and measurements of power quality and harmonics. Once the basics are established the authors move on to harmonic modeling of power systems, including components and apparatus (electric machines). The final part of the book is devoted to power quality mitigation approaches and devices, and the fourth part extends the analysis to power quality solutions for renewable

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...

Review of the Tandem Mirror Experiment-Upgrade (TMX-U) machine-parameter-instrumentation system

International Nuclear Information System (INIS)

Kane, R.J.; Coffield, F.E.; Coutts, G.W.; Hornady, R.S.

1983-01-01

The Tandem Mirror Experiment-Upgrade (TMX-U) machine consists of seven major machine subsystems: magnet system, neutral beam system, microwave heating (ECRH), ion heating (ICRH), gas fueling, stream guns, and vacuum system. Satisfactory performance of these subsystems is necessary to achieve the experimental objectives planned for TMX-U operations. Since the performance quality of the subsystem is important and can greatly affect plasma parameters, a 233-channel instrumentation system has been installed. Data from the instrumentation system are acquired and stored with the plasma diagnostic information. Thus, the details of the machine performance are available during post-shot analysis. This paper describes all the machine-parameter-instrumentation hardware, presents some typical data, and outlines how the data are used

THE GARDEN AND THE MACHINE

DEFF Research Database (Denmark)

Clemmensen, Thomas Juel

2012-01-01

The aim of this paper is to explore how the concepts of garden and machine might inform our understanding of the complex relationship between infrastructure and nature. The garden is introduced as a third nature and used to shed a critical light on the promotion of landscape as infrastructure...... in relation to the environmental problems being addressed, and that we need gardens of reflection, interrogation and doubt, in order to engage with the deeper complexities of territorial transformations....

The Garden and the Machine

DEFF Research Database (Denmark)

Clemmensen, Thomas Juel

2014-01-01

The aim of this paper is to explore how the concepts of garden and machine might inform our understanding of the complex relationship between infrastructure and nature. The garden is introduced as a third nature and used to shed a critical light on the promotion of landscape ‘as’ infrastructure...... in relation to the environmental problems being addressed, and that we need gardens of reflection, interrogation and doubt, in order to engage with the deeper complexities of territorial transformations....

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

Directory of Open Access Journals (Sweden)

Cameron Alyse FM

2009-11-01

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

Combining extreme learning machines using support vector machines for breast tissue classification.

Science.gov (United States)

Daliri, Mohammad Reza

2015-01-01

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

Blue gum gaming machine: an evaluation of responsible gambling features.

Science.gov (United States)

Blaszczynski, Alexander; Gainsbury, Sally; Karlov, Lisa

2014-09-01

Structural characteristics of gaming machines contribute to persistence in play and excessive losses. The purpose of this study was to evaluate the effectiveness of five proposed responsible gaming features: responsible gaming messages; a bank meter quarantining winnings until termination of play; alarm clock facilitating setting time-reminders; demo mode allowing play without money; and a charity donation feature where residual amounts can be donated rather than played to zero credits. A series of ten modified gaming machines were located in five Australian gambling venues. The sample comprised 300 patrons attending the venue and who played the gaming machines. Participants completed a structured interview eliciting gambling and socio-demographic data and information on their perceptions and experience of play on the index machines. Results showed that one-quarter of participants considered that these features would contribute to preventing recreational gamblers from developing problems. Just under half of the participants rated these effects to be at least moderate or significant. The promising results suggest that further refinements to several of these features could represent a modest but effective approach to minimising excessive gambling on gaming machines.

Manifold learning in machine vision and robotics

Science.gov (United States)

Bernstein, Alexander

2017-02-01

Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all "feasible and usable data" occupy only a very small part in high dimensional "observation space" with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from "natural" sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.

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.

Evaluation of containment failure and cleanup time for Pu shots on the Z machine.

Energy Technology Data Exchange (ETDEWEB)

Darby, John L.

