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

Treesearch

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

2012-01-01

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

Electronic vending machines for dispensing rapid HIV self-testing kits: a case study.

PubMed

Young, Sean D; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-02-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV self-testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: (1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, (2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and (3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits.

Electronic vending machines for dispensing rapid HIV self-testing kits: A case study

PubMed Central

Young, Sean D.; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-01-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV-self testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: 1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, 2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and 3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits. PMID:23777528

Test build from Robotic Fiber Placement Machine

NASA Image and Video Library

2015-10-01

MAJID BABAI, LEFT, CHIEF OF THE NONMETALLIC MANUFACTURING BRANCH AT MARSHALL, AND STEPHEN RICHARDSON, LEAD FOR THE STRUCTURAL DEVELOPMENT TEAM, TAKE A CLOSER LOOK AT ONE OF THE FIRST TEST BUILDS MADE BY THE NEW ROBOTIC FIBER PLACEMENT MACHINE BEHIND THEM.

Testing of Anesthesia Machines and Defibrillators in Healthcare Institutions.

PubMed

Gurbeta, Lejla; Dzemic, Zijad; Bego, Tamer; Sejdic, Ervin; Badnjevic, Almir

2017-09-01

To improve the quality of patient treatment by improving the functionality of medical devices in healthcare institutions. To present the results of the safety and performance inspection of patient-relevant output parameters of anesthesia machines and defibrillators defined by legal metrology. This study covered 130 anesthesia machines and 161 defibrillators used in public and private healthcare institutions, during a period of two years. Testing procedures were carried out according to international standards and legal metrology legislative procedures in Bosnia and Herzegovina. The results show that in 13.84% of tested anesthesia machine and 14.91% of defibrillators device performance is not in accordance with requirements and should either have its results be verified, or the device removed from use or scheduled for corrective maintenance. Research emphasizes importance of independent safety and performance inspections, and gives recommendations for the frequency of inspection based on measurements. Results offer implications for adequacy of preventive and corrective maintenance performed in healthcare institutions. Based on collected data, the first digital electronical database of anesthesia machines and defibrillators used in healthcare institutions in Bosnia and Herzegovina is created. This database is a useful tool for tracking each device's performance over time.

A new class of high-G and long-duration shock testing machines

NASA Astrophysics Data System (ADS)

Rastegar, Jahangir

2018-03-01

Currently available methods and systems for testing components for survival and performance under shock loading suffer from several shortcomings for use to simulate high-G acceleration events with relatively long duration. Such events include most munitions firing and target impact, vehicular accidents, drops from relatively high heights, air drops, impact between machine components, and other similar events. In this paper, a new class of shock testing machines are presented that can be used to subject components to be tested to high-G acceleration pulses of prescribed amplitudes and relatively long durations. The machines provide for highly repeatable testing of components. The components are mounted on an open platform for ease of instrumentation and video recording of their dynamic behavior during shock loading tests.

Dynamic impact testing with servohydraulic testing machines

NASA Astrophysics Data System (ADS)

Bardenheier, R.; Rogers, G.

2006-08-01

The design concept of “Crashworthiness” requires the information on material behaviour under dynamic impact loading in order to describe and predict the crash behaviour of structures. Especially the transport related industries, like car, railway or aircraft industry, pursue the concept of lightweight design for a while now. The materials' maximum constraint during loading is pushed to permanently increasing figures. This means in terms of crashworthiness that the process of energy absorption in structures and the mechanical behaviour of materials must well understood and can be described appropriately by material models. In close cooperation with experts from various industries and research institutes Instron has developed throughout the past years a new family of servohydraulic testing machines specifically designed to cope with the dynamics of high rate testing. Main development steps are reflected versus their experimental necessities.

Development of An Assessment Test for An Anesthetic Machine.

PubMed

Tiviraj, Supinya; Yokubol, Bencharatana; Amornyotin, Somchai

2016-05-01

The study is aimed to develop and assess the quality of an evaluation form used to evaluate the nurse anesthetic trainees' skills in undertaking a pre-use check of an anesthetic machine. An evaluation form comprising 25 items was developed, informed by the guidelines published by national anesthesiologist societies and refined to reflect the anesthetic machine used in our institution. The item-checking included the cylinder supplies and medical gas pipelines, vaporizer back bar, ventilator anesthetic breathing system, scavenging system and emergency back-up equipment. The authors sought the opinions of five experienced anesthetic trainers to judge the validity of the content. The authors measured its inter-rater reliability when used by two achievement scores evaluating the performance of 36 nurse anesthetic trainees undertaking 15-minute anesthetic machine checks and test-retest the reliability correlation scores between the two performances in the seven days interval. The five experienced anesthesiologists agreed that the evaluation form accurately reflected the objectives of anesthetic machine checking, equating to an index of congruency of 1.00. The inter-rater reliability of the independent assessors scoring was 0.977 (p = 0.01) and the test-retest reliability was 0.883 (p = 0.01). An evaluation form proved to be a reliable and effective tool for assessing the anesthetic nurse trainees' checking of an anesthetic machine before the use. This evaluation form was brief clear and practical to use, and should help to improve anesthetic nurse education and the patient safety.

Radial-piston pump for drive of test machines

NASA Astrophysics Data System (ADS)

Nizhegorodov, A. I.; Gavrilin, A. N.; Moyzes, B. B.; Cherkasov, A. I.; Zharkevich, O. M.; Zhetessova, G. S.; Savelyeva, N. A.

2018-01-01

The article reviews the development of radial-piston pump with phase control and alternating-flow mode for seismic-testing platforms and other test machines. The prospects for use of the developed device are proved. It is noted that the method of frequency modulation with the detection of the natural frequencies is easily realized by using the radial-piston pump. The prospects of further research are given proof.

A cost-effective, accurate machine for testing the torsional strength of sheep long bones.

PubMed

Jämsä, T; Jalovaara, P

1996-07-01

A cost-effective torsional testing machine for sheep long bones was constructed. The machine was fabricated on a disused standard turning lathe. The angular speed used was 6.5 degrees/s. A precision amplifier using modern low-noise, low-drift operational amplifiers was developed. The maximum torsional load was 250 Nm, the sensitivity 0.5 Nm and the total machine inaccuracy less than 1.0%. The standard error of torsional testing was 3.0% when seven pairs of intact sheep tibiae were tested.

Fabrication and Tests of M240 Machine Gun Barrels Lined with Stellite 25

DTIC Science & Technology

2016-04-01

ARL-TR-7662 ● APR 2016 US Army Research Laboratory Fabrication and Tests of M240 Machine Gun Barrels Lined with Stellite 25...Fabrication and Tests of M240 Machine Gun Barrels Lined with Stellite 25 by William S de Rosset and Sean Fudger Weapons and Materials Research...

Human Machine Interface Programming and Testing

NASA Technical Reports Server (NTRS)

Foster, Thomas Garrison

2013-01-01

Human Machine Interface (HMI) Programming and Testing is about creating graphical displays to mimic mission critical ground control systems in order to provide NASA engineers with the ability to monitor the health management of these systems in real time. The Health Management System (HMS) is an online interactive human machine interface system that monitors all Kennedy Ground Control Subsystem (KGCS) hardware in the field. The Health Management System is essential to NASA engineers because it allows remote control and monitoring of the health management systems of all the Programmable Logic Controllers (PLC) and associated field devices. KGCS will have equipment installed at the launch pad, Vehicle Assembly Building, Mobile Launcher, as well as the Multi-Purpose Processing Facility. I am designing graphical displays to monitor and control new modules that will be integrated into the HMS. The design of the display screen will closely mimic the appearance and functionality of the actual modules. There are many different field devices used to monitor health management and each device has its own unique set of health management related data, therefore each display must also have its own unique way to display this data. Once the displays are created, the RSLogix5000 application is used to write software that maps all the required data read from the hardware to the graphical display. Once this data is mapped to its corresponding display item, the graphical display and hardware device will be connected through the same network in order to test all possible scenarios and types of data the graphical display was designed to receive. Test Procedures will be written to thoroughly test out the displays and ensure that they are working correctly before being deployed to the field. Additionally, the Kennedy Ground Controls Subsystem's user manual will be updated to explain to the NASA engineers how to use the new module displays.

Investigation of Dynamic Force/Vibration Transmission Characteristics of Four-Square Type Gear Durability Test Machines

NASA Technical Reports Server (NTRS)

Kahraman, Ahmet

2002-01-01

In this study, design requirements for a dynamically viable, four-square type gear test machine are investigated. Variations of four-square type gear test machines have been in use for durability and dynamics testing of both parallel- and cross-axis gear set. The basic layout of these machines is illustrated. The test rig is formed by two gear pairs, of the same reduction ratio, a test gear pair and a reaction gear pair, connected to each other through shafts of certain torsional flexibility to form an efficient, closed-loop system. A desired level of constant torque is input to the circuit through mechanical (a split coupling with a torque arm) or hydraulic (a hydraulic actuator) means. The system is then driven at any desired speed by a small DC motor. The main task in hand is the isolation of the test gear pair from the reaction gear pair under dynamic conditions. Any disturbances originated at the reaction gear mesh might potentially travel to the test gearbox, altering the dynamic loading conditions of the test gear mesh, and hence, influencing the outcome of the durability or dynamics test. Therefore, a proper design of connecting structures becomes a major priority. Also, equally important is the issue of how close the operating speed of the machine is to the resonant frequencies of the gear meshes. This study focuses on a detailed analysis of the current NASA Glenn Research Center gear pitting test machine for evaluation of its resonance and vibration isolation characteristics. A number of these machines as the one illustrated has been used over last 30 years to establish an extensive database regarding the influence of the gear materials, processes surface treatments and lubricants on gear durability. This study is intended to guide an optimum design of next generation test machines for the most desirable dynamic characteristics.

Can machines think? A report on Turing test experiments at the Royal Society

NASA Astrophysics Data System (ADS)

Warwick, Kevin; Shah, Huma

2016-11-01

In this article we consider transcripts that originated from a practical series of Turing's Imitation Game that was held on 6 and 7 June 2014 at the Royal Society London. In all cases the tests involved a three-participant simultaneous comparison by an interrogator of two hidden entities, one being a human and the other a machine. Each of the transcripts considered here resulted in a human interrogator being fooled such that they could not make the 'right identification', that is, they could not say for certain which was the machine and which was the human. The transcripts presented all involve one machine only, namely 'Eugene Goostman', the result being that the machine became the first to pass the Turing test, as set out by Alan Turing, on unrestricted conversation. This is the first time that results from the Royal Society tests have been disclosed and discussed in a paper.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine 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 SMV as a method for classification. From trial to trial, SVM produces consistent results

[The testing system for OCP of the digital X-ray machine].

PubMed

Wang, Yan; Mo, Guoming; Wang, Juru; Zhou, Tao; Yu, Jianguo

2011-09-01

In this paper, we designed a testing system for operator control panel of a high-voltage and high-frequency X-ray machine, and an online testing software for functional components, in order to help the testing engineers to improve their work efficiency.

Coordinate measuring machine test standard apparatus and method

DOEpatents

Bieg, L.F.

1994-08-30

A coordinate measuring machine test standard apparatus and method are disclosed which includes a rotary spindle having an upper phase plate and an axis of rotation, a kinematic ball mount attached to the phase plate concentric with the axis of rotation of the phase plate, a groove mounted at the circumference of the phase plate, and an arm assembly which rests in the groove. The arm assembly has a small sphere at one end and a large sphere at the other end. The small sphere may be a coordinate measuring machine probe tip and may have variable diameters. The large sphere is secured in the kinematic ball mount and the arm is held in the groove. The kinematic ball mount includes at least three mounting spheres and the groove is an angular locating groove including at least two locking spheres. The arm may have a hollow inner core and an outer layer. The rotary spindle may be a ratio reducer. The device is used to evaluate the measuring performance of a coordinate measuring machine for periodic recertification, including 2 and 3 dimensional accuracy, squareness, straightness, and angular accuracy. 5 figs.

Coordinate measuring machine test standard apparatus and method

DOEpatents

Bieg, Lothar F.

1994-08-30

A coordinate measuring machine test standard apparatus and method which iudes a rotary spindle having an upper phase plate and an axis of rotation, a kinematic ball mount attached to the phase plate concentric with the axis of rotation of the phase plate, a groove mounted at the circumference of the phase plate, and an arm assembly which rests in the groove. The arm assembly has a small sphere at one end and a large sphere at the other end. The small sphere may be a coordinate measuring machine probe tip and may have variable diameters. The large sphere is secured in the kinematic ball mount and the arm is held in the groove. The kinematic ball mount includes at least three mounting spheres and the groove is an angular locating groove including at least two locking spheres. The arm may have a hollow inner core and an outer layer. The rotary spindle may be a ratio reducer. The device is used to evaluate the measuring performance of a coordinate measuring machine for periodic recertification, including 2 and 3 dimensional accuracy, squareness, straightness, and angular accuracy.

VIEW EASTLEFTBUILDING 2 PHYSICAL TESTING HOUSE (1928) RIGHTBUILDING 7 MACHINE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

VIEW EAST-LEFT-BUILDING 2 PHYSICAL TESTING HOUSE (1928) RIGHT-BUILDING 7 MACHINE SHOP (1901 SECTION) - John A. Roebling's Sons Company & American Steel & Wire Company, South Broad, Clark, Elmer, Mott & Hudson Streets, Trenton, Mercer County, NJ

Comparison between laser interferometric and calibrated artifacts for the geometric test of machine tools

NASA Astrophysics Data System (ADS)

Sousa, Andre R.; Schneider, Carlos A.

2001-09-01

A touch probe is used on a 3-axis vertical machine center to check against a hole plate, calibrated on a coordinate measuring machine (CMM). By comparing the results obtained from the machine tool and CMM, the main machine tool error components are measured, attesting the machine accuracy. The error values can b used also t update the error compensation table at the CNC, enhancing the machine accuracy. The method is easy to us, has a lower cost than classical test techniques, and preliminary results have shown that its uncertainty is comparable to well established techniques. In this paper the method is compared with the laser interferometric system, regarding reliability, cost and time efficiency.

The Critical Compression Load for a Universal Testing Machine When the Specimen Is Loaded Through Knife Edges

NASA Technical Reports Server (NTRS)

Lundquist, Eugene E; Schwartz, Edward B

1942-01-01

The results of a theoretical and experimental investigation to determine the critical compression load for a universal testing machine are presented for specimens loaded through knife edges. The critical load for the testing machine is the load at which one of the loading heads becomes laterally instable in relation to the other. For very short specimens the critical load was found to be less than the rated capacity given by the manufacturer for the machine. A load-length diagram is proposed for defining the safe limits of the test region for the machine. Although this report is particularly concerned with a universal testing machine of a certain type, the basic theory which led to the derivation of the general equation for the critical load, P (sub cr) = alpha L can be applied to any testing machine operated in compression where the specimen is loaded through knife edges. In this equation, L is the length of the specimen between knife edges and alpha is the force necessary to displace the upper end of the specimen unit horizontal distance relative to the lower end of the specimen in a direction normal to the knife edges through which the specimen is loaded.

Fracture Tests of Etched Components Using a Focused Ion Beam Machine

NASA Technical Reports Server (NTRS)

Kuhn, Jonathan, L.; Fettig, Rainer K.; Moseley, S. Harvey; Kutyrev, Alexander S.; Orloff, Jon; Powers, Edward I. (Technical Monitor)

2000-01-01

Many optical MEMS device designs involve large arrays of thin (0.5 to 1 micron components subjected to high stresses due to cyclic loading. These devices are fabricated from a variety of materials, and the properties strongly depend on size and processing. Our objective is to develop standard and convenient test methods that can be used to measure the properties of large numbers of witness samples, for every device we build. In this work we explore a variety of fracture test configurations for 0.5 micron thick silicon nitride membranes machined using the Reactive Ion Etching (RIE) process. Testing was completed using an FEI 620 dual focused ion beam milling machine. Static loads were applied using a probe. and dynamic loads were applied through a piezo-electric stack mounted at the base of the probe. Results from the tests are presented and compared, and application for predicting fracture probability of large arrays of devices are considered.

Prioritizing individual genetic variants after kernel machine testing using variable selection.

PubMed

He, Qianchuan; Cai, Tianxi; Liu, Yang; Zhao, Ni; Harmon, Quaker E; Almli, Lynn M; Binder, Elisabeth B; Engel, Stephanie M; Ressler, Kerry J; Conneely, Karen N; Lin, Xihong; Wu, Michael C

2016-12-01

Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity by State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach. © 2016 WILEY PERIODICALS, INC.

Can Machine Scoring Deal with Broad and Open Writing Tests as Well as Human Readers?

ERIC Educational Resources Information Center

McCurry, Doug

2010-01-01

This article considers the claim that machine scoring of writing test responses agrees with human readers as much as humans agree with other humans. These claims about the reliability of machine scoring of writing are usually based on specific and constrained writing tasks, and there is reason for asking whether machine scoring of writing requires…

Tests of Lead-bronze Bearings in the DVL Bearing-testing Machine

NASA Technical Reports Server (NTRS)

Fischer, G

1940-01-01

The lead-bronze bearings tested in the DVL machine have proven themselves very sensitive to load changes as in comparison with bearings of light metal. In order to prevent surface injuries and consequently running interruptions, the increase of the load has to be made in small steps with sufficient run-in time between steps. The absence of lead in the running surface, impurities in the alloy (especially iron) and surface irregularities (pores) decreases the load-carrying capacity of the bearing to two or three times that of the static load.

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

NASA Astrophysics Data System (ADS)

Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

2018-03-01

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

A microdynamic version of the tensile test machine

NASA Technical Reports Server (NTRS)

Glaser, R. J.

1991-01-01

Very large space structures require structural reactions to control forces associated with nanometer-level displacements; JPL has accordingly built a tensile test machine capable of mN-level force measurements and nm-level displacement measurements, with a view to the study of structural linear joining technology at the lower limit of its resolution. The tester is composed of a moving table that is supported by six flexured legs and a test specimen cantilevered off the table to ground. Three vertical legs contain piezoactuators allowing changes in length up to 200 microns while generating axial load and bending moments. Displacements between ground and table are measured by means of three laser-interferometric channels.

Acceptability of using electronic vending machines to deliver oral rapid HIV self-testing kits: a qualitative study.

PubMed

Young, Sean D; Daniels, Joseph; Chiu, ChingChe J; Bolan, Robert K; Flynn, Risa P; Kwok, Justin; Klausner, Jeffrey D

2014-01-01

Rates of unrecognized HIV infection are significantly higher among Latino and Black men who have sex with men (MSM). Policy makers have proposed that HIV self-testing kits and new methods for delivering self-testing could improve testing uptake among minority MSM. This study sought to conduct qualitative assessments with MSM of color to determine the acceptability of using electronic vending machines to dispense HIV self-testing kits. African American and Latino MSM were recruited using a participant pool from an existing HIV prevention trial on Facebook. If participants expressed interest in using a vending machine to receive an HIV self-testing kit, they were emailed a 4-digit personal identification number (PIN) code to retrieve the test from the machine. We followed up with those who had tested to assess their willingness to participate in an interview about their experience. Twelve kits were dispensed and 8 interviews were conducted. In general, participants expressed that the vending machine was an acceptable HIV test delivery method due to its novelty and convenience. Acceptability of this delivery model for HIV testing kits was closely associated with three main factors: credibility, confidentiality, and convenience. Future research is needed to address issues, such as user-induced errors and costs, before scaling up the dispensing method.

Comparison of several asphalt design methods.

DOT National Transportation Integrated Search

1998-01-01

This laboratory study compared several methods of selecting the optimum asphalt content of surface mixes. Six surface mixes were tested using the 50-blow Marshall design, the 75-blow Marshall design, two brands of SHRP gyratory compactors, and the U....

Antifungal Susceptibility Testing in HIV/AIDS Patients: a Comparison Between Automated Machine and Manual Method.

PubMed

Nelwan, Erni J; Indrasanti, Evi; Sinto, Robert; Nurchaida, Farida; Sosrosumihardjo, Rustadi

2016-01-01

to evaluate the performance of Vitek2 compact machine (Biomerieux Inc. ver 04.02, France) in reference to manual methods for susceptibility test for Candida resistance among HIV/AIDS patients. a comparison study to evaluate Vitek2 compact machine (Biomerieux Inc. ver 04.02, France) in reference to manual methods for susceptibility test for Candida resistance among HIV/AIDS patient was done. Categorical agreement between manual disc diffusion and Vitek2 machine was calculated using predefined criteria. Time to susceptibility result for automated and manual methods were measured. there were 137 Candida isolates comprising eight Candida species with C.albicans and C. glabrata as the first (56.2%) and second (15.3%) most common species, respectively. For fluconazole drug, among the C. albicans, 2.6% was found resistant on manual disc diffusion methods and no resistant was determined by Vitek2 machine; whereas 100% C. krusei was identified as resistant on both methods. Resistant patterns for C. glabrata to fluconazole, voriconazole and amphotericin B were 52.4%, 23.8%, 23.8% vs. 9.5%, 9.5%, 4.8% respectively between manual diffusion disc methods and Vitek2 machine. Time to susceptibility result for automated methods compared to Vitex2 machine was shorter for all Candida species. there is a good categorical agreement between manual disc diffusion and Vitek2 machine, except for C. glabrata for measuring the antifungal resistant. Time to susceptibility result for automated methods is shorter for all Candida species.

A New Method for Incremental Testing of Finite State Machines

NASA Technical Reports Server (NTRS)

Pedrosa, Lehilton Lelis Chaves; Moura, Arnaldo Vieira

2010-01-01

The automatic generation of test cases is an important issue for conformance testing of several critical systems. We present a new method for the derivation of test suites when the specification is modeled as a combined Finite State Machine (FSM). A combined FSM is obtained conjoining previously tested submachines with newly added states. This new concept is used to describe a fault model suitable for incremental testing of new systems, or for retesting modified implementations. For this fault model, only the newly added or modified states need to be tested, thereby considerably reducing the size of the test suites. The new method is a generalization of the well-known W-method and the G-method, but is scalable, and so it can be used to test FSMs with an arbitrarily large number of states.

Acceptability of Using Electronic Vending Machines to Deliver Oral Rapid HIV Self-Testing Kits: A Qualitative Study

PubMed Central

Young, Sean D.; Daniels, Joseph; Chiu, ChingChe J.; Bolan, Robert K.; Flynn, Risa P.; Kwok, Justin; Klausner, Jeffrey D.

2014-01-01

Introduction Rates of unrecognized HIV infection are significantly higher among Latino and Black men who have sex with men (MSM). Policy makers have proposed that HIV self-testing kits and new methods for delivering self-testing could improve testing uptake among minority MSM. This study sought to conduct qualitative assessments with MSM of color to determine the acceptability of using electronic vending machines to dispense HIV self-testing kits. Materials and Methods African American and Latino MSM were recruited using a participant pool from an existing HIV prevention trial on Facebook. If participants expressed interest in using a vending machine to receive an HIV self-testing kit, they were emailed a 4-digit personal identification number (PIN) code to retrieve the test from the machine. We followed up with those who had tested to assess their willingness to participate in an interview about their experience. Results Twelve kits were dispensed and 8 interviews were conducted. In general, participants expressed that the vending machine was an acceptable HIV test delivery method due to its novelty and convenience. Discussion Acceptability of this delivery model for HIV testing kits was closely associated with three main factors: credibility, confidentiality, and convenience. Future research is needed to address issues, such as user-induced errors and costs, before scaling up the dispensing method. PMID:25076208

Growth of plant root cultures in liquid- and gas-dispersed reactor environments.

PubMed

McKelvey, S A; Gehrig, J A; Hollar, K A; Curtis, W R

1993-01-01

The growth of Agrobacterium transformed "hairy root" cultures of Hyoscyamus muticus was examined in various liquid- and gas-dispersed bioreactor configurations. Reactor runs were replicated to provide statistical comparisons of nutrient availability on culture performance. Accumulated tissue mass in submerged air-sparged reactors was 31% of gyratory shake-flask controls. Experiments demonstrate that poor performance of sparged reactors is not due to bubble shear damage, carbon dioxide stripping, settling, or flotation of roots. Impaired oxygen transfer due to channeling and stagnation of the liquid phase are the apparent causes of poor growth. Roots grown on a medium-perfused inclined plane grew at 48% of gyratory controls. This demonstrates the ability of cultures to partially compensate for poor liquid distribution through vascular transport of nutrients. A reactor configuration in which the medium is sprayed over the roots and permitted to drain down through the root tissue was able to provide growth rates which are statistically indistinguishable (95% T-test) from gyratory shake-flask controls. In this type of spray/trickle-bed configuration, it is shown that distribution of the roots becomes a key factor in controlling the rate of growth. Implications of these results regarding design and scale-up of bioreactors to produce fine chemicals from root cultures are discussed.

Adding Test Generation to the Teaching Machine

ERIC Educational Resources Information Center

Bruce-Lockhart, Michael; Norvell, Theodore; Crescenzi, Pierluigi

2009-01-01

We propose an extension of the Teaching Machine project, called Quiz Generator, that allows instructors to produce assessment quizzes in the field of algorithm and data structures quite easily. This extension makes use of visualization techniques and is based on new features of the Teaching Machine that allow third-party visualizers to be added as…

Stirling machine operating experience

NASA Technical Reports Server (NTRS)

Ross, Brad; Dudenhoefer, James E.

1991-01-01

Numerous Stirling machines have been built and operated, but the operating experience of these machines is not well known. It is important to examine this operating experience in detail, because it largely substantiates the claim that Stirling machines are capable of reliable and lengthy lives. The amount of data that exists is impressive, considering that many of the machines that have been built are developmental machines intended to show proof of concept, and were not expected to operate for any lengthy period of time. Some Stirling machines (typically free-piston machines) achieve long life through non-contact bearings, while other Stirling machines (typically kinematic) have achieved long operating lives through regular seal and bearing replacements. In addition to engine and system testing, life testing of critical components is also considered.

Standard method of test for grindability of coal by the Hardgrove-machine method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1975-01-01

A procedure is described for sampling coal, grinding in a Hardgrove grinding machine, and passing through standard sieves to determine the degree of pulverization of coals. The grindability index of the coal tested is calculated from a calibration chart prepared by plotting weight of material passing a No. 200 sieve versus the Hardgrove Grindability Index for the standard reference samples. The Hardgrove machine is shown schematically. The method for preparing and determining grindability indexes of standard reference samples is given in the appendix. (BLM)

Development of a small specimen test machine to evaluate irradiation embrittlement of fusion reactor materials

NASA Astrophysics Data System (ADS)

Ishii, T.; Ohmi, M.; Saito, J.; Hoshiya, T.; Ooka, N.; Jitsukawa, S.; Eto, M.

2000-12-01

Small specimen test techniques (SSTT) are essential to use an accelerator-driven deuterium-lithium stripping reaction neutron source for the study of fusion reactor materials because of the limitation of the available irradiation volume. A remote-controlled small punch (SP) test machine was developed at the hot laboratory of the Japan Materials Testing Reactor (JMTR) in the Japan Atomic Energy Research Institute (JAERI). This report describes the SP test method and machine for use in a hot cell, and test results on irradiated ferritic steels. The specimen was either a coupon 10×10×0.25 mm 3 or a TEM disk 3 mm in diameter by 0.25 mm in thickness. Tests can be performed at temperatures ranging from 93 to 1123 K in a vacuum or in an inert gas environment. The ductile to brittle transition temperature of the irradiated ferritic steel as determined by the SP test is also evaluated.

The Kansas Squat Test Modality Comparison: Free Weights vs. Smith Machine.

PubMed

Luebbers, Paul E; Fry, Andrew C

2016-08-01

Luebbers, PE and Fry, AC. The Kansas squat test modality comparison: free weights vs. smith machine. J Strength Cond Res 30(8): 2186-2193, 2016-Standardized methods of testing power are instrumental in planning and implementing training regimens for many athletes, and also in tracking training adaptations. Previous work has demonstrated that the Kansas squat test (KST) is a valid test for measuring indices of mean and peak power when compared with the Wingate anaerobic cycle test. Although the KST was designed for use with a Smith machine (SM), many power athletes use free weights for training. The purpose of this study was to determine the feasibility of using free weights (FW) for the KST by comparing it with the SM modality. Twenty-three track and field athletes participated (mean ± SD; weight, 69.7 ± 10.6 kg; age, 20.1 ± 1.1 years) in this study. Each completed familiarization sessions with the FW and SM modalities before data collection. A 1-repetition maximum squat was also determined for both the FW and SM. Correlation coefficients indicated significant relationships between the FW KST and SM KST on measures of peak test power (r = 0.955; p < 0.01) and mean test power (r = 0.959; p < 0.01) but not for relative fatigue (r = -0.198; p > 0.05) or posttest lactate (r = 0.109; p > 0.05). Paired samples t-tests indicated that the FW KST resulted in significantly higher measures of peak power and mean power (p ≤ 0.01), although no differences were observed for relative fatigue or lactate (p > 0.05). These data indicate that the FW KST is a valid and feasible alternative to the SM KST in measuring peak and mean power.

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

NASA Astrophysics Data System (ADS)

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.

Machine Learning and Data Mining for Comprehensive Test Ban Treaty Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Russell, S; Vaidya, S

2009-07-30

The Comprehensive Test Ban Treaty (CTBT) is gaining renewed attention in light of growing worldwide interest in mitigating risks of nuclear weapons proliferation and testing. Since the International Monitoring System (IMS) installed the first suite of sensors in the late 1990's, the IMS network has steadily progressed, providing valuable support for event diagnostics. This progress was highlighted at the recent International Scientific Studies (ISS) Conference in Vienna in June 2009, where scientists and domain experts met with policy makers to assess the current status of the CTBT Verification System. A strategic theme within the ISS Conference centered on exploring opportunitiesmore » for further enhancing the detection and localization accuracy of low magnitude events by drawing upon modern tools and techniques for machine learning and large-scale data analysis. Several promising approaches for data exploitation were presented at the Conference. These are summarized in a companion report. In this paper, we introduce essential concepts in machine learning and assess techniques which could provide both incremental and comprehensive value for event discrimination by increasing the accuracy of the final data product, refining On-Site-Inspection (OSI) conclusions, and potentially reducing the cost of future network operations.« less

Evaluation of I-FIT results and machine variability using MnRoad test track mixtures.

DOT National Transportation Integrated Search

2017-06-01

The Illinois Flexibility Index Test (I-FIT) was developed to distinguish between different mixtures in terms of potential cracking. Several : machines were manufactured and are currently available to perform the I-FIT. This report presents the result...

Whole-machine calibration approach for phased array radar with self-test

NASA Astrophysics Data System (ADS)

Shen, Kai; Yao, Zhi-Cheng; Zhang, Jin-Chang; Yang, Jian

2017-06-01

The performance of the missile-borne phased array radar is greatly influenced by the inter-channel amplitude and phase inconsistencies. In order to ensure its performance, the amplitude and the phase characteristics of radar should be calibrated. Commonly used methods mainly focus on antenna calibration, such as FFT, REV, etc. However, the radar channel also contains T / R components, channels, ADC and messenger. In order to achieve on-based phased array radar amplitude information for rapid machine calibration and compensation, we adopt a high-precision plane scanning test platform for phase amplitude test. A calibration approach for the whole channel system based on the radar frequency source test is proposed. Finally, the advantages and the application prospect of this approach are analysed.

Electrical test prediction using hybrid metrology and machine learning

NASA Astrophysics Data System (ADS)

Breton, Mary; Chao, Robin; Muthinti, Gangadhara Raja; de la Peña, Abraham A.; Simon, Jacques; Cepler, Aron J.; Sendelbach, Matthew; Gaudiello, John; Emans, Susan; Shifrin, Michael; Etzioni, Yoav; Urenski, Ronen; Lee, Wei Ti

2017-03-01

Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology

Humanizing machines: Anthropomorphization of slot machines increases gambling.

PubMed

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).

Dynamic analysis and vibration testing of CFRP drive-line system used in heavy-duty machine tool

NASA Astrophysics Data System (ADS)

Yang, Mo; Gui, Lin; Hu, Yefa; Ding, Guoping; Song, Chunsheng

2018-03-01

Low critical rotary speed and large vibration in the metal drive-line system of heavy-duty machine tool affect the machining precision seriously. Replacing metal drive-line with the CFRP drive-line can effectively solve this problem. Based on the composite laminated theory and the transfer matrix method (TMM), this paper puts forward a modified TMM to analyze dynamic characteristics of CFRP drive-line system. With this modified TMM, the CFRP drive-line of a heavy vertical miller is analyzed. And the finite element modal analysis model of the shafting is established. The results of the modified TMM and finite element analysis (FEA) show that the modified TMM can effectively predict the critical rotary speed of CFRP drive-line. And the critical rotary speed of CFRP drive-line is 20% higher than that of the original metal drive-line. Then, the vibration of the CFRP and the metal drive-line were tested. The test results show that application of the CFRP drive shaft in the drive-line can effectively reduce the vibration of the heavy-duty machine tool.

Improving Non-Destructive Concrete Strength Tests Using Support Vector Machines

PubMed Central

Shih, Yi-Fan; Wang, Yu-Ren; Lin, Kuo-Liang; Chen, Chin-Wen

2015-01-01

Non-destructive testing (NDT) methods are important alternatives when destructive tests are not feasible to examine the in situ concrete properties without damaging the structure. The rebound hammer test and the ultrasonic pulse velocity test are two popular NDT methods to examine the properties of concrete. The rebound of the hammer depends on the hardness of the test specimen and ultrasonic pulse travelling speed is related to density, uniformity, and homogeneity of the specimen. Both of these two methods have been adopted to estimate the concrete compressive strength. Statistical analysis has been implemented to establish the relationship between hammer rebound values/ultrasonic pulse velocities and concrete compressive strength. However, the estimated results can be unreliable. As a result, this research proposes an Artificial Intelligence model using support vector machines (SVMs) for the estimation. Data from 95 cylinder concrete samples are collected to develop and validate the model. The results show that combined NDT methods (also known as SonReb method) yield better estimations than single NDT methods. The results also show that the SVMs model is more accurate than the statistical regression model. PMID:28793627

SU-E-T-660: Quantitative Fault Testing for Commissioning of Proton Therapy Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reilly, M; Rankine, L; Grantham, K

2015-06-15

Purpose: To ensure proper fault testing for the first single room proton therapy machine by establishing a common set of acceptance testing and commissioning parameters with the manufacturer. The following work details the parameters tested and associated results. Methods: Dose rates in service mode were varied to ensure that when the threshold for maximum or minimum MU/min was met, the beam promptly shut off. The flatness parameter was tested by purposely assigning an incorrect secondary scatter, to ensure the beam shut off when detecting a heterogeneous profile. The beam symmetry parameter was tested by altering the steering coil up tomore » 3.0A, thereby forcing the beam to be asymmetric and shut off. Lastly, the quench system was tested by ramping down the magnet to 5% capacity, whereby the quench button was engaged to bring down the magnet current to a safe level. Results: A dose rate increase or decrease in excess of 10% shut the beam off within 5 seconds as observed by the current on a Matrixx ionization chamber array (IBA Dosimetry, Bartlett, TN) A 3.0A change in the beam steering coil introduced a 2% change in the flatness and symmetry profiles with respect to baseline measurements resulting in the beam shutting off within 5 seconds. An incorrect 2nd scatterer introduced a flatness of 4.1% and symmetry of 6.4% which immediately triggered a beam shut off. Finally, the quench system worked as expected during the ramp down procedure. Conclusion: A fault testing plan to check dosimetric faults and the quench system was performed for the first single room proton therapy system. All dosimetric parameters and machine conditions were met to our satisfaction. We propose that the same type of fault testing should be applied to any proton system during commissioning, including scanning beam systems.« less

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Development of a New Utm (universal Testing Machine) System for the Nano/micro In-Process Measurement

NASA Astrophysics Data System (ADS)

Kweon, Hyunkyu; Choi, Sungdae; Kim, Youngsik; Nam, Kiho

Micro UTM (Universal Testing Machines) are becoming increasingly popular for testing the mechanical properties of MEMS materials, metal thin films, and micro-molecule materials1-2. And, new miniature testing machines that can perform in-process measurement in SEM, TEM, and SPM are also needed. In this paper, a new micro UTM with a precision positioning system that can be fine positioning stage. Coarse positioning is implemented by step motor. The size, load output and used in SEM, TEM, and SPM have been proposed. Bimorph type PZT precision actuator is used in displacement output of bimorph type UTM are 109×64×22(mm), about 35g, and 0.4 mm, respectively. And the displacement output is controlled in the block digital form. The results of the analysis and basic properties of positioning system and the UTM system are presented. In addition, the experiment results of in-process measurement during tensile load in SEM and AFM are showed.

Quantitative evaluation of rejuvenators to restore embrittlement temperatures in oxidized asphalt mixtures using acoustic emission

NASA Astrophysics Data System (ADS)

Sun, Zhe; Farace, Nicholas; Arnold, Jacob; Behnia, Behzad; Buttlar, William G.; Reis, Henrique

2015-03-01

Towards developing a method capable to assess the efficiency of rejuvenators to restore embrittlement temperatures of oxidized asphalt binders towards their original, i.e., unaged values, three gyratory compacted specimens were manufactured with mixtures oven-aged for 36 hours at 135 °C. In addition, one gyratory compacted specimen manufactured using a short-term oven-aged mixture for two hours at 155 °C was used for control to simulate aging during plant production. Each of these four gyratory compacted specimens was then cut into two cylindrical specimen 5 cm thick for a total of six 36-hour oven-aged specimens and two short term aging specimens. Two specimens aged for 36 hours and the two short-term specimens were then tested using an acoustic emission approach to obtain base acoustic emission response of short-term and severely-aged specimens. The remaining four specimens oven-aged for 36 hours were then treated by spreading their top surface with rejuvenator in the amount of 10% of the binder by weight. These four specimens were then tested using the same acoustic emission approach after two, four, six, and eight weeks of dwell time. It was observed that the embrittlement temperatures of the short-term aged and severely oven-aged specimens were -25 °C and - 15 °C, respectively. It was also observed that after four weeks of dwell time, the rejuvenator-treated samples had recuperated the original embrittlement temperatures. In addition, it was also observed that the rejuvenator kept acting upon the binder after four weeks of dwell time; at eight weeks of dwell time, the specimens had an embrittlement temperature about one grade cooler than the embrittlement temperature corresponding to the short-term aged specimen.

Drilling Machines: Vocational Machine Shop.

ERIC Educational Resources Information Center

Thomas, John C.

The lessons and supportive information in this field tested instructional block provide a guide for teachers in developing a machine shop course of study in drilling. The document is comprised of operation sheets, information sheets, and transparency masters for 23 lessons. Each lesson plan includes a performance objective, material and tools,…

The relationship between reinforcement and gaming machine choice.

PubMed

Haw, John

2008-03-01

The present study assessed whether prior reinforcement experiences were related to gaming machine choice and the decision to change gaming machines during a session of gambling. Seventy undergraduate students (48 women, 22 men; mean age = 22.05 years) were presented with two visually identical simulated gaming machines in a practice phase. These simulated machines differed only in the rate of reinforcement. After the practice phase, participants were asked to choose a machine to play in the test phase and were allowed to change machines at will. Two measures of reinforcement were employed; frequency of wins and payback rate. Results indicated that neither measure of reinforcement was related to machine choice, but both were predictors of when participants changed machines. A post-hoc analysis of the 33 participants who changed machines during the test phase found a significant relationship between machine choice and prior reinforcement. For these participants, payback rate was significantly related to machine choice, unlike frequency of wins.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

PubMed Central

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

Design of a hydraulic bending machine

Treesearch

Steven G. Hankel; Marshall Begel

2004-01-01

To keep pace with customer demands while phasing out old and unserviceable test equipment, the staff of the Engineering Mechanics Laboratory (EML) at the USDA Forest Service, Forest Products Laboratory, designed and assembled a hydraulic bending test machine. The EML built this machine to test dimension lumber, nominal 2 in. thick and up to 12 in. deep, at spans up to...

IQ Tests Are Not for Machines, Yet

ERIC Educational Resources Information Center

Dowe, David L.; Hernandez-Orallo, Jose

2012-01-01

Complex, but specific, tasks--such as chess or "Jeopardy!"--are popularly seen as milestones for artificial intelligence (AI). However, they are not appropriate for evaluating the intelligence of machines or measuring the progress in AI. Aware of this delusion, Detterman has recently raised a challenge prompting AI researchers to evaluate their…

Bias-Free Chemically Diverse Test Sets from Machine Learning.

PubMed

Swann, Ellen T; Fernandez, Michael; Coote, Michelle L; Barnard, Amanda S

2017-08-14

Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

Results of Investigative Tests of Gas Turbine Engine Compressor Blades Obtained by Electrochemical Machining

NASA Astrophysics Data System (ADS)

Kozhina, T. D.; Kurochkin, A. V.

2016-04-01

The paper highlights results of the investigative tests of GTE compressor Ti-alloy blades obtained by the method of electrochemical machining with oscillating tool-electrodes, carried out in order to define the optimal parameters of the ECM process providing attainment of specified blade quality parameters given in the design documentation, while providing maximal performance. The new technological methods suggested based on the results of the tests; in particular application of vibrating tool-electrodes and employment of locating elements made of high-strength materials, significantly extend the capabilities of this method.

Laser Machining of Melt Infiltrated Ceramic Matrix Composite

NASA Technical Reports Server (NTRS)

Jarmon, D. C.; Ojard, G.; Brewer, D.

2012-01-01

As interest grows in considering the use of ceramic matrix composites for critical components, the effects of different machining techniques, and the resulting machined surfaces, on strength need to be understood. This work presents the characterization of a Melt Infiltrated SiC/SiC composite material system machined by different methods. While a range of machining approaches were initially considered, only diamond grinding and laser machining were investigated on a series of tensile coupons. The coupons were tested for residual tensile strength, after a stressed steam exposure cycle. The data clearly differentiated the laser machined coupons as having better capability for the samples tested. These results, along with micro-structural characterization, will be presented.

An analysis of a digital variant of the Trail Making Test using machine learning techniques.

PubMed

Dahmen, Jessamyn; Cook, Diane; Fellows, Robert; Schmitter-Edgecombe, Maureen

2017-01-01

The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. Predicted TMT scores correlate well with clinical digital test scores (r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS exhibited a small correlation with clinically derived TICS scores (r = 0.12 Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B). Digitally derived features were also used to predict diagnosis (AUC of 0.65). Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.

Machinability of IPS Empress 2 framework ceramic.

PubMed

Schmidt, C; Weigl, P

2000-01-01

Using ceramic materials for an automatic production of ceramic dentures by CAD/CAM is a challenge, because many technological, medical, and optical demands must be considered. The IPS Empress 2 framework ceramic meets most of them. This study shows the possibilities for machining this ceramic with economical parameters. The long life-time requirement for ceramic dentures requires a ductile machined surface to avoid the well-known subsurface damages of brittle materials caused by machining. Slow and rapid damage propagation begins at break outs and cracks, and limits life-time significantly. Therefore, ductile machined surfaces are an important demand for machine dental ceramics. The machining tests were performed with various parameters such as tool grain size and feed speed. Denture ceramics were machined by jig grinding on a 5-axis CNC milling machine (Maho HGF 500) with a high-speed spindle up to 120,000 rpm. The results of the wear test indicate low tool wear. With one tool, you can machine eight occlusal surfaces including roughing and finishing. One occlusal surface takes about 60 min machining time. Recommended parameters for roughing are middle diamond grain size (D107), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 1000 mm/min, depth of cut a(e) = 0.06 mm, width of contact a(p) = 0.8 mm, and for finishing ultra fine diamond grain size (D46), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 100 mm/min, depth of cut a(e) = 0.02 mm, width of contact a(p) = 0.8 mm. The results of the machining tests give a reference for using IPS Empress(R) 2 framework ceramic in CAD/CAM systems. Copyright 2000 John Wiley & Sons, Inc.

Development of a remote-controlled fatigue test machine using a laser extensometer for investigation of irradiation effect on fatigue properties

NASA Astrophysics Data System (ADS)

Yonekawa, M.; Ishii, T.; Ohmi, M.; Takada, F.; Hoshiya, T.; Niimi, M.; Ioka, I.; Miwa, Y.; Tsuji, H.

2002-12-01

In order to investigate effects of neutron irradiation on fatigue properties of nuclear materials, a remote-controlled high temperature fatigue test machine was developed at the hot laboratory of the Japan Materials Testing Reactor (JMTR) in the Japan Atomic Energy Research Institute (JAERI). A small-sized fatigue specimen having double blades to measure strain with a laser extensometer was designed for this machine. A strain amplitude in fatigue tests of a completely reversed push-pull type using a triangular wave was controlled with an accuracy of ±3% of the total strain range during test. Low cycle fatigue tests of type 304 stainless steel irradiated in JMTR at 823 K up to a fast neutron fluence of 1×10 25 n/m 2 ( E>1 MeV) were performed in total strain ranges of 0.7-1.4% at 823 K using the designed small-sized specimens.

Machining and characterization of self-reinforced polymers

NASA Astrophysics Data System (ADS)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

This Paper focuses on obtaining the mechanical properties and the effect of the different machining techniques on self-reinforced composites sample and to derive the best machining method with remarkable properties. Each sample was tested by the Tensile and Flexural tests, fabricated using hot compaction test and those loads were calculated. These composites are machined using conventional methods because of lack of advanced machinery in most of the industries. The advanced non-conventional methods like Abrasive water jet machining were used. These machining techniques are used to get the better output for the composite materials with good mechanical properties compared to conventional methods. But the use of non-conventional methods causes the changes in the work piece, tool properties and more economical compared to the conventional methods. Finding out the best method ideal for the designing of these Self Reinforced Composites with and without defects and the use of Scanning Electron Microscope (SEM) analysis for the comparing the microstructure of the PP and PE samples concludes our process.

Metal release from coffee machines and electric kettles.

PubMed

Müller, Frederic D; Hackethal, Christin; Schmidt, Roman; Kappenstein, Oliver; Pfaff, Karla; Luch, Andreas

2015-01-01

The release of elemental ions from 8 coffee machines and 11 electric kettles into food simulants was investigated. Three different types of coffee machines were tested: portafilter espresso machines, pod machines and capsule machines. All machines were tested subsequently on 3 days before and on 3 days after decalcification. Decalcification of the machines was performed with agents according to procedures as specified in the respective manufacturer's manuals. The electric kettles showed only a low release of the elements analysed. For the coffee machines decreasing concentrations of elements were found from the first to the last sample taken in the course of 1 day. Metal release on consecutive days showed a decreasing trend as well. After decalcification a large increase in the amounts of elements released was encountered. In addition, the different machine types investigated clearly differed in their extent of element release. By far the highest leaching, both quantitatively and qualitatively, was found for the portafilter machines. With these products releases of Pb, Ni, Mn, Cr and Zn were in the range and beyond the release limits as proposed by the Council of Europe. Therefore, a careful rinsing routine, especially after decalcification, is recommended for these machines. The comparably lower extent of release of one particular portafilter machine demonstrates that metal release at levels above the threshold that triggers health concerns are technically avoidable.

Machinability of hypereutectic silicon-aluminum alloys

NASA Astrophysics Data System (ADS)

Tanaka, T.; Akasawa, T.

1999-08-01

The machinability of high-silicon aluminum alloys made by a P/M process and by casting was compared. The cutting test was conducted by turning on lathes with the use of cemented carbide tools. The tool wear by machining the P/M alloy was far smaller than the tool wear by machining the cast alloy. The roughness of the machined surface of the P/M alloy is far better than that of the cast alloy, and the turning speed did not affect it greatly at higher speeds. The P/M alloy produced long chips, so the disposal can cause trouble. The size effect of silicon grains on the machinability is discussed.

An Analysis of a Digital Variant of the Trail Making Test Using Machine Learning Techniques

PubMed Central

Dahmen, Jessamyn; Cook, Diane; Fellows, Robert; Schmitter-Edgecombe, Maureen

2017-01-01

BACKGROUND The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. OBJECTIVE This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. METHODS Using digital Trail Making Test (dTMT) data collected from (N=54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. RESULTS Predicted TMT scores correlate well with clinical digital test scores (r=0.98) and paper time to completion scores (r=0.65). Predicted TICS exhibited a small correlation with clinically-derived TICS scores (r=0.12 Part A, r=0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically-derived FAB scores (r=0.13 Part A, r=0.29 for Part B). Digitally-derived features were also used to predict diagnosis (AUC of 0.65). CONCLUSION Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT’s additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone. PMID:27886019

Effect of Thermal and Chemical Treatment on the Microstructural, Mechanical and Machining Performance of W319 Al-Si-Cu Cast Alloy Engine Blocks and Directionally Solidified Machinability Test Blocks

NASA Astrophysics Data System (ADS)

Szablewski, Daniel

The research presented in this work is focused on making a link between casting microstructural, mechanical and machining properties for 319 Al-Si sand cast components. In order to achieve this, a unique Machinability Test Block (MTB) is designed to simulate the Nemak V6 Al-Si engine block solidification behavior. This MTB is then utilized to cast structures with in-situ nano-alumina particle master alloy additions that are Mg based, as well as independent in-situ Mg additions, and Sr additions to the MTB. The Universal Metallurgical Simulator and Analyzer (UMSA) Technology Platform is utilized for characterization of each cast structure at different Secondary Dendrite Arm Spacing (SDAS) levels. The rapid quench method and Jominy testing is used to assess the capability of the nano-alumina master alloy to modify the microstructure at different SDAS levels. Mechanical property assessment of the MTB is done at different SDAS levels on cast structures with master alloy additions described above. Weibull and Quality Index statistical analysis tools are then utilized to assess the mechanical properties. The MTB is also used to study single pass high speed face milling and bi-metallic cutting operations where the Al-Si hypoeutectic structure is combined with hypereutectoid Al-Si liners and cast iron cylinder liners. These studies are utilized to aid the implementation of Al-Si liners into the Nemak V6 engine block and bi-metallic cutting of the head decks. Machining behavior is also quantified for the investigated microstructures, and the Silicon Modification Level (SiML) is utilized for microstructural analysis as it relates to the machining behavior.

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

PubMed Central

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

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

PubMed

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

2016-11-28

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.

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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.

Machinability of cast commercial titanium alloys.

PubMed

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

2002-01-01

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

Effect of focusing flow on stationary spot machining properties in elastic emission machining

PubMed Central

2013-01-01

Ultraprecise optical elements are applied in advanced optical apparatus. Elastic emission machining (EEM) is one of the ultraprecision machining methods used to fabricate shapes with 0.1-nm accuracy. In this study, we proposed and experimentally tested the control of the shape of a stationary spot profile by introducing a focusing-flow state between the nozzle outlet and the workpiece surface in EEM. The simulation results indicate that the focusing-flow nozzle sharpens the distribution of the velocity on the workpiece surface. The results of machining experiments verified those of the simulation. The obtained stationary spot conditions will be useful for surface processing with a high spatial resolution. PMID:23680043

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

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

PubMed

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.

The Machine Scoring of Writing

ERIC Educational Resources Information Center

McCurry, Doug

2010-01-01

This article provides an introduction to the kind of computer software that is used to score student writing in some high stakes testing programs, and that is being promoted as a teaching and learning tool to schools. It sketches the state of play with machines for the scoring of writing, and describes how these machines work and what they do.…

MACHINING TEST SPECIMENS FROM HARVESTED ZION RPV SEGMENTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nanstad, Randy K; Rosseel, Thomas M; Sokolov, Mikhail A

2015-01-01

The decommissioning of the Zion Nuclear Generating Station (NGS) in Zion, Illinois, presents a special and timely opportunity for developing a better understanding of materials degradation and other issues associated with extending the lifetime of existing nuclear power plants (NPPs) beyond 60 years of service. In support of extended service and current operations of the US nuclear reactor fleet, the Oak Ridge National Laboratory (ORNL), through the Department of Energy (DOE), Light Water Reactor Sustainability (LWRS) Program, is coordinating and contracting with Zion Solutions, LLC, a subsidiary of Energy Solutions, an international nuclear services company, the selective procurement of materials,more » structures, components, and other items of interest from the decommissioned reactors. In this paper, we will discuss the acquisition of segments of the Zion Unit 2 Reactor Pressure Vessel (RPV), cutting these segments into blocks from the beltline and upper vertical welds and plate material and machining those blocks into mechanical (Charpy, compact tension, and tensile) test specimens and coupons for microstructural (TEM, SEM, APT, SANS and nano indention) characterization. Access to service-irradiated RPV welds and plate sections will allow through wall attenuation studies to be performed, which will be used to assess current radiation damage models [1].« less

A testing machine for dental air-turbine handpiece characteristics: free-running speed, stall torque, bearing resistance.

PubMed

Darvell, Brain W; Dyson, J E

2005-01-01

The measurement of performance characteristics of dental air turbine handpieces is of interest with respect to product comparisons, standards specifications and monitoring of bearing longevity in clinical service. Previously, however, bulky and expensive laboratory equipment was required. A portable test machine is described for determining three key characteristics of dental air-turbine handpieces: free-running speed, stall torque and bearing resistance. It relies on a special circuit design for performing a hardware integration of a force signal with respect to rotational position, independent of the rate at which the turbine is allowed to turn during both stall torque and bearing resistance measurements. Free-running speed without the introduction of any imbalance can be readily monitored. From the essential linear relationship between torque and speed, dynamic torque and, hence, power, can then be calculated. In order for these measurements to be performed routinely with the necessary precision of location on the test stage, a detailed procedure for ensuring proper gripping of the handpiece is described. The machine may be used to verify performance claims, standard compliance checks should this be established as appropriate, monitor deterioration with time and usage in the clinical environment and for laboratory investigation of design development.

Machine rates for selected forest harvesting machines

Treesearch

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...

Testing of machine wound second generation HTS tape Vacuum Pressure Impregnated coils

NASA Astrophysics Data System (ADS)

Swaffield, D.; Lewis, C.; Eugene, J.; Ingles, M.; Peach, D.

2014-05-01

Delamination of second generation (2G) High Temperature Superconducting (HTS) tapes has previously been reported when using resin based insulation systems for wound coils. One proposed root cause is the differential thermal contraction between the coil former and the resin encapsulated coil turns resulting in the tape c-axis tensile stress being exceeded. Importantly, delamination results in unacceptable degradation of the superconductor critical current level. To mitigate the delamination risk and prove winding, jointing and Vacuum Pressure Impregnation (VPI) processes in the production of coils for superconducting rotating machines at GE Power Conversion two scaled trial coils have been wound and extensively tested. The coils are wound from 12mm wide 2G HTS tape supplied by AMSC onto stainless steel 'racetrack' coil formers. The coils are wound in two layers which include both in-line and layer-layer joints subject to in-process test. The resin insulation system chosen is VPI and oven cured. Tests included; insulation resistance, repeat quench and recovery of the superconductor, heat runs and measurement of n-value, before and after multiple thermal cycling between ambient and 35 Kelvin. No degradation of coil performance is evidenced.

Machine learning enhanced optical distance sensor

NASA Astrophysics Data System (ADS)

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 <0.8 mm and <2.2 mm, respectively. The test measurement error is at least a factor of 4 improvement over our prior sensor demonstration without the use of machine 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.

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

PubMed

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

2017-11-01

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

Development of a low energy micro sheet forming machine

NASA Astrophysics Data System (ADS)

Razali, A. R.; Ann, C. T.; Shariff, H. M.; Kasim, N. I.; Musa, M. A.; Ahmad, A. F.

2017-10-01

It is expected that with the miniaturization of materials being processed, energy consumption is also being `miniaturized' proportionally. The focus of this study was to design a low energy micro-sheet-forming machine for thin sheet metal application and fabricate a low direct current powered micro-sheet-forming machine. A prototype of low energy system for a micro-sheet-forming machine which includes mechanical and electronic elements was developed. The machine was tested for its performance in terms of natural frequency, punching forces, punching speed and capability, energy consumption (single punch and frequency-time based). Based on the experiments, the machine can do 600 stroke per minute and the process is unaffected by the machine's natural frequency. It was also found that sub-Joule of power was required for a single stroke of punching/blanking process. Up to 100micron thick carbon steel shim was successfully tested and punched. It concludes that low power forming machine is feasible to be developed and be used to replace high powered machineries to form micro-products/parts.

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

PubMed

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.

Anaesthesia machine: checklist, hazards, scavenging.

PubMed

Goneppanavar, Umesh; Prabhu, Manjunath

2013-09-01

From a simple pneumatic device of the early 20(th) century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered.

Anaesthesia Machine: Checklist, Hazards, Scavenging

PubMed Central

Goneppanavar, Umesh; Prabhu, Manjunath

2013-01-01

From a simple pneumatic device of the early 20th century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered. PMID:24249887

MEASUREMENT OF INDOOR AIR EMISSIONS FROM DRY-PROCESS PHOTOCOPY MACHINES

EPA Science Inventory

The article provides background information on indoor air emissions from office equipment, with emphasis on dry-process photocopy machines. The test method is described in detail along with results of a study to evaluate the test method using four dry-process photocopy machines. ...

Self-Calibrating Surface Measuring Machine

NASA Astrophysics Data System (ADS)

Greenleaf, Allen H.

1983-04-01

A new kind of surface-measuring machine has been developed under government contract at Itek Optical Systems, a Division of Itek Corporation, to assist in the fabrication of large, highly aspheric optical elements. The machine uses four steerable distance-measuring interferometers at the corners of a tetrahedron to measure the positions of a retroreflective target placed at various locations against the surface being measured. Using four interferometers gives redundant information so that, from a set of measurement data, the dimensions of the machine as well as the coordinates of the measurement points can be determined. The machine is, therefore, self-calibrating and does not require a structure made to high accuracy. A wood-structured prototype of this machine was made whose key components are a simple form of air bearing steering mirror, a wide-angle cat's eye retroreflector used as the movable target, and tracking sensors and servos to provide automatic tracking of the cat's eye by the four laser beams. The data are taken and analyzed by computer. The output is given in terms of error relative to an equation of the desired surface. In tests of this machine, measurements of a 0.7 m diameter mirror blank have been made with an accuracy on the order of 0.2µm rms.

Defining and Testing the Influence of Servo System Response on Machine Tool Compliance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hopkins, D J

2004-03-24

Compliance can be defined as the measurement of displacement per unit of force applied e.g. nano-meters per Newton (m/N). Compliance is the reciprocal of stiffness. High stiffness means low compliance and visa versa. It is an important factor in machine tool characteristics because it reflects the ability of the machine axis to maintain a desired position as it encounters a force or torque. Static compliance is a measurement made with a constant force applied e.g. the average depth of cut. Dynamic compliance is a measurement made as a function of frequency, e.g. a fast too servo (FTS) that applies amore » varying cutting force or load, interrupted cuts and external disturbances such as ground vibrations or air conditioning induced forces on the machine. Compliance can be defined for both a linear and rotary axis of a machine tool. However, to properly define compliance for a rotary axis, the axis must allow a commanded angular position. Note that this excludes velocity only axes. In this paper, several factors are discussed that affect compliance but emphasis is placed on how the machine servo system plays a key role in compliance at low to mid frequency regions. The paper discusses several techniques for measuring compliance and provides examples of results from these measurements.« less

Thrown object testing of forest machine operator protective structures

Treesearch

S.E. Taylor; M.W. Veal; R.B. Rummer

2003-01-01

High-speed chains or rotating disks are commonly used to cut and process trees during forest harvesting operations. Mechanical failure or fatigue of these tools can lead to a potentially hazardous situation where fragments of chain or sawteeth are thrown through the operator enclosures on forest machines. This poster presentation discusses the development and...

Investigation of approximate models of experimental temperature characteristics of machines

NASA Astrophysics Data System (ADS)

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

2018-05-01

This work is devoted to the investigation of various approaches to the approximation of experimental data and the creation of simulation mathematical models of thermal processes in machines with the aim of finding ways to reduce the time of their field tests and reducing the temperature error of the treatments. The main methods of research which the authors used in this work are: the full-scale thermal testing of machines; realization of various approaches at approximation of experimental temperature characteristics of machine tools by polynomial models; analysis and evaluation of modelling results (model quality) of the temperature characteristics of machines and their derivatives up to the third order in time. As a result of the performed researches, rational methods, type, parameters and complexity of simulation mathematical models of thermal processes in machine tools are proposed.

Nano Mechanical Machining Using AFM Probe

NASA Astrophysics Data System (ADS)

Mostofa, Md. Golam

Complex miniaturized components with high form accuracy will play key roles in the future development of many products, as they provide portability, disposability, lower material consumption in production, low power consumption during operation, lower sample requirements for testing, and higher heat transfer due to their very high surface-to-volume ratio. Given the high market demand for such micro and nano featured components, different manufacturing methods have been developed for their fabrication. Some of the common technologies in micro/nano fabrication are photolithography, electron beam lithography, X-ray lithography and other semiconductor processing techniques. Although these methods are capable of fabricating micro/nano structures with a resolution of less than a few nanometers, some of the shortcomings associated with these methods, such as high production costs for customized products, limited material choices, necessitate the development of other fabricating techniques. Micro/nano mechanical machining, such an atomic force microscope (AFM) probe based nano fabrication, has, therefore, been used to overcome some the major restrictions of the traditional processes. This technique removes material from the workpiece by engaging micro/nano size cutting tool (i.e. AFM probe) and is applicable on a wider range of materials compared to the photolithographic process. In spite of the unique benefits of nano mechanical machining, there are also some challenges with this technique, since the scale is reduced, such as size effects, burr formations, chip adhesions, fragility of tools and tool wear. Moreover, AFM based machining does not have any rotational movement, which makes fabrication of 3D features more difficult. Thus, vibration-assisted machining is introduced into AFM probe based nano mechanical machining to overcome the limitations associated with the conventional AFM probe based scratching method. Vibration-assisted machining reduced the cutting forces

Design Methodology for Automated Construction Machines

DTIC Science & Technology

1987-12-11

along with the design of a pair of machines which automate framework installation.-,, 20. DISTRIBUTION IAVAILABILITY OF ABSTRACT 21. ABSTRACT SECURITY... Development Assistant Professor of Civil Engineering and Laura A . Demsetz, David H. Levy, Bruce Schena Graduate Research Assistants December 11, 1987 U.S...are discussed along with the design of a pair of machines which automate framework installation. Preliminary analysis and testing indicate that these

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

Improvement of automatic fish feeder machine design

NASA Astrophysics Data System (ADS)

Chui Wei, How; Salleh, S. M.; Ezree, Abdullah Mohd; Zaman, I.; Hatta, M. H.; Zain, B. A. Md; Mahzan, S.; Rahman, M. N. A.; Mahmud, W. A. W.

2017-10-01

Nation Plan of action for management of fishing is target to achieve an efficient, equitable and transparent management of fishing capacity in marine capture fisheries by 2018. However, several factors influence the fishery production and efficiency of marine system such as automatic fish feeder machine could be taken in consideration. Two latest fish feeder machines have been chosen as the reference for this study. Based on the observation, it has found that the both machine was made with heavy structure, low water and temperature resistance materials. This research’s objective is to develop the automatic feeder machine to increase the efficiency of fish feeding. The experiment has conducted to testing the new design of machine. The new machine with maximum storage of 5 kg and functioning with two DC motors. This machine able to distribute 500 grams of pellets within 90 seconds and longest distance of 4.7 meter. The higher speed could reduce time needed and increase the distance as well. The minimum speed range for both motor is 110 and 120 with same full speed range of 255.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Machinability of some dentin simulating materials.

PubMed

Möllersten, L

1985-01-01

Machinability in low speed drilling was investigated for pure aluminium, Frasaco teeth, ivory, plexiglass and human dentin. The investigation was performed in order to find a suitable test material for drilling experiments using paralleling instruments. A material simulating human dentin in terms of cuttability at low drilling speeds was sought. Tests were performed using a specially designed apparatus. Holes to a depth of 2 mm were drilled with a twist drill using a constant feeding force. The time required was registered. The machinability of the materials tested was determined by direct comparison of the drilling times. As regards cuttability, first aluminium and then ivory were found to resemble human dentin most closely. By comparing drilling time variances the homogeneity of the materials tested was estimated. Aluminium, Frasaco teeth and plexiglass demonstrated better homogeneity than ivory and human dentin.

The Efficacy of Machine Learning Programs for Navy Manpower Analysis

DTIC Science & Technology

1993-03-01

This thesis investigated the efficacy of two machine learning programs for Navy manpower analysis. Two machine learning programs, AIM and IXL, were...to generate models from the two commercial machine learning programs. Using a held out sub-set of the data the capabilities of the three models were...partial effects. The author recommended further investigation of AIM’s capabilities, and testing in an operational environment.... Machine learning , AIM, IXL.

380 kW synchronous machine with HTS rotor windings--development at Siemens and first test results

NASA Astrophysics Data System (ADS)

Nick, W.; Nerowski, G.; Neumüller, H.-W.; Frank, M.; van Hasselt, P.; Frauenhofer, J.; Steinmeyer, F.

2002-08-01

Applying HTS conductors in the rotor of synchronous machines allows the design of future motors or generators that are lighter, more compact and feature an improved coefficient of performance. To address these goals a project collaboration was installed within Siemens, including Automation & Drives, Large Drives as a leading supplier of electrical machines, Corporate Technology as a competence center for superconducting technology, and other partners. The main task of the project was to demonstrate the feasibility of basic concepts. The rotor was built from racetrack coils of Bi-2223 HTS tape conductor, these were assembled on a core and fixed by a bandage of glass-fibre composite. Rotor coil cooling is performed by thermal conduction, one end of the motor shaft is hollow to give access for the cooling system. Two cooling systems were designed and operated successfully: firstly an open circuit using cold gaseous helium from a storage vessel, but also a closed circuit system based on a cryogenerator. To take advantage of the increased rotor induction levels the stator winding was designed as an air gap winding. This was manufactured and fitted in a standard motor housing. After assembling of the whole system in a test facility with a DC machine load experiments have been started to prove the validity of our design, including operation with both cooling systems and driving the stator from the grid as well as by a power inverter.

Machining and brazing of accelerating RF cavity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghodke, S.R.; Barnwal, Rajesh; Mondal, Jayant, E-mail: ghodke_barc@yahoo.co.in

2014-07-01

BARC has developed 2856 MHz accelerating cavities for 6 MeV, 9 MeV and 10 MeV RF Linac. New vendors are developed for mass production of accelerating cavity for future projects. New vendors are developing for diamond turning machining, cleaning and brazing processes. Fabrication involved material testing, CNC diamond turning of cavity, cavity cleaning and brazing. Before and after brazing resonance frequency (RF) of cavity was checked with vector network analyser (VNA). A power feed test setup is also fabricated to test power feed cavity before brazing. This test setup will be used to find out assembly performance of power feedmore » cavity and its coupler. This paper discusses about nano machining, cleaning and brazing processes of RF cavities. (author)« less

Light and short arc rubs in rotating machines: Experimental tests and modelling

NASA Astrophysics Data System (ADS)

Pennacchi, P.; Bachschmid, N.; Tanzi, E.

2009-10-01

Rotor-to-stator rub is a non-linear phenomenon which has been analyzed many times in rotordynamics literature, but very often these studies are devoted simply to highlight non-linearities, using very simple rotors, rather than to present reliable models. However, rotor-to-stator rub is actually one of the most common faults during the operation of rotating machinery. The frequency of its occurrence is increasing due to the trend of reducing the radial clearance between the seal and the rotor in modern turbine units, pumps and compressors in order to increase efficiency. Often the rub occurs between rotor and seals and the analysis of the phenomenon cannot set aside the consideration of the different relative stiffness. This paper presents some experimental results obtained by means of a test rig in which rub conditions of real machines are reproduced. In particular short arc rubs are considered and the shaft is stiffer than the obstacle. Then a model, suitable to be employed for real rotating machinery, is presented and the simulations obtained are compared with the experimental results. The model is able to reproduce the behaviour of the test rig.

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

Using Phun to Study ``Perpetual Motion'' Machines

NASA Astrophysics Data System (ADS)

Koreš, Jaroslav

2012-05-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th- century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over the centuries numerous proposals for PM have been made, involving ever more elements of modern science in their construction. It is possible to test a variety of PM machines in the classroom using a program called Phun2 or its commercial version Algodoo.3 The programs are designed to simulate physical processes and we can easily simulate mechanical machines using them. They provide an intuitive graphical environment controlled with a mouse; a programming language is not needed. This paper describes simulations of four different (supposed) PM machines.4

Learning Machine Learning: A Case Study

ERIC Educational Resources Information Center

Lavesson, N.

2010-01-01

This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

Modification of Upper Thread Tensioner of Sewing Machine

NASA Astrophysics Data System (ADS)

Klouček, P.; Škop, P.

Standard mechanical upper thread tensioner of sewing machines is more and more limited in use for industrial sewing machines due to increasing requests for quality and raising velocity of machines. If we omit mostly manual settings of force made only by sense, the most problematic things are influence of different friction coefficient of the different batch of threads and strong relation between thread tension and sewing machine velocity. The article describes the development focused to the elimination of the most significant disadvantages of a standard tensioner and mainly finding of new conception of the tensioner with electromagnetic brake, development and testing of its prototype.

Communication Studies of DMP and SMP Machines

NASA Technical Reports Server (NTRS)

Sohn, Andrew; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

Understanding the interplay between machines and problems is key to obtaining high performance on parallel machines. This paper investigates the interplay between programming paradigms and communication capabilities of parallel machines. In particular, we explicate the communication capabilities of the IBM SP-2 distributed-memory multiprocessor and the SGI PowerCHALLENGEarray symmetric multiprocessor. Two benchmark problems of bitonic sorting and Fast Fourier Transform are selected for experiments. Communication-efficient algorithms are developed to exploit the overlapping capabilities of the machines. Programs are written in Message-Passing Interface for portability and identical codes are used for both machines. Various data sizes and message sizes are used to test the machines' communication capabilities. Experimental results indicate that the communication performance of the multiprocessors are consistent with the size of messages. The SP-2 is sensitive to message size but yields a much higher communication overlapping because of the communication co-processor. The PowerCHALLENGEarray is not highly sensitive to message size and yields a low communication overlapping. Bitonic sorting yields lower performance compared to FFT due to a smaller computation-to-communication ratio.

Effect of end-point compaction on superpave hot mix asphalt (HMA) mix designs.

DOT National Transportation Integrated Search

2004-01-09

In the Superpave hot mix asphalt (HMA) mix design system, gyratory specime ns are compacted to varying levels of initial (Ninitial), : design (Ndesign) and maximum (Nmaximum) gyrations. Initially, in the Superpave system, specimens were compacted to ...

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1989-01-01

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved

Mixing and Compaction Temperatures for Hot Mix Asphalt Concrete

DOT National Transportation Integrated Search

2000-01-01

According to Superpave mixture design, gyratory specimens are mixed and compacted at equiviscous binder temperatures corresponding to viscosities of 0.17 and 0.28 Pa's, respectively. These were the values previously used in the Marshal mix design met...

Machining of Aircraft Titanium with Abrasive-Waterjets for Fatigue Critical Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, H. T.; Hovanski, Yuri; Dahl, Michael E.

2010-10-04

Laboratory tests were conducted to determine the fatigue performance of AWJ-machined aircraft titanium. Dog-bone specimens machined with AWJs were prepared and tested with and without sanding and dry-grit blasting with Al2O3 as secondary processes. The secondary processes were applied to remove the visual appearance of AWJ-generated striations and to clean up the garnet embedment. The fatigue performance of AWJ-machined specimens was compared with baseline specimens machined with CNC milling. Fatigue test results not only confirmed the findings of the aluminum dog-bone specimens but also further enhance the fatigue performance. In addition, titanium is known to be notoriously difficult to cutmore » with contact tools while AWJs cut it 34% faster than stainless steel. AWJ cutting and dry-grit blasting are shown to be a preferred combination for processing aircraft titanium that is fatigue critical.« less

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

PubMed

Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

2017-10-25

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

Realistic Free-Spins Features Increase Preference for Slot Machines.

PubMed

Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J

2017-06-01

Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.

Machine Learning for Biological Trajectory Classification Applications

NASA Technical Reports Server (NTRS)

Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

2002-01-01

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Impact resistance of guards on grinding machines.

PubMed

Mewes, Detlef; Mewes, Olaf; Herbst, Peter

2011-01-01

Guards on machine tools are meant to protect persons from injuries caused by parts ejected with high kinetic energy from the machine's working zone. With respect to stationary grinding machines, Standard No. EN 13218:2002, therefore, specifies minimum wall thicknesses for guards. These values are mainly based on estimations and experience instead of systematic experimental investigations. This paper shows to what extent simple impact tests with standardizable projectiles can be used as basis for the evaluation of the impact resistance of guards, provided that not only the kinetic energy of the projectiles used but also, among others, their geometry corresponds to the abrasive product fragments to be expected.

Evaluating Georgia DOT's compaction requirements for stone matrix asphalt mixes.

DOT National Transportation Integrated Search

2006-06-01

This study determined a compactive effort for Stone Mastic Asphalt (SMA) mixes with the Superpave gyratory compactor (SGC) that would match a 50-blow Marshall compactive effort using aggregates and mix designs common in Georgia. SMA mix designs were ...

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

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

Machine learning methods in chemoinformatics

PubMed Central

Mitchell, John B O

2014-01-01

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

Machining of Aircraft Titanium with Abrasive-Waterjets for Fatigue Critical Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, H. T.; Hovanski, Yuri; Dahl, Michael E.

2012-02-01

Laboratory tests were conducted to determine the fatigue performance of abrasive-waterjet- (AWJ-) machined aircraft titanium. Dog-bone specimens machined with AWJs were prepared and tested with and without sanding and dry-grit blasting with Al2O3 as secondary processes. The secondary processes were applied to remove the visual appearance of AWJ-generated striations and to clean up the garnet embedment. The fatigue performance of AWJ-machined specimens was compared with baseline specimens machined with CNC milling. Fatigue test results of the titanium specimens not only confirmed our previous findings in aluminum dog-bone specimens but in comparison also further enhanced the fatigue performance of the titanium.more » In addition, titanium is known to be difficult to cut, particularly for thick parts, however AWJs cut the material 34% faster han stainless steel. AWJ cutting and dry-grit blasting are shown to be a preferred ombination for processing aircraft titanium that is fatigue critical.« less

Research note : field control of asphalt concrete paving mixtures.

DOT National Transportation Integrated Search

1995-01-01

The goal of this study was to develop information and evaluate new methods for controlling quality of the AC mixture in the mat. Specifically, this research project evaluated a gyratory compactor in the field laboratory to determine mix quality. Spec...

16. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

16. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to south (90mm lens). Note the large segmental-arched doorway to move locomotives in and out of Machine Shop. - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Application of Abrasive-Waterjets for Machining Fatigue-Critical Aircraft Aluminum Parts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, H T; Hovanski, Yuri; Dahl, Michael E

2010-08-19

Current specifications require AWJ-cut aluminum parts for fatigue critical aerospace structures to go through subsequent processing due to concerns of degradation in fatigue performance. The requirement of secondary process for AWJ-machined parts greatly negates the cost effectiveness of waterjet technology. Some cost savings are envisioned if it can be shown that AWJ net cut parts have comparable durability properties as those conventionally machined. To revisit and upgrade the specifications for AWJ machining of aircraft aluminum, “Dog-bone” specimens, with and without secondary processes, were prepared for independent fatigue tests at Boeing and Pacific Northwest National Laboratory (PNNL). Test results show thatmore » the fatigue life is proportional to quality levels of machined edges or inversely proportional to the surface roughness Ra . Even at highest quality level, the average fatigue life of AWJ-machined parts is about 30% shorter than those of conventionally machined counterparts. Between two secondary processes, dry-grit blasting with aluminum oxide abrasives until the striation is removed visually yields excellent result. It actually prolongs the fatigue life of parts at least three times higher than that achievable with conventional machining. Dry-grit blasting is relatively simple and inexpensive to administrate and, equally important, alleviates the concerns of garnet embedment.« less

Machine characterization based on an abstract high-level language machine

NASA Technical Reports Server (NTRS)

Saavedra-Barrera, Rafael H.; Smith, Alan Jay; Miya, Eugene

1989-01-01

Measurements are presented for a large number of machines ranging from small workstations to supercomputers. The authors combine these measurements into groups of parameters which relate to specific aspects of the machine implementation, and use these groups to provide overall machine characterizations. The authors also define the concept of pershapes, which represent the level of performance of a machine for different types of computation. A metric based on pershapes is introduced that provides a quantitative way of measuring how similar two machines are in terms of their performance distributions. The metric is related to the extent to which pairs of machines have varying relative performance levels depending on which benchmark is used.

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques

PubMed Central

Cui, De-Mi; Wang, Xiao-Quan; Lu, Lie-Min

2017-01-01

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT’s turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts’ interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology’s effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction. PMID:29068431

30 CFR 18.96 - Preparation of machines for inspection; requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection... place at which a field approval investigation will be conducted with respect to any machine, the...

30 CFR 18.96 - Preparation of machines for inspection; requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection... place at which a field approval investigation will be conducted with respect to any machine, the...

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

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

High speed operation of permanent magnet machines

NASA Astrophysics Data System (ADS)

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.

Model development, testing and experimentation in a CyberWorkstation for Brain-Machine Interface research.

PubMed

Rattanatamrong, Prapaporn; Matsunaga, Andrea; Raiturkar, Pooja; Mesa, Diego; Zhao, Ming; Mahmoudi, Babak; Digiovanna, Jack; Principe, Jose; Figueiredo, Renato; Sanchez, Justin; Fortes, Jose

2010-01-01

The CyberWorkstation (CW) is an advanced cyber-infrastructure for Brain-Machine Interface (BMI) research. It allows the development, configuration and execution of BMI computational models using high-performance computing resources. The CW's concept is implemented using a software structure in which an "experiment engine" is used to coordinate all software modules needed to capture, communicate and process brain signals and motor-control commands. A generic BMI-model template, which specifies a common interface to the CW's experiment engine, and a common communication protocol enable easy addition, removal or replacement of models without disrupting system operation. This paper reviews the essential components of the CW and shows how templates can facilitate the processes of BMI model development, testing and incorporation into the CW. It also discusses the ongoing work towards making this process infrastructure independent.

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

PubMed

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol; Jang, Huisu

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

Re-Design and Beat Testing of the Man-Machine Integration Design and Analysis System: MIDAS

NASA Technical Reports Server (NTRS)

Shively, R. Jay; Rutkowski, Michael (Technical Monitor)

1999-01-01

The Man-machine Design and Analysis System (MIDAS) is a human factors design and analysis system that combines human cognitive models with 3D CAD models and rapid prototyping and simulation techniques. MIDAS allows designers to ask 'what if' types of questions early in concept exploration and development prior to actual hardware development. The system outputs predictions of operator workload, situational awareness and system performance as well as graphical visualization of the cockpit designs interacting with models of the human in a mission scenario. Recently, MIDAS was re-designed to enhance functionality and usability. The goals driving the redesign include more efficient processing, GUI interface, advances in the memory structures, implementation of external vision models and audition. These changes were detailed in an earlier paper. Two Beta test sites with diverse applications have been chosen. One Beta test site is investigating the development of a new airframe and its interaction with the air traffic management system. The second Beta test effort will investigate 3D auditory cueing in conjunction with traditional visual cueing strategies including panel-mounted and heads-up displays. The progress and lessons learned on each of these projects will be discussed.

Machine for Automatic Bacteriological Pour Plate Preparation

PubMed Central

Sharpe, A. N.; Biggs, D. R.; Oliver, R. J.

1972-01-01

A fully automatic system for preparing poured plates for bacteriological analyses has been constructed and tested. The machine can make decimal dilutions of bacterial suspensions, dispense measured amounts into petri dishes, add molten agar, mix the dish contents, and label the dishes with sample and dilution numbers at the rate of 2,000 dishes per 8-hr day. In addition, the machine can be programmed to select different media so that plates for different types of bacteriological analysis may be made automatically from the same sample. The machine uses only the components of the media and sterile polystyrene petri dishes; requirements for all other materials, such as sterile pipettes and capped bottles of diluents and agar, are eliminated. Images PMID:4560475

Wire electric-discharge machining and other fabrication techniques

NASA Technical Reports Server (NTRS)

Morgan, W. H.

1983-01-01

Wire electric discharge machining and extrude honing were used to fabricate a two dimensional wing for cryogenic wind tunnel testing. Electric-discharge cutting is done with a moving wire electrode. The cut track is controlled by means of a punched-tape program and the cutting feed is regulated according to the progress of the work. Electric-discharge machining involves no contact with the work piece, and no mechanical force is exerted. Extrude hone is a process for honing finish-machined surfaces by the extrusion of an abrasive material (silly putty), which is forced through a restrictive fixture. The fabrication steps are described and production times are given.

14. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to north (90mm lens). - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates

NASA Astrophysics Data System (ADS)

Borgnat, Pierre; Flandrin, Patrick; Richard, Cédric; Ferrari, André; Amoud, Hassan; Honeine, Paul

2012-03-01

Time-frequency representations provide a powerful tool for nonstationary signal analysis and classification, supporting a wide range of applications [12]. As opposed to conventional Fourier analysis, these techniques reveal the evolution in time of the spectral content of signals. In Ref. [7,38], time-frequency analysis is used to test stationarity of any signal. The proposed method consists of a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogate signals for defining the null hypothesis of stationarity and, based upon this information, to derive statistical tests. An open question remains, however, about how to choose relevant time-frequency features. Over the last decade, a number of new pattern recognition methods based on reproducing kernels have been introduced. These learning machines have gained popularity due to their conceptual simplicity and their outstanding performance [30]. Initiated by Vapnik’s support vector machines (SVM) [35], they offer now a wide class of supervised and unsupervised learning algorithms. In Ref. [17-19], the authors have shown how the most effective and innovative learning machines can be tuned to operate in the time-frequency domain. This chapter follows this line of research by taking advantage of learning machines to test and quantify stationarity. Based on one-class SVM, our approach uses the entire time-frequency representation and does not require arbitrary feature extraction. Applied to a set of surrogates, it provides the domain boundary that includes most of these stationarized signals. This allows us to test the stationarity of the signal under investigation. This chapter is organized as follows. In Section 22.2, we introduce the surrogate data method to generate stationarized signals, namely, the null hypothesis of stationarity. The concept of time-frequency learning machines is presented in Section 22.3, and applied to one-class SVM in order

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

Use of system code to estimate equilibrium tritium inventory in fusion DT machines, such as ARIES-AT and components testing facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

C.P.C. Wong; B. Merrill

2014-10-01

ITER is under construction and will begin operation in 2020. This is the first 500 MWfusion class DT device, and since it is not going to breed tritium, it will consume most of the limited supply of tritium resources in the world. Yet, in parallel, DT fusion nuclear component testing machines will be needed to provide technical data for the design of DEMO. It becomes necessary to estimate the tritium burn-up fraction and corresponding initial tritium inventory and the doubling time of these machines for the planning of future supply and utilization of tritium. With the use of a systemmore » code, tritium burn-up fraction and initial tritium inventory for steady state DT machines can be estimated. Estimated tritium burn-up fractions of FNSF-AT, CFETR-R and ARIES-AT are in the range of 1–2.8%. Corresponding total equilibrium tritium inventories of the plasma flow and tritium processing system, and with the DCLL blanket option are 7.6 kg, 6.1 kg, and 5.2 kg for ARIES-AT, CFETR-R and FNSF-AT, respectively.« less

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jha, Sumit Kumar; Pullum, Laura L; Ramanathan, Arvind

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studyingmore » the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.« less

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

PubMed

Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

2017-12-19

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness

Machine Shop Lathes.

ERIC Educational Resources Information Center

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…

Combination of Universal Mechanical Testing Machine with Atomic Force Microscope for Materials Research

PubMed Central

Zhong, Jian; He, Dannong

2015-01-01

Surface deformation and fracture processes of materials under external force are important for understanding and developing materials. Here, a combined horizontal universal mechanical testing machine (HUMTM)-atomic force microscope (AFM) system is developed by modifying UMTM to combine with AFM and designing a height-adjustable stabilizing apparatus. Then the combined HUMTM-AFM system is evaluated. Finally, as initial demonstrations, it is applied to analyze the relationship among macroscopic mechanical properties, surface nanomorphological changes under external force, and fracture processes of two kinds of representative large scale thin film materials: polymer material with high strain rate (Parafilm) and metal material with low strain rate (aluminum foil). All the results demonstrate the combined HUMTM-AFM system overcomes several disadvantages of current AFM-combined tensile/compression devices including small load force, incapability for large scale specimens, disability for materials with high strain rate, and etc. Therefore, the combined HUMTM-AFM system is a promising tool for materials research in the future. PMID:26265357

Combination of Universal Mechanical Testing Machine with Atomic Force Microscope for Materials Research.

PubMed

Zhong, Jian; He, Dannong

2015-08-12

Surface deformation and fracture processes of materials under external force are important for understanding and developing materials. Here, a combined horizontal universal mechanical testing machine (HUMTM)-atomic force microscope (AFM) system is developed by modifying UMTM to combine with AFM and designing a height-adjustable stabilizing apparatus. Then the combined HUMTM-AFM system is evaluated. Finally, as initial demonstrations, it is applied to analyze the relationship among macroscopic mechanical properties, surface nanomorphological changes under external force, and fracture processes of two kinds of representative large scale thin film materials: polymer material with high strain rate (Parafilm) and metal material with low strain rate (aluminum foil). All the results demonstrate the combined HUMTM-AFM system overcomes several disadvantages of current AFM-combined tensile/compression devices including small load force, incapability for large scale specimens, disability for materials with high strain rate, and etc. Therefore, the combined HUMTM-AFM system is a promising tool for materials research in the future.

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

PubMed

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.

Efficient forced vibration reanalysis method for rotating electric machines

NASA Astrophysics Data System (ADS)

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.

Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop

DTIC Science & Technology

2007-01-01

machine learning components natural language processing, and optimization...was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting...study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

PubMed Central

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction. PMID:29136004

Ship Model Testing

DTIC Science & Technology

2016-01-15

state-of-the-art equipment and to continue to produce excellent graduates in our field. Technical Approach In order to address our current testing ...New Additions • New material testing machine with environmental chamber • New dual-fuel test bed for Haeberle Laboratory • Upgrade existing...Southwark Emery universal test machine • 3D printer with ultra-high surface definition • CFD Workstations Since the inception of this grant, Webb

Machine tool locator

DOEpatents

Hanlon, John A.; Gill, Timothy J.

2001-01-01

Machine tools can be accurately measured and positioned on manufacturing machines within very small tolerances by use of an autocollimator on a 3-axis mount on a manufacturing machine and positioned so as to focus on a reference tooling ball or a machine tool, a digital camera connected to the viewing end of the autocollimator, and a marker and measure generator for receiving digital images from the camera, then displaying or measuring distances between the projection reticle and the reference reticle on the monitoring screen, and relating the distances to the actual position of the autocollimator relative to the reference tooling ball. The images and measurements are used to set the position of the machine tool and to measure the size and shape of the machine tool tip, and examine cutting edge wear. patent

An industrial sewing machine variable speed controller

NASA Technical Reports Server (NTRS)

Estes, Christa; Spiggle, Charles; Swift, Shannon; Vangeffen, Stephen; Youngner, Frank

1992-01-01

The apparel industry is attempting to move in a new direction in the coming decade. Since the invention of an electrically powered sewing machine, the operator has been seated. Today, companies are switching from a sit down operation to a stand up operation involving modular stations. The old treadle worked well with the sitting operator, but problems have been found when trying to use the same treadle with a standing operator. This report details a new design for a treadle to operate an industrial sewing machine that has a standing operator. Emphasis is placed on the ease of use by the operator, as well as the ergonomics involved. Procedures for testing the design are included along with possible uses for the treadle in other applications besides an industrial sewing machine.

An industrial sewing machine variable speed controller

NASA Astrophysics Data System (ADS)

Estes, Christa; Spiggle, Charles; Swift, Shannon; Vangeffen, Stephen; Youngner, Frank

The apparel industry is attempting to move in a new direction in the coming decade. Since the invention of an electrically powered sewing machine, the operator has been seated. Today, companies are switching from a sit down operation to a stand up operation involving modular stations. The old treadle worked well with the sitting operator, but problems have been found when trying to use the same treadle with a standing operator. This report details a new design for a treadle to operate an industrial sewing machine that has a standing operator. Emphasis is placed on the ease of use by the operator, as well as the ergonomics involved. Procedures for testing the design are included along with possible uses for the treadle in other applications besides an industrial sewing machine.

Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

NASA Astrophysics Data System (ADS)

Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

2018-02-01

The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

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.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

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.

Ultra precision machining

NASA Astrophysics Data System (ADS)

Debra, Daniel B.; Hesselink, Lambertus; Binford, Thomas

1990-05-01

There are a number of fields that require or can use to advantage very high precision in machining. For example, further development of high energy lasers and x ray astronomy depend critically on the manufacture of light weight reflecting metal optical components. To fabricate these optical components with machine tools they will be made of metal with mirror quality surface finish. By mirror quality surface finish, it is meant that the dimensions tolerances on the order of 0.02 microns and surface roughness of 0.07. These accuracy targets fall in the category of ultra precision machining. They cannot be achieved by a simple extension of conventional machining processes and techniques. They require single crystal diamond tools, special attention to vibration isolation, special isolation of machine metrology, and on line correction of imperfection in the motion of the machine carriages on their way.

Quantum machine learning.

PubMed

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.

Quantum machine learning

NASA Astrophysics Data System (ADS)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-01

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

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.

A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation.

PubMed

Talaminos-Barroso, Alejandro; Estudillo-Valderrama, Miguel A; Roa, Laura M; Reina-Tosina, Javier; Ortega-Ruiz, Francisco

2016-06-01

M2M (Machine-to-Machine) communications represent one of the main pillars of the new paradigm of the Internet of Things (IoT), and is making possible new opportunities for the eHealth business. Nevertheless, the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications. In the first place, to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions. In the second place, to validate the tool with a particular use case: the respiratory rehabilitation. A software tool, called Distributed Computing Framework (DFC), has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines, with independence of the protocol and performance metrics selected. DDS, MQTT, CoAP, JMS, AMQP and XMPP protocols were evaluated considering different specific performance metrics, including CPU usage, memory usage, bandwidth consumption, latency and jitter. The results obtained allowed to validate a case of use: respiratory rehabilitation of chronic obstructive pulmonary disease (COPD) patients in two scenarios with different types of requirement: Home-Based and Ambulatory. The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies. In this regard, the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Methodology for creating dedicated machine and algorithm on sunflower counting

NASA Astrophysics Data System (ADS)

Muracciole, Vincent; Plainchault, Patrick; Mannino, Maria-Rosaria; Bertrand, Dominique; Vigouroux, Bertrand

2007-09-01

In order to sell grain lots in European countries, seed industries need a government certification. This certification requests purity testing, seed counting in order to quantify specified seed species and other impurities in lots, and germination testing. These analyses are carried out within the framework of international trade according to the methods of the International Seed Testing Association. Presently these different analyses are still achieved manually by skilled operators. Previous works have already shown that seeds can be characterized by around 110 visual features (morphology, colour, texture), and thus have presented several identification algorithms. Until now, most of the works in this domain are computer based. The approach presented in this article is based on the design of dedicated electronic vision machine aimed to identify and sort seeds. This machine is composed of a FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor) and a PC bearing the GUI (Human Machine Interface) of the system. Its operation relies on the stroboscopic image acquisition of a seed falling in front of a camera. A first machine was designed according to this approach, in order to simulate all the vision chain (image acquisition, feature extraction, identification) under the Matlab environment. In order to perform this task into dedicated hardware, all these algorithms were developed without the use of the Matlab toolbox. The objective of this article is to present a design methodology for a special purpose identification algorithm based on distance between groups into dedicated hardware machine for seed counting.

Shock Mounting for Heavy Machines

NASA Technical Reports Server (NTRS)

Thompson, A. R.

1984-01-01

Elastomeric bearings eliminate extraneous forces. Rocket thrust transmitted from motor to load cells via support that absorbs extraneous forces so they do not affect accuracy of thrust measurements. Adapter spoked cone fits over forward end of rocket motor. Shock mounting developed for rocket engines under test used as support for heavy machines, bridges, or towers.

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

PubMed

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

2015-11-01

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

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Machining Test Specimens from Harvested Zion RPV Segments for Through Wall Attenuation Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosseel, Thomas M; Sokolov, Mikhail A; Nanstad, Randy K

2015-01-01

The decommissioning of the Zion Units 1 and 2 Nuclear Generating Station (NGS) in Zion, Illinois presents a special opportunity for developing a better understanding of materials degradation and other issues associated with extending the lifetime of existing Nuclear Power Plants (NPPs) beyond 60 years of service. In support of extended service and current operations of the US nuclear reactor fleet, the Oak Ridge National Laboratory (ORNL), through the Department of Energy (DOE), Light Water Reactor Sustainability (LWRS) Program, is coordinating and contracting with Zion Solutions, LLC, a subsidiary of Energy Solutions, the selective procurement of materials, structures, and componentsmore » from the decommissioned reactors. In this paper, we will discuss the acquisition of segments of the Zion Unit 2 Reactor Pressure Vessel (RPV), the cutting of these segments into sections and blocks from the beltline and upper vertical welds and plate material, the current status of machining those blocks into mechanical (Charpy, compact tension, and tensile) test specimens and coupons for chemical and microstructural (TEM, APT, SANS, and nano indention) characterization, as well as the current test plans and possible collaborative projects. Access to service-irradiated RPV welds and plate sections will allow through wall attenuation studies to be performed, which will be used to assess current radiation damage models (Rosseel et al. (2012) and Rosseel et al. (2015)).« less

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

PubMed Central

Su, Yi; Routhu, Sudhamayee; Moon, Kee S.; Lee, Sung Q.; Youm, WooSub; Ozturk, Yusuf

2016-01-01

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time. PMID:27669264

Numerical Simulation of Earth Pressure on Head Chamber of Shield Machine with FEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Shouju; Kang Chengang; Sun, Wei

2010-05-21

Model parameters of conditioned soils in head chamber of shield machine are determined based on tree-axial compression tests in laboratory. The loads acting on tunneling face are estimated according to static earth pressure principle. Based on Duncan-Chang nonlinear elastic constitutive model, the earth pressures on head chamber of shield machine are simulated in different aperture ratio cases for rotating cutterhead of shield machine. Relationship between pressure transportation factor and aperture ratio of shield machine is proposed by using aggression analysis.

Bypassing the Kohn-Sham equations with machine learning.

PubMed

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.

THE ROLE OF REVIEW MATERIAL IN CONTINUOUS PROGRAMMING WITH TEACHING MACHINES.

ERIC Educational Resources Information Center

FERSTER, C.B.

STUDENTