Filter Influence on Rotor Losses in Coreless Axial Flux Permanent Magnet Machines

Directory of Open Access Journals (Sweden)

SANTIAGO, J.

2013-02-01

Full Text Available This paper investigates the eddy current losses induced in the rotor of coreless Axial-Flux machines. The calculation of eddy currents in the magnets requires the simulation of the inverter and the filter to obtain the harmonic content of the stator currents and FEM analysis of the magnets in the rotor. Due to the low inductance in coreless machines, the induced eddy current losses in the rotor remain lower than in traditional slotted machines. If only machine losses are considered, filters in DC/AC converters are not required in machines with wide airgaps as time harmonic losses in the rotor are very low.The harmonic content both from simulations and experimental results of a DC/AC converter are used to calculate the eddy currents in the rotor magnets. The properties of coreless machine topologies are investigated and some simplifications are proposed for time efficient 3D-FEM analysis. The time varying magnetic field can be considered constant over the magnets when the pole is divided in several magnets.The simplified FEM method to calculate eddy current losses is applicable to coreless machines with poles split into several magnets, although the conclusions are applicable to all coreless and slotless motors and generators.

Introduction to machine learning

OpenAIRE

Baştanlar, Yalın; Özuysal, Mustafa

2014-01-01

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

Nutrient Intake Values for Folate during Pregnancy and Lactation Vary Widely around the World

Directory of Open Access Journals (Sweden)

Lisa A. Houghton

2013-09-01

Full Text Available Folate is a B-vitamin with particular importance during reproduction due to its role in the synthesis and maintenance of DNA. Folate is well known for its role in preventing neural tube defects (NTDs during the periconceptional period. There is also an increased need for folate throughout pregnancy to support optimal growth and development of the fetus and blood volume expansion and tissue growth of the mother. During lactation, women are at risk of folate deficiency due to increased demands to accommodate milk folate levels. Nutrient Intake Values (NIVs for folate have been calculated to take into account additional needs during pregnancy and lactation. However, these values vary widely between countries. For example, the folate requirement that is set to meet the needs of almost all healthy women during pregnancy varies from 300 µg/day in the United Kingdom to 750 µg/day in Mexico. Currently, there is no accepted standardized terminology or framework for establishing NIVs. This article reviews country-specific NIVs for folate during pregnancy and lactation and the basis for setting these reference values.

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.

Tribological and Wear Performance of Carbide Tools with TiB2 PVD Coating under Varying Machining Conditions of TiAl6V4 Aerospace Alloy

Directory of Open Access Journals (Sweden)

Jose Mario Paiva

2017-11-01

Full Text Available Tribological phenomena and tool wear mechanisms during machining of hard-to-cut TiAl6V4 aerospace alloy have been investigated in detail. Since cutting tool wear is directly affected by tribological phenomena occurring between the surfaces of the workpiece and the cutting tool, the performance of the cutting tool is strongly associated with the conditions of the machining process. The present work shows the effect of different machining conditions on the tribological and wear performance of TiB2-coated cutting tools compared to uncoated carbide tools. FEM modeling of the temperature profile on the friction surface was performed for wet machining conditions under varying cutting parameters. Comprehensive characterization of the TiB2 coated vs. uncoated cutting tool wear performance was made using optical 3D imaging, SEM/EDX and XPS methods respectively. The results obtained were linked to the FEM modeling. The studies carried out show that during machining of the TiAl6V4 alloy, the efficiency of the TiB2 coating application for carbide cutting tools strongly depends on cutting conditions. The TiB2 coating is very efficient under roughing at low speeds (with strong buildup edge formation. In contrast, it shows similar wear performance to the uncoated tool under finishing operations at higher cutting speeds when cratering wear predominates.

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.

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.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

Science.gov (United States)

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-01-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

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.

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

Soft computing in machine learning

CERN Document Server

Park, Jooyoung; Inoue, Atsushi

2014-01-01

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

Estimates of Continental Ancestry Vary Widely among Individuals with the Same mtDNA Haplogroup

Science.gov (United States)

Emery, Leslie S.; Magnaye, Kevin M.; Bigham, Abigail W.; Akey, Joshua M.; Bamshad, Michael J.

2015-01-01

The association between a geographical region and an mtDNA haplogroup(s) has provided the basis for using mtDNA haplogroups to infer an individual’s place of origin and genetic ancestry. Although it is well known that ancestry inferences using mtDNA haplogroups and those using genome-wide markers are frequently discrepant, little empirical information exists on the magnitude and scope of such discrepancies between multiple mtDNA haplogroups and worldwide populations. We compared genetic-ancestry inferences made by mtDNA-haplogroup membership to those made by autosomal SNPs in ∼940 samples of the Human Genome Diversity Panel and recently admixed populations from the 1000 Genomes Project. Continental-ancestry proportions often varied widely among individuals sharing the same mtDNA haplogroup. For only half of mtDNA haplogroups did the highest average continental-ancestry proportion match the highest continental-ancestry proportion of a majority of individuals with that haplogroup. Prediction of an individual’s mtDNA haplogroup from his or her continental-ancestry proportions was often incorrect. Collectively, these results indicate that for most individuals in the worldwide populations sampled, mtDNA-haplogroup membership provides limited information about either continental ancestry or continental region of origin. PMID:25620206

Computer vision and machine learning for robust phenotyping in genome-wide studies.

Science.gov (United States)

Zhang, Jiaoping; Naik, Hsiang Sing; Assefa, Teshale; Sarkar, Soumik; Reddy, R V Chowda; Singh, Arti; Ganapathysubramanian, Baskar; Singh, Asheesh K

2017-03-08

Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems.

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.

Science.gov (United States)

Choi, Sungwoon; Lee, Jangho; Kang, Min-Gyu; Min, Hyeyoung; Chang, Yoon-Seok; Yoon, Sungroh

2017-10-01

From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single-stranded RNA virus belonging to the family Coronaviridae. Despite expert opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions. Copyright © 2017 Elsevier Inc. All rights reserved.

Power Electronics and Electric Machines Facilities | Transportation

Science.gov (United States)

Research | NREL Facilities Power Electronics and Electric Machines Facilities NREL's power electronics and electric machines thermal management experimentation facilities feature a wide range of four researchers in discussion around a piece of laboratory equipment. Power electronics researchers

Learning with Support Vector Machines

CERN Document Server

Campbell, Colin

2010-01-01

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a

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...

Refining Nodes and Edges of State Machines

DEFF Research Database (Denmark)

Hallerstede, Stefan; Snook, Colin

2011-01-01

State machines are hierarchical automata that are widely used to structure complex behavioural specifications. We develop two notions of refinement of state machines, node refinement and edge refinement. We compare the two notions by means of examples and argue that, by adopting simple conventions...... refinement theory and UML-B state machine refinement influences the style of node refinement. Hence we propose a method with direct proof of state machine refinement avoiding the detour via Event-B that is needed by UML-B....

Machine learning for adaptive many-core machines a practical approach

CERN Document Server

Lopes, Noel

2015-01-01

The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.

Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

Science.gov (United States)

Haque, M Muksitul; Holder, Lawrence B; Skinner, Michael K

2015-01-01

Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs). Different environmental toxicants have been shown to promote exposure (i.e., toxicant) specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm) epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT) and methoxychlor (MXC) exposure lineage F3 generation. Analysis of this positive validation data set showed a 100% prediction accuracy for all the DDT-MXC sperm epimutations. Observations further elucidate the genomic features associated with transgenerational germline epimutations and identify a genome-wide

Soft-Deep Boltzmann Machines

OpenAIRE

Kiwaki, Taichi

2015-01-01

We present a layered Boltzmann machine (BM) that can better exploit the advantages of a distributed representation. It is widely believed that deep BMs (DBMs) have far greater representational power than its shallow counterpart, restricted Boltzmann machines (RBMs). However, this expectation on the supremacy of DBMs over RBMs has not ever been validated in a theoretical fashion. In this paper, we provide both theoretical and empirical evidences that the representational power of DBMs can be a...

Program Design Report of the CNC Machine Tool(II)

International Nuclear Information System (INIS)

Kim, Jong Kiun; Youm, K. U.; Kim, K. S.; Lee, I. B.; Yoon, K. B.; Lee, C. K.; Youm, J. H.

2007-06-01

The application of CNC machine tool being widely expanded according to variety of machine work method and rapid promotion of machine tool, cutting tool, for high speed efficient machine work. In order to conduct of the project of manufacture and maintenance of laboratory equipment, production design and machine work technology are continually developed, especially the application of CNC machine tool is very important for the improvement of productivity, quality and clearing up a manpower shortage. We publish technical report which it includes CNC machine tool program and drawing, it contributes to the systematic development of CNC program design and machine work technology

Program Design Report of the CNC Machine Tool(II)

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong Kiun; Youm, K. U.; Kim, K. S.; Lee, I. B.; Yoon, K. B.; Lee, C. K.; Youm, J. H

2007-06-15

The application of CNC machine tool being widely expanded according to variety of machine work method and rapid promotion of machine tool, cutting tool, for high speed efficient machine work. In order to conduct of the project of manufacture and maintenance of laboratory equipment, production design and machine work technology are continually developed, especially the application of CNC machine tool is very important for the improvement of productivity, quality and clearing up a manpower shortage. We publish technical report which it includes CNC machine tool program and drawing, it contributes to the systematic development of CNC program design and machine work technology.

Program Design Report of the CNC Machine Tool(III)

International Nuclear Information System (INIS)

Kim, Jong Kiun; Youm, K. U.; Kim, K. S.; Lee, I. B.; Yoon, K. B.; Lee, C. K.; Youm, J. H.

2008-08-01

The application of CNC machine tool being widely expanded according to variety of machine work method and rapid promotion of machine tool, cutting tool, for high speed efficient machine work. In order to conduct of the project of manufacture and maintenance of laboratory equipment, production design and machine work technology are continually developed, especially the application of CNC machine tool is very important for the improvement of productivity, quality and clearing up a manpower shortage. We publish technical report which it includes CNC machine tool program and drawing, it contributes to the systematic development of CNC program design and machine work technology

Program Design Report of the CNC Machine Tool (I)

International Nuclear Information System (INIS)

Kim, Jong Kiun; Youm, K. U.; Kim, K. S.

2006-08-01

The application of CNC machine tool being widely expanded according to variety of machine work method and rapid promotion of machine tool, cutting tool, for high speed efficient machine work. In order to conduct of the project of manufacture and maintenance of laboratory equipment, production design and machine work technology are continually developed, especially the application of CNC machine tool is very important for the improvement of productivity, quality and clearing up a manpower shortage. We publish technical report which it includes CNC machine tool program and drawing, it contributes to the systematic development of CNC program design and machine work technology

Program Design Report of the CNC Machine Tool(IV)

International Nuclear Information System (INIS)

Youm, Ki Un; Lee, I. B.; Youm, J. H.

2009-09-01

The application of CNC machine tool being widely expanded according to variety of machine work method and rapid promotion of machine tool, cutting tool, for high speed efficient machine work. In order to conduct of the project of manufacture and maintenance of laboratory equipment, production design and machine work technology are continually developed, especially the application of CNC machine tool is very important for the improvement of productivity, quality and clearing up a manpower shortage. We publish technical report which it includes CNC machine tool program and drawing, it contributes to the systematic development of CNC program design and machine work technology

Machine learning and medical imaging

CERN Document Server

Shen, Dinggang; Sabuncu, Mert

2016-01-01

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, a...

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.

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.

Ensuring proper and safe use of the cryotherapy machine

Directory of Open Access Journals (Sweden)

Ismael Cordero

2015-04-01

Full Text Available Cryotherapy machines, also known as cryosurgery machines, continue to be widely used for surgical procedures of the eye such as retinal detachment repair, cataract extraction, glaucoma and so on.

Machinability of glass fiber reinforced plastic (GFRP) composite ...

African Journals Online (AJOL)

This paper deals with the study of machinability of GFRP composite tubes of different fiber orientation angle vary from 300 to 900. Machining studies were carried out on an all geared lathe using three different cutting tools: namely Carbide (K-20), Cubic Boron Nitride (CBN) and Poly-Crystalline Diamond (PCD). Experiments ...

Impact of Cutting Forces and Chip Microstructure in High Speed Machining of Carbon Fiber – Epoxy Composite Tube

Directory of Open Access Journals (Sweden)

Roy Y. Allwin

2017-09-01

Full Text Available Carbon fiber reinforced polymeric (CFRP composite materials are widely used in aerospace, automobile and biomedical industries due to their high strength to weight ratio, corrosion resistance and durability. High speed machining (HSM of CFRP material is needed to study the impact of cutting parameters on cutting forces and chip microstructure which offer vital inputs to the machinability and deformation characteristics of the material. In this work, the orthogonal machining of CFRP was conducted by varying the cutting parameters such as cutting speed and feed rate at high cutting speed/feed rate ranges up to 346 m/min/ 0.446 mm/rev. The impact of the cutting parameters on cutting forces (principal cutting, feed and thrust forces and chip microstructure were analyzed. A significant impact on thrust forces and chip segmentation pattern was seen at higher feed rates and low cutting speeds.

Chatter and machine tools

CERN Document Server

Stone, Brian

2014-01-01

Focussing on occurrences of unstable vibrations, or Chatter, in machine tools, this book gives important insights into how to eliminate chatter with associated improvements in product quality, surface finish and tool wear. Covering a wide range of machining processes, including turning, drilling, milling and grinding, the author uses his research expertise and practical knowledge of vibration problems to provide solutions supported by experimental evidence of their effectiveness. In addition, this book contains links to supplementary animation programs that help readers to visualise the ideas detailed in the text. Advancing knowledge in chatter avoidance and suggesting areas for new innovations, Chatter and Machine Tools serves as a handbook for those desiring to achieve significant reductions in noise, longer tool and grinding wheel life and improved product finish.

Program Design Report of the CNC Machine Tool(V-1)

International Nuclear Information System (INIS)

Youm, Ki Un; Moon, J. S.; Lee, I. B.; Youn, J. H.

2010-08-01

The application of CNC machine tool being widely expanded according to variety of machine work method and rapid promotion of machine tool, cutting tool, for high speed efficient machine work. In order to conduct of the project of manufacture and maintenance of laboratory equipment, production design and machine work technology are continually developed, especially the application of CNC machine tool is very important for the improvement of productivity, quality and clearing up a manpower shortage. We publish technical report which it includes CNC machine tool program and drawing, it contributes to the systematic development of CNC program design and machine work technology

Research on the tool holder mode in high speed machining

Science.gov (United States)

Zhenyu, Zhao; Yongquan, Zhou; Houming, Zhou; Xiaomei, Xu; Haibin, Xiao

2018-03-01

High speed machining technology can improve the processing efficiency and precision, but also reduce the processing cost. Therefore, the technology is widely regarded in the industry. With the extensive application of high-speed machining technology, high-speed tool system has higher and higher requirements on the tool chuck. At present, in high speed precision machining, several new kinds of clip heads are as long as there are heat shrinkage tool-holder, high-precision spring chuck, hydraulic tool-holder, and the three-rib deformation chuck. Among them, the heat shrinkage tool-holder has the advantages of high precision, high clamping force, high bending rigidity and dynamic balance, etc., which are widely used. Therefore, it is of great significance to research the new requirements of the machining tool system. In order to adapt to the requirement of high speed machining precision machining technology, this paper expounds the common tool holder technology of high precision machining, and proposes how to select correctly tool clamping system in practice. The characteristics and existing problems are analyzed in the tool clamping system.

Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

Energy Technology Data Exchange (ETDEWEB)

Varpula, T. [VTT Automation, Espoo (Finland). Measurement Technology

1998-12-31

A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

Energy Technology Data Exchange (ETDEWEB)

Varpula, T [VTT Automation, Espoo (Finland). Measurement Technology

1999-12-31

A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

Laser machining micro-structures on diamond surface with a sub-nanosecond pulsed laser

Science.gov (United States)

Wu, Mingtao; Guo, Bing; Zhao, Qingliang

2018-02-01

Micro-structure surface on diamond material is widely used in a series of industrial and scientific applications, such as micro-electromechanical systems (MEMS), nanoelectromechanical systems (NEMS), microelectronics, textured or micro-structured diamond machining tools. The efficient machining of micro-structure on diamond surface is urgently demanded in engineering. In this paper, laser machining square micro-structure on diamond surface was studied with a sub-nanosecond pulsed laser. The influences of laser machining parameters, including the laser power, scanning speed, defocusing quantity and scanning pitch, were researched in view of the ablation depth, material removal rate and machined surface topography. Both the ablation depth and material removal rate increased with average laser power. A reduction of the growth rate of the two parameters was induced by the absorption of the laser plasma plume at high laser power. The ablation depth non-linearly decreased with the increasing of the scanning speed while the material removal rate showed an opposite tendency. The increasing of the defocusing quantity induced complex variation of the ablation depth and the material removal rate. The maximum ablation depth and material removal rate were achieved at a defocusing position. The ablation depth and material removal rate oppositely varied about the scanning pitch. A high overlap ratio was meaningful for achieving a smooth micro-structure surface topography. Laser machining with a large defocusing quantity, high laser power and small scanning pitch was helpful for acquiring the desired micro-structure which had a large depth and smooth micro-structure surface topography.

An R package "VariABEL" for genome-wide searching of potentially interacting loci by testing genotypic variance heterogeneity

Directory of Open Access Journals (Sweden)

Struchalin Maksim V

2012-01-01

Full Text Available Abstract Background Hundreds of new loci have been discovered by genome-wide association studies of human traits. These studies mostly focused on associations between single locus and a trait. Interactions between genes and between genes and environmental factors are of interest as they can improve our understanding of the genetic background underlying complex traits. Genome-wide testing of complex genetic models is a computationally demanding task. Moreover, testing of such models leads to multiple comparison problems that reduce the probability of new findings. Assuming that the genetic model underlying a complex trait can include hundreds of genes and environmental factors, testing of these models in genome-wide association studies represent substantial difficulties. We and Pare with colleagues (2010 developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants. Results In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests. Conclusions Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.

Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

Directory of Open Access Journals (Sweden)

M Muksitul Haque

Full Text Available Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs. Different environmental toxicants have been shown to promote exposure (i.e., toxicant specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (<3 CpG / 100bp termed CpG deserts and a number of unique DNA sequence motifs. The rat genome was annotated for these and additional relevant features. The objective of the current study was to use a machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT and methoxychlor (MXC exposure lineage F3 generation. Analysis of this positive validation

Iron Losses in Electrical Machines Due to Non Sinusoidal Alternating Fluxes

DEFF Research Database (Denmark)

Ritchie, Ewen; Walker, J.A.; Dorrell, D. G.

2007-01-01

This paper shows how the flux waveform in the core of an electrical machine can be vary non- sinusoidally which complicates the calculation of the iron loss in a machine. A set of tests are conducted on a steel sample using an Epstein square where harmonics are injected into the flux waveform which...... of a machine....

Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

Science.gov (United States)

Ohsugi, Hideharu; Tabuchi, Hitoshi; Enno, Hiroki; Ishitobi, Naofumi

2017-08-25

Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep learning, a machine-learning technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

The Bearingless Electrical Machine

Science.gov (United States)

Bichsel, J.

1992-01-01

Electromagnetic bearings allow the suspension of solids. For rotary applications, the most important physical effect is the force of a magnetic circuit to a high permeable armature, called the MAXWELL force. Contrary to the commonly used MAXWELL bearings, the bearingless electrical machine will take advantage of the reaction force of a conductor carrying a current in a magnetic field. This kind of force, called Lorentz force, generates the torque in direct current, asynchronous and synchronous machines. The magnetic field, which already exists in electrical machines and helps to build up the torque, can also be used for the suspension of the rotor. Besides the normal winding of the stator, a special winding was added, which generates forces for levitation. So a radial bearing, which is integrated directly in the active part of the machine, and the motor use the laminated core simultaneously. The winding was constructed for the levitating forces in a special way so that commercially available standard ac inverters for drives can be used. Besides wholly magnetic suspended machines, there is a wide range of applications for normal drives with ball bearings. Resonances of the rotor, especially critical speeds, can be damped actively.

Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis

Science.gov (United States)

Wang, Shibin; Chen, Xuefeng; Selesnick, Ivan W.; Guo, Yanjie; Tong, Chaowei; Zhang, Xingwu

2018-02-01

Synchrosqueezing transform (SST) can effectively improve the readability of the time-frequency (TF) representation (TFR) of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF). However, for signals composed of multiple components with fast varying IF, SST still suffers from TF blurs. In this paper, we introduce a time-frequency analysis (TFA) method called matching synchrosqueezing transform (MSST) that achieves a highly concentrated TF representation comparable to the standard TF reassignment methods (STFRM), even for signals with fast varying IF, and furthermore, MSST retains the reconstruction benefit of SST. MSST captures the philosophy of STFRM to simultaneously consider time and frequency variables, and incorporates three estimators (i.e., the IF estimator, the group delay estimator, and a chirp-rate estimator) into a comprehensive and accurate IF estimator. In this paper, we first introduce the motivation of MSST with three heuristic examples. Then we introduce a precise mathematical definition of a class of chirp-like intrinsic-mode-type functions that locally can be viewed as a sum of a reasonably small number of approximate chirp signals, and we prove that MSST does indeed succeed in estimating chirp-rate and IF of arbitrary functions in this class and succeed in decomposing these functions. Furthermore, we describe an efficient numerical algorithm for the practical implementation of the MSST, and we provide an adaptive IF extraction method for MSST reconstruction. Finally, we verify the effectiveness of the MSST in practical applications for machine fault diagnosis, including gearbox fault diagnosis for a wind turbine in variable speed conditions and rotor rub-impact fault diagnosis for a dual-rotor turbofan engine.

A Review of Virtual Machine Attack Based on Xen

Directory of Open Access Journals (Sweden)

Ren xun-yi

2016-01-01

Full Text Available Virtualization technology as the foundation of cloud computing gets more and more attention because the cloud computing has been widely used. Analyzing the threat with the security of virtual machine and summarizing attack about virtual machine based on XEN to predict visible security hidden recently. Base on this paper can provide a reference for the further research on the security of virtual machine.

Scikit-learn: Machine Learning in Python

OpenAIRE

Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu

2011-01-01

International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...

Scikit-learn: Machine Learning in Python

OpenAIRE

Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Louppe, Gilles; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu

2012-01-01

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....

Dynamics and Thermodynamics of Molecular Machines

DEFF Research Database (Denmark)

Golubeva, Natalia

2014-01-01

to their microscopic size, molecular motors are governed by principles fundamentally different from those describing the operation of man-made motors such as car engines. In this dissertation the dynamic and thermodynamic properties of molecular machines are studied using the tools of nonequilibrium statistical......Molecular machines, or molecular motors, are small biophysical devices that perform a variety of essential metabolic processes such as DNA replication, protein synthesis and intracellular transport. Typically, these machines operate by converting chemical energy into motion and mechanical work. Due...... mechanics. The first part focuses on noninteracting molecular machines described by a paradigmatic continuum model with the aim of comparing and contrasting such a description to the one offered by the widely used discrete models. Many molecular motors, for example, kinesin involved in cellular cargo...

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

International Nuclear Information System (INIS)

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

2013-01-01

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

Machine learning in manufacturing: advantages, challenges, and applications

Directory of Open Access Journals (Sweden)

Thorsten Wuest

2016-01-01

Full Text Available The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

Frontal Neurons Modulate Memory Retrieval across Widely Varying Temporal Scales

Science.gov (United States)

Zhang, Wen-Hua; Williams, Ziv M.

2015-01-01

Once a memory has formed, it is thought to undergo a gradual transition within the brain from short- to long-term storage. This putative process, however, also poses a unique problem to the memory system in that the same learned items must also be retrieved across broadly varying time scales. Here, we find that neurons in the ventrolateral…

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.

Model-based machine learning.

Science.gov (United States)

Bishop, Christopher M

2013-02-13

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

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.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

OpenAIRE

Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael

2016-01-01

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algo...

Time-varying BRDFs.

Science.gov (United States)

Sun, Bo; Sunkavalli, Kalyan; Ramamoorthi, Ravi; Belhumeur, Peter N; Nayar, Shree K

2007-01-01

The properties of virtually all real-world materials change with time, causing their bidirectional reflectance distribution functions (BRDFs) to be time varying. However, none of the existing BRDF models and databases take time variation into consideration; they represent the appearance of a material at a single time instance. In this paper, we address the acquisition, analysis, modeling, and rendering of a wide range of time-varying BRDFs (TVBRDFs). We have developed an acquisition system that is capable of sampling a material's BRDF at multiple time instances, with each time sample acquired within 36 sec. We have used this acquisition system to measure the BRDFs of a wide range of time-varying phenomena, which include the drying of various types of paints (watercolor, spray, and oil), the drying of wet rough surfaces (cement, plaster, and fabrics), the accumulation of dusts (household and joint compound) on surfaces, and the melting of materials (chocolate). Analytic BRDF functions are fit to these measurements and the model parameters' variations with time are analyzed. Each category exhibits interesting and sometimes nonintuitive parameter trends. These parameter trends are then used to develop analytic TVBRDF models. The analytic TVBRDF models enable us to apply effects such as paint drying and dust accumulation to arbitrary surfaces and novel materials.

A Study of the Resolution of Dental Intraoral X-Ray Machines

International Nuclear Information System (INIS)

Kim, Seon Ju; Chung, Hyon De

1990-01-01

The purpose of this study was to assess the resolution and focal spot size of dental X-ray machines. Fifty dental X-ray machines were selected for measuring resolution and focal spot size. These machines were used in general dental clinics. The time on installation of the X-ray machine varies from 1 years to 10 years. The resolution of these machines was measured with the test pattern. The focal spot size of these machines was measured with the star test pattern. The following results were obtained: 1. The resolution of dental intraoral X-ray machines was not significantly changed in ten years. 2. The focal spot size of dental intraoral X-ray machines was not significantly increased in ten years. The statistical analysis between the mean focal spot size and nominal focal spot size was significant at the 0.05 level about the more than 3 years used machines.

Genome-wide CpG island methylation and intergenic demethylation propensities vary among different tumor sites.

Science.gov (United States)

Lee, Seung-Tae; Wiemels, Joseph L

2016-02-18

The epigenetic landscape of cancer includes both focal hypermethylation and broader hypomethylation in a genome-wide manner. By means of a comprehensive genomic analysis on 6637 tissues of 21 tumor types, we here show that the degrees of overall methylation in CpG island (CGI) and demethylation in intergenic regions, defined as 'backbone', largely vary among different tumors. Depending on tumor type, both CGI methylation and backbone demethylation are often associated with clinical, epidemiological and biological features such as age, sex, smoking history, anatomic location, histological type and grade, stage, molecular subtype and biological pathways. We found connections between CGI methylation and hypermutability, microsatellite instability, IDH1 mutation, 19p gain and polycomb features, and backbone demethylation with chromosomal instability, NSD1 and TP53 mutations, 5q and 19p loss and long repressive domains. These broad epigenetic patterns add a new dimension to our understanding of tumor biology and its clinical implications. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Circular causality and indeterminism in machines for design

Directory of Open Access Journals (Sweden)

Thomas Fischer

2014-12-01

Full Text Available Presenting a hard-to-predict typography-varying system predicated on Nazi-era cryptography, the Enigma cipher machine, this paper illustrates conditions under which unrepeatable phenomena can arise, even from straight-forward mechanisms. Such conditions arise where systems are observed from outside of boundaries that arise through their observation, and where such systems refer to themselves in a circular fashion. It argues that the Enigma cipher machine is isomorphous with Heinz von Foersters portrayals of non-triviality in his non-trivial machine (NTM, but not with surprising human behaviour, and it demonstrates that the NTM does not account for spontaneity as it is observed in humans in general.

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

OpenAIRE

Tang, Yuan

2016-01-01

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

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.

A Study of the Interaction between Batting Cage Baseballs and Pitching Machine

Directory of Open Access Journals (Sweden)

Patrick Drane

2018-02-01

Full Text Available Batting cage pitching machines are widely used across the sports of baseball and softball for training and recreation purposes. The balls are specifically designed for the machines and for the environment to ensure high durability and typically do not have seams. Polymeric foam balls are widely used in these automated pitching machines for batting practice in a cage environment and are similar in weight and size compared with the regulation balls used in leagues. The primary objective of this paper is to characterize the polymeric balls and their interaction with the pitching machine. The paper will present measured ball properties and measured relationships between various pitching machine parameters such as wheel speed, and the ratio of wheel speeds on the ball exit velocity and rotation. This paper will also characterize some of the effects of wear on the baseballs and wheels from their prolonged use.

Density Based Support Vector Machines for Classification

OpenAIRE

Zahra Nazari; Dongshik Kang

2015-01-01

Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used cl...

X-ray evaluation of residual stress distributions within surface machined layer generated by surface machining and sequential welding

International Nuclear Information System (INIS)

Taniguchi, Yuu; Okano, Shigetaka; Mochizuki, Masahito

2017-01-01

The excessive tensile residual stress generated by welding after surface machining may be an important factor to cause stress corrosion cracking (SCC) in nuclear power plants. Therefore we need to understand and control the residual stress distribution appropriately. In this study, residual stress distributions within surface machined layer generated by surface machining and sequential welding were evaluated by X-ray diffraction method. Depth directional distributions were also investigated by electrolytic polishing. In addition, to consider the effect of work hardened layer on the residual stress distributions, we also measured full width at half maximum (FWHM) obtained from X-ray diffraction. Testing material was a low-carbon austenitic stainless steel type SUS316L. Test specimens were prepared by surface machining with different cutting conditions. Then, bead-on-plate welding under the same welding condition was carried out on the test specimens with different surface machined layer. As a result, the tensile residual stress generated by surface machining increased with increasing cutting speed and showed nearly uniform distributions on the surface. Furthermore, the tensile residual stress drastically decreased with increasing measurement depth within surface machined layer. Then, the residual stress approached 0 MPa after the compressive value showed. FWHM also decreased drastically with increasing measurement depth and almost constant value from a certain depth, which was almost equal regardless of the machining condition, within surface machined layer in all specimens. After welding, the transverse distribution of the longitudinal residual stress varied in the area apart from the weld center according to machining conditions and had a maximum value in heat affected zone. The magnitude of the maximum residual stress was almost equal regardless of the machining condition and decreased with increasing measurement depth within surface machined layer. Finally, the

Languages, compilers and run-time environments for distributed memory machines

CERN Document Server

Saltz, J

1992-01-01

Papers presented within this volume cover a wide range of topics related to programming distributed memory machines. Distributed memory architectures, although having the potential to supply the very high levels of performance required to support future computing needs, present awkward programming problems. The major issue is to design methods which enable compilers to generate efficient distributed memory programs from relatively machine independent program specifications. This book is the compilation of papers describing a wide range of research efforts aimed at easing the task of programmin

Introduction to machine learning: k-nearest neighbors.

Science.gov (United States)

Zhang, Zhongheng

2016-06-01

Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm, and then focuses on how to perform kNN modeling with R. The dataset should be prepared before running the knn() function in R. After prediction of outcome with kNN algorithm, the diagnostic performance of the model should be checked. Average accuracy is the mostly widely used statistic to reflect the kNN algorithm. Factors such as k value, distance calculation and choice of appropriate predictors all have significant impact on the model performance.

Impact of Model Detail of Synchronous Machines on Real-time Transient Stability Assessment

DEFF Research Database (Denmark)

Weckesser, Johannes Tilman Gabriel; Jóhannsson, Hjörtur; Østergaard, Jacob

2013-01-01

In this paper, it is investigated how detailed the model of a synchronous machine needs to be in order to assess transient stability using a Single Machine Equivalent (SIME). The results will show how the stability mechanism and the stability assessment are affected by the model detail. In order...... of the machine models is varied. Analyses of the results suggest that a 4th-order model may be sufficient to represent synchronous machines in transient stability studies....

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.

The hidden costs of installing Xpert machines in a tuberculosis high-burden country: experiences from Nigeria.

Science.gov (United States)

Abdurrahman, Saddiq Tsimiri; Emenyonu, Nnamdi; Obasanya, Olusegun Joshua; Lawson, Lovett; Dacombe, Russell; Muhammad, Muhammad; Oladimeji, Olanrewaju; Cuevas, Luis Eduardo

2014-01-01

Since the endorsement of GeneXpert MTB/RIF by the WHO, many countries have embarked on implementing this technology. We outline the cost of installing GeneXpert in district hospitals in Abuja, Nigeria. We prospectively documented costs related to the installation of GeneXpert at five sites. Costs were collected from receipts received from suppliers and normalized to USD 2012 values. Costs were often identified after initiating installation for many reasons. Installation varied widely between sites with sufficient space and power supply; sites with insufficient space or power supply and costs not directly associated with site installation. The basic cost for installation was USD 2,621.98 per machine. Sites that required additional space cost close to USD 7,000.00. Space and power requirements have a significant effect on installation costs. Countries need to carefully consider the placement of Xpert machines based on the quality and size of the available infrastructure.

Development and analysis of U-core switched reluctance machine

DEFF Research Database (Denmark)

Jæger, Rasmus; Nielsen, Simon Staal; Rasmussen, Peter Omand

2016-01-01

Switched reluctance machines (SRMs) have seen a lot of interest due to their rugged and fault tolerant construction as well as their high efficiency over a wide speed range. The technology however suffers from torque ripple, acoustic noise and low torque density. Many concepts to address these di......Switched reluctance machines (SRMs) have seen a lot of interest due to their rugged and fault tolerant construction as well as their high efficiency over a wide speed range. The technology however suffers from torque ripple, acoustic noise and low torque density. Many concepts to address...... and reduced flux reversal, reducing core losses. Due to an increased number of poles, torque density is increased and torque ripple reduced. A prototype is built and through a number of tests, the machine is mapped and all loss components are analysed. As a result of the analysis, an assessment is presented...

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...

Structural Analysis of Advanced Refueling Machine of APR1400

International Nuclear Information System (INIS)

Cho, J. R.; Kim, Y. H.; Park, B. T.; Park, J. B.; Jung, J. H.

2007-01-01

The Refueling Machine (RM) consists of two structural parts of bridge and trolley. The bridge structure is approximately 8.5 m long and 5 m wide and is primarily composed of two deep wide flange sections spanning the rector area at the operating level. The trolley is mounted on wheels that roll on the rails of the bridge. Vertical movements of trolley and bridge are restricted by guide rollers. In this paper, dynamic and structural analyses based on the earthquake spectrum are carried out to verify the structural integrity of advanced refueling machine. It is done by 3-dimensional finite element analysis using ANSYS software

Peak thrust operation of linear induction machines from parameter identification

Energy Technology Data Exchange (ETDEWEB)

Zhang, Z.; Eastham, T.R.; Dawson, G.E. [Queen`s Univ., Kingston, Ontario (Canada). Dept. of Electrical and Computer Engineering

1995-12-31

Various control strategies are being used to achieve high performance operation of linear drives. To maintain minimum volume and weight of the power supply unit on board the transportation vehicle, peak thrust per unit current operation is a desirable objective. True peak thrust per unit current through slip control is difficult to achieve because the parameters of linear induction machines vary during normal operation. This paper first develops a peak thrust per unit current control law based on the per-phase equivalent circuit for linear induction machines. The algorithm for identification of the variable parameters in induction machines is then presented. Application to an operational linear induction machine (LIM) demonstrates the utility of this algorithm. The control strategy is then simulated, based on an operational transit LIM, to show the capability of achieving true peak thrust operation for linear induction machines.

Asynchronous machines. Direct torque control; Machines asynchrones. Commande par controle direct de couple

Energy Technology Data Exchange (ETDEWEB)

Fornel, B. de [Institut National Polytechnique, 31 - Toulouse (France)

2006-05-15

The asynchronous machine, with its low cost and robustness, is today the most widely used motor to make speed variators. However, its main drawback is that the same current generates both the magnetic flux and the torque, and thus any torque variation creates a flux variation. Such a coupling gives to the asynchronous machine a nonlinear behaviour which makes its control much more complex. The direct self control (DSC) method has been developed to improve the low efficiency of the scalar control method and for the specific railway drive application. The direct torque control (DTC) method is derived from the DSC method but corresponds to other type of applications. The DSC and DTC algorithms for asynchronous motors are presented in this article: 1 - direct control of the stator flux (DSC): principle, flux control, torque control, switching frequency of the inverter, speed estimation; 2 - direct torque control (DTC): principle, electromagnetic torque derivative, signals shape and switching frequency, some results, DTC speed variator without speed sensor, DTC application to multi-machine multi-converter systems; 3 - conclusion. (J.S.)

Momentum transport studies from multi-machine comparisons

International Nuclear Information System (INIS)

Yoshida, M.; Kamada, Y.; Sakamoto, Y.; Kaye, S.; Solomon, W.; Bell, R.E.; Rice, J.; Podpaly, Y.; Reinke, M.L.; Tala, T.; Salmi, A.; Burrell, K.H.; Ferreira, J.; McDonald, D.; Mantica, P.

2012-01-01

A database of toroidal momentum transport on five tokamaks, Alcator C-Mod, DIII-D, JET, NSTX and JT-60U, has been constructed under a wide range of conditions in order to understand the characteristics of toroidal momentum transport coefficients, namely the toroidal momentum diffusivity (χ φ ) and the pinch velocity (V pinch ). Through an inter-machine comparison, the similarities and differences in the properties of χ φ and V pinch among the machines have been clarified. Parametric dependences of these momentum transport coefficients have been investigated over a wide range of plasma parameters taking advantage of the different operation regimes in machines. The approach offers insights into the parametric dependences as follows. The toroidal momentum diffusivity (χ φ ) generally increases with increasing heat diffusivity (χ i ). The correlation is observed over a wide range of χ φ , covering roughly two orders of magnitude, and within each of the machines over the whole radius. Through the inter-machine comparison, it is found that χ φ becomes larger in the outer region of the plasma. Also observed is a general trend for V pinch in tokamaks; the inward pinch velocity (−V pinch ) increases with increasing χ φ . The results that are commonly observed in machines will support a toroidal rotation prediction in future devices. On the other hand, differences among machines have been observed. The toroidal momentum diffusivity, χ φ , is larger than or equal to χ i in JET and JT-60U; on the other hand, χ φ is smaller than or equal to χ i in NSTX, DIII-D and Alcator C-Mod. In DIII-D, the ratio −RV pinch /χ φ at r/a = 0.5–0.6 is about 2, which is small compared with that in other tokamaks (−RV pinch /χ φ ≈ 5). Based on these different observations, parametric dependences of χ φ /χ i , RV pinch /χ φ and χ φ have been investigated in H-mode plasmas. Across the dataset from all machines, the ratio χ φ /χ i tends to be larger in low �

RSO Characterization from Photometric Data Using Machine Learning

Science.gov (United States)

Howard, M.; Klem, B.; Gorman, J.

Object characterization is the description of a resident space object (RSO), its capabilities and its behavior. While astrometric data has been used extensively for object detection, location, and characterization, photometric data has been less widely applied and remains a promising area for improving RSO characterization. RSO characteristics which may influence changes in light intensity with respect to changes in viewing angle or orientation signature include geometry, orientation, components material properties, stability and other characteristics. However, most RSO characterization is presently performed manually and on an individual basis by space analysts and there is a need for efficient and automated methods to perform characterization. This paper discusses the application of machine learning techniques to characterization of RSOs in the geosynchronous altitude regime using photometric data. We develop simulated signatures in the visible spectral band of three basic RSO types, with variations in object orientation, material characteristics, size and attitude and attempt to recover these properties through object characterization techniques. We generate observations by sampling noisy measurements from the simulated signature. Next, we extract a set of features from the observations and train machine learning algorithms to classify the signatures. We consider the effectiveness of a set of binary classifiers trained to individually recognize separate cases. The results of each classifier are combined together to produce a final output characterization of an input observation. Experiments with varying levels of noise are presented, and we evaluate models with respect to classification accuracy and other criteria. The end result of this process is a unique methodology for exploiting the use usefulness and applicability of machine learning to an important space sensing and identification process. This material is based upon work supported by the United States

Deep learning versus traditional machine learning methods for aggregated energy demand prediction

NARCIS (Netherlands)

Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

2018-01-01

In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

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...

Machine learning enhanced optical distance sensor

Science.gov (United States)

Amin, M. Junaid; Riza, N. A.

2018-01-01

Presented for the first time is a machine learning enhanced optical distance sensor. The distance sensor is based on our previously demonstrated distance measurement technique that uses an Electronically Controlled Variable Focus Lens (ECVFL) with a laser source to illuminate a target plane with a controlled optical beam spot. This spot with varying spot sizes is viewed by an off-axis camera and the spot size data is processed to compute the distance. In particular, proposed and demonstrated in this paper is the use of a regularized polynomial regression based supervised machine learning algorithm to enhance the accuracy of the operational sensor. The algorithm uses the acquired features and corresponding labels that are the actual target distance values to train a machine learning model. The optimized training model is trained over a 1000 mm (or 1 m) experimental target distance range. Using the machine learning algorithm produces a training set and testing set distance measurement errors of learning. Applications for the proposed sensor include industrial scenario distance sensing where target material specific training models can be generated to realize low <1% measurement error distance measurements.

Beam Dynamics Studies in Recirculating Machines

CERN Document Server

Pellegrini, Dario; Latina, A

The LHeC and the CLIC Drive Beam share not only the high-current beams that make them prone to show instabilities, but also unconventional lattice topologies and operational schemes in which the time sequence of the bunches varies along the machine. In order to asses the feasibility of these projects, realistic simulations taking into account the most worrisome effects and their interplays, are crucial. These include linear and non-linear optics with time dependent elements, incoherent and coherent synchrotron radiation, short and long-range wakefields, beam-beam effect and ion cloud. In order to investigate multi-bunch effects in recirculating machines, a new version of the tracking code PLACET has been developed from scratch. PLACET2, already integrates most of the effects mentioned before and can easily receive additional physics. Its innovative design allows to describe complex lattices and track one or more bunches accordingly to the machine operation, reproducing the bunch train splitting and recombinat...

Human and machine perception communication, interaction, and integration

CERN Document Server

Cantoni, Virginio; Setti, Alessandra

2005-01-01

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

Study on effect of tool electrodes on surface finish during electrical discharge machining of Nitinol

Science.gov (United States)

Sahu, Anshuman Kumar; Chatterjee, Suman; Nayak, Praveen Kumar; Sankar Mahapatra, Siba

2018-03-01

Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

Machine learning derived risk prediction of anorexia nervosa.

Science.gov (United States)

Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

2016-01-20

Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

Amp: A modular approach to machine learning in atomistic simulations

Science.gov (United States)

Khorshidi, Alireza; Peterson, Andrew A.

2016-10-01

Electronic structure calculations, such as those employing Kohn-Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties-namely that they are noiseless and targeted training data can be produced on-demand-that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which

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...

Safety of stationary grinding machines - impact resistance of work zone enclosures.

Science.gov (United States)

Mewes, Detlef; Adler, Christian

2017-09-01

Guards on machine tools are intended to protect persons from being injured by parts ejected with high kinetic energy from the work zone of the machine. Stationary grinding machines are a typical example. Generally such machines are provided with abrasive product guards closely enveloping the grinding wheel. However, many machining tasks do not allow the use of abrasive product guards. In such cases, the work zone enclosure has to be dimensioned so that, in case of failure, grinding wheel fragments remain inside the machine's working zone. To obtain data for the dimensioning of work zone enclosures on stationary grinding machines, which must be operated without an abrasive product guard, burst tests were conducted with vitrified grinding wheels. The studies show that, contrary to widely held opinion, narrower grinding wheels can be more critical concerning the impact resistance than wider wheels although their fragment energy is smaller.

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

Radiofrequency (RF) radiation measurement for diathermy machine

International Nuclear Information System (INIS)

Rozaimah Abdul Rahim; Roha Tukimin; Mohd Amirul Nizam; Ahmad Fadzli; Mohd Azizi

2010-01-01

Full-text: Diathermy machine is one of medical device that use widely in hospital and clinic. During the diathermy treatment, high radiofrequency (RF) currents (shortwave and microwave) are used to heat deep muscular tissues through electromagnetic energy to body tissues. The heat increases blood flow, relieve pain and speeding up recovery. The stray RF radiation from the machine can exposes to unintended tissue of the patient, to the operator (physical therapist) and also can cause electromagnetic interference (EMI) effect to medical devices around the machine. The main objective of this study is to establish a database of the RF radiation exposure levels experienced by the operator and patient during the treatments. RF radiation (electric and magnetic field) produced by the diathermy machines were measured using special RF survey meters. The finding of this study confirms that radiation levels on the surface and near the applicator of the diathermy machine much more elevated due to the much closer distance to the source and they exceeding the occupational and general public exposure limit. The results also shows the field strengths drop very significantly when the distance of measurement increase. (author)

Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies.

Science.gov (United States)

Zhao, Ni; Zhan, Xiang; Huang, Yen-Tsung; Almli, Lynn M; Smith, Alicia; Epstein, Michael P; Conneely, Karen; Wu, Michael C

2018-03-01

Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association. A composite kernel is constructed to measure the similarities in the genotype and methylation values between pairs of samples. We demonstrate through simulations and real data applications that the proposed approach can correctly control type I error, and is more robust and powerful than using only the genotype or methylation data in detecting trait-associated genes. We applied our method to investigate the genetic and epigenetic regulation of gene expression in response to stressful life events using data that are collected from the Grady Trauma Project. Within the kernel machine testing framework, our methods allow for heterogeneity in effect sizes, nonlinear, and interactive effects, as well as rapid P-value computation. © 2017 WILEY PERIODICALS, INC.

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

OpenAIRE

Rui Zhao; Ruqiang Yan; Jinjiang Wang; Kezhi Mao

2017-01-01

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression mode...

Gelcasting compositions having improved drying characteristics and machinability

Science.gov (United States)

Janney, Mark A.; Walls, Claudia A. H.

2001-01-01

A gelcasting composition has improved drying behavior, machinability and shelf life in the dried and unfired state. The composition includes an inorganic powder, solvent, monomer system soluble in the solvent, an initiator system for polymerizing the monomer system, and a plasticizer soluble in the solvent. Dispersants and other processing aides to control slurry properties can be added. The plasticizer imparts an ability to dry thick section parts, to store samples in the dried state without cracking under conditions of varying relative humidity, and to machine dry gelcast parts without cracking or chipping. A method of making gelcast parts is also disclosed.

Design and fabrication of a cassava peeling machine | Akintunde ...

African Journals Online (AJOL)

Design and fabrication of a cassava peeling machine. ... Journal Home > Vol 23, No 1 (2005) > ... The varying shapes and sizes of cassava tubers have made cassava peeling to be one of the major problems in the mechanization of cassava ...

Efficiency trends in electric machines and drives

International Nuclear Information System (INIS)

Mecrow, B.C.; Jack, A.G.

2008-01-01

Almost all electricity in the UK is generated by rotating electrical generators, and approximately half of it is used to drive electrical motors. This means that efficiency improvements to electrical machines can have a very large impact on energy consumption. The key challenges to increased efficiency in systems driven by electrical machines lie in three areas: to extend the application of variable-speed electric drives into new areas through reduction of power electronic and control costs; to integrate the drive and the driven load to maximise system efficiency; and to increase the efficiency of the electrical drive itself. In the short to medium term, efficiency gains within electrical machines will result from the development of new materials and construction techniques. Approximately a quarter of new electrical machines are driven by variable-speed drives. These are a less mature product than electrical machines and should see larger efficiency gains over the next 50 years. Advances will occur, with new types of power electronic devices that reduce switching and conduction loss. With variable-speed drives, there is complete freedom to vary the speed of the driven load. Replacing fixed-speed machines with variable-speed drives for a high proportion of industrial loads could mean a 15-30% energy saving. This could save the UK 15 billion kWh of electricity per year which, when combined with motor and drive efficiency gains, would amount to a total annual saving of 24 billion kWh

Convective Heat Transfer Coefficients of Automatic Transmission Fluid Jets with Implications for Electric Machine Thermal Management: Preprint

Energy Technology Data Exchange (ETDEWEB)

Bennion, Kevin; Moreno, Gilberto

2015-09-29

Thermal management for electric machines (motors/ generators) is important as the automotive industry continues to transition to more electrically dominant vehicle propulsion systems. Cooling of the electric machine(s) in some electric vehicle traction drive applications is accomplished by impinging automatic transmission fluid (ATF) jets onto the machine's copper windings. In this study, we provide the results of experiments characterizing the thermal performance of ATF jets on surfaces representative of windings, using Ford's Mercon LV ATF. Experiments were carried out at various ATF temperatures and jet velocities to quantify the influence of these parameters on heat transfer coefficients. Fluid temperatures were varied from 50 degrees C to 90 degrees C to encompass potential operating temperatures within an automotive transaxle environment. The jet nozzle velocities were varied from 0.5 to 10 m/s. The experimental ATF heat transfer coefficient results provided in this report are a useful resource for understanding factors that influence the performance of ATF-based cooling systems for electric machines.

Machinability of Al 6061 Deposited with Cold Spray Additive Manufacturing

Science.gov (United States)

Aldwell, Barry; Kelly, Elaine; Wall, Ronan; Amaldi, Andrea; O'Donnell, Garret E.; Lupoi, Rocco

2017-10-01

Additive manufacturing techniques such as cold spray are translating from research laboratories into more mainstream high-end production systems. Similar to many additive processes, finishing still depends on removal processes. This research presents the results from investigations into aspects of the machinability of aluminum 6061 tubes manufactured with cold spray. Through the analysis of cutting forces and observations on chip formation and surface morphology, the effect of cutting speed, feed rate, and heat treatment was quantified, for both cold-sprayed and bulk aluminum 6061. High-speed video of chip formation shows changes in chip form for varying material and heat treatment, which is supported by the force data and quantitative imaging of the machined surface. The results shown in this paper demonstrate that parameters involved in cold spray directly impact on machinability and therefore have implications for machining parameters and strategy.

[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.

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

Energy Technology Data Exchange (ETDEWEB)

Chen, Anyuan

2011-01-15

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

Research in the field of development of a new generation of metal-processing machines

Directory of Open Access Journals (Sweden)

L.Tanovič

2017-12-01

Full Text Available The paper presents a review of research in the field of development of a new generation of metal-working machines for manufacturing high-precision products with dimensions of several micrometers from various materials. Presented are several new, newly created micro-machines that have the ability to manufacture products of a wide range. Studies in the field of metalworking machines and robots of a new generation with parallel kinematics, creation of machining systems for multi-axis machining, improvement and application of controllable systems, testing of processing systems in production conditions, which are conducted at the Machine-Building Faculty of the Belgrade University (Serbia are considered.

Linear Parameter Varying Control of Induction Motors

DEFF Research Database (Denmark)

Trangbæk, Klaus

The subject of this thesis is the development of linear parameter varying (LPV) controllers and observers for control of induction motors. The induction motor is one of the most common machines in industrial applications. Being a highly nonlinear system, it poses challenging control problems...... for high performance applications. This thesis demonstrates how LPV control theory provides a systematic way to achieve good performance for these problems. The main contributions of this thesis are the application of the LPV control theory to induction motor control as well as various contributions...

Calibration device for wide range monitor

International Nuclear Information System (INIS)

Kodoku, Masaya; Sato, Toshifumi.

1989-01-01

The calibration device for a wide range monitor according to the present invention can continuously calibrate the entire counting regions of a wide range monitor. The wide range monitor detect the reactor power in the neutron source region by means of a pulse counting method and detects the reactor power in the intermediate region by means of a cambell method. A calibration signal outputting means is disposed for continuously outputting, as such calibration signals, pulse number varying signals in which the number of pulses per unit time varies depending on the reactor power in the neutron source region to be simulated and amplitude square means varying signal in which the mean square value of amplitude varies depending on the reactor power in the intermediate region to be simulated. By using both of the calibration signals, calibration can be conducted for the nuclear reactor power in the neutron source region and the intermediate region even if the calibration is made over two regions, further, calibration for the period present over the two region can be conducted easily as well. (I.S.)

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare.

Science.gov (United States)

Mozaffari-Kermani, Mehran; Sur-Kolay, Susmita; Raghunathan, Anand; Jha, Niraj K

2015-11-01

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

Study of a Variable Mass Atwood's Machine Using a Smartphone

Science.gov (United States)

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

2018-01-01

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…

High speed operation of permanent magnet machines

Science.gov (United States)

El-Refaie, Ayman M.

investigated. A 6kW, 36slot/30pole prototype SPM machine has been designed and built. Experimental measurements have been used to verify the analytical and FEA results. These test results have demonstrated that wide constant-power speed range can be achieved. Other important machine features such as the near-sinusoidal back-emf, high efficiency, and low cogging torque have also been demonstrated.

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

Science.gov (United States)

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

2018-03-20

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

Torque capability improvement of sensorless FOC induction machine in field weakening for propulsion purposes

Directory of Open Access Journals (Sweden)

Nisha G.K.

2017-05-01

Full Text Available An electric propulsion system is generally based on torque controlled electric drive and DC series motors are traditionally used for propulsion system. Induction machines, which are reliable, low cost and have less maintenance, satisfy the characteristics of the propulsion and reinstating the DC series motor. Field oriented control (FOC of induction machines can decouple its torque control from field control which allows the induction motor to act like a separately excited DC motor. In this paper, the characteristic control of induction motor is achieved through appropriate design modification of induction motor by varying magnetizing current to produce maximum torque in field weakening (FW region. Thus to improve the torque capability of induction machine in FW region by varying machine parameters. The sensorless operation of the induction motor is carried out by adopting model reference adaptive system (MRAS using sliding mode control (SMC and a FW algorithm based on the voltage and current constraints. The simulation of the induction motor drive models based on the design options have been carried out and analyzed the simulation results.

Wired World-Wide Web Interactive Remote Event Display

Energy Technology Data Exchange (ETDEWEB)

De Groot, Nicolo

2003-05-07

WIRED (World-Wide Web Interactive Remote Event Display) is a framework, written in the Java{trademark} language, for building High Energy Physics event displays. An event display based on the WIRED framework enables users of a HEP collaboration to visualize and analyze events remotely using ordinary WWW browsers, on any type of machine. In addition, event displays using WIRED may provide the general public with access to the research of high energy physics. The recent introduction of the object-oriented Java{trademark} language enables the transfer of machine independent code across the Internet, to be safely executed by a Java enhanced WWW browser. We have employed this technology to create a remote event display in WWW. The combined Java-WWW technology hence assures a world wide availability of such an event display, an always up-to-date program and a platform independent implementation, which is easy to use and to install.

Machine Learning in Medicine.

Science.gov (United States)

Deo, Rahul C

2015-11-17

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. © 2015 American Heart Association, Inc.

Machine Learning in Medicine

Science.gov (United States)

Deo, Rahul C.

2015-01-01

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

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 ...

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

Science.gov (United States)

Martin, Laura M. W.; Beach, King

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

Target fabrication using laser and spark erosion machining

International Nuclear Information System (INIS)

Clement, X.; Coudeville, A.; Eyharts, P.; Perrine , J.P.; Rouillard, R.

1981-11-01

Lasers and E.D.M. (electrical discharge machining) are both extremely useful tools for machining the small targets needed in inertial confinement studies. Lasers are currently used in a wide range of target problems and it appears that E.D.M. has a still wider range of applications for plane and spherical targets. The problems of material deformation and tool breaking are practically eliminated as the electrode and the machined part are not in mechanical contact. In comparison with laser micromachining E.D.M. offers: larger versatility with the possibility of new developments and applications; higher production speed for thin conducting materials; lower initial and operating costs; the processes are well controlled, reproducible and can be easily automated; the operation is safe without the dangers associted with lasers

Influence of electrical resistivity and machining parameters on electrical discharge machining performance of engineering ceramics.

Science.gov (United States)

Ji, Renjie; Liu, Yonghong; Diao, Ruiqiang; Xu, Chenchen; Li, Xiaopeng; Cai, Baoping; Zhang, Yanzhen

2014-01-01

Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge

Bypassing the Kohn-Sham equations with machine learning.

Science.gov (United States)

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Machine Translation from Text

Science.gov (United States)

Habash, Nizar; Olive, Joseph; Christianson, Caitlin; McCary, John

Machine translation (MT) from text, the topic of this chapter, is perhaps the heart of the GALE project. Beyond being a well defined application that stands on its own, MT from text is the link between the automatic speech recognition component and the distillation component. The focus of MT in GALE is on translating from Arabic or Chinese to English. The three languages represent a wide range of linguistic diversity and make the GALE MT task rather challenging and exciting.

Vending machine assessment methodology. A systematic review.

Science.gov (United States)

Matthews, Melissa A; Horacek, Tanya M

2015-07-01

The nutritional quality of food and beverage products sold in vending machines has been implicated as a contributing factor to the development of an obesogenic food environment. How comprehensive, reliable, and valid are the current assessment tools for vending machines to support or refute these claims? A systematic review was conducted to summarize, compare, and evaluate the current methodologies and available tools for vending machine assessment. A total of 24 relevant research studies published between 1981 and 2013 met inclusion criteria for this review. The methodological variables reviewed in this study include assessment tool type, study location, machine accessibility, product availability, healthfulness criteria, portion size, price, product promotion, and quality of scientific practice. There were wide variations in the depth of the assessment methodologies and product healthfulness criteria utilized among the reviewed studies. Of the reviewed studies, 39% evaluated machine accessibility, 91% evaluated product availability, 96% established healthfulness criteria, 70% evaluated portion size, 48% evaluated price, 52% evaluated product promotion, and 22% evaluated the quality of scientific practice. Of all reviewed articles, 87% reached conclusions that provided insight into the healthfulness of vended products and/or vending environment. Product healthfulness criteria and complexity for snack and beverage products was also found to be variable between the reviewed studies. These findings make it difficult to compare results between studies. A universal, valid, and reliable vending machine assessment tool that is comprehensive yet user-friendly is recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

CrN-based wear resistant hard coatings for machining and forming tools

Energy Technology Data Exchange (ETDEWEB)

Yang, S; Cooke, K E; Teer, D G [Teer Coatings Ltd, West Stone House, Berry Hill Industrial Estate, Droitwich, Worcestershire WR9 9AS (United Kingdom); Li, X [School of Metallurgy and Materials, University of Birmingham, Birmingham B15 2TT (United Kingdom); McIntosh, F [Rolls-Royce plc, Inchinnan, Renfrewshire PA4 9AF, Scotland (United Kingdom)

2009-05-21

Highly wear resistant multicomponent or multilayer hard coatings, based on CrN but incorporating other metals, have been developed using closed field unbalanced magnetron sputter ion plating technology. They are exploited in coated machining and forming tools cutting and forming of a wide range of materials in various application environments. These coatings are characterized by desirable properties including good adhesion, high hardness, high toughness, high wear resistance, high thermal stability and high machining capability for steel. The coatings appear to show almost universal working characteristics under operating conditions of low and high temperature, low and high machining speed, machining of ordinary materials and difficult to machine materials, and machining under lubricated and under minimum lubricant quantity or even dry conditions. These coatings can be used for cutting and for forming tools, for conventional (macro-) machining tools as well as for micromachining tools, either as a single coating or in combination with an advanced, self-lubricating topcoat.

Electromagnetic interference of implantable cardiac devices from a shoulder massage machine.

Science.gov (United States)

Yoshida, Saeko; Fujiwara, Kousaku; Kohira, Satoshi; Hirose, Minoru

2014-09-01

Shoulder massage machines have two pads that are driven by solenoid coils to perform a per cussive massage on the shoulders. There have been concerns that such machines might create electromagnetic interference (EMI) in implantable cardiac devices because of the time-varying magnetic fields produced by the alternating current in the solenoid coils. The objective of this study was to investigate the potential EMI from one such shoulder massage machine on implantable cardiac devices. We measured the distribution profile of the magnetic field intensity around the massage machine. Furthermore, we performed an inhibition test and an asynchronous test on an implantable cardiac pacemaker using the standardized Irnich human body model. We examined the events on an implantable cardioverter-defibrillator (ICD) using a pacemaker programmer while the massage machine was in operation. The magnetic field distribution profile exhibited a peak intensity of 212 (A/m) in one of the solenoid coils. The maximal interference distance between the massage machine and the implantable cardiac pacemaker was 28 cm. Ventricular fibrillation was induced when the massage machine was brought near the electrode of the ICD and touched the Irnich human body model. It is necessary to provide a "don't use" warning on the box or the exterior of the massage machines or in the user manuals and to caution patients with implanted pacemakers about the dangers and appropriate usage of massage machines.

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.

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.

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.

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

Science.gov (United States)

Lin, Y.; Zhang, W. J.

2005-02-01

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

Chapter 10: Mining genome-wide genetic markers.

Directory of Open Access Journals (Sweden)

Xiang Zhang

Full Text Available Genome-wide association study (GWAS aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1 An introduction to the background of GWAS. (2 The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3 The limitations of current approaches and future directions.

On the issue of particularities of design and simulation of welded constructions of machine's basic parts

OpenAIRE

Mihaylov, V.; Porhynov, S.

2010-01-01

In current conditions of machine industry competitive strength of firms effectively to make the process equipment has an essential value. It predetermines the necessity of wide application of welded constructions of machine tools basic parts. The solution of this task involves development o technique permitting to calculate and optimize the basic parts of machine tools depending on conditions in each case. In the present paper the results of research of deflected mode of machine tool basic pa...

SU-F-T-521: Flattening-Filter-Free Beam Parameters Comparison From Different Linac Machine Types

Energy Technology Data Exchange (ETDEWEB)

Hussain, A [King Faisal Specialist Hospital, Riyadh, Saudi Arabia, Arkansas Cancer Institute, Pine Bluff, AR (Saudi Arabia)

2016-06-15

Purpose: Novel linac machines, TrueBeam (TB) and Elekta Versa have updated head designing and software control system, include flattening-filter-free (FFF) photon and electron beams. Later on FFF beams were also introduced on C-Series machines. In this work FFF beams for same energy 6MV but from different machine versions were studied with reference to beam data parameters. Methods: The 6MV-FFF percent depth doses, profile symmetry and flatness, dose rate tables, and multi-leaf collimator (MLC) transmission factors were measured during commissioning process of both C-series and Truebeam machines. The scanning and dosimetric data for 6MV-FFF beam from Truebeam and C-Series linacs was compared. A correlation of 6MV-FFF beam from Elekta Versa with that of Varian linacs was also found. Results: The scanning files were plotted for both qualitative and quantitative analysis. The dosimetric leaf gap (DLG) for C-Series 6MV-FFF beam is 1.1 mm. Published values for Truebeam dosimetric leaf gap is 1.16 mm. 6MV MLC transmission factor varies between 1.3 % and 1.4 % in two separate measurements and measured DLG values vary between 1.32 mm and 1.33 mm on C-Series machine. MLC transmission factor from C-Series machine varies between 1.5 % and 1.6 %. Some of the measured data values from C-Series FFF beam are compared with Truebeam representative data. 6MV-FFF beam parameter values like dmax, OP factors, beam symmetry and flatness and additional parameters for C-Series and Truebeam liancs will be presented and compared in graphical form and tabular data form if selected. Conclusion: The 6MV flattening filter (FF) beam data from C-Series & Truebeam and 6MV-FFF beam data from Truebeam has already presented. This particular analysis to compare 6MV-FFF beam from C-Series and Truebeam provides opportunity to better elaborate FFF mode on novel machines. It was found that C-Series and Truebeam 6MV-FFF dosimetric and beam data was quite similar.

Machine Selection in A Dairy Product Company with Entropy and SAW Method Integration

Directory of Open Access Journals (Sweden)

Aşkın Özdağoğlu

2017-07-01

Full Text Available Machine selection is an important and difficult process for the firms, and its results may generate more problems than anticipated. In order to find the best alternative, managers should define the requirements of the factory and determine the necessary criteria. On the other hand, the decision making criteria in order to choose the right equipment may vary according to the type of the manufacturing facility, market requirements, and consumer assigned criteria. This study aims to find the best machine alternative  among  the three machine offerings according to twelve evaluation criteria by integrating entropy method with SAW method.

Machine Induced Experimental Background Conditions in the LHC

CERN Document Server

Levinsen, Yngve Inntjore; Stapnes, Steinar

2012-09-19

The Large Hadron Collider set a new energy record for particle accelerators in late 2009, breaking the previous record held by Tevatron of 2 TeV collision energy. The LHC today operates at a collision energy of 7 TeV. With higher beam energy and intensity, measures have to be taken to ensure optimal experimental conditions and safety of the machine and detectors. Machine induced experimental background can severely reduce the quality of experimental triggers and track reconstruction. In a worst case, the radiation levels can be damaging for some of the subdetectors. The LHC is a particular challenge in this regard due to the vastly different operating conditions of the different experiments. The nominal luminosity varies by four orders of magnitude. The unprecedented stored beam energy and the amount of superconducting elements can make it challenging to protect the accelerator itself as well. In this work we have simulated and measured the machine induced background originating from various sources: the beam...

Quantum machine learning for quantum anomaly detection

Science.gov (United States)

Liu, Nana; Rebentrost, Patrick

2018-04-01

Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.

2015 International Conference on Machine Learning and Signal Processing

CERN Document Server

Woo, Wai; Sulaiman, Hamzah; Othman, Mohd; Saat, Mohd

2016-01-01

This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learni...

Bearing Condition Recognition and Degradation Assessment under Varying Running Conditions Using NPE and SOM

Directory of Open Access Journals (Sweden)

Shaohui Zhang

2014-01-01

Full Text Available Manifold learning methods have been widely used in machine condition monitoring and fault diagnosis. However, the results reported in these studies focus on the machine faults under stable loading and rotational speeds, which cannot interpret the practical machine running. Rotating machine is always running under variable speeds and loading, which makes the vibration signal more complicated. To address such concern, the NPE (neighborhood preserving embedding is applied for bearing fault classification. Compared with other algorithms (PCA, LPP, LDA, and ISOP, the NPE performs well in feature extraction. Since the traditional time domain signal denoising is time consuming and memory consuming, we denoise the signal features directly in feature space. Furthermore, NPE and SOM (self-organizing map are combined to assess the bearing degradation performance. Simulation and experiment results validate the effectiveness of the proposed method.

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

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

A new accurate curvature matching and optimal tool based five-axis machining algorithm

International Nuclear Information System (INIS)

Lin, Than; Lee, Jae Woo; Bohez, Erik L. J.

2009-01-01

Free-form surfaces are widely used in CAD systems to describe the part surface. Today, the most advanced machining of free from surfaces is done in five-axis machining using a flat end mill cutter. However, five-axis machining requires complex algorithms for gouging avoidance, collision detection and powerful computer-aided manufacturing (CAM) systems to support various operations. An accurate and efficient method is proposed for five-axis CNC machining of free-form surfaces. The proposed algorithm selects the best tool and plans the tool path autonomously using curvature matching and integrated inverse kinematics of the machine tool. The new algorithm uses the real cutter contact tool path generated by the inverse kinematics and not the linearized piecewise real cutter location tool path

Machine Learning for Quantification of Small Vessel Disease Imaging Biomarkers

NARCIS (Netherlands)

Ghafoorian, M.

2018-01-01

This thesis is devoted to developing fully automated methods for quantification of small vessel disease imaging bio-markers, namely WMHs and lacunes, using vari- ous machine learning/deep learning and computer vision techniques. The rest of the thesis is organized as follows: Chapter 2 describes

Media-Augmented Exercise Machines

Science.gov (United States)

Krueger, T.

2002-01-01

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

Prediction of tunnel boring machine performance using machine and rock mass data

International Nuclear Information System (INIS)

Dastgir, G.

2012-01-01

too low or too high. An empirical linear model for advance rate was predicted with a high accuracy. On the other hand, in Hemerwald tunnel thrust and AR have the same correlation as in Hieflau. A significant correlation between machine parameters and rock mass properties was found. An empirical linear equation with great accuracy was achieved to predict AR as a function of different rock mass properties and machine parameters. After analyzing the data from seven tunnel sites, based on rock strength, fracture class and behavior of thrust versus advance rate (AR), seven case histories have been divided into two major groups. Group one consists of Hieflau, Hemerwald, Maen and Pieve tunnels. Rock mass strata mainly comprise of Limestone, Schistose-Gneiss, Micaschist and Meta-granite. For group one rock strength (UCS) ranges from 162-226 MPa, that contains high strength rocks. In this group AR decreases linearly with increase of thrust. Reason for this trend is very clear from data analysis that is due to very high strength, presence of less joints and very low fracture class. For high strength rocks a prediction model for AR may be used with slight variations from case to case. On other hand group two comprises Queen water tunnel, Vereina and Varzo tunnels. Rock mass strata mainly consists of Micaschists and Gneiss. Rock strength (UCS) varies between 55 - 162 MPa, which is low to medium strength rocks. In group two AR linearly increases with increase of TBM thrust. The reason is low rock strength and presence of medium to high frequency of joints and a high rock fracture class. For the low strength rocks, another AR prediction model is suggested. (author) [de

Load bearing and deformation behaviour of dynamically loaded wide plate specimens

International Nuclear Information System (INIS)

Julisch, P.; Haedrich, H.J.; Stadtmueller, W.; Sturm, D.

1989-01-01

For the testing of large-scale specimens, a 12 MN-High Loading Rate Tensile Testing Machine was designed and built at MPA Stuttgart. The aim was to determine the influence of high loading rates on the stress and strain behaviour of unwelded and welded components of ferritic and austenitic materials. This new generation of testing machines is driven by a propellant charge, and generates a maximum tensile force of 12 MN with a piston velocity of 25 m/s after a stroke of 20 mm, or a maximum velocity of 60 m/s after a stroke of 400 mm. In a first test programme, welded and unwelded wide plate specimens made of material X 6 CrNi 18 11 were tested at room temperature with different strain rates from 10 -3 /s to 63/s. In addition to a description of the 12 MN-High Loading Rate Tensile Testing Machine, the results of the high loading rate tensile tests performed will be presented and compared with quasistatically tested wide plate specimens. (orig.)

Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

OpenAIRE

Zhang, James; Vukotic, Ilija; Gardner, Robert

2018-01-01

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT...

Inverse Problems in Geodynamics Using Machine Learning Algorithms

Science.gov (United States)

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

2018-01-01

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

Evaluation of Machine Learning Methods for LHC Optics Measurements and Corrections Software

CERN Document Server

AUTHOR|(CDS)2206853; Henning, Peter

The field of artificial intelligence is driven by the goal to provide machines with human-like intelligence. However modern science is currently facing problems with high complexity that cannot be solved by humans in the same timescale as by machines. Therefore there is a demand on automation of complex tasks. To identify the category of tasks which can be performed by machines in the domain of optics measurements and correction on the Large Hadron Collider (LHC) is one of the central research subjects of this thesis. The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In High Energy Physics these concepts are mostly used in offline analysis of experiments data and to perform regression tasks. In Accelerator Physics the machine learning approach has not found a wide application yet. Therefore potential tasks for machine learning solutions can be specified in this domain. The appropriate methods and their suitability for...

Efficacy of Nanocutting Fluids in Machining-an Experimental Investigation

Directory of Open Access Journals (Sweden)

Vamsi Krishna Pasam

2018-01-01

Full Text Available This paper presents the experimental investigations on the performance of eco-friendly vegetable oil based nanofluids in turning operation. In order to assess the quality of nano cutting fluids used during machining, cutting temperatures, cutting forces, tool wear and surface roughness under constant cutting conditions are measured. The influence of two types of nanofluids prepared from nano boric acid (NBA and carbon nanotubes (CNT mixed separately with coconut oil (CC, on machining performance during turning operation is examined. Comparative analysis of the results obtained is done under dry, soluble oil (SL and lubricant environments at constant cutting conditions and 0.25% nano particle inclusions (NPI. To understand the influence of NPI experiments were conducted using CCNBA and CCCNT at varying NPI as well. Results obtained using cutting fluids prepared from vegetable oil based nanofluids are encouraging and more pronouncing by the application of CCCNT at machining zone. The extent of improvement in reduction of cutting temperatures, main cutting force, tool wear and surface roughness is tracked to be 13%, 37.5%, 44% and 40% respectively by the application of CCCNT compared to dry machining. It was found that application of CCNBA and CCCNT at 0.5% NPI is more effective in improving the machining performance.

Tool feed influence on the machinability of CO(2) laser optics.

Science.gov (United States)

Arnold, J B; Steger, P J; Saito, T T

1975-08-01

Influence of tool feed on reflectivity of diamond-machined surfaces was evaluated using materials (gold, silver, and copper) from which CO(2) laser optics are primarily produced. Fifteen specimens were machined by holding all machining parameters constant, except tool feed. Tool feed was allowed to vary by controlled amounts from one evaluation zone (or part) to another. Past experience has verified that the quality of a diamond-machined surface is not a function of the cutting velocity; therefore, this experiment was conducted on the basis that a variation in cutting velocity was not an influencing factor on the diamondturning process. Inspection results of the specimens indicated that tool feeds significantly higher than 5.1 micro/rev (200 microin./rev) produced detrimental effects on the machined surfaces. In some cases, at feeds as high as 13 microm/rev (500 microin./rev), visible scoring was evident. Those surfaces produced with tool feeds less than 5.1 microm/rev had little difference in reflectivity. Measurements indicat d that their reflectivity existed in a range from 96.7% to 99.3% at 10.6 microm.

Machine Learning for Mapping Groundwater Salinity with Oil Well Log Data

Science.gov (United States)

Chang, W. H.; Shimabukuro, D.; Gillespie, J. M.; Stephens, M.

2016-12-01

An oil field may have thousands of wells with detailed petrophysical logs, and far fewer direct measurements of groundwater salinity. Can the former be used to extrapolate the latter into a detailed map of groundwater salinity? California Senate Bill 4, with its requirement to identify Underground Sources of Drinking Water, makes this a question worth answering. A well-known obstacle is that the basic petrophysical equations describe ideal scenarios ("clean wet sand") and even these equations contain many parameters that may vary with location and depth. Accounting for other common scenarios such as high-conductivity shaly sands or low-permeability diatomite (both characteristic of California's Central Valley) causes parameters to proliferate to the point where the model is underdetermined by the data. When parameters outnumber data points, however, is when machine learning methods are most advantageous. We present a method for modeling a generic oil field, where groundwater salinity and lithology are depth series parameters, and the constants in petrophysical equations are scalar parameters. The data are well log measurements (resistivity, porosity, spontaneous potential, and gamma ray) and a small number of direct groundwater salinity measurements. Embedded in the model are petrophysical equations that account for shaly sand and diatomite formations. As a proof of concept, we feed in well logs and salinity measurements from the Lost Hills Oil Field in Kern County, California, and show that with proper regularization and validation the model makes reasonable predictions of groundwater salinity despite the large number of parameters. The model is implemented using Tensorflow, which is an open-source software released by Google in November, 2015 that has been rapidly and widely adopted by machine learning researchers. The code will be made available on Github, and we encourage scrutiny and modification by machine learning researchers and hydrogeologists alike.

Reducing the uncertainty in robotic machining by modal analysis

Science.gov (United States)

Alberdi, Iñigo; Pelegay, Jose Angel; Arrazola, Pedro Jose; Ørskov, Klaus Bonde

2017-10-01

The use of industrial robots for machining could lead to high cost and energy savings for the manufacturing industry. Machining robots offer several advantages respect to CNC machines such as flexibility, wide working space, adaptability and relatively low cost. However, there are some drawbacks that are preventing a widespread adoption of robotic solutions namely lower stiffness, vibration/chatter problems and lower accuracy and repeatability. Normally due to these issues conservative cutting parameters are chosen, resulting in a low material removal rate (MRR). In this article, an example of a modal analysis of a robot is presented. For that purpose the Tap-testing technology is introduced, which aims at maximizing productivity, reducing the uncertainty in the selection of cutting parameters and offering a stable process free from chatter vibrations.

A robust PSSs design using PSO in a multi-machine environment

Energy Technology Data Exchange (ETDEWEB)

Shayeghi, H., E-mail: hshayeghi@gmail.co [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Safari, A.; Aghmasheh, R. [Technical Engineering Department, Zanjan University, Zanjan (Iran, Islamic Republic of)

2010-04-15

In this paper, multi-objective design of multi-machine power system stabilizers (PSSs) using particle swarm optimization (PSO) is proposed. The potential of the proposed approach for optimal setting of the widely used conventional lead-lag PSSs has been investigated. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electromechanical modes of all machines to a prescribed zone in the s-plane. The PSSs parameters tuning problem is converted to an optimization problem with the eigenvalue-based multi-objective function comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes, which is solved by a PSO algorithm which has a strong ability to find the most optimistic results. The robustness of the proposed PSO-based PSSs (PSOPSS) is verified on a multi-machine power system under different operating conditions and disturbances. The results of the proposed PSOPSS are compared with the genetic algorithm based tuned PSS and classical PSSs through eigenvalue analysis, nonlinear time-domain simulation and some performance indices to illustrate its robust performance for a wide range of loading conditions.

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.

High-speed micro-electro-discharge machining.

Energy Technology Data Exchange (ETDEWEB)

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

2005-09-01

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

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)

Defining the Post-Machined Sub-surface in Austenitic Stainless Steels

Science.gov (United States)

Srinivasan, N.; Sunil Kumar, B.; Kain, V.; Birbilis, N.; Joshi, S. S.; Sivaprasad, P. V.; Chai, G.; Durgaprasad, A.; Bhattacharya, S.; Samajdar, I.

2018-06-01

Austenitic stainless steels grades, with differences in chemistry, stacking fault energy, and thermal conductivity, were subjected to vertical milling. Anodic potentiodynamic polarization was able to differentiate (with machining speed/strain rate) between different post-machined sub-surfaces in SS 316L and Alloy A (a Cu containing austenitic stainless steel: Sanicroe 28™), but not in SS 304L. However, such differences (in the post-machined sub-surfaces) were revealed in surface roughness, sub-surface residual stresses and misorientations, and in the relative presence of sub-surface Cr2O3 films. It was shown, quantitatively, that higher machining speed reduced surface roughness and also reduced the effective depths of the affected sub-surface layers. A qualitative explanation on the sub-surface microstructural developments was provided based on the temperature-dependent thermal conductivity values. The results herein represent a mechanistic understanding to rationalize the corrosion performance of widely adopted engineering alloys.

Predicting the dissolution kinetics of silicate glasses using machine learning

Science.gov (United States)

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

2018-05-01

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

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.

Defining the Post-Machined Sub-surface in Austenitic Stainless Steels

Science.gov (United States)

Srinivasan, N.; Sunil Kumar, B.; Kain, V.; Birbilis, N.; Joshi, S. S.; Sivaprasad, P. V.; Chai, G.; Durgaprasad, A.; Bhattacharya, S.; Samajdar, I.

2018-04-01

Austenitic stainless steels grades, with differences in chemistry, stacking fault energy, and thermal conductivity, were subjected to vertical milling. Anodic potentiodynamic polarization was able to differentiate (with machining speed/strain rate) between different post-machined sub-surfaces in SS 316L and Alloy A (a Cu containing austenitic stainless steel: Sanicroe 28™), but not in SS 304L. However, such differences (in the post-machined sub-surfaces) were revealed in surface roughness, sub-surface residual stresses and misorientations, and in the relative presence of sub-surface Cr2O3 films. It was shown, quantitatively, that higher machining speed reduced surface roughness and also reduced the effective depths of the affected sub-surface layers. A qualitative explanation on the sub-surface microstructural developments was provided based on the temperature-dependent thermal conductivity values. The results herein represent a mechanistic understanding to rationalize the corrosion performance of widely adopted engineering alloys.

Kernel Methods for Machine Learning with Life Science Applications

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie

Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

Summary of Working Group 3: Machine Design and R and D

International Nuclear Information System (INIS)

Ohmori, C.; Kirk, H.; Haseroth, H.

2006-01-01

Various topics on machine studies and component R and D for Neutrino Factory subsystems were discussed in Working Group 3 (WG3) at the NuFact05 Workshop. In this working group, 29 talks and 2 posters were presented. We also had special focus sessions for lively discussions on several important issues; targets, cooling, proton driver, FFAGs and acceleration, and a World Wide Design study. Joint Sessions were held with WG1 and WG4 on machine requirements

Advanced induction machine model in phase coordinates for wind turbine applications

DEFF Research Database (Denmark)

Fajardo, L.A.; Iov, F.; Hansen, Anca Daniela

2007-01-01

In this paper an advanced phase coordinates squirrel cage induction machine model with time varying electrical parameters affected by magnetic saturation and rotor deep bar effects, is presented. The model uses standard data sheet for characterization of the electrical parameters, it is developed...

An analysis of switching and non-switching slot machine player behaviour.

Science.gov (United States)

Coates, Ewan; Blaszczynski, Alex

2013-12-01

Learning theory predicts that, given the repeated choice to bet between two concurrently available slot machines, gamblers will learn to bet more money on the machine with higher expected return (payback percentage) or higher win probability per spin (volatility). The purpose of this study was to investigate whether this occurs when the two machines vary orthogonally on payback percentage and volatility. The sample comprised 52 first year psychology students (mean age = 20.3 years, 20 females, 32 males) who had played a gaming machine at least once in the previous 12 months. Participants were administered a battery of questionnaires designed to assess level of knowledge on the characteristics and operation of poker machines, frequency of poker machine play in the past 12 months, personality traits of impulsivity and capacity for cognitive reflection, and gambling beliefs. For the experimental task, participants were instructed to play on two PC-simulated electronic gaming machines (EGMs or slot machines) that differed on payback percentage and volatility, with the option of freely switching between EGMs after a practice phase. Results indicated that participants were able to easily discriminate between machines and manifested a preference to play machines offering higher payback or volatility. These findings diverged from previous findings of no preference for play on higher payback/volatility machines, potentially due to of the current study's absence of the option to make multi-line and multi-credit bets. It was concluded that return rate parameters like payback percentage and volatility strongly influenced slot machine preference in the absence of betting options like multi-line bets, though more research is needed to determine the effects of such betting options on player distribution of money between multiple EGMs.

Genome-wide transcriptome analysis of soybean primary root under varying water-deficit conditions.

Science.gov (United States)

Song, Li; Prince, Silvas; Valliyodan, Babu; Joshi, Trupti; Maldonado dos Santos, Joao V; Wang, Jiaojiao; Lin, Li; Wan, Jinrong; Wang, Yongqin; Xu, Dong; Nguyen, Henry T

2016-01-15

Soybean is a major crop that provides an important source of protein and oil to humans and animals, but its production can be dramatically decreased by the occurrence of drought stress. Soybeans can survive drought stress if there is a robust and deep root system at the early vegetative growth stage. However, little is known about the genome-wide molecular mechanisms contributing to soybean root system architecture. This study was performed to gain knowledge on transcriptome changes and related molecular mechanisms contributing to soybean root development under water limited conditions. The soybean Williams 82 genotype was subjected to very mild stress (VMS), mild stress (MS) and severe stress (SS) conditions, as well as recovery from the severe stress after re-watering (SR). In total, 6,609 genes in the roots showed differential expression patterns in response to different water-deficit stress levels. Genes involved in hormone (Auxin/Ethylene), carbohydrate, and cell wall-related metabolism (XTH/lipid/flavonoids/lignin) pathways were differentially regulated in the soybean root system. Several transcription factors (TFs) regulating root growth and responses under varying water-deficit conditions were identified and the expression patterns of six TFs were found to be common across the stress levels. Further analysis on the whole plant level led to the finding of tissue-specific or water-deficit levels specific regulation of transcription factors. Analysis of the over-represented motif of different gene groups revealed several new cis-elements associated with different levels of water deficit. The expression patterns of 18 genes were confirmed byquantitative reverse transcription polymerase chain reaction method and demonstrated the accuracy and effectiveness of RNA-Seq. The primary root specific transcriptome in soybean can enable a better understanding of the root response to water deficit conditions. The genes detected in root tissues that were associated with

Machine vision theory, algorithms, practicalities

CERN Document Server

Davies, E R

2005-01-01

In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directl

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

Towards Measuring the Abstractness of State Machines based on Mutation Testing

Directory of Open Access Journals (Sweden)

Thomas Baar

2017-01-01

Full Text Available Abstract. The notation of state machines is widely adopted as a formalism to describe the behaviour of systems. Usually, multiple state machine models can be developed for the very same software system. Some of these models might turn out to be equivalent, but, in many cases, different state machines describing the same system also differ in their level of abstraction. In this paper, we present an approach to actually measure the abstractness level of state machines w.r.t. a given implemented software system. A state machine is considered to be less abstract when it is conceptionally closer to the implemented system. In our approach, this distance between state machine and implementation is measured by applying coverage criteria known from software mutation testing. Abstractness of state machines can be considered as a new metric. As for other metrics as well, a known value for the abstractness of a given state machine allows to assess its quality in terms of a simple number. In model-based software development projects, the abstract metric can help to prevent model degradation since it can actually measure the semantic distance from the behavioural specification of a system in form of a state machine to the current implementation of the system. In contrast to other metrics for state machines, the abstractness cannot be statically computed based on the state machine’s structure, but requires to execute both state machine and corresponding system implementation. The article is published in the author’s wording. 

Effect of processing conditions on residual stress distributions by bead-on-plate welding after surface machining

International Nuclear Information System (INIS)

Ihara, Ryohei; Mochizuki, Masahito

2014-01-01

Residual stress is important factor for stress corrosion cracking (SCC) that has been observed near the welded zone in nuclear power plants. Especially, surface residual stress is significant for SCC initiation. In the joining processes of pipes, butt welding is conducted after surface machining. Residual stress is generated by both processes, and residual stress distribution due to surface machining is varied by the subsequent butt welding. In previous paper, authors reported that residual stress distribution generated by bead on plate welding after surface machining has a local maximum residual stress near the weld metal. The local maximum residual stress shows approximately 900 MPa that exceeds the stress threshold for SCC initiation. Therefore, for the safety improvement of nuclear power plants, a study on the local maximum residual stress is important. In this study, the effect of surface machining and welding conditions on residual stress distribution generated by welding after surface machining was investigated. Surface machining using lathe machine and bead on plate welding with tungsten inert gas (TIG) arc under various conditions were conducted for plate specimens made of SUS316L. Then, residual stress distributions were measured by X-ray diffraction method (XRD). As a result, residual stress distributions have the local maximum residual stress near the weld metal in all specimens. The values of the local maximum residual stresses are almost the same. The location of the local maximum residual stress is varied by welding condition. It could be consider that the local maximum residual stress is generated by same generation mechanism as welding residual stress in surface machined layer that has high yield stress. (author)

Zeeman catastrophe machines as a toolkit for teaching chaos

International Nuclear Information System (INIS)

Nagy, Péter; Tasnádi, Péter

2014-01-01

The investigation of chaotic motions and cooperative systems offers a magnificent opportunity to involve modern physics in the basic course of mechanics taught to engineering students. In this paper, it will be demonstrated that the Zeeman machine can be a versatile and motivating tool for students to acquire introductory knowledge about chaotic motion via interactive simulations. The Zeeman catastrophe machine is a typical example of a quasi-static system with hysteresis. It works in a relatively simple way and its properties can be understood very easily. Since the machine can be built easily and the simulation of its movement is also simple, the experimental investigation and the theoretical description can be connected intuitively. Although the Zeeman machine is known mainly for its quasi-static and catastrophic behaviour, its dynamic properties are also of interest with its typical chaotic features. By means of a periodically driven Zeeman machine, a wide range of chaotic properties of the simple systems can be demonstrated, such as bifurcation diagrams, chaotic attractors, transient chaos, Lyapunov exponents and so on. This paper is organically linked to our website (http://csodafizika.hu/zeeman) where the discussed simulation programs can be downloaded. In a second paper, the novel construction of a network of Zeeman machines will be presented to study the properties of cooperative systems. (paper)

MAINTENANCE PLANNING OF THE SEWING NEEDLES OF SIMPLE SEWING MACHINES

Directory of Open Access Journals (Sweden)

ŞUTEU Marius Darius

2017-05-01

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

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.

Enhancing the Current Automated Teller Machine (ATM) in Nigerian ...

African Journals Online (AJOL)

This is going to be achieved by creating another input device that collects the money into the ATM system, reads its denomination and either saves it or transfers it the required customer ... Keywords: Automated Teller Machine (ATM), Interswitch, Local Area Network (LAN), Wide Area Network (WAN), Telecommunication.

High speed laser cutting machine. Kosoku reza kakoki

Energy Technology Data Exchange (ETDEWEB)

Shinno, N. (Matsushita Electric Industrial Co. Ltd., Kadoma, Osaka (Japan))

1993-11-01

The carbon dioxide gas laser cutting machine is being used widely for from cutting soft steel and stainless steel, etc. to intermetallic welding and in the field of cutting in particular, concerning sheet cutting, it has been changing the existing monopoly of the turret punch press, and as for medium and thick plate cutting, that of the gas plasma fusing device. This article is the general description of high speed laser cutting machine. Concerning the laser cutting (sheet cutting in particular), as the essential items for securing severe cutting accuracy and, at the same time, improving the cutting speed, the following matters are picked up for respective explanation; improvement of stationary machine accuracy, improvement of dynamic machine accuracy, improvement of quality of laser beam as well as optimization of cutting conditions, and shortening of piercing time. Also explanation is given to the respective items, namely speeding-up of medium and thick plate cutting, and reduction of load onto the operator by improved operation. Finally, feeding and removing of a sheet only, and feeding and removing with a pallet are mentioned as the efforts for automation and energy saving. 3 figs., 1 tab.

A variable-mode stator consequent pole memory machine

Science.gov (United States)

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

2018-05-01

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

Fundamental investigation on influence of external heat on chip formation during thermal assisted machining

Science.gov (United States)

Alkali, A. U.; Ginta, T. L.; Abdulrani, A. M.; Elsiti, N. M.

2018-04-01

Various heat sources have been investigated by numerous researchers to reveal machinability benefits of thermally assisted machining (TAM) process. Fewer engineering materials have been tested. In the same vein, those researches continue to demonstrate effective performance of TAM in terms of bulk material removal rate, improved surface finish, prolong tool life and reduction of cutting forces among others. Experimental investigation on the strain-hardenability and flow stress of material removed with respect to increase in temperature in TAM has not been given attention in previous studies. This study investigated the pattern of chip morphology and segmentation giving close attention to influence of external heat source responsible for strain – hardenability of the material removed during TAM and dry machining at room temperature. Full immersion down cut milling was used throughout the machining conditions. Machining was conducted on AISI 316L using uncoated tungsten carbide end mill insert at varying cutting speeds (V) of 50, 79, and 100 m/min, and feed rates (f) of 0.15, 0.25, and 0.4 mm/tooth while the depth of cut was maintained at 0.2mm throughout the machining trials. The analyses of chip formation, segmentations and stain hardenability were carried out by using LMU light microscope, field emission microscopy and micro indentation. The study observed that build up edge is formed when a stagnation zone develops in front of tool tip which give rise to poor thermal gradient for conduction heat to be transferred within the bulk material during dry machining. This promotes varying strain – hardening of the material removed with evident high chips hardness and thickness, whereas TAM circumvents such impairment by softening the shear zone through local preheat.

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.

2nd Machine Learning School for High Energy Physics

CERN Document Server

2016-01-01

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

Machine learning of molecular properties: Locality and active learning

Science.gov (United States)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

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. .

TEACHING MACHINES AND PROGRAMMED LEARNING, A SOURCE BOOK.

Science.gov (United States)

LUMSDAINE, A.A., ED.; GLASER, ROBERT, ED.

BROUGHT TOGETHER HERE IS THE WIDELY-SCATTERED LITERATURE ON SELF-INSTRUCTIONAL PROGRAMS AND DEVICES BY LEADERS, PAST AND PRESENT, IN THEIR DEVELOPMENT. S.L. PRESSEY IN HIS ARTICLES DESCRIBES THE APPARATUS, METHODS, THEORY, AND RESULTS ATTENDANT UPON USE OF HIS TEST-SCORING DEVICES. B.F. SKINNER IN HIS ARTICLES DEVELOPS THEORY, DESCRIBES MACHINES,…

Numerical controlled diamond fly cutting machine for grazing incidence X-ray reflection mirrors

International Nuclear Information System (INIS)

Uchida, Fumihiko; Moriyama, Shigeo; Seya, Eiiti

1992-01-01

Synchrotron radiation has reached the stage of practical use, and the application to the wide fields that support future advanced technologies such as spectroscopy, the structural analysis of matters, semiconductor lithography and medical light source is expected. For the optical system of the equipment utilizing synchrotron radiation, the total reflection mirrors of oblique incidence are used for collimating and collecting X-ray. In order to restrain their optical aberration, nonspherical shape is required, and as the manufacturing method with high precision for nonspherical mirrors, a numerically controlled diamond cutting machine was developed. As for the cutting of soft metals with diamond tools, the high precision machining of any form can be done by numerical control, the machining time can be reduced as compared with grinding, and the cooling effect is large in metals. The construction of the cutting machine, the principle of machining, the control system, the method of calculating numerical control data, the investigation of machinable forms and the result of evaluation are reported. (K.I.)

Surface and canopy fuels vary widely in 24-yr old postfire lodgepole pine forests

Science.gov (United States)

Nelson, K. N.; Turner, M.; Romme, W. H.; Tinker, D. B.

2013-12-01

Extreme fire seasons have become common in western North America, and the extent of young postfire forests has grown as fire frequency and annual area burned have increased. These young forests will set the stage for future fires, but an assessment of fuel loads in young forests is lacking. The rate of fuel re-accumulation and fuels variability in postfire forest landscapes is needed to anticipate future fire occurrence and behavior in the American West. We studied fuel characteristics in young lodgepole pine forests that regenerated after the 1988 fires in Yellowstone National Park to address two questions: (1) How do surface fuel characteristics change with time-since-fire? (2) How do canopy and surface fuels vary across the Yellowstone landscape 24 years postfire? During summer 2012, we re-measured surface fuels in 11 plots that were established in 1996 (8 yrs post fire), and we measured surface and canopy fuels in 82 stands (each 0.25 ha) distributed across the Yellowstone post-1988 fire landscape. In the remeasured plots, surface fuel loads generally increased over the last 16 years. One-hr fuels did not change between sample dates, but all other fuel classes (i.e., 10-hr, 100-hr, and 1000-hr) increased by a factor of two or three. Within the sample timeframe, variability of fuel loads within stands decreased significantly. The coefficients of variation decreased for all fuel classes by 23% to 67%. Data from the 82 plots revealed that canopy and surface fuels in 24-year-old stands varied tremendously across the Yellowstone landscape. Live tree densities spanned 0 to 344,067 trees ha-1, producing a mean available canopy fuel load of 7.7 Mg ha-1 and a wide range from 0 to 47 Mg ha-1. Total surface fuel loads averaged 130 Mg ha-1 and ranged from 49 to 229 Mg ha-1, of which 90% was in the 1000-hr fuel class. The mass of fine surface fuels (i.e., litter/duff, 1-hr, 10-hr, and herbaceous fuels) and canopy fuels (i.e., foliage and 1-hr branches) were strongly and

Surface texturing of Si3N4–SiC ceramic tool components by pulsed laser machining

CSIR Research Space (South Africa)

Tshabalala, LC

2016-03-01

Full Text Available texturing of Si(sub3)N(sub4)–SiC composites in the fabrication of machining tool inserts for various tribological applications. The samples were machined at varied laser energy (0.1–0.6 mJ) and lateral pulse overlap (50–88%) in order to generate a sequence...

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.

Contribution to the design and the control of synchronous double excitation machines: hybrid vehicle application; Contribution a la conception et a la commande des machines synchrones a double excitation: application au vehicule hybride

Energy Technology Data Exchange (ETDEWEB)

Amara, Y

2001-12-01

Double excitation machines are synchronous machines where two excitation circuits coexist: one with permanent magnets and the other with windings. This study shows that double excitation allows to combine the advantages of synchronous machines with winded inductor with those of permanent magnet machines. This concept allows a better dimensioning of the converter-machine set and a better energy management. In order to allow the operation of permanent magnet machines over a wide range of speeds, it is necessary to have a magnetic reaction of the induced circuit of the same order than the excitation flux. On the other hand, the power factor is weaker and the power supply converter is over-dimensioned. The double excitation allows the permanent magnet machines to work over a large speed range with a better power factor, even when the magnetic reaction of the induced circuit is relatively weak with respect to the excitation flux. (J.S.)

Influence of edge radius of sintered-carbide tip on roughness of machined surface

Directory of Open Access Journals (Sweden)

Jiří Votava

2013-01-01

Full Text Available Increasing of cutting speed and thus increasing labour productivity is observed as a current trend in engineering production. This effort results to development of new cutting materials which are more capable to resist increased requirements on machined surface as well as operating life of the instrument. Nowadays, the most widely used materials used for cutting instruments are sintered carbides which are alloyed by other metals. The goal of this paper is to analyse change in quality of machined surface depending on the change of cutting conditions. For cutting operation, there were used a milling cutter high-speed steel 90 (HSS and removable sintered-carbide tips with different radius. Steel 12 050 hardened for 17 HRC was used as a machined material. Firstly, hardness of machined as well as machining materials was analysed. Further, metallographic analysis and measurement of microhardness of the individual structure phases was processed. Cutting conditions of both instruments were selected depending on the machined material. Surface roughness indicates the quality of machined surface.

Machine integrated optical measurement of honed surfaces in presence of cooling lubricant

International Nuclear Information System (INIS)

Schmitt, R; Koenig, N; Zheng, H

2011-01-01

The measurement of honed surfaces is one of the most important tasks in tribology. Although many established techniques exist for texture characterization, such as SEM, tactile stylus or white-light interferometry, none of them is suited for a machine integrated measurement. Harsh conditions such as the presence of cooling lubricant or vibrations prohibit the use of commercial sensors inside a honing machine. Instead, machined engine blocks need time-consuming cleaning and preparation while taken out of the production line for inspection. A full inspection of all produced parts is hardly possible this way. Within this paper, an approach for a machine-integrated measurement is presented, which makes use of optical sensors for texture profiling. The cooling lubricant here serves as immersion medium. The results of test measurements with a chromatic-confocal sensor and a fiber-optical low-coherence interferometer show the potential of both measuring principles for our approach. Cooling lubricant temperature and flow, scanning speed and measurement frequency have been varied in the tests. The sensor with best performance will later be chosen for machine integration.

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.

Machining of AISI D2 Tool Steel with Multiple Hole Electrodes by EDM Process

Science.gov (United States)

Prasad Prathipati, R.; Devuri, Venkateswarlu; Cheepu, Muralimohan; Gudimetla, Kondaiah; Uzwal Kiran, R.

2018-03-01

In recent years, with the increasing of technology the demand for machining processes is increasing for the newly developed materials. The conventional machining processes are not adequate to meet the accuracy of the machining of these materials. The non-conventional machining processes of electrical discharge machining is one of the most efficient machining processes is being widely used to machining of high accuracy products of various industries. The optimum selection of process parameters is very important in machining processes as that of an electrical discharge machining as they determine surface quality and dimensional precision of the obtained parts, even though time consumption rate is higher for machining of large dimension features. In this work, D2 high carbon and chromium tool steel has been machined using electrical discharge machining with the multiple hole electrode technique. The D2 steel has several applications such as forming dies, extrusion dies and thread rolling. But the machining of this tool steel is very hard because of it shard alloyed elements of V, Cr and Mo which enhance its strength and wear properties. However, the machining is possible by using electrical discharge machining process and the present study implemented a new technique to reduce the machining time using a multiple hole copper electrode. In this technique, while machining with multiple holes electrode, fin like projections are obtained, which can be removed easily by chipping. Then the finishing is done by using solid electrode. The machining time is reduced to around 50% while using multiple hole electrode technique for electrical discharge machining.

Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

Science.gov (United States)

Matsuba, Shinji; Tabuchi, Hitoshi; Ohsugi, Hideharu; Enno, Hiroki; Ishitobi, Naofumi; Masumoto, Hiroki; Kiuchi, Yoshiaki

2018-05-09

To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system. First, to evaluate the diagnostic accuracy of DCNN, 364 photographic images (AMD: 137) were amplified and the area under the curve (AUC), sensitivity and specificity were examined. Furthermore, in order to compare the diagnostic abilities between DCNN and six ophthalmologists, we prepared yield 84 sheets comprising 50% of normal and wet-AMD data each, and calculated the correct answer rate, specificity, sensitivity, and response times. DCNN exhibited 100% sensitivity and 97.31% specificity for wet-AMD images, with an average AUC of 99.76%. Moreover, comparing the diagnostic abilities of DCNN versus six ophthalmologists, the average accuracy of the DCNN was 100%. On the other hand, the accuracy of ophthalmologists, determined only by Optos images without a fundus examination, was 81.9%. A combination of DCNN with Optos images is not better than a medical examination; however, it can identify exudative AMD with a high level of accuracy. Our system is considered useful for screening and telemedicine.

Agarwood oil quality classifier using machine learning | Abas ...

African Journals Online (AJOL)

Agarwood oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies and religious prayers. Its quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine ...

Effect of different machining processes on the tool surface integrity and fatigue life

Energy Technology Data Exchange (ETDEWEB)

Cao, Chuan Liang [College of Mechanical and Electrical Engineering, Nanchang University, Nanchang (China); Zhang, Xianglin [School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan (China)

2016-08-15

Ultra-precision grinding, wire-cut electro discharge machining and lapping are often used to machine the tools in fine blanking industry. And the surface integrity from these machining processes causes great concerns in the research field. To study the effect of processing surface integrity on the fine blanking tool life, the surface integrity of different tool materials under different processing conditions and its influence on fatigue life were thoroughly analyzed in the present study. The result shows that the surface integrity of different materials was quite different on the same processing condition. For the same tool material, the surface integrity on varying processing conditions was quite different too and deeply influenced the fatigue life.

Power System Event Ranking Using a New Linear Parameter-Varying Modeling with a Wide Area Measurement System-Based Approach

Directory of Open Access Journals (Sweden)

Mohammad Bagher Abolhasani Jabali

2017-07-01

Full Text Available Detecting critical power system events for Dynamic Security Assessment (DSA is required for reliability improvement. The approach proposed in this paper investigates the effects of events on dynamic behavior during nonlinear system response while common approaches use steady-state conditions after events. This paper presents some new and enhanced indices for event ranking based on time-domain simulation and polytopic linear parameter-varying (LPV modeling of a power system. In the proposed approach, a polytopic LPV representation is generated via linearization about some points of the nonlinear dynamic behavior of power system using wide-area measurement system (WAMS concepts and then event ranking is done based on the frequency response of the system models on the vertices. Therefore, the nonlinear behaviors of the system in the time of fault occurrence are considered for events ranking. The proposed algorithm is applied to a power system using nonlinear simulation. The comparison of the results especially in different fault conditions shows the advantages of the proposed approach and indices.

Chaotic behaviour of Zeeman machines at introductory course of mechanics

Science.gov (United States)

Nagy, Péter; Tasnádi, Péter

2016-05-01

Investigation of chaotic motions and cooperative systems offers a magnificent opportunity to involve modern physics into the basic course of mechanics taught to engineering students. In the present paper it will be demonstrated that Zeeman Machine can be a versatile and motivating tool for students to get introductory knowledge about chaotic motion via interactive simulations. It works in a relatively simple way and its properties can be understood very easily. Since the machine can be built easily and the simulation of its movement is also simple the experimental investigation and the theoretical description can be connected intuitively. Although Zeeman Machine is known mainly for its quasi-static and catastrophic behaviour, its dynamic properties are also of interest with its typical chaotic features. By means of a periodically driven Zeeman Machine a wide range of chaotic properties of the simple systems can be demonstrated such as bifurcation diagrams, chaotic attractors, transient chaos and so on. The main goal of this paper is the presentation of an interactive learning material for teaching the basic features of the chaotic systems through the investigation of the Zeeman Machine.

Chaotic behaviour of Zeeman machines at introductory course of mechanics

International Nuclear Information System (INIS)

Nagy, P.; Tasnádi, P.

2015-01-01

Investigation of chaotic motions and cooperative systems offers a magnificent opportunity to involve modern physics into the basic course of mechanics taught to engineering students. In the present paper it will be demonstrated that Zeeman Machine can be a versatile and motivating tool for students to get introductory knowledge about chaotic motion via interactive simulations. It works in a relatively simple way and its properties can be understood very easily. Since the machine can be built easily and the simulation of its movement is also simple the experimental investigation and the theoretical description can be connected intuitively. Although Zeeman Machine is known mainly for its quasi-static and catastrophic behaviour, its dynamic properties are also of interest with its typical chaotic features. By means of a periodically driven Zeeman Machine a wide range of chaotic properties of the simple systems can be demonstrated such as bifurcation diagrams, chaotic attractors, transient chaos and so on. The main goal of this paper is the presentation of an interactive learning material for teaching the basic features of the chaotic systems through the investigation of the Zeeman Machine. 1. –

Protyping machine vision software on the World Wide Web

Science.gov (United States)

Karantalis, George; Batchelor, Bruce G.

1998-10-01

Interactive image processing is a proven technique for analyzing industrial vision applications and building prototype systems. Several of the previous implementations have used dedicated hardware to perform the image processing, with a top layer of software providing a convenient user interface. More recently, self-contained software packages have been devised and these run on a standard computer. The advent of the Java programming language has made it possible to write platform-independent software, operating over the Internet, or a company-wide Intranet. Thus, there arises the possibility of designing at least some shop-floor inspection/control systems, without the vision engineer ever entering the factories where they will be used. It successful, this project will have a major impact on the productivity of vision systems designers.

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...

Machine learning to analyze images of shocked materials for precise and accurate measurements

Energy Technology Data Exchange (ETDEWEB)

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.; Meehan, B. T.; Ramos, Kyle J.; Bolme, Cindy A.; Sandberg, Richard L.; Nelson, Keith A.

2017-09-14

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

Oil analysis in machine diagnostics

Energy Technology Data Exchange (ETDEWEB)

Vaehaeoja, P.

2006-07-01

This study concentrates on developing and tuning various oil analysis methods to meet the requirements of modern industry and environmental analytics. Oil analysis methods form a vital part of techniques used to monitor the condition of machines and may help to improve the overall equipment effectiveness value of a factory in a significant manner. Worm gears are used in various production machines, and their breakdowns may cause significant production losses. Wearing of these gears is relatively difficult to monitor with vibration analysis. Analysis of two indicator metals, copper and iron, may reveal wearing phenomena of worm gears effectively, and savings can be significant. Effective wear metal analysis requires good tools. ICP-OES with kerosene dilution is widely used in wear metal analysis, but purchasing and using of ICP-OES is expensive. A cheaper FAAS technique with similar pre-treatment of oil samples was tested and it proved to be useful especially in analyzing small amounts of samples. The accuracy of FAAS was sufficient for quantitative work in machine diagnostics and waste oil characterization. Solid debris analyses are useful in oil contamination control as well as in detection of wearing mechanisms. Membrane filtration, optical microscopy, SEM and automatic particle counting were applied in analysis of rolling and gear oils. Particle counting is an effective way to detect oil contamination, but in the studied cases even larger particles than those detected in normal ISO classes would be informative. However, membrane filtration and optical microscopy may reveal the wearing machine element exactly. Additives provide oils with desired properties thus they should be monitored intensively. A FTIR method for quantitative analysis of fatty alcohols and fatty acid esters in machinery oils was developed during this work. It has already been used successfully in quantitative and qualitative analysis of machinery oil samples. Various kinds of oils may be

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

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.

The Basics of Stellites in Machining Perspective

Directory of Open Access Journals (Sweden)

Md Shahanur Hasan

2016-12-01

Full Text Available Stellites are cobalt (Co-based superalloys available in two main combinations: (a a Tungsten (W group with composition of Co-Cr-W-C, and (b a Molybdenum (Mo group containing Co-Cr-Mo-C. Stellites possess outstanding corrosion resistance, oxidation resistance, wear resistance, heat resistance, and low magnetic permeability. Components made of stellites work well in highly corrosive environments and maintain these advantageous properties at elevated temperatures. Components made of stellites are widely used in the oil and gas, automotive, nuclear power, paper and pulp, chemical and petrochemical, refineries, automobile, aerospace and aircraft industries. By virtue of their nonmagnetic, anticorrosive and non-reactivity to human body-fluid properties, stellites are used in medical surgery and in surgical tools, tooth and bone implants and replacements, heart valves, and in heart pacemakers. The hardness range of stellites is from 32 to 55 HRC, which makes stellites brittle materials but they have a low Young’s modulus. Due to their high hardness, dense but non-homogeneous molecular structure and lower thermal conductivity, machining operations for parts made of stellites are extremely difficult, categorising stellites as difficult-to-machine materials like Ti-alloys, inconels, composites and stainless steels. Usually, machine components made of stellites are produced by a deposition method onto steel substrates instead of expensive solid stellite bars. The rough surfaces of deposited stellites are then finished by grinding, rather than some other economic machining process, which is costly and time-consuming, making stellite products very expensive. This paper provides a basic overview of stellites applicable in engineering, their significances and specific applications, advantages and disadvantages in respect of machining processes. A brief review on experimental research on economically rational cutting parameters for turning operations of

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).

Calculation of Airborne Radioactivity Hazard from Machining Volume-Activated Materials

International Nuclear Information System (INIS)

E.T. Marshall; S.O. Schwahn

1997-01-01

When evaluating a task involving the machining of volume-activated materials, accelerator health physicists must consider more than the surface contamination levels of the equipment and containment of loose shavings, dust or filings. Machining operations such as sawing, routing, welding, and grinding conducted on volume-activated material may pose a significant airborne radioactivity hazard to the worker. This paper presents a computer spreadsheet notebook that conservatively estimates the airborne radioactivity levels generated during machining operations performed on volume-activated materials. By knowing (1) the size and type of materials, (2) the dose rate at a given distances, and (3) limited process knowledge, the Derived Air Concentration (DAC) fraction can be estimated. This tool is flexible, taking into consideration that the process knowledge available for the different materials varies. It addresses the two most common geometries: thick plane and circular cylinder. Once the DAC fraction has been estimated, controls can be implemented to mitigate the hazard to the worker

Calculation of airborne radioactivity hazard from machining volume-activated materials

International Nuclear Information System (INIS)

Marshall, E.T.; Schwahn, S.O.

1996-10-01

When evaluating a task involving the machining of volume-activated materials, accelerator health physicists must consider more than the surface contamination levels of the equipment and containment of loose shavings, dust or filings. Machining operations such as sawing, routing, welding, and grinding conducted on volume-activated material may pose a significant airborne radioactivity hazard to the worker. This paper presents a computer spreadsheet notebook that conservatively estimates the airborne radioactivity levels generated during machining operations performed on volume-activated materials. By knowing (1) the size and type of materials, (2) the dose rate at a given distances, and (3) limited process knowledge, the Derived Air Concentration (DAC) fraction can be estimated. This tool is flexible, taking into consideration that the process knowledge available for the different materials varies. It addresses the two most common geometries: thick plane and circular cylinder. Once the DAC fraction has been estimated, controls can be implemented to mitigate the hazard to the worker

Visualizing time-varying harmonics using filter banks

NARCIS (Netherlands)

Duque, C.A.; Da Silveira, P.M.; Ribeiro, P.F.

2011-01-01

Although it is well known that Fourier analysis is in reality only accurately applicable to steady state waveforms, it is a widely used tool to study and monitor time-varying signals, such as are commonplace in electrical power systems. The disadvantages of Fourier analysis, such as frequency

Variance inflation in high dimensional Support Vector Machines

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2013-01-01

Many important machine learning models, supervised and unsupervised, are based on simple Euclidean distance or orthogonal projection in a high dimensional feature space. When estimating such models from small training sets we face the problem that the span of the training data set input vectors...... the case of Support Vector Machines (SVMS) and we propose a non-parametric scheme to restore proper generalizability. We illustrate the algorithm and its ability to restore performance on a wide range of benchmark data sets....... follow a different probability law with less variance. While the problem and basic means to reconstruct and deflate are well understood in unsupervised learning, the case of supervised learning is less well understood. We here investigate the effect of variance inflation in supervised learning including...

The application of machine learning techniques in the clinical drug therapy.

Science.gov (United States)

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Analysis of Unbalanced Magnetic Pull in Wound Rotor Induction Machines using Finite Element Analysis – Transient, Motoring and Generating Modes

DEFF Research Database (Denmark)

Dorrell, David G.; Hermann, Alexander Niels August; Jensen, Bogi Bech

2013-01-01

eccentricity. The operating conditions are varied so that transient, motoring and doubly-fed induction generator modes are studied. This allows greater understanding of the radial forces involved. Wound rotor induction machines exhibit higher unbalanced magnetic pull than cage induction machines so......There has been much literature on unbalanced magnetic pull in various types of electrical machine. This can lead to bearing wear and additional vibrations in the machine. In this paper a wound rotor induction is studied. Finite element analysis studies are conducted when the rotor has 10 % rotor...

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...

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

Science.gov (United States)

Sakiyama, Yojiro

2009-02-01

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

Calibration of diagnostic x-ray machines using radiation exposure and radiographic parameters

International Nuclear Information System (INIS)

Agba, E.H.; Uloko, P. I.; Tyovenda, A. A.

2011-01-01

Calibration of diagnostic x-ray machines using radiation exposure and radiographic parameters of the x-ray machines has been carried out. Three phase diagnostic x-ray machines situated at Federal Medical Centre, Makurdi, General Hospital, Otukpo and Christian Hospital, Mkar were used for the calibration work. The radiation meter was used to measure x-ray radiation exposure. The result of this work demonstrates mR/mAs=C(KV p ) that there exist a power law relation of the form between the radiation exposure and the radiographic parameters of diagnostic x-ray machines, which can be used to estimate patient exposure during routine x-ray diagnostic examinations for wide range of operating parameters. The values of the power exponent n, constant c and total filtrations of the diagnostic x-ray machines have been estimated. These values for the diagnostic x-ray machines at the Federal Medical Centre, Makurdi are: 2.14, 0.88 and 2.77 respectively, for the one at the General Hospital, Otukpo are: 2.07, 0.76 and 2.68 respectively and that of the Christian Hospital, Mkar are: 2.01,0.69 and 2.61 respectively.

Optimization of Support Vector Machine (SVM) for Object Classification

Science.gov (United States)

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

2012-01-01

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

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.

Ultra wide band antennas

CERN Document Server

Begaud, Xavier

2013-01-01

Ultra Wide Band Technology (UWB) has reached a level of maturity that allows us to offer wireless links with either high or low data rates. These wireless links are frequently associated with a location capability for which ultimate accuracy varies with the inverse of the frequency bandwidth. Using time or frequency domain waveforms, they are currently the subject of international standards facilitating their commercial implementation. Drawing up a complete state of the art, Ultra Wide Band Antennas is aimed at students, engineers and researchers and presents a summary of internationally recog

BELM: Bayesian extreme learning machine.

Science.gov (United States)

Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

2011-03-01

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

Simple machines

CERN Document Server

Graybill, George

2007-01-01

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

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

Science.gov (United States)

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

2018-02-01

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

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

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

1998-03-19

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

Superconducting rotating machines

International Nuclear Information System (INIS)

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

1975-01-01

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

Development and evaluation of a threshing machine for Prosopis ...

African Journals Online (AJOL)

A decorticating machine for Prosopis Africana was developed, tested and evaluated. Prosopis Africana is popular in the Sub-Saharan Africa, with all the parts of the tree useful for medicinal purposes. This study focuses on the threshing of the pod to obtain the seed, which is the most widely used of the parts of the Prosopis ...

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.

Turbo-machine deployment of HTR-10 GT

International Nuclear Information System (INIS)

Zhu Shutang; Wang Jie; Zhang Zhengming; Yu Suyuan

2005-01-01

As a testing project of gas turbine modular High Temperature Gas-cooled Reactor (HTGR), HTR-10GT has been studied and developed by Institute of Nuclear and New Energy Technology (INET) of Tsinghua University after the success of HTR-10 with steam turbine cycle. The main purposes of this project are to demonstrate the gas turbine modular HTGR, to optimize the deployment of Power Conversion Unit (PCU) and to verify the techniques of turbo-machine, operating modes and controlling measures. HTR-10GT is concentrated on the PCU design and the turbo-machine deployment. Possible turbo-machine deployments have been investigated and two of them are introduced in this paper. The preliminary design for the turbo-machine of HTR-10GT is single-shaft of vertical layout, arranged by the side of the reactor and the turbo-compressor rotary speed was selected to be 250 s -1 (15000 r/min) by considering the efficiency of turbo-compressor blade systems, the strength conditions and the mass and size characteristics of the turbo-compressor. The rotor system will be supported by electromagnetic bearings (EMBs) to curb the possible pollutions of the primary loop. Of all the components in this design, the high speed turbo-generator seems to be a world-wide technical nut. As an alternative design, a gearbox complex is used to reduce the rotary speed from the turbo-compressor 250 s -1 to 50 s -1 so that the ordinary generator can be used. (authors)

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

Wire Electrical Discharge Machining of a Hybrid Composite: Evaluation of Kerf Width and Surface Roughness

Directory of Open Access Journals (Sweden)

Abdil KUŞ

2016-06-01

Full Text Available In this study, the machinability characteristics of Al/B4C-Gr hybrid composite were investigated using wire electrical discharge machining (WEDM. In the experiments, the machining parameters of wire speed, pulse-on time and pulse-off time were varied in order to explaiın their effects on machining performance, including the width of slit (kerf and surface roughness values (Rz and Rt. According to the Taguchi quality design concept, a L18 (21×32 orthogonal array was used to determine the S/N ratio, and analysis of variance (ANOVA and the F-test were used to indicate the significant machining parameters affecting the machining performance. From the ANOVA and F-test results, the significant factors were determined for each of the machining performance criteria of kerf, Rz and Rt. The variations of kerf, Rz and Rt with the machining parameters were statistically modeled via the regression analysis method. The optimum levels of the control factors for kerf, Rz and Rt were specified as A1B1C1, A1B1C2 and A1B1C2, respectively. The correlation coefficients of the predictive equations developed for kerf, Rz and Rt were calculated as 0.98, 0.828 and 0.855, respectively.

Measurement of properties of a lithium bromide aqueous solution for the determination of the concentration for a prototype absorption machine

International Nuclear Information System (INIS)

Labra, L.; Juárez-Romero, D.; Siqueiros, J.; Coronas, A.; Salavera, D.

2017-01-01

Highlights: • Determination of concentration of absorption mixture for absorption heat transformers. • Measurement of physical properties for heat transformer assessment. • Comparative behavior of Electric conductivity, Refractive index, and density of LiBr-H_2O. - Abstract: An electrolyte solution of Lithium Bromide (LiBr) water was chosen for study because of its wide use in prototype absorption machines. The LiBr must be operated close to the temperature and mass fraction at which lithium bromide achieves the highest efficiency. For the purpose of establishing the concentration in a prototype absorption machines, measurements were made of the properties that vary with temperature and concentration. The selected properties are electrical conductivity, density, refractive indexes and sound velocity. The resulting measured properties values were compared with some values found in previous works. The properties of aqueous lithium bromide solutions were measured at the concentration range of 45–65% of LiBr and temperatures range of 20–80 °C. Semi-empirical correlations that determine the properties of lithium bromide are also proposed. The methods for measuring the properties of aqueous solutions were considered taking into account their reliability, simplicity and sampling time.

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…

Mineral mining machines

Energy Technology Data Exchange (ETDEWEB)

Mc Gaw, B H

1984-01-01

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

Analysis of surface integrity in machining of AISI 304 stainless steel under various cooling and cutting conditions

Science.gov (United States)

Klocke, F.; Döbbeler, B.; Lung, S.; Seelbach, T.; Jawahir, I. S.

2018-05-01

Recent studies have shown that machining under specific cooling and cutting conditions can be used to induce a nanocrystalline surface layer in the workspiece. This layer has beneficial properties, such as improved fatigue strength, wear resistance and tribological behavior. In machining, a promising approach for achieving grain refinement in the surface layer is the application of cryogenic cooling. The aim is to use the last step of the machining operation to induce the desired surface quality to save time-consuming and expensive post machining surface treatments. The material used in this study was AISI 304 stainless steel. This austenitic steel suffers from low yield strength that limits its technological applications. In this paper, liquid nitrogen (LN2) as cryogenic coolant, as well as minimum quantity lubrication (MQL), was applied and investigated. As a reference, conventional flood cooling was examined. Besides the cooling conditions, the feed rate was varied in four steps. A large rounded cutting edge radius and finishing cutting parameters were chosen to increase the mechanical load on the machined surface. The surface integrity was evaluated at both, the microstructural and the topographical levels. After turning experiments, a detailed analysis of the microstructure was carried out including the imaging of the surface layer and hardness measurements at varying depths within the machined layer. Along with microstructural investigations, different topological aspects, e.g., the surface roughness, were analyzed. It was shown that the resulting microstructure strongly depends on the cooling condition. This study also shows that it was possible to increase the micro hardness in the top surface layer significantly.

Direct Drive Synchronous Machine Models for Stability Assessment of Wind Farms

Energy Technology Data Exchange (ETDEWEB)

Poeller, Markus; Achilles, Sebastian [DIgSILENT GmbH, Gomaringen (Germany)

2003-11-01

The increasing size of wind farms requires power system stability analysis including dynamic wind generator models. For turbines above 1MW doubly-fed induction machines are the most widely used concept. However, especially in Germany, direct-drive wind generators based on converter-driven synchronous generator concepts have reached considerable market penetration. This paper presents converter driven synchronous generator models of various order that can be used for simulating transients and dynamics in a very wide time range.

THE INFLUENCE SEWING MACHINE ON FOOTWEAR MOCCASIN ECONOMIC INDICATORS AI ASSEMBLY OPERATIONS

Directory of Open Access Journals (Sweden)

MALCOCI Marina

2014-05-01

Full Text Available With the evolution of time changes, grows and improves, construction, form, rationality and argumentation usefulness of various types of footwear, namely machines. In the present paper analyzes a number of sewing machines, bound for achieving moccasin shoes. With sewing machines can perform a wide variety of stitches, they create an aesthetically pleasing, but all at once enable product diversification with minimum expenses. Sewing machines moccasins are distinguished by technological parameters, number of stitches, design and affordability. Sewing operation carried out in these machines is carried out within 72 seconds to manual operation - 22 minutes. A flow diagram mechanical requires a reduced number of workers (e.g., 3 workers, to a manual flow diagram - 38 workers. Labour productivity in the use of sewing machines increase by 10 times, and operation cost decreases from 3,7 to 5,7 lei. Regardless of the sewing moccasins construction company helps to increase productivity, quality completion of the operation, ie products, reducing the time required to manufacture the products, shortening manufacturing cycle. Among the cars analyzed, the most recommended sewing machine as OS 7700 P Global company because it represents the best technical features. Sewing machines for manufacturing footwear moccasin were implemented in Moldova in 2010, at the "Cristina Mold Rom Simpex" in Chisinau. Because, company management understood beneficial role of sewing moccasins on quality operation, but also on other economic indicators. Currently the majority of footwear enterprises in Moldova sewing moccasins are done manually. One problem is the high price of sewing machines moccasins.

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

Directory of Open Access Journals (Sweden)

Adel Taha Abbas

2018-05-01

Full Text Available Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN is presented in this paper for surface roughness (Ra prediction of one component in computer numerical control (CNC turning over minimal machining time (Tm and at prime machining costs (C. An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP, to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

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

Power quality improvement in highly varying loads using thyristor-switched capacitor

Energy Technology Data Exchange (ETDEWEB)

Poshtan, M. [Petroleum Inst., Abu Dhabi (United Arab Emirates). Dept. of Electrical Engineering; Mokhtari, H.; Esmaeili, A. [Sharif Univ. of Technology, Tehran (Iran, Islamic Republic of). Dept. of Electrical Engineering

2007-07-01

Ordinary contactor-based-capacitor (CBC) banks may not be able to response quickly enough in highly varying electrical loads such as welding machines or arc furnace loads. Thyristor-switched capacitor (TSC) banks are therefore used to compensate for reactive power of highly varying loads. In this paper, the performance of a TSC was compared to CBC banks. The 2 systems, were also compared in terms of energy saving in transmission systems. Simulations carried out using PSCAD/EMTDC software showed that there was a considerable difference in the performance of the 2 systems. The shortcomings of existing CBC systems include slow response of mechanical switching systems; problem of switching more than one bank into the system; and, voltage/current transients during on-off switching. 3 refs., 6 tabs., 14 figs.

Adenoma detection rate varies greatly during colonoscopy training

NARCIS (Netherlands)

van Doorn, Sascha C.; Klanderman, Robert B.; Hazewinkel, Yark; Fockens, Paul; Dekker, Evelien

2015-01-01

The adenoma detection rate (ADR) is considered the most important quality indicator for colonoscopy and varies widely among colonoscopists. It is unknown whether the ADR of gastroenterology consultants can already be predicted during their colonoscopy training. To evaluate the ADR of fellows in

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

Directory of Open Access Journals (Sweden)

Juliette Bessette

2018-01-01

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

Operation of micro and molecular machines: a new concept with its origins in interface science.

Science.gov (United States)

Ariga, Katsuhiko; Ishihara, Shinsuke; Izawa, Hironori; Xia, Hong; Hill, Jonathan P

2011-03-21

A landmark accomplishment of nanotechnology would be successful fabrication of ultrasmall machines that can work like tweezers, motors, or even computing devices. Now we must consider how operation of micro- and molecular machines might be implemented for a wide range of applications. If these machines function only under limited conditions and/or require specialized apparatus then they are useless for practical applications. Therefore, it is important to carefully consider the access of functionality of the molecular or nanoscale systems by conventional stimuli at the macroscopic level. In this perspective, we will outline the position of micro- and molecular machines in current science and technology. Most of these machines are operated by light irradiation, application of electrical or magnetic fields, chemical reactions, and thermal fluctuations, which cannot always be applied in remote machine operation. We also propose strategies for molecular machine operation using the most conventional of stimuli, that of macroscopic mechanical force, achieved through mechanical operation of molecular machines located at an air-water interface. The crucial roles of the characteristics of an interfacial environment, i.e. connection between macroscopic dimension and nanoscopic function, and contact of media with different dielectric natures, are also described.

Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine

Directory of Open Access Journals (Sweden)

Wahyudi Setiawan

2014-02-01

Full Text Available Diabetic Retinopathy is a complication of Diabetes Melitus. It can be a blindness if untreated settled as early as possible. System created in this thesis is the detection of diabetic retinopathy level of the image obtained from fundus photographs. There are three main steps to resolve the problems, preprocessing, feature extraction and classification. Preprocessing methods that used in this system are Grayscale Green Channel, Gaussian Filter, Contrast Limited Adaptive Histogram Equalization and Masking. Two Dimensional Linear Discriminant Analysis (2DLDA is used for feature extraction. Support Vector Machine (SVM is used for classification. The test result performed by taking a dataset of MESSIDOR with number of images that vary for the training phase, otherwise is used for the testing phase. Test result show the optimal accuracy are 84% .   Keywords : Diabetic Retinopathy, Support Vector Machine, Two Dimensional Linear Discriminant Analysis, MESSIDOR

Feature recognition and detection for ancient architecture based on machine vision

Science.gov (United States)

Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

2018-03-01

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

Bounds and asymptotics for orthogonal polynomials for varying weights

CERN Document Server

Levin, Eli

2018-01-01

This book establishes bounds and asymptotics under almost minimal conditions on the varying weights, and applies them to universality limits and entropy integrals.  Orthogonal polynomials associated with varying weights play a key role in analyzing random matrices and other topics.  This book will be of use to a wide community of mathematicians, physicists, and statisticians dealing with techniques of potential theory, orthogonal polynomials, approximation theory, as well as random matrices. .

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

Science.gov (United States)

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

2018-05-01

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

Machine learning for epigenetics and future medical applications

OpenAIRE

Holder, Lawrence B.; Haque, M. Muksitul; Skinner, Michael K.

2017-01-01

ABSTRACT Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems w...

TEA CO2 laser machining of CFRP composite

OpenAIRE

Salama, Adel; Li, Lin; Mativenga, Paul; Whitehead, David

2016-01-01

Carbon fibre-reinforced polymer (CFRP) composites have found wide applications in the aerospace, marine, sports and automotive industries owing to their lightweight and acceptable mechanical properties compared to the commonly used metallic materials. Machining of CFRP composites using lasers can be challenging due to inhomogeneity in the material properties and structures, which can lead to thermal damages during laser processing. In the previous studies, Nd:YAG, diode-pumped solid-state, CO...

An Evaluation of Online Machine Translation of Arabic into English News Headlines: Implications on Students' Learning Purposes

Science.gov (United States)

Kadhim, Kais A.; Habeeb, Luwaytha S.; Sapar, Ahmad Arifin; Hussin, Zaharah; Abdullah, Muhammad Ridhuan Tony Lim

2013-01-01

Nowadays, online Machine Translation (MT) is used widely with translation software, such as Google and Babylon, being easily available and downloadable. This study aims to test the translation quality of these two machine systems in translating Arabic news headlines into English. 40 Arabic news headlines were selected from three online sources,…

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

Science.gov (United States)

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

2017-01-01

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

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.)

Comparison between wire mesh and plate electrodes during Wide-pattern machining on invar fine sheet using through-mask electrochemical micromachining

Energy Technology Data Exchange (ETDEWEB)

Chun, Kwang-ho; Jin, Da-som; Kim, Seong-hyun; Lee, Eun-sang [Inha University, Incheon (Korea, Republic of)

2017-04-15

Many research on the fabrication of Organic light emitting diodes (OLED) shadow masks with high resolution have been carried out because of the development of the smart-display industry. It is the parts of display panel which has millions of micro holes on invar (Fe- Ni) fine sheet. Various techniques such as laser machining, chemical etching and Electrochemical micro-machining (EMM) are used to produce micro-hole arrays. In this study, Through-mask electrochemical machining (TMEMM) combine with portion of photolithography process was applied to fabricate micro-hole arrays on invar fine sheet. The sheet was coated with dry film photoresist. Two types of electrode, plate and mesh, was used to compare the influence of electrode type. The sheet was coated with dry film photoresist with micro- sized through holes. The results were compared in regard to uniformity and taper angle. Compared with the plate electrode, the mesh electrode has better uniformity and taper angle which is important criteria of OLED shadow mask. These results could be used to improve TMEMM for invar fine sheet when it is applied to fabricate micro-hole arrays and help to obtain optical uniformity and desired taper angles.

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.

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

Science.gov (United States)

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

2009-10-01

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

RESONANCE BEHAVIOUR ANALYSIS OF CARRYING SYSTEM IN HEAVY MACHINE WITH TRAVELING-COLUMN

Directory of Open Access Journals (Sweden)

Yu. V. Vasilevich

2015-01-01

Full Text Available A finite elements method (FEM-analysis has been carried out with the purpose to study dynamics of carrying system in a machine tool of unique height. This is a one-off machine with high traveling column and a support with horizontal slide can move vertically along the column. Spindle milling and boring units are mounted telescopically at slide butt end. The FEM-analysis of the machine tool has been made due to its renovation. Machine resonances, responses to cutting force, static and dynamic rigidity in spindles have been estimated in the paper. The machine carrying system is subtle, heavy and structurally complicated. Its structural chain includes a carriage, a column, a support, a slide, spindle units interconnected with the help of hydro-static guides. While varying main parameters their influence on rigidity has been investigated in the paper. Height of  support lifting, column material and rigidity of longitudinal drive have been changed during the investigations.Static, modal and harmonic MEF-analyses have been executed in the paper. Frequency-response characteristic of the machine has been constructed in the process of the investigations. Two support bending resonances have been detected in the column at low frequency. Such Due to this there are limits for usage of the machine in sub-resonance and static frequency range. The most powerful resonance has been observed at frequency of 27.8 Hz. The resonance has manifested itself as torsional oscillations of the column together with the support and the slide.Slide bending resonances have been revealed at higher frequencies 65–105 Hz. Three intervals which are fit for machine operation have been determined between resonance ranges. There is a prospective usage of the machine in inter-resonance (32–65 Hz and super-resonance (more than 105 Hz intervals. It has been shown that rigidity in spindle is a sequence higher in dynamic intervals than in a static interval. Machine subtility can be

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.

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

Directory of Open Access Journals (Sweden)

Marek Zombroń

2014-07-01

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

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)

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

NARCIS (Netherlands)

Toral Ruiz, Antonio

2017-01-01

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

Distributed state machine supervision for long-baseline gravitational-wave detectors

International Nuclear Information System (INIS)

Rollins, Jameson Graef

2016-01-01

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.

Distributed state machine supervision for long-baseline gravitational-wave detectors

Energy Technology Data Exchange (ETDEWEB)

Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org [LIGO Laboratory, California Institute of Technology, Pasadena, California 91125 (United States)

2016-09-15

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.

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

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.

Performance of a solar chimney by varying design parameters

CSIR Research Space (South Africa)

Kumirai, T

2015-08-01

Full Text Available the design of solar chimneys to ensure optimal performance. The purpose of this chapter is to discuss the performance of an example solar chimney by varying the design parameters and examining their effects on the interior ventilation performance... chimney by varying design parameters Tichaona Kumirai, Researcher, Built Environment CSIR Jan-Hendrik Grobler, DPSS CSIR Dr D.C.U. Conradie, Senior researcher, Built Environment CSIR 1 Introduction Trombe walls and solar chimneys are not widely...

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

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

Energy Technology Data Exchange (ETDEWEB)

Johnson, Brian B [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lin, Yashen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Gevorgian, Vahan [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Purba, Victor [University of Minnesota; Dhople, Sairaj [University of Minnesota

2017-09-28

From the inception of power systems, synchronous machines have acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, power electronics interfaces are playing a growing role as they are the primary interface for several types of renewable energy sources and storage technologies. As the role of power electronics in systems continues to grow, it is crucial to investigate the properties of bulk power systems in low inertia settings. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator, three-phase inverter, and a load. Furthermore, the inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings and, hence, differing levels of inertia. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the interaction between the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.

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

Directory of Open Access Journals (Sweden)

Marc Wieland

2014-03-01

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

The PEP-II/BaBar Project-Wide Database using World Wide Web and Oracle*Case

International Nuclear Information System (INIS)

Chan, A.; Crane, G.; MacGregor, I.; Meyer, S.

1995-12-01

The PEP-II/BaBar Project Database is a tool for monitoring the technical and documentation aspects of the accelerator and detector construction. It holds the PEP-II/BaBar design specifications, fabrication and installation data in one integrated system. Key pieces of the database include the machine parameter list, components fabrication and calibration data, survey and alignment data, property control, CAD drawings, publications and documentation. This central Oracle database on a UNIX server is built using Oracle*Case tools. Users at the collaborating laboratories mainly access the data using World Wide Web (WWW). The Project Database is being extended to link to legacy databases required for the operations phase

Experimental analysis of the performance of machine learning algorithms in the classification of navigation accident records

Directory of Open Access Journals (Sweden)

REIS, M V. S. de A.

2017-06-01

Full Text Available This paper aims to evaluate the use of machine learning techniques in a database of marine accidents. We analyzed and evaluated the main causes and types of marine accidents in the Northern Fluminense region. For this, machine learning techniques were used. The study showed that the modeling can be done in a satisfactory manner using different configurations of classification algorithms, varying the activation functions and training parameters. The SMO (Sequential Minimal Optimization algorithm showed the best performance result.

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.

How state taxes and policies targeting soda consumption modify the association between school vending machines and student dietary behaviors: a cross-sectional analysis.

Science.gov (United States)

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

2014-01-01

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. Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.). Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference  =  -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption.

How state taxes and policies targeting soda consumption modify the association between school vending machines and student dietary behaviors: a cross-sectional analysis.

Directory of Open Access Journals (Sweden)

Daniel R Taber

Full Text Available 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.Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1 estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2 determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors..Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11 and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05 if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference  =  -4.02, 95% CI: -7.28, -0.76. However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors.Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption.

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

Science.gov (United States)

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 were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.) Results Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Conclusion Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption. PMID:25083906

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.

The deforestation debate; estimates vary widely over the extent of forest loss

International Nuclear Information System (INIS)

Monastersky, R.

1993-01-01

While tropical forests are vanishing at a disturbing rate, the wide-spread disagreement over deforestation estimates makes it difficult for government officials and scientists to assess the problem. In turn this hampers efforts to gauge the threat of related issues such as habitat destruction and global warming. Creating more confusion is the realization that partly deforested, but not stripped, lands have not been completely taken into account. In addition lands in tropical regions but outside the tropical rain forest are poorly represented. This article uses Brazil as an example of the conflicting estimates. The efforts of the United Nations Food and Agriculture Organization (FAO) to improve its estimates are described. An on-going NASA project to help is also described

TEA CO2 laser machining of CFRP composite

Science.gov (United States)

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

2016-05-01

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

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs.

Science.gov (United States)

Abbas, Adel Taha; Pimenov, Danil Yurievich; Erdakov, Ivan Nikolaevich; Taha, Mohamed Adel; Soliman, Mahmoud Sayed; El Rayes, Magdy Mostafa

2018-05-16

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth⁻Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness ( Ra ) prediction of one component in computer numerical control (CNC) turning over minimal machining time ( T m ) and at prime machining costs ( C ). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra , T m , and C , in relation to cutting speed, v c , depth of cut, a p , and feed per revolution, f r . For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values v c , a p , and f r . The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, T m = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed v c = 250 m/min, cutting depth a p = 1.0 mm, and feed per revolution f r = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

An experimental investigation of pulsed laser-assisted machining of AISI 52100 steel

Science.gov (United States)

Panjehpour, Afshin; Soleymani Yazdi, Mohammad R.; Shoja-Razavi, Reza

2014-11-01

Grinding and hard turning are widely used for machining of hardened bearing steel parts. Laser-assisted machining (LAM) has emerged as an efficient alternative to grinding and hard turning for hardened steel parts. In most cases, continuous-wave lasers were used as a heat source to cause localized heating prior to material removal by a cutting tool. In this study, an experimental investigation of pulsed laser-assisted machining of AISI 52100 bearing steel was conducted. The effects of process parameters (i.e., laser mean power, pulse frequency, pulse energy, cutting speed and feed rate) on state variables (i.e., material removal temperature, specific cutting energy, surface roughness, microstructure, tool wear and chip formation) were investigated. At laser mean power of 425 W with frequency of 120 Hz and cutting speed of 70 m/min, the benefit of LAM was shown by 25% decrease in specific cutting energy and 18% improvement in surface roughness, as compared to those of the conventional machining. It was shown that at constant laser power, the increase of laser pulse energy causes the rapid increase in tool wear rate. Pulsed laser allowed efficient control of surface temperature and heat penetration in material removal region. Examination of the machined subsurface microstructure and microhardness profiles showed no change under LAM and conventional machining. Continuous chips with more uniform plastic deformation were produced in LAM.

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

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

International Nuclear Information System (INIS)

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

1991-01-01

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

Quality Assessment of Adaptive Bitrate Videos using Image Metrics and Machine Learning

DEFF Research Database (Denmark)

Søgaard, Jacob; Forchhammer, Søren; Brunnström, Kjell

2015-01-01

Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method...

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

Energy Technology Data Exchange (ETDEWEB)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

International Nuclear Information System (INIS)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

2016-01-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

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.

New Cogging Torque Reduction Methods for Permanent Magnet Machine

Science.gov (United States)

Bahrim, F. S.; Sulaiman, E.; Kumar, R.; Jusoh, L. I.

2017-08-01

Permanent magnet type motors (PMs) especially permanent magnet synchronous motor (PMSM) are expanding its limbs in industrial application system and widely used in various applications. The key features of this machine include high power and torque density, extending speed range, high efficiency, better dynamic performance and good flux-weakening capability. Nevertheless, high in cogging torque, which may cause noise and vibration, is one of the threat of the machine performance. Therefore, with the aid of 3-D finite element analysis (FEA) and simulation using JMAG Designer, this paper proposed new method for cogging torque reduction. Based on the simulation, methods of combining the skewing with radial pole pairing method and skewing with axial pole pairing method reduces the cogging torque effect up to 71.86% and 65.69% simultaneously.

Introduction to AC machine design

CERN Document Server

Lipo, Thomas A

2018-01-01

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

Precision machining commercialization

International Nuclear Information System (INIS)

1978-01-01

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

Are there intelligent Turing machines?

OpenAIRE

Bátfai, Norbert

2015-01-01

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

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Science.gov (United States)

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

Directory of Open Access Journals (Sweden)

Gonzalo Pajares

2016-11-01

Full Text Available Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a spectral bands (visible and infrared; (b imaging sensors and optical systems (including intrinsic parameters and (c geometric visual system arrangement (considering extrinsic parameters and stereovision systems. A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management project for effective weed control in maize fields (wide-rows crops, funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.

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.)

Electromagnetic Analysis and Design of Switched Reluctance Double-Rotor Machine for Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Shouliang Han

2014-10-01

Full Text Available The double-rotor machine is a kind of multiple input and output electromechanical energy transducer with two electrical ports and two mechanical ports, which is an ideal transmission system for hybrid electric vehicles and has a series of advantages such as integration of power and energy, high efficiency and compaction. In this paper, a switched reluctance double-rotor machine (SRDRM is proposed for hybrid electric vehicles, while no conductor or PM in the middle rotor. This machine not only inherits the merits of switched reluctance machine, such as simple salient rotor structure, high reliability and wide speed range, but also can avoid the outer rotor’s cooling problem effectively. By using an equivalent magnetic circuit model, the function of middle rotor yoke is analyzed. Electromagnetic analyses of the SRDRM are performed with analytical calculations and 2-D finite element methods, including the effects of main parameters on performance. Finally, a 4.4 kW prototype machine is designed and manufactured, and the tests are performed, which validate the proposed design method.

‘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. 

Development and Evaluation of a Wide-Bed Former for Vegetable Cultivation in Controlled Tractor Traffic

Science.gov (United States)

Dixit, Anoop; Khurana, Rohinish; Verma, Aseem; Singh, Arshdeep; Manes, G. S.

2018-05-01

India is the second largest producer of vegetables in the world. For vegetable cultivation, a good seed bed preparation is an important task which involves 6-10 different operations. To tackle the issue of multiple operations, a prototype of tractor operated wide bed former was developed and evaluated. The machine comprises of a rotary tiller and a bed forming setup. It forms bed of 1000 mm top width which is suitable as per the track width of an average sized tractor in India. The height of the beds formed is 130 mm whereas the top and bottom width of channel formed on both sides of the bed is 330 and 40 mm respectively at soil moisture content of 12.5-16% (db). The forward speed of 2.75 km/h was observed to be suitable for proper bed formation. The average fuel consumption of the machine was 5.9 l/h. The average bulk density of soil before and after the bed formation was 1.46 and 1.63 g/cc respectively. Field capacity of the machine was found to be 0.31 ha/h. The machine resulted in 93.8% labour saving and 80.4% saving in cost of bed preparation as compared to conventional farmer practice. Overall performance of wide-bed former was found to be satisfactory.

Ignition properties of n-butane and iso-butane in a rapid compression machine

NARCIS (Netherlands)

Gersen, S.; Mokhov, A. V.; Darmeveil, J. H.; Levinsky, H. B.

Autoignition delay times of n-butane and iso-butane have been measured in a Rapid Compression Machine in the temperature range 660-1010 K, at pressures varying from 14 to 36 bar and at equivalence ratios phi = 1.0 and phi = 0.5. Both butane isomers exhibit a negative-temperature-coefficient (NTC)

Towards artificial intelligence based diesel engine performance control under varying operating conditions using support vector regression

Directory of Open Access Journals (Sweden)

Naradasu Kumar Ravi

2013-01-01

Full Text Available Diesel engine designers are constantly on the look-out for performance enhancement through efficient control of operating parameters. In this paper, the concept of an intelligent engine control system is proposed that seeks to ensure optimized performance under varying operating conditions. The concept is based on arriving at the optimum engine operating parameters to ensure the desired output in terms of efficiency. In addition, a Support Vector Machines based prediction model has been developed to predict the engine performance under varying operating conditions. Experiments were carried out at varying loads, compression ratios and amounts of exhaust gas recirculation using a variable compression ratio diesel engine for data acquisition. It was observed that the SVM model was able to predict the engine performance accurately.

Selection of parameters for advanced machining processes using firefly algorithm

Directory of Open Access Journals (Sweden)

Rajkamal Shukla

2017-02-01

Full Text Available Advanced machining processes (AMPs are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM and abrasive water jet machining (AWJM are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70 gm/min to 181.6723 gm/min, while “Ra” and “REWR” are decreased from 6.21 μm to 3.6767 μm and 6.21% to 6.324 × 10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858 mm to 0.3704 mm and 5.41 mm to 4.443 mm respectively. In both the processes, the obtained results show a significant improvement in the responses.

A control approach for plasma density in tokamak machines

Energy Technology Data Exchange (ETDEWEB)

Boncagni, Luca, E-mail: luca.boncagni@enea.it [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Pucci, Daniele; Piesco, F.; Zarfati, Emanuele [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy); Mazzitelli, G. [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Monaco, S. [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy)

2013-10-15

Highlights: •We show a control approach for line plasma density in tokamak. •We show a control approach for pressure in a tokamak chamber. •We show experimental results using one valve. -- Abstract: In tokamak machines, chamber pre-fill is crucial to attain plasma breakdown, while plasma density control is instrumental for several tasks such as machine protection and achievement of desired plasma performances. This paper sets the principles of a new control strategy for attaining both chamber pre-fill and plasma density regulation. Assuming that the actuation mean is a piezoelectric valve driven by a varying voltage, the proposed control laws ensure convergence to reference values of chamber pressure during pre-fill, and of plasma density during plasma discharge. Experimental results at FTU are presented to discuss weaknesses and strengths of the proposed control strategy. The whole system has been implemented by using the MARTe framework [1].

Machine Learning and Inverse Problem in Geodynamics

Science.gov (United States)

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

2017-12-01

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

Effect of surface machining on corrosion behavior of SA182-304 in simulated

International Nuclear Information System (INIS)

Zhang Zhiming; Wang Jianqiu; Han Enhou; Ke Wei

2015-01-01

Different machining processes of mechanical parts can cause surface damage layers with different levels. The surface deformed layer can affect the corrosion behavior and service life of these mechanical parts a lot during the following service process. As a result, it is a key issue for the fabrication of the mechanical parts with long life that the selection of proper machining parameters and the removal of surface damage. The purpose of this study is to study the influence of different turning parameters on the corrosion behavior of nuclear grade SA182-304 stainless steel widely used in the advanced pressured water reactors (PWRs). 6 kinds of samples with different surface state are prepared by a lathe with different machining parameters, such as the feed, cutting speed and back engagement of the cutting edge. The high temperature and high pressure immersion test of these samples in the simulated PWR primary watershows that machining processes can affect the microstructure and chemical composition of the formed surface oxide scales a lot. According to the experimental results, the proper machining parameters for the studied SA182-304 are suggested. (authors)

Combined machine-readable and visually authenticated optical devices

Science.gov (United States)

Souparis, Hugues

1996-03-01

Optical variable devices are now widely used on documents or values. The most recent optical visual features with high definition, animation, brightness, special color tune, provide excellent first and second levels of authentication. Human eye is the only instrument required to check the authenticity. This is a major advantage of OVDs in many circumstances, such as currency exchange, ID street control . . . But, under other circumstances, such as automatic payments with banknotes, volume ID controls at boarders, ID controls in shops . . . an automatic authentication will be necessary or more reliable. When both a visual and automated authentication are required, the combination, on the same security component, of a variable image and a machine readable optical element is a very secure and cost effective solution for the protection of documents. Several techniques are now available an can be selected depending upon the respective roles of the machine readability and visual control.

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...

Towards an automatic model transformation mechanism from UML state machines to DEVS models

Directory of Open Access Journals (Sweden)

Ariel González

2015-08-01

Full Text Available The development of complex event-driven systems requires studies and analysis prior to deployment with the goal of detecting unwanted behavior. UML is a language widely used by the software engineering community for modeling these systems through state machines, among other mechanisms. Currently, these models do not have appropriate execution and simulation tools to analyze the real behavior of systems. Existing tools do not provide appropriate libraries (sampling from a probability distribution, plotting, etc. both to build and to analyze models. Modeling and simulation for design and prototyping of systems are widely used techniques to predict, investigate and compare the performance of systems. In particular, the Discrete Event System Specification (DEVS formalism separates the modeling and simulation; there are several tools available on the market that run and collect information from DEVS models. This paper proposes a model transformation mechanism from UML state machines to DEVS models in the Model-Driven Development (MDD context, through the declarative QVT Relations language, in order to perform simulations using tools, such as PowerDEVS. A mechanism to validate the transformation is proposed. Moreover, examples of application to analyze the behavior of an automatic banking machine and a control system of an elevator are presented.

Machine Learning.

Science.gov (United States)

Kirrane, Diane E.

1990-01-01

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

Machining of Metal Matrix Composites

CERN Document Server

2012-01-01

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

Machine technology: a survey

International Nuclear Information System (INIS)

Barbier, M.M.

1981-01-01

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

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

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

International Nuclear Information System (INIS)

Saito, A; Kuroishi, M; Nakai, H

2016-01-01

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

Toolsets for Airborne Data (TAD): Improving Machine Readability for ICARTT Data Files

Science.gov (United States)

Northup, E. A.; Early, A. B.; Beach, A. L., III; Kusterer, J.; Quam, B.; Wang, D.; Chen, G.

2015-12-01

NASA has conducted airborne tropospheric chemistry studies for about three decades. These field campaigns have generated a great wealth of observations, including a wide range of the trace gases and aerosol properties. The ASDC Toolsets for Airborne Data (TAD) is designed to meet the user community needs for manipulating aircraft data for scientific research on climate change and air quality relevant issues. TAD makes use of aircraft data stored in the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) file format. ICARTT has been the NASA standard since 2010, and is widely used by NOAA, NSF, and international partners (DLR, FAAM). Its level of acceptance is due in part to it being generally self-describing for researchers, i.e., it provides necessary data descriptions for proper research use. Despite this, there are a number of issues with the current ICARTT format, especially concerning the machine readability. In order to overcome these issues, the TAD team has developed an "idealized" file format. This format is ASCII and is sufficiently machine readable to sustain the TAD system, however, it is not fully compatible with the current ICARTT format. The process of mapping ICARTT metadata to the idealized format, the format specifics, and the actual conversion process will be discussed. The goal of this presentation is to demonstrate an example of how to improve the machine readability of ASCII data format protocols.

Innovation of High Voltage Supply Adjustment Device on Diagnostic X-Ray Machine

International Nuclear Information System (INIS)

Sujatno; Wiranto Budi Santoso

2010-01-01

Innovation of high voltage supply adjustment device on diagnostic x-ray machine has been carried out. The innovation is conducted by utilizing an electronic circuit as a high voltage adjustment device. Usually a diagnostic x-ray machine utilizes a transformer or an auto-transformer as a high voltage supply adjustment device. A high power diagnostic x-ray machine needs a high power transformer which has big physical dimension. Therefore a box control where the transformer is located has to have big physical dimension. Besides, the price of the transformer is expensive and hardly found in local markets. In this innovation, the transformer is replaced by an electronic circuit. The main component of the electronic circuit is Triac BTA-40. As adjustment device, the triac is controlled by a variable resistor which is coupled by a stepper motor. A step movement of stepper motor varies a value of resistor. The resistor value determines the triac gate voltage. Furthermore the triac will open according to the value of electrical current flowing to the gate. When the gate is open, electrical voltage and current will flow from cathode to anode of the triac. The value of these electrical voltage and current depend on gate open condition. Then this triac output voltage is feed to diagnostic x-ray machine high voltage supply. Therefore the high voltage value of diagnostic x-ray machine is adjusted by the output voltage of the electronic circuit. By using this electronic circuit, the physical dimension of diagnostic x-ray machine box control and the price of the equipment can be reduced. (author)

WWER NPPs fuel handling machine control system

International Nuclear Information System (INIS)

Mini, G.; Rossi, G.; Barabino, M.; Casalini, M.

2001-01-01

In order to increase the safety level of the fuel handling machine on WWER NPPs, Ansaldo Nucleare was asked to design and supply a new Control System. Two FHM Control System units have been already supplied for Temelin NPP and others supplies are in process for the Atommash company, which has in charge the supply of FHMs for NPPs located in Russia, Ukraine and China. The Fuel Handling Machine (FHM) Control System is an integrated system capable of a complete management of nuclear fuel assemblies. The computer-based system takes into account all the operational safety interlocks so that it is able to avoid incorrect and dangerous manoeuvres in the case of operator error. Control system design criteria, hardware and software architecture, and quality assurance control, are in accordance with the most recent international requirements and standards, and in particular for electromagnetic disturbance immunity demands and seismic compatibility. The hardware architecture of the control system is based on ABB INFI 90 system. The microprocessor-based ABB INFI 90 system incorporates and improves upon many of the time proven control capabilities of Bailey Network 90, validated over 14,000 installations world-wide. The control system complies all the former designed sensors and devices of the machine and markedly the angular position measurement sensors named 'selsyn' of Russian design. Nevertheless it is fully compatible with all the most recent sensors and devices currently available on the market (for ex. Multiturn absolute encoders). All control logic components were developed using standard INFI 90 Engineering Work Station, interconnecting blocks extracted from an extensive SAMA library by using a graphical approach (CAD) and allowing an easier intelligibility, more flexibility and updated and coherent documentation. The data acquisition system and the Man Machine Interface are implemented by ABB in co-operation with Ansaldo. The flexible and powerful software structure

VVER NPPs fuel handling machine control system

International Nuclear Information System (INIS)

Mini, G.; Rossi, G.; Barabino, M.; Casalini, M.

2002-01-01

In order to increase the safety level of the fuel handling machine on WWER NPPs, Ansaldo Nucleare was asked to design and supply a new Control System. Two Fuel Handling Machine (FHM) Control System units have been already supplied for Temelin NPP and others supply are in process for the Atommash company, which has in charge the supply of FHMs for NPPs located in Russia, Ukraine and China.The computer-based system takes into account all the operational safety interlocks so that it is able to avoid incorrect and dangerous manoeuvres in the case of operator error. Control system design criteria, hardware and software architecture, and quality assurance control, are in accordance with the most recent international requirements and standards, and in particular for electromagnetic disturbance immunity demands and seismic compatibility. The hardware architecture of the control system is based on ABB INFI 90 system. The microprocessor-based ABB INFI 90 system incorporates and improves upon many of the time proven control capabilities of Bailey Network 90, validated over 14,000 installations world-wide.The control system complies all the former designed sensors and devices of the machine and markedly the angular position measurement sensors named 'selsyn' of Russian design. Nevertheless it is fully compatible with all the most recent sensors and devices currently available on the market (for ex. Multiturn absolute encoders).All control logic were developed using standard INFI 90 Engineering Work Station, interconnecting blocks extracted from an extensive SAMA library by using a graphical approach (CAD) and allowing and easier intelligibility, more flexibility and updated and coherent documentation. The data acquisition system and the Man Machine Interface are implemented by ABB in co-operation with Ansaldo. The flexible and powerful software structure of 1090 Work-stations (APMS - Advanced Plant Monitoring System, or Tenore NT) has been successfully used to interface the

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.

Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

Directory of Open Access Journals (Sweden)

Supriya Kinger

2014-01-01

Full Text Available Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

Prediction based proactive thermal virtual machine scheduling in green clouds.

Science.gov (United States)

Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

2014-01-01

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

Science.gov (United States)

Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

2014-01-01

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated. PMID:24737962

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.

Cytocompatibility of a free machining titanium alloy containing lanthanum.

Science.gov (United States)

Feyerabend, Frank; Siemers, Carsten; Willumeit, Regine; Rösler, Joachim

2009-09-01

Titanium alloys like Ti6Al4V are widely used in medical engineering. However, the mechanical and chemical properties of titanium alloys lead to poor machinability, resulting in high production costs of medical products. To improve the machinability of Ti6Al4V, 0.9% of the rare earth element lanthanum (La) was added. The microstructure, the mechanical, and the corrosion properties were determined. Lanthanum containing alloys exhibited discrete particles of cubic lanthanum. The mechanical properties and corrosion resistance were slightly decreased but are still sufficient for many applications in the field of medical engineering. In vitro experiments with mouse macrophages (RAW 264.7) and human bone-derived cells (MG-63, HBDC) were performed and revealed that macrophages showed a dose response below and above a LaCl3 concentration of 200 microM, while MG-63 and HBDC tolerated three times higher concentrations without reduction of viability. The viability of cells cultured on disks of the materials showed no differences between the reference and the lanthanum containing alloy. We therefore propose that lanthanum containing alloy appears to be a good alternative for biomedical applications, where machining of parts is necessary.

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

Directory of Open Access Journals (Sweden)

Ireneusz Zagórski

2014-11-01

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

The ‘full sleeve’ application in the horizontal cold-chamber machine for pressure die casting of aluminium alloys

Directory of Open Access Journals (Sweden)

Z. Konopka

2008-04-01

Full Text Available The ‘full sleeve’ construction has been designed and accomplished in the horizontal cold-chamber pressure die casting machine. Main part of this solution is a counter plunger placed in a movable die half which allows for full filling of the shot sleeve and precisely fixes the metal quantity needed for casting. The purpose of this new construction solution is mainly the reduction of the casting porosity caused by air entrapment and the improvement of both castability and accuracy of the die cavity reproduction. For such a redesigned machine there have been performed examinations consisting in pressure casting of AlSi9Cu alloy (EN AC-46000 at varying plunger velocity in the second stage of injection and varying intensification pressure. The alloy castability (the die filling ability has been measured for each parameter setting. For the purpose of comparison, similar measurements have been performed also for the conventional system without a counter plunger. The castability examination has been done by means of a specially designed die with an impression of a trial casting of variable wall thickness. The experiments have been held according to the assumed factor design 22, what allowed for determining the mathematical models describing the influence of die filling parameters on the castability and the die cavity reproduction level. Both alternatives of the experiment confirmed the positive influence of plunger velocity and intensification pressure increase on the improvement of castability, the measure of the latter being the filled length of the impression. Applying of the new ‘full sleeve’ solution has improved castability for each experiment by about 20% as compared with conventional alternative. Castability in the ‘full sleeve’ system has been increased even for low values of plunger velocity and intensification pressure. For both alternative systems the influence of plunger velocity has been found, as an average, by four times

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.

Wide-band segmented power distribution networks

NARCIS (Netherlands)

Tereshchenko, O.V.; Buesink, Frederik Johannes Karel; Leferink, Frank Bernardus Johannes

2013-01-01

This paper discusses a novel design of Power Distribution Network (PDN). By physical structuring of the power plane into repetitive symmetrical and asymmetrical segments of varying size, suppression of the propagation of unwanted noise throughout the PDN over a wide frequency range is achieved.

Design of 2D time-varying vector fields.

Science.gov (United States)

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.

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, ...

A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.

Science.gov (United States)

Levman, Jacob E D; Warner, Ellen; Causer, Petrina; Martel, Anne L

2014-02-01

This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.

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

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.

CRDM motion analysis using machine learning technique

International Nuclear Information System (INIS)

Nishimura, Takuya; Nakayama, Hiroyuki; Saitoh, Mayumi; Yaguchi, Seiji

2017-01-01

Magnetic jack type Control Rod Drive Mechanism (CRDM) for pressurized water reactor (PWR) plant operates control rods in response to electrical signals from a reactor control system. CRDM operability is evaluated by quantifying armature's response of closed/opened time which means interval time between coil energizing/de-energizing points and armature closed/opened points. MHI has already developed an automatic CRDM motion analysis and applied it to actual plants so far. However, CRDM operational data has wide variation depending on their characteristics such as plant condition, plant, etc. In the existing motion analysis, there is an issue of analysis accuracy for applying a single analysis technique to all plant conditions, plants, etc. In this study, MHI investigated motion analysis using machine learning (Random Forests) which is flexibly accommodated to CRDM operational data with wide variation, and is improved analysis accuracy. (author)

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...

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.

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

Science.gov (United States)

Pitts, James Daniel

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

Survey of practical application fields of micro-machine and micro-factory technologies in Japan; Nippon ni okeru maikuro machine oyobi maikuro factory gijutsu no jitsuyoka bun`ya chosa

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-02-01

As for micro-machine and micro-factory technologies, research and development trends promoted by private companies were surveyed except for national projects. In the field of main technology development by private companies which do not participate in national projects, developments of micro-devices, such as micro-sensor, and micro-actuator, as well as basic technologies, such as machining, assembly, and material technology, are predominant. The applied fields of these aim at electronic industries for measurement and analysis equipment, motorcar sensors, information, communication, and home electric products. While, there are a few research and developments as to micro-robots. Research and developments aiming at application to medical field are widely promoted by private companies. In this field, micro-machining technology for micro-surgery and endoscopes is prospective. There is a photo-forceps technology for handling the micro-parts. However, there are few researches considering the micro-factory. 146 refs., 73 figs., 7 tabs.

Machine learning search for variable stars

Science.gov (United States)

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

2018-04-01

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

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

Vapor pumps and gas-driven machines

International Nuclear Information System (INIS)

Guillet, R.

1991-01-01

The vapor pump, patented in 1979 by Gaz de France, is an additional mass and heat exchanger which uses the combustion air of fuel-burning machines as an additional cold source. This cold source is preheated and, above all, humidified before reaching the burner, by means of the residual sensible and latent heat in the combustion products of the fuel-burning process. This final exchanger thus makes it possible, in many cases, to recover all the gross calorific value of natural gas, even when the combustion products leave the process at a wet temperature greater than 60 0 C, the maximum dew point of the products of normal combustion. Another significant advantage of the vapor pump being worth highlighting is the selective recycling of water vapor by the vapor pump which reduces the adiabatic combustion temperature and the oxygen concentration in the combustion air, two factors which lead to considerable reductions in nitrogen oxides formation, hence limiting atmospheric pollution. Alongside a wide range of configurations which make advantageous use of the vapor pump in association with gas-driven machines and processes, including gas turbines, a number of boiler plant installations are also presented [fr

Running accuracy analysis of a 3-RRR parallel kinematic machine considering the deformations of the links

Science.gov (United States)

Wang, Liping; Jiang, Yao; Li, Tiemin

2014-09-01

Parallel kinematic machines have drawn considerable attention and have been widely used in some special fields. However, high precision is still one of the challenges when they are used for advanced machine tools. One of the main reasons is that the kinematic chains of parallel kinematic machines are composed of elongated links that can easily suffer deformations, especially at high speeds and under heavy loads. A 3-RRR parallel kinematic machine is taken as a study object for investigating its accuracy with the consideration of the deformations of its links during the motion process. Based on the dynamic model constructed by the Newton-Euler method, all the inertia loads and constraint forces of the links are computed and their deformations are derived. Then the kinematic errors of the machine are derived with the consideration of the deformations of the links. Through further derivation, the accuracy of the machine is given in a simple explicit expression, which will be helpful to increase the calculating speed. The accuracy of this machine when following a selected circle path is simulated. The influences of magnitude of the maximum acceleration and external loads on the running accuracy of the machine are investigated. The results show that the external loads will deteriorate the accuracy of the machine tremendously when their direction coincides with the direction of the worst stiffness of the machine. The proposed method provides a solution for predicting the running accuracy of the parallel kinematic machines and can also be used in their design optimization as well as selection of suitable running parameters.

Optimizing Distributed Machine Learning for Large Scale EEG Data Set

Directory of Open Access Journals (Sweden)

M Bilal Shaikh

2017-06-01

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

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

Directory of Open Access Journals (Sweden)

Żak Krzysztof

2017-06-01

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

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.

Investigation of surface roughness and tool wear length with varying combination of depth of cut and feed rate of Aluminium alloy and P20 steel machining

International Nuclear Information System (INIS)

Varmma Suparmaniam, Madan; Yusoff, Ahmad Razlan

2016-01-01

High-speed milling technique is often used in many industries to boost productivity of the manufacturing of high-technology components. The occurrence of wear highly limits the efficiency and accuracy of high- speed milling operations. In this paper, analysis of high-speed milling process parameters such as material removal rate, cutting speed, feed rate and depth of cut carried out by implemented to conventional milling. This experiment investigate the effects of varying combination of depth of cut and feed rate to tool wear rate length using metallurgical microscope and surface roughness using portable surface roughness tester after end milling of Aluminium and P20 steel. Results showed that feed rate significantly influences the surface roughness value while depth of cut does not as the surface roughness value keep increasing with the increase of feed rate and decreasing depth of cut. Whereas, tool wear rate almost remain unchanged indicates that material removal rate strongly contribute the wear rate. It believe that with no significant tool wear rate the results of this experiment are useful by showing that HSM technique is possible to be applied in conventional machine with extra benefits of high productivity, eliminating semi-finishing operation and reducing tool load for finishing. (paper)

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

Science.gov (United States)

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

2010-01-01

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

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Directory of Open Access Journals (Sweden)

Marc Wieland

2016-09-01

Full Text Available This study evaluates the performance of a Support Vector Machine (SVM classifier to learn and detect changes in single- and multi-temporal X- and L-band Synthetic Aperture Radar (SAR images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however

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...

Novel Simplified Model for Asynchronous Machine with Consideration of Frequency Characteristic

Directory of Open Access Journals (Sweden)

Changchun Cai

2014-01-01

Full Text Available The frequency characteristic of electric equipment should be considered in the digital simulation of power systems. The traditional asynchronous machine third-order transient model excludes not only the stator transient but also the frequency characteristics, thus decreasing the application sphere of the model and resulting in a large error under some special conditions. Based on the physical equivalent circuit and Park model for asynchronous machines, this study proposes a novel asynchronous third-order transient machine model with consideration of the frequency characteristic. In the new definitions of variables, the voltages behind the reactance are redefined as the linear equation of flux linkage. In this way, the rotor voltage equation is not associated with the derivative terms of frequency. However, the derivative terms of frequency should not always be ignored in the application of the traditional third-order transient model. Compared with the traditional third-order transient model, the novel simplified third-order transient model with consideration of the frequency characteristic is more accurate without increasing the order and complexity. Simulation results show that the novel third-order transient model for the asynchronous machine is suitable and effective and is more accurate than the widely used traditional simplified third-order transient model under some special conditions with drastic frequency fluctuations.

Online learning control using adaptive critic designs with sparse kernel machines.

Science.gov (United States)

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Global Stability of Polytopic Linear Time-Varying Dynamic Systems under Time-Varying Point Delays and Impulsive Controls

Directory of Open Access Journals (Sweden)

M. de la Sen

2010-01-01

Full Text Available This paper investigates the stability properties of a class of dynamic linear systems possessing several linear time-invariant parameterizations (or configurations which conform a linear time-varying polytopic dynamic system with a finite number of time-varying time-differentiable point delays. The parameterizations may be timevarying and with bounded discontinuities and they can be subject to mixed regular plus impulsive controls within a sequence of time instants of zero measure. The polytopic parameterization for the dynamics associated with each delay is specific, so that (q+1 polytopic parameterizations are considered for a system with q delays being also subject to delay-free dynamics. The considered general dynamic system includes, as particular cases, a wide class of switched linear systems whose individual parameterizations are timeinvariant which are governed by a switching rule. However, the dynamic system under consideration is viewed as much more general since it is time-varying with timevarying delays and the bounded discontinuous changes of active parameterizations are generated by impulsive controls in the dynamics and, at the same time, there is not a prescribed set of candidate potential parameterizations.

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.

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

Investigation of surface roughness in micro-electro discharge machining of nonconductive ZrO2 for MEMS application

International Nuclear Information System (INIS)

Sabur, A; Moudood, A; Ali, M Y; Maleque, M A

2013-01-01

Micro-electro discharge machining technique, a noncontact machining process, is applied for drilling blind hole on nonconductive ZrO 2 ceramic for MEMS application. A conductive layer of adhesive copper is applied on the workpiece surface to initiate the sparks. Kerosene is used as dielectric for creation of continuous conductive pyrolytic carbon layer on the machined surface. Experiments are conducted by varying the voltage (V), capacitance (C) and rotational speed (N). Correlating these variables a mathematical model for surface roughness (SR) is developed using Taguchi method. The results showed that the V and C are the significant parameters of SR in micro-EDM for nonconductive ZrO 2 ceramic. The model also showed that SR increases with the increase of V and C

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

Analysis of the Chip Geometry in Dry Machining of Aeronautical Aluminum Alloys

Directory of Open Access Journals (Sweden)

Francisco Javier Trujillo Vilches

2017-01-01

Full Text Available Aluminum alloys are widely used in the manufacturing of structural parts for aircraft, frequently in combination with other materials such as CFRP (Carbon Fiber Reinforced Polymer, to form FML (Fiber Metal Laminates structures (CFRP/Al. The dry machining of these structures presents several problems, some of which are related to chip evacuation, either when machining aluminum alloys as an isotropic material, or during hybridization with composites. In this work, a study of the way in which cutting parameters influence the chip morphology in the dry machining of UNS A97075-T6 (Al-Zn and UNS A92024-T3 (Al-Cu alloys, is performed. Thus, different geometric parameters of the chip morphology have been obtained, and their evolution with feed has been analysed. Finally, the different relationships which occur between these geometric parameters and feed, have been obtained. These relationships allow a prediction of the evolution of some of the geometric parameters of the chip, as a function of feed.

Flexible software architecture for user-interface and machine control in laboratory automation.

Science.gov (United States)

Arutunian, E B; Meldrum, D R; Friedman, N A; Moody, S E

1998-10-01

We describe a modular, layered software architecture for automated laboratory instruments. The design consists of a sophisticated user interface, a machine controller and multiple individual hardware subsystems, each interacting through a client-server architecture built entirely on top of open Internet standards. In our implementation, the user-interface components are built as Java applets that are downloaded from a server integrated into the machine controller. The user-interface client can thereby provide laboratory personnel with a familiar environment for experiment design through a standard World Wide Web browser. Data management and security are seamlessly integrated at the machine-controller layer using QNX, a real-time operating system. This layer also controls hardware subsystems through a second client-server interface. This architecture has proven flexible and relatively easy to implement and allows users to operate laboratory automation instruments remotely through an Internet connection. The software architecture was implemented and demonstrated on the Acapella, an automated fluid-sample-processing system that is under development at the University of Washington.

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...

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...

Design of 2D Time-Varying Vector Fields

KAUST Repository

Chen, Guoning

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects. © 1995-2012 IEEE.

Design of 2D Time-Varying Vector Fields

KAUST Repository

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D.; Zhang, Eugene

2012-01-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects. © 1995-2012 IEEE.

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…

Development of new metal matrix composite electrodes for electrical discharge machining through powder metallurgy process

Directory of Open Access Journals (Sweden)

C. Mathalai Sundaram

2014-12-01

Full Text Available Electrical discharge machining (EDM is one of the widely used nontraditional machining methods to produce die cavities by the erosive effect of electrical discharges. This method is popular due to the fact that a relatively soft electrically conductive tool electrode can machine hard work piece. Copper electrode is normally used for machining process. Electrode wear rate is the major drawback for EDM researchers. This research focus on fabrication of metal matrix composite (MMC electrode by mixing copper powder with titanium carbide (TiC and Tungsten carbide (WC powder through powder metallurgy process, Copper powder is the major amount of mixing proportion with TiC and WC. However, this paper focus on the early stage of the project where powder metallurgy route was used to determine suitable mixing time, compaction pressure and sintering and compacting process in producing EDM electrode. The newly prepared composite electrodes in different composition are tested in EDM for OHNS steel.

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.

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.

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...

Effective Cost Mechanism for Cloudlet Retransmission and Prioritized VM Scheduling Mechanism over Broker Virtual Machine Communication Framework

OpenAIRE

Raj, Gaurav; Setia, Sonika

2012-01-01

In current scenario cloud computing is most widely increasing platform for task execution. Lot of research is going on to cut down the cost and execution time. In this paper, we propose an efficient algorithm to have an effective and fast execution of task assigned by the user. We proposed an effective communication framework between broker and virtual machine for assigning the task and fetching the results in optimum time and cost using Broker Virtual Machine Communication Framework (BVCF). ...

Machine Protection

International Nuclear Information System (INIS)

Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

2012-01-01

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

Machine Protection

Energy Technology Data Exchange (ETDEWEB)

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

2012-07-01

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

Machine Protection

CERN Document Server

Zerlauth, Markus; Wenninger, Jörg

2012-01-01

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

Dictionary of machine terms

International Nuclear Information System (INIS)

1990-06-01

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

HTS machine laboratory prototype

DEFF Research Database (Denmark)

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

Real-time wavelet-based inline banknote-in-bundle counting for cut-and-bundle machines

Science.gov (United States)

Petker, Denis; Lohweg, Volker; Gillich, Eugen; Türke, Thomas; Willeke, Harald; Lochmüller, Jens; Schaede, Johannes

2011-03-01

Automatic banknote sheet cut-and-bundle machines are widely used within the scope of banknote production. Beside the cutting-and-bundling, which is a mature technology, image-processing-based quality inspection for this type of machine is attractive. We present in this work a new real-time Touchless Counting and perspective cutting blade quality insurance system, based on a Color-CCD-Camera and a dual-core Computer, for cut-and-bundle applications in banknote production. The system, which applies Wavelet-based multi-scale filtering is able to count banknotes inside a 100-bundle within 200-300 ms depending on the window size.

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

SU-D-204-06: Integration of Machine Learning and Bioinformatics Methods to Analyze Genome-Wide Association Study Data for Rectal Bleeding and Erectile Dysfunction Following Radiotherapy in Prostate Cancer

Energy Technology Data Exchange (ETDEWEB)

Oh, J; Deasy, J [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Kerns, S [University of Rochester Medical Center, Rochester, NY (United States); Ostrer, H [Albert Einstein College of Medicine, Bronx, NY (United States); Rosenstein, B [Mount Sinai School of Medicine, New York, NY (United States)

2016-06-15

Purpose: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. Methods: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction. The model building process was performed using 2/3 of samples (training) and the predictive model was tested with 1/3 of samples (validation). To identify important single nucleotide polymorphisms (SNPs), we computed the SNP importance score, resulting from our random forest regression model. We performed gene ontology (GO) enrichment analysis for nearby genes of the important SNPs. Results: After univariate analysis on the training dataset, we filtered out many SNPs with p>0.001, resulting in 749 and 367 SNPs that were used in the model building process for rectal bleeding and erectile dysfunction, respectively. On the validation dataset, our random forest regression model achieved the area under the curve (AUC)=0.70 and 0.62 for rectal bleeding and erectile dysfunction, respectively. We performed GO enrichment analysis for the top 25%, 50%, 75%, and 100% SNPs out of the select SNPs in the univariate analysis. When we used the top 50% SNPs, more plausible biological processes were obtained for both toxicities. An additional test with the top 50% SNPs improved predictive power with AUC=0.71 and 0.65 for rectal bleeding and erectile dysfunction. A better performance was achieved with AUC=0.67 when age and androgen deprivation therapy were added to the model for erectile dysfunction. Conclusion: Our approach that combines machine learning and bioinformatics techniques

SU-D-204-06: Integration of Machine Learning and Bioinformatics Methods to Analyze Genome-Wide Association Study Data for Rectal Bleeding and Erectile Dysfunction Following Radiotherapy in Prostate Cancer

International Nuclear Information System (INIS)

Oh, J; Deasy, J; Kerns, S; Ostrer, H; Rosenstein, B

2016-01-01

Purpose: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. Methods: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction. The model building process was performed using 2/3 of samples (training) and the predictive model was tested with 1/3 of samples (validation). To identify important single nucleotide polymorphisms (SNPs), we computed the SNP importance score, resulting from our random forest regression model. We performed gene ontology (GO) enrichment analysis for nearby genes of the important SNPs. Results: After univariate analysis on the training dataset, we filtered out many SNPs with p>0.001, resulting in 749 and 367 SNPs that were used in the model building process for rectal bleeding and erectile dysfunction, respectively. On the validation dataset, our random forest regression model achieved the area under the curve (AUC)=0.70 and 0.62 for rectal bleeding and erectile dysfunction, respectively. We performed GO enrichment analysis for the top 25%, 50%, 75%, and 100% SNPs out of the select SNPs in the univariate analysis. When we used the top 50% SNPs, more plausible biological processes were obtained for both toxicities. An additional test with the top 50% SNPs improved predictive power with AUC=0.71 and 0.65 for rectal bleeding and erectile dysfunction. A better performance was achieved with AUC=0.67 when age and androgen deprivation therapy were added to the model for erectile dysfunction. Conclusion: Our approach that combines machine learning and bioinformatics techniques

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

OpenAIRE

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

2015-01-01

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

Machine vision systems using machine learning for industrial product inspection

Science.gov (United States)

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

2002-02-01

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

Distinguished figures in mechanism and machine science

CERN Document Server

2014-01-01

This book is composed of chapters that focus specifically on technological developments by distinguished figures in the history of MMS (Mechanism and Machine Science).  Biographies of well-known scientists are also included to describe their efforts and experiences, and surveys of their work and achievements, and a modern interpretation of their legacy are presented. After the first two volumes, the papers in this third volume again cover a wide range within the field of the History of Mechanical Engineering with specific focus on MMS and will be of interest and motivation to the work (historical or not) of many.

Handbook of asynchronous machines with variable speed

CERN Document Server

Razik, Hubert

2013-01-01

This handbook deals with the asynchronous machine in its close environment. It was born from a reflection on this electromagnetic converter whose integration in industrial environments takes a wide part. Previously this type of motor operated at fixed speed, from now on it has been integrated more and more in processes at variable speed. For this reason it seemed useful, or necessary, to write a handbook on the various aspects from the motor in itself, via the control and while finishing by the diagnosis aspect. Indeed, an asynchronous motor is used nowadays in industry where variation speed a

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.

Non-machinery dialysis that achieves blood purification therapy without using full-scale dialysis machines.

Science.gov (United States)

Abe, Takaya; Onoda, Mistutaka; Matsuura, Tomohiko; Sugimura, Jun; Obara, Wataru; Sato, Toshiya; Takahashi, Mihoko; Chiba, Kenta; Abe, Tomiya

2017-09-01

An electrical or water supply and a blood purification machine are required for renal replacement therapy. There is a possibility that acute kidney injury can occur in large numbers and on a wide scale in the case of a massive earthquake, and there is the potential risk that the current supply will be unable to cope with acute kidney injury cases. However, non-machinery dialysis requires exclusive circuits and has the characteristic of not requiring the full-scale dialysis machines. We performed perfusion experiments that used non-machinery dialysis and recent blood purification machines in 30-min intervals, and the effectiveness of non-machinery dialysis was evaluated by the assessing the removal efficiency of potassium, which causes lethal arrhythmia during acute kidney injury. The non-machinery dialysis potassium removal rate was at the same level as continuous blood purification machines with a dialysate flow rate of 5 L/h after 15 min and continuous blood purification machines with a dialysate flow rate of 3 L/h after 30 min. Non-machinery dialysis required an exclusive dialysate circuit, the frequent need to replace bags, and new dialysate exchanged once every 30 min. However, it can be seen as an effective renal replacement therapy for crush-related acute kidney injury patients, even in locations or facilities not having the full-scale dialysis machines.

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,…

Weighted K-means support vector machine for cancer prediction.

Science.gov (United States)

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

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.

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

Science.gov (United States)

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

2011-01-01

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

Application of full-face round by the sequential blasting machine in tunnel excavation

Energy Technology Data Exchange (ETDEWEB)

Cho, Y.D.; Park, B.K.; Lee, S.E.; Lim, H.U.

1995-12-31

Many methods and techniques have been developed to reduce ground vibrations. Some of them are an adoption of electric millisecond detonators with a sequential blasting machine and an improvement of initiating system with an adequate number of delay intervals. To reduce the level of ground vibration in tunnel excavation, the sequential blasting machine (S.B.M.) with decisecond detonators was adopted. A total of 134 blasts was recorded at various sites and the results were analyzed. The distances blast-to-structures were ranged from 20.3 to 42.0 meter, where charge weights were varied from 0.25 to 0.75kg per delay. It is proved that the sequential blasting in tunnel excavation is very effective to control ground vibration.

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...

A Machine Learning Approach to Test Data Generation

DEFF Research Database (Denmark)

Christiansen, Henning; Dahmcke, Christina Mackeprang

2007-01-01

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

Mankind, machines and people

Energy Technology Data Exchange (ETDEWEB)

Hugli, A

1984-01-01

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

A Concrete Framework for Environment Machines

DEFF Research Database (Denmark)

Biernacka, Malgorzata; Danvy, Olivier

2007-01-01

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

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

Science.gov (United States)

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

2016-09-01

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

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

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-03-01

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

Machining of uranium and uranium alloys

International Nuclear Information System (INIS)

Morris, T.O.

1981-01-01

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

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

Science.gov (United States)

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

2002-11-01

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

Re-establishing filtering capabilities of machined porous beryllium via chemical reduction and cleaning

International Nuclear Information System (INIS)

Randall, W.L.

1975-01-01

Porous beryllium is furnished in sheets of varying sizes and thickness; it is therefore necessary that it be machined into specified sizes. A chemical reduction and cleaning procedure was devised to remove the disrupted surface, open the sealed pores of the material, and clean entrapped contaminates from the internal structure. Dimensional stability can be closely controlled and material size is of no consequence. (U.S.)

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

OpenAIRE

Ming Cheng; Le Sun; Giuseppe Buja; Lihua Song

2015-01-01

The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs). Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator perm...

Maintenance management of Holke milling machine's universal dividing head

International Nuclear Information System (INIS)

Bambang L; Dewanto; Tony R; Subroto

2013-01-01

Management in general can be interpreted as a rule for controlling the number of people to achieve the goals desired. Management of maintenance activities can be developed from the above definition by describing the activities of the universal dividing head treatment at Holke milling machine. The equipment is a major tool in the milling machine used to manufacture a wide range of very precise grooves, the hole and diameter should have the same distance, and the even and uneven distribution. The tool was damaged and cannot be functioned. The bearing of fulcrum axle was crushed and the screw shaft was worn out due to aging. Therefore, maintenance activity was carried out by replacing of bearing. Function test was then made, and the result shows that the universal dividing head can function well. (author)

Pileup Mitigation with Machine Learning (PUMML)

Science.gov (United States)

Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin; Schwartz, Matthew D.

2017-12-01

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.

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

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

Chen, Hsinchun

2003-01-01

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

Machine Tool Software

Science.gov (United States)

1988-01-01

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

Machine Ethics: Creating an Ethical Intelligent Agent

OpenAIRE

Anderson, Michael; Anderson, Susan Leigh

2007-01-01

The newly emerging field of machine ethics (Anderson and Anderson 2006) is concerned with adding an ethical dimension to machines. Unlike computer ethics -- which has traditionally focused on ethical issues surrounding humans' use of machines -- machine ethics is concerned with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. In this article we discuss the importance of machine ethics, the need for machines that represent ...

Nanocomposites for Machining Tools

Directory of Open Access Journals (Sweden)

Daria Sidorenko

2017-10-01

Full Text Available Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance.

Machine learning with R

CERN Document Server

Lantz, Brett

2013-01-01

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

Severe plastic deformation of copper by machining: Microstructure refinement and nanostructure evolution with strain

International Nuclear Information System (INIS)

Swaminathan, S.; Brown, T.L.; Chandrasekar, S.; McNelley, T.R.; Compton, W.D.

2007-01-01

The microstructures of copper chips created by plane strain machining at ambient temperature have been analyzed using transmission electron microscopy (TEM) and orientation imaging microscopy (OIM). The strain imposed in the chips was varied by changing the tool rake angle. Characterization of orthogonal faces of the chips showed the microstructure to be essentially uniform through the chip volume, indicative also of uniform deformation

Restrictions of process machine retooling at machine-building enterprises

OpenAIRE

Kuznetsova Elena; Tipner Ludmila; Ershov Alexey

2017-01-01

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...

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Parallel-Machine Scheduling with Time-Dependent and Machine Availability Constraints

Directory of Open Access Journals (Sweden)

Cuixia Miao

2015-01-01

Full Text Available We consider the parallel-machine scheduling problem in which the machines have availability constraints and the processing time of each job is simple linear increasing function of its starting times. For the makespan minimization problem, which is NP-hard in the strong sense, we discuss the Longest Deteriorating Rate algorithm and List Scheduling algorithm; we also provide a lower bound of any optimal schedule. For the total completion time minimization problem, we analyze the strong NP-hardness, and we present a dynamic programming algorithm and a fully polynomial time approximation scheme for the two-machine problem. Furthermore, we extended the dynamic programming algorithm to the total weighted completion time minimization problem.

A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

Energy Technology Data Exchange (ETDEWEB)

Kumar, Sanjeev; Batish, Ajay [Thapar University, Patiala (India); Singh, Rupinder [GNDEC, Ludhiana (India); Singh, T. P. [Symbiosis Institute of Technology, Pune (India)

2014-07-15

In the present study, electric discharge machining process was used for machining of titanium alloys. Eight process parameters were varied during the process. Experimental results showed that current and pulse-on-time significantly affected the performance characteristics. Artificial neural network coupled with Taguchi approach was applied for optimization and prediction of surface roughness. The experimental results and the predicted results showed good agreement. SEM was used to investigate the surface integrity. Analysis for migration of different chemical elements and formation of compounds on the surface was performed using EDS and XRD pattern. The results showed that high discharge energy caused surface defects such as cracks, craters, thick recast layer, micro pores, pin holes, residual stresses and debris. Also, migration of chemical elements both from electrode and dielectric media were observed during EDS analysis. Presence of carbon was seen on the machined surface. XRD results showed formation of titanium carbide compound which precipitated on the machined surface.

Machine learning for epigenetics and future medical applications.

Science.gov (United States)

Holder, Lawrence B; Haque, M Muksitul; Skinner, Michael K

2017-07-03

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

Guest Editorial Electric Machines in Renewable Energy Applications

Energy Technology Data Exchange (ETDEWEB)

Aliprantis, Dionysios; El-Sharkawi, Mohamed; Muljadi, Eduard; Brown, Ian; Chiba, Akira; Dorrell, David; Erlich, Istvan; Kerszenbaum, Isidor Izzy; Levi, Emil; Mayor, Kevin; Mohammed, Osama; Papathanassiou, Stavros; Popescu, Mircea; Qiao, Wei; Wu, Dezheng

2015-12-01

The main objective of this special issue is to collect and disseminate publications that highlight recent advances and breakthroughs in the area of renewable energy resources. The use of these resources for production of electricity is increasing rapidly worldwide. As of 2015, a majority of countries have set renewable electricity targets in the 10%-40% range to be achieved by 2020-2030, with a few notable exceptions aiming for 100% generation by renewables. We are experiencing a truly unprecedented transition away from fossil fuels, driven by environmental, energy security, and socio-economic factors.Electric machines can be found in a wide range of renewable energy applications, such as wind turbines, hydropower and hydrokinetic systems, flywheel energy storage devices, and low-power energy harvesting systems. Hence, the design of reliable, efficient, cost-effective, and controllable electric machines is crucial in enabling even higher penetrations of renewable energy systems in the smart grid of the future. In addition, power electronic converter design and control is critical, as they provide essential controllability, flexibility, grid interface, and integration functions.

The Hooey Machine.

Science.gov (United States)

Scarnati, James T.; Tice, Craig J.

1992-01-01

Describes how students can make and use Hooey Machines to learn how mechanical energy can be transferred from one object to another within a system. The Hooey Machine is made using a pencil, eight thumbtacks, one pushpin, tape, scissors, graph paper, and a plastic lid. (PR)

Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

Science.gov (United States)

Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

2018-04-01

The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

Evaluation of machinability and flexural strength of a novel dental machinable glass-ceramic.

Science.gov (United States)

Qin, Feng; Zheng, Shucan; Luo, Zufeng; Li, Yong; Guo, Ling; Zhao, Yunfeng; Fu, Qiang

2009-10-01

To evaluate the machinability and flexural strength of a novel dental machinable glass-ceramic (named PMC), and to compare the machinability property with that of Vita Mark II and human enamel. The raw batch materials were selected and mixed. Four groups of novel glass-ceramics were formed at different nucleation temperatures, and were assigned to Group 1, Group 2, Group 3 and Group 4. The machinability of the four groups of novel glass-ceramics, Vita Mark II ceramic and freshly extracted human premolars were compared by means of drilling depth measurement. A three-point bending test was used to measure the flexural strength of the novel glass-ceramics. The crystalline phases of the group with the best machinability were identified by X-ray diffraction. In terms of the drilling depth, Group 2 of the novel glass-ceramics proves to have the largest drilling depth. There was no statistical difference among Group 1, Group 4 and the natural teeth. The drilling depth of Vita MK II was statistically less than that of Group 1, Group 4 and the natural teeth. Group 3 had the least drilling depth. In respect of the flexural strength, Group 2 exhibited the maximum flexural strength; Group 1 was statistically weaker than Group 2; there was no statistical difference between Group 3 and Group 4, and they were the weakest materials. XRD of Group 2 ceramic showed that a new type of dental machinable glass-ceramic containing calcium-mica had been developed by the present study and was named PMC. PMC is promising for application as a dental machinable ceramic due to its good machinability and relatively high strength.

Materials and optimized designs for human-machine interfaces via epidermal electronics.

Science.gov (United States)

Jeong, Jae-Woong; Yeo, Woon-Hong; Akhtar, Aadeel; Norton, James J S; Kwack, Young-Jin; Li, Shuo; Jung, Sung-Young; Su, Yewang; Lee, Woosik; Xia, Jing; Cheng, Huanyu; Huang, Yonggang; Choi, Woon-Seop; Bretl, Timothy; Rogers, John A

2013-12-17

Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mechanics of Wood Machining

CERN Document Server

Csanády, Etele

2013-01-01

Wood is one of the most valuable materials for mankind, and since our earliest days wood materials have been widely used. Today we have modern woodworking machine and tools; however, the raw wood materials available are continuously declining. Therefore we are forced to use this precious material more economically, reducing waste wherever possible. This new textbook on the “Mechanics of Wood Machining” combines the quantitative, mathematical analysis of the mechanisms of wood processing with practical recommendations and solutions. Bringing together materials from many sources, the book contains new theoretical and experimental approaches and offers a clear and systematic overview of the theory of wood cutting, thermal loading in wood-cutting tools, dynamic behaviour of tool and work piece, optimum choice of operational parameters and energy consumption, the wear process of the tools, and the general regularities of wood surface roughness. Diagrams are provided for the quick estimation of various process ...

Machining a glass rod with a lathe-type electro-chemical discharge machine

International Nuclear Information System (INIS)

Furutani, Katsushi; Maeda, Hideaki

2008-01-01

This paper deals with the performance of electro-chemical discharge machining (ECDM) of a revolving glass rod. ECDM has been studied for machining insulating materials such as glass and ceramics. In conventional ECDM, an insulating workpiece is dipped in an electrolyte as a working fluid and a tool electrode is pressed on the surface with a small load. In the experiments, a workpiece was revolved to provide fresh working fluid into a gap between the tool electrode and the workpiece. A soda lime grass rod was machined with a thin tungsten rod in NaCl solution. The applied voltage was changed up to 40 V. The rotation speed was set to 0, 0.3, 3 and 30 min −1 . Discharge was observed over an applied voltage of 30 V. The width and depth of the machined grooves and the surface roughness of their bottom were increased with increase of the applied voltage. Although the depth of machining at 3 min −1 was the same as that at 30 min −1 , the width and roughness at 30 min −1 were smaller than those at 3 min −1 . Moreover, because the thickness of vaporization around the tool electrode was decreased with increase of the rotation speed, the width of the machined groove became smaller

An HTS machine laboratory prototype

DEFF Research Database (Denmark)

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

2012-01-01

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

LHC Report: machine development

CERN Multimedia

Rogelio Tomás García for the LHC team

2015-01-01

Machine development weeks are carefully planned in the LHC operation schedule to optimise and further study the performance of the machine. The first machine development session of Run 2 ended on Saturday, 25 July. Despite various hiccoughs, it allowed the operators to make great strides towards improving the long-term performance of the LHC.   The main goals of this first machine development (MD) week were to determine the minimum beam-spot size at the interaction points given existing optics and collimation constraints; to test new beam instrumentation; to evaluate the effectiveness of performing part of the beam-squeezing process during the energy ramp; and to explore the limits on the number of protons per bunch arising from the electromagnetic interactions with the accelerator environment and the other beam. Unfortunately, a series of events reduced the machine availability for studies to about 50%. The most critical issue was the recurrent trip of a sextupolar corrector circuit –...

Probability distribution of machining center failures

International Nuclear Information System (INIS)

Jia Yazhou; Wang Molin; Jia Zhixin

1995-01-01

Through field tracing research for 24 Chinese cutter-changeable CNC machine tools (machining centers) over a period of one year, a database of operation and maintenance for machining centers was built, the failure data was fitted to the Weibull distribution and the exponential distribution, the effectiveness was tested, and the failure distribution pattern of machining centers was found. Finally, the reliability characterizations for machining centers are proposed

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...

The Newest Machine Material

International Nuclear Information System (INIS)

Seo, Yeong Seop; Choe, Byeong Do; Bang, Meong Sung

2005-08-01

This book gives descriptions of machine material with classification of machine material and selection of machine material, structure and connection of material, coagulation of metal and crystal structure, equilibrium diagram, properties of metal material, elasticity and plasticity, biopsy of metal, material test and nondestructive test. It also explains steel material such as heat treatment of steel, cast iron and cast steel, nonferrous metal materials, non metallic materials, and new materials.

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.

Machinability of advanced materials

CERN Document Server

Davim, J Paulo

2014-01-01

Machinability of Advanced Materials addresses the level of difficulty involved in machining a material, or multiple materials, with the appropriate tooling and cutting parameters.  A variety of factors determine a material's machinability, including tool life rate, cutting forces and power consumption, surface integrity, limiting rate of metal removal, and chip shape. These topics, among others, and multiple examples comprise this research resource for engineering students, academics, and practitioners.

A one-dimensional Q-machine model taking into account charge-exchange collisions

International Nuclear Information System (INIS)

Maier, H.; Kuhn, S.

1992-01-01

The Q-machine is a nontrivial bounded plasma system which is excellently suited not only for fundamental plasma physics investigations but also for the development and testing of new theoretical methods for modeling such systems. However, although Q-machines have now been around for over thirty years, it appears that there exist no comprehensive theoretical models taking into account their considerable geometrical and physical complexity with a reasonable degree of self-consistency. In the present context we are concerned with the low-density, single-emitter Q-machine, for which the most widely used model is probably the (one-dimensional) ''collisionless plane-diode model'', which has originally been developed for thermionic diodes. Although the validity of this model is restricted to certain ''axial'' phenomena, we consider it a suitable starting point for extensions of various kinds. While a generalization to two-dimensional geometry (with still collisionless plasma) is being reported elsewhere, the present work represents a first extension to collisional plasma (with still one-dimensional geometry). (author) 12 refs., 2 figs

Conceptual Modeling of Time-Varying Information

DEFF Research Database (Denmark)

Gregersen, Heidi; Jensen, Christian S.

2004-01-01

A wide range of database applications manage information that varies over time. Many of the underlying database schemas of these were designed using the Entity-Relationship (ER) model. In the research community as well as in industry, it is common knowledge that the temporal aspects of the mini......-world are important, but difficult to capture using the ER model. Several enhancements to the ER model have been proposed in an attempt to support the modeling of temporal aspects of information. Common to the existing temporally extended ER models, few or no specific requirements to the models were given...

C4.5 programs for machine learning

CERN Document Server

Quinlan, J Ross

1992-01-01

Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available for download (see below). C4.5 starts with large sets of cases belonging to known classes. The cases,

The Use of Tygon Tubing Sterilized by Gamma Radiation in Heart-Lung By-Pass Machines

Energy Technology Data Exchange (ETDEWEB)

Alladine, M. F.; Gibbons, J. R. P. [London Chest Hospital, London (United Kingdom)

1967-09-15

Tygon tubing is a co-polymer polyvinyl chloride. This tubing is used throughout the circuit of our heart-lung by-pass machines and had been sterilized by autoclaving or exposure to ethylene oxide gas. Tubing sterilized by autoclaving was noted to have a decrease in tensile strength and the transparency of the tubing was affected. Sterilization by ethylene oxide had the disadvantage that gas was often present in the lumen of the tube when connected to the machine. Lengths of Tygon tubing were sterilized by gamma radiation at varying radiation levels and temperatures. Bacteriological studies were done on the tubing and estimation of tensile strength carried out. We have found that a level of radiation of 2. 5 to 3. 0 Mrad at 20°C produces little or no change in tensile strength and the tubing remains bacteriologically sterile. Tygon tubing sterilized in this way has now been used in over sixty heart-lung by-pass machine operations. (author)

Control system of mutually coupled switched reluctance motor drive of mining machines in generator mode

Science.gov (United States)

Ivanov, A. S.; Kalanchin, I. Yu; Pugacheva, E. E.

2017-09-01

One of the first electric motors, based on the use of electromagnets, was a reluctance motor in the XIX century. Due to the complexities in the implementation of control system the development of switched reluctance electric machines was repeatedly initiated only in 1960 thanks to the development of computers and power electronic devices. The main feature of these machines is the capacity to work both in engine mode and in generator mode. Thanks to a simple and reliable design in which there is no winding of the rotor, commutator, permanent magnets, a reactive gate-inductor electric drive operating in the engine mode is actively being introduced into various areas such as car industry, production of household appliances, wind power engineering, as well as responsible production processes in the oil and mining industries. However, the existing shortcomings of switched reluctance electric machines, such as nonlinear pulsations of electromagnetic moment, the presence of three or four phase supply system and sensor of rotor position prevent wide distribution of this kind of electric machines.

Machine Learning-Empowered Biometric Methods for Biomedicine Applications

Directory of Open Access Journals (Sweden)

Qingxue Zhang

2017-07-01

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

Nuclear reactor machine refuelling system

International Nuclear Information System (INIS)

Cashen, W.S.; Erwin, D.

1977-01-01

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

Machine Translation Effect on Communication

DEFF Research Database (Denmark)

Jensen, Mika Yasuoka; Bjørn, Pernille

2011-01-01

Intercultural collaboration facilitated by machine translation has gradually spread in various settings. Still, little is known as for the practice of machine-translation mediated communication. This paper investigates how machine translation affects intercultural communication in practice. Based...... on communication in which multilingual communication system is applied, we identify four communication types and its’ influences on stakeholders’ communication process, especially focusing on establishment and maintenance of common ground. Different from our expectation that quality of machine translation results...

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...

Formal modeling of virtual machines

Science.gov (United States)

Cremers, A. B.; Hibbard, T. N.

1978-01-01

Systematic software design can be based on the development of a 'hierarchy of virtual machines', each representing a 'level of abstraction' of the design process. The reported investigation presents the concept of 'data space' as a formal model for virtual machines. The presented model of a data space combines the notions of data type and mathematical machine to express the close interaction between data and control structures which takes place in a virtual machine. One of the main objectives of the investigation is to show that control-independent data type implementation is only of limited usefulness as an isolated tool of program development, and that the representation of data is generally dictated by the control context of a virtual machine. As a second objective, a better understanding is to be developed of virtual machine state structures than was heretofore provided by the view of the state space as a Cartesian product.

Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

Science.gov (United States)

Ardi, S.; Ardyansyah, D.

2018-02-01

In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

Permutation parity machines for neural synchronization

International Nuclear Information System (INIS)

Reyes, O M; Kopitzke, I; Zimmermann, K-H

2009-01-01

Synchronization of neural networks has been studied in recent years as an alternative to cryptographic applications such as the realization of symmetric key exchange protocols. This paper presents a first view of the so-called permutation parity machine, an artificial neural network proposed as a binary variant of the tree parity machine. The dynamics of the synchronization process by mutual learning between permutation parity machines is analytically studied and the results are compared with those of tree parity machines. It will turn out that for neural synchronization, permutation parity machines form a viable alternative to tree parity machines

Manipulator for plasma-assisted machining of components made of materials with low machinability

International Nuclear Information System (INIS)

Lyaoshchukov, M.M.; Agadzhanyan, R.A.

1984-01-01

The All-Union Scientific-Research and Technological Institute of Pump Engineering developed, and the ''Uralgidromash'' Production Association has adopted, a manipulator with remote control for the plasma-assisted machining (PAM) of components made of materials with low machinability. The manipulator is distinguished by its universal design and can be used for machining both external and internal surfaces of the bodies of revolution and also end faces and various curvilinear surfaces

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

Science.gov (United States)

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

2018-01-16

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

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

Science.gov (United States)

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Distributing flight dynamics products via the World Wide Web

Science.gov (United States)

Woodard, Mark; Matusow, David

1996-01-01

The NASA Flight Dynamics Products Center (FDPC), which make available selected operations products via the World Wide Web, is reported on. The FDPC can be accessed from any host machine connected to the Internet. It is a multi-mission service which provides Internet users with unrestricted access to the following standard products: antenna contact predictions; ground tracks; orbit ephemerides; mean and osculating orbital elements; earth sensor sun and moon interference predictions; space flight tracking data network summaries; and Shuttle transport system predictions. Several scientific data bases are available through the service.

29 CFR 1910.218 - Forging machines.

Science.gov (United States)

2010-07-01

... 29 Labor 5 2010-07-01 2010-07-01 false Forging machines. 1910.218 Section 1910.218 Labor... OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.218 Forging machines. (a... other identifier, for the forging machine which was inspected. (ii) Scheduling and recording the...

The achievements of the Z-machine

International Nuclear Information System (INIS)

Larousserie, D.

2008-01-01

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.)

Vector control of induction machines

CERN Document Server

Robyns, Benoit

2012-01-01

After a brief introduction to the main law of physics and fundamental concepts inherent in electromechanical conversion, ""Vector Control of Induction Machines"" introduces the standard mathematical models for induction machines - whichever rotor technology is used - as well as several squirrel-cage induction machine vector-control strategies. The use of causal ordering graphs allows systematization of the design stage, as well as standardization of the structure of control devices. ""Vector Control of Induction Machines"" suggests a unique approach aimed at reducing parameter sensitivity for

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

Science.gov (United States)

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

2014-01-01

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