2010-02-01

Between November 30 and December 11, 2009 an evaluation was performed of the probability of containment failure and the time for cleanup of contamination of the Z machine given failure, for plutonium (Pu) experiments on the Z machine at Sandia National Laboratories (SNL). Due to the unique nature of the problem, there is little quantitative information available for the likelihood of failure of containment components or for the time to cleanup. Information for the evaluation was obtained from Subject Matter Experts (SMEs) at the Z machine facility. The SMEs provided the State of Knowledge (SOK) for the evaluation. There is significant epistemic- or state of knowledge- uncertainty associated with the events that comprise both failure of containment and cleanup. To capture epistemic uncertainty and to allow the SMEs to reason at the fidelity of the SOK, we used the belief/plausibility measure of uncertainty for this evaluation. We quantified two variables: the probability that the Pu containment system fails given a shot on the Z machine, and the time to cleanup Pu contamination in the Z machine given failure of containment. We identified dominant contributors for both the time to cleanup and the probability of containment failure. These results will be used by SNL management to decide the course of action for conducting the Pu experiments on the Z machine.

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. .

Financial signal processing and machine learning

CERN Document Server

Kulkarni,Sanjeev R; Dmitry M. Malioutov

2016-01-01

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analy...

Tattoo machines, needles and utilities.

Science.gov (United States)

Rosenkilde, Frank

2015-01-01

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

Design of rotating electrical machines

CERN Document Server

Pyrhonen , Juha; Hrabovcova , Valeria

2013-01-01

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

The Employment Effects of High-Technology: A Case Study of Machine Vision. Research Report No. 86-19.

Science.gov (United States)

Chen, Kan; Stafford, Frank P.

A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. (Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as…

Technology Time Machine 2012

DEFF Research Database (Denmark)

Lehner, Wolfgang; Fettweis, Gerhard; Fitzek, Frank

2013-01-01

The IEEE Technology Time Machine (TTM) is a unique event for industry leaders, academics, and decision making government officials who direct R&D activities, plan research programs or manage portfolios of research activities. This report covers the main topics of the 2nd Symposium of future...... technologies. The Symposium brought together world renowned experts to discuss the evolutionary and revolutionary advances in technology landscapes as we look towards 2020 and beyond. TTM facilitated informal discussions among the participants and speakers thus providing an excellent opportunity for informal...... interaction between attendees, senior business leaders, world-renowned innovators, and the press. The goal of the Symposium is to discover key critical innovations across technologies which will alter the research and application space of the future. Topics covered the future of Wireless Technology, Smart...

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.

The effects of a nutrition education intervention on vending machine sales on a university campus.

Science.gov (United States)

Brown, Mary V; Flint, Matthew; Fuqua, James

2014-01-01

To determine the effects of a nutrition information intervention on the vending machine purchases on a college campus. Five high-use vending machines were selected for the intervention, which was conducted in the fall of 2011. Baseline sales data were collected in the 5 machines prior to the intervention. At the time of the intervention, color-coded stickers were placed near each item selection to identify less healthy (red), moderately healthy (yellow), and more healthy (green) snack items. Sales data were collected during the 2-week intervention. Purchases of red- and yellow-stickered foods were reduced in most of the machines; moreover, sales of the green-stickered items increased in all of the machines. The increased purchases of healthier snack options demonstrate encouraging patterns that support more nutritious and healthy alternatives in vending machines.

MITS machine operations

International Nuclear Information System (INIS)

Flinchem, J.

1980-01-01

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

Coordinate measuring machines

DEFF Research Database (Denmark)

De Chiffre, Leonardo

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

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.

Enhancement of plant metabolite fingerprinting by machine learning.

Science.gov (United States)

Scott, Ian M; Vermeer, Cornelia P; Liakata, Maria; Corol, Delia I; Ward, Jane L; Lin, Wanchang; Johnson, Helen E; Whitehead, Lynne; Kular, Baldeep; Baker, John M; Walsh, Sean; Dave, Anuja; Larson, Tony R; Graham, Ian A; Wang, Trevor L; King, Ross D; Draper, John; Beale, Michael H

2010-08-01

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Directory of Open Access Journals (Sweden)

Tiziana Segreto

2017-12-01

Full Text Available Nickel-Titanium (Ni-Ti alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT. The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

Science.gov (United States)

Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

2017-12-12

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Machine Learning and Radiology

Science.gov (United States)

Wang, Shijun; Summers, Ronald M.

2012-01-01

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

Tribology in machine design

CERN Document Server

Stolarski, Tadeusz

1999-01-01

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

Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine

Directory of Open Access Journals (Sweden)

Masayuki Yarimizu

2015-01-01

Full Text Available Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs, and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.

76 FR 37291 - Food Labeling; Calorie Labeling of Articles of Food in Vending Machines; Correction

Science.gov (United States)

2011-06-27

.... FDA-2011-F-0171] Food Labeling; Calorie Labeling of Articles of Food in Vending Machines; Correction... certain articles of food sold from vending machines. The document published with several errors including... FURTHER INFORMATION CONTACT: Daniel Y. Reese, Center for Food Safety and Applied Nutrition (HFS-820), Food...

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

Directory of Open Access Journals (Sweden)

Woong-Kirl Choi

2018-01-01

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

Features of measurement and processing of vibration signals registered on the moving parts of electrical machines

OpenAIRE

Gyzhko, Yuri

2011-01-01

Measurement and processing of vibration signals registered on the moving parts of the electrical machines using the diagnostic information-measuring system that uses Bluetooth wireless standard for the transmission of the measured data from moving parts of electrical machine is discussed.

A Universal Reactive Machine

DEFF Research Database (Denmark)

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

1997-01-01

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

MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

OpenAIRE

Jerzy Balicki; Waldemar Korłub

2017-01-01

In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because ...

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

International Nuclear Information System (INIS)

Leuch, M

2011-01-01

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

Virtual Machine in Automation Projects

OpenAIRE

Xing, Xiaoyuan

2010-01-01

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

Machine-learning-assisted materials discovery using failed experiments

Science.gov (United States)

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

2016-05-01

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

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

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

A comparative study of machine learning models for ethnicity classification

Science.gov (United States)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Machine learning methods for planning

CERN Document Server

Minton, Steven

1993-01-01

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

Internal combustion engines for alcohol motor fuels: a compilation of background technical information

Energy Technology Data Exchange (ETDEWEB)

Blaser, Richard

1980-11-01

This compilation, a draft training manual containing technical background information on internal combustion engines and alcohol motor fuel technologies, is presented in 3 parts. The first is a compilation of facts from the state of the art on internal combustion engine fuels and their characteristics and requisites and provides an overview of fuel sources, fuels technology and future projections for availability and alternatives. Part two compiles facts about alcohol chemistry, alcohol identification, production, and use, examines ethanol as spirit and as fuel, and provides an overview of modern evaluation of alcohols as motor fuels and of the characteristics of alcohol fuels. The final section compiles cross references on the handling and combustion of fuels for I.C. engines, presents basic evaluations of events leading to the use of alcohols as motor fuels, reviews current applications of alcohols as motor fuels, describes the formulation of alcohol fuels for engines and engine and fuel handling hardware modifications for using alcohol fuels, and introduces the multifuel engines concept. (LCL)

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

Science.gov (United States)

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

2017-01-01

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

The Buttonhole Machine. Module 13.

Science.gov (United States)

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

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

Introduction to machine learning.

Science.gov (United States)

Baştanlar, Yalin; Ozuysal, Mustafa

2014-01-01

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

Applied machining technology

CERN Document Server

Tschätsch, Heinz

2010-01-01

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

A new machine for continuous renal replacement therapy: from development to clinical testing.

Science.gov (United States)

Ricci, Zaccaria; Salvatori, Gabriella; Bonello, Monica; Ratanarat, Ranistha; Andrikos, Emilios; Dan, Maurizio; Piccinni, Pasquale; Ronco, Claudio

2005-01-01

A new continuous renal replacement therapy machine has been designed to fulfill the expectations of nephrologists and intensivists operating in the common ground of critical care nephrology. The new equipment is called Prismaflex and it is the natural evolution of the PRISMA machine that has been utilized worldwide for continuous renal replacement therapy in the last 10 years. The authors performed a preliminary alpha-trial to establish the usability, flexibility and reliability of the new device. Accuracy was also tested by recording various operational parameters during different intermittent and continuous renal replacement modalities during 62 treatments. This article will describe our first experience with this new device and touch upon the historic and technologic background leading to its development.

Machine vision based quality inspection of flat glass products

Science.gov (United States)

Zauner, G.; Schagerl, M.

2014-03-01

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

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction.

Science.gov (United States)

Napolitano, Giulio; Marshall, Adele; Hamilton, Peter; Gavin, Anna T

2016-06-01

Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging. The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: 'semi-structured' and 'unstructured'. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry. The best result of 99.4% accuracy - which included only one semi-structured report predicted as unstructured - was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured. These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought. Copyright �

A comparative analysis of machine learning approaches for plant disease identification

Directory of Open Access Journals (Sweden)

Hidayat ur Rahman

2017-08-01

Full Text Available Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages. Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG have been used. Results: 382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves. Conclusion: From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants.

OPMILL - MICRO COMPUTER PROGRAMMING ENVIRONMENT FOR CNC MILLING MACHINES THREE AXIS EQUATION PLOTTING CAPABILITIES

Science.gov (United States)

Ray, R. B.

1994-01-01

OPMILL is a computer operating system for a Kearney and Trecker milling machine that provides a fast and easy way to program machine part manufacture with an IBM compatible PC. The program gives the machinist an "equation plotter" feature which plots any set of equations that define axis moves (up to three axes simultaneously) and converts those equations to a machine milling program that will move a cutter along a defined path. Other supported functions include: drill with peck, bolt circle, tap, mill arc, quarter circle, circle, circle 2 pass, frame, frame 2 pass, rotary frame, pocket, loop and repeat, and copy blocks. The system includes a tool manager that can handle up to 25 tools and automatically adjusts tool length for each tool. It will display all tool information and stop the milling machine at the appropriate time. Information for the program is entered via a series of menus and compiled to the Kearney and Trecker format. The program can then be loaded into the milling machine, the tool path graphically displayed, and tool change information or the program in Kearney and Trecker format viewed. The program has a complete file handling utility that allows the user to load the program into memory from the hard disk, save the program to the disk with comments, view directories, merge a program on the disk with one in memory, save a portion of a program in memory, and change directories. OPMILL was developed on an IBM PS/2 running DOS 3.3 with 1 MB of RAM. OPMILL was written for an IBM PC or compatible 8088 or 80286 machine connected via an RS-232 port to a Kearney and Trecker Data Mill 700/C Control milling machine. It requires a "D:" drive (fixed-disk or virtual), a browse or text display utility, and an EGA or better display. Users wishing to modify and recompile the source code will also need Turbo BASIC, Turbo C, and Crescent Software's QuickPak for Turbo BASIC. IBM PC and IBM PS/2 are registered trademarks of International Business Machines. Turbo

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

Directory of Open Access Journals (Sweden)

J. Rhee

2016-06-01

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

Machine Learning

Energy Technology Data Exchange (ETDEWEB)

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

2017-04-21

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

Machine Protection

International Nuclear Information System (INIS)

Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

2012-01-01

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

Machine Protection

Energy Technology Data Exchange (ETDEWEB)

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

2012-07-01

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

Machine Protection

CERN Document Server

Zerlauth, Markus; Wenninger, Jörg

2012-01-01

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

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.

Thermalization of mutual information in hyperscaling violating backgrounds

Energy Technology Data Exchange (ETDEWEB)

Tanhayi, M. Reza [Department of Physics, Faculty of Basic Science,Islamic Azad University Central Tehran Branch (IAUCTB),P.O. Box 14676-86831, Tehran (Iran, Islamic Republic of); School of Physics, Institute for Research in Fundamental Sciences (IPM),P.O. Box 19395-5531, Tehran (Iran, Islamic Republic of)

2016-03-31

We study certain features of scaling behaviors of the mutual information during a process of thermalization, more precisely we extend the time scaling behavior of mutual information which has been discussed in http://dx.doi.org/10.1007/JHEP09(2015)165 to time-dependent hyperscaling violating geometries. We use the holographic description of entanglement entropy for two disjoint system consisting of two parallel strips whose widths are much larger than the separation between them. We show that during the thermalization process, the dynamical exponent plays a crucial rule in reading the general time scaling behavior of mutual information (e.g., at the pre-local-equilibration regime). It is shown that the scaling violating parameter can be employed to define an effective dimension.

Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks

OpenAIRE

Airola, Rasmus; Hager, Kristoffer

2017-01-01

The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple of years in the light of an increasing need to handle big data and large information flows. Machine learning has a broad area of application, such as human-computer interaction, predicting stock prices, real-time translation, and self driving vehicles. Large companies such as Microsoft and Google have already implemented machine learning in some o...

Dictionary of machine terms

International Nuclear Information System (INIS)

1990-06-01

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

HTS machine laboratory prototype

DEFF Research Database (Denmark)

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

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.

Machine vision systems using machine learning for industrial product inspection

Science.gov (United States)

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

2002-02-01

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

Advanced man-machine interaction. Fundamentals and implementation

Energy Technology Data Exchange (ETDEWEB)

Kraiss, K.F. (ed.) [Aachen Technische Hochschule (Germany). Lehrstuhl fuer Technische Informatik und Computerwissenschaften

2006-07-01

Man-machine interaction is the gateway providing access to functions and services, which, due to the ever increasing complexity of smart systems, threatens to become a bottleneck. This book therefore introduces not only advanced interfacing concepts, but also gives insight into the related theoretical background.This refers mainly to the realization of video-based multimodal interaction via gesture, mimics, and speech, but also to interacting with virtual object in virtual environments, cooperating with local or remote robots, and user assistance. While most publications in the field of human factors engineering focus on interface design, this book puts special emphasis on implementation aspects. To this end it is accompanied by software development environments for image processing, classification, and virtual environment implementation. In addition a test data base is included for gestures, head pose, facial expressions, full-body person recognition, and people tracking. These data are used for the examples throughout the book, but are also meant to encourage the reader to start experimentation on his own. Thus the book may serve as a self-contained introduction both for researchers and developers of man-machine interfaces. It may also be used for graduate-level university courses. (orig.)

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

Directory of Open Access Journals (Sweden)

Shanhui Liu

2013-01-01

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

Machine learning and radiology.

Science.gov (United States)

Wang, Shijun; Summers, Ronald M

2012-07-01

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

Machine Learning of Fault Friction

Science.gov (United States)

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

2017-12-01

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

Machining with abrasives

CERN Document Server

Jackson, Mark J

2011-01-01

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

The Knife Machine. Module 15.

Science.gov (United States)

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

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

Comparative study of Moore and Mealy machine models adaptation ...

African Journals Online (AJOL)

Information and Communications Technology has influenced the need for automated machines that can carry out important production procedures and, automata models are among the computational models used in design and construction of industrial processes. The production process of the popular African Black Soap ...

State Machine Framework And Its Use For Driving LHC Operational states

CERN Document Server

Misiowiec, M; Solfaroli Camilloci, M

2011-01-01

The LHC follows a complex operational cycle with 12 major phases that include equipment tests, preparation, beam injection, ramping and squeezing, finally followed by the physics phase. This cycle is modelled and enforced with a state machine, whereby each operational phase is represented by a state. On each transition, before entering the next state, a series of conditions is verified to make sure the LHC is ready to move on. The State Machine framework was developed to cater for building independent or embedded state machines. They safely drive between the states executing tasks bound to transitions and broadcast related information to interested parties. The framework encourages users to program their own actions. Simple configuration management allows the operators to define and maintain complex models themselves. An emphasis was also put on easy interaction with the remote state machine instances through standard communication protocols. On top of its core functionality, the framework offers a transparen...

How much information is in a jet?

Science.gov (United States)

Datta, Kaustuv; Larkoski, Andrew

2017-06-01

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

How much information is in a jet?

International Nuclear Information System (INIS)

Datta, Kaustuv; Larkoski, Andrew

2017-01-01

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

Restrictions of process machine retooling at machine-building enterprises

Directory of Open Access Journals (Sweden)

Kuznetsova Elena

2017-01-01

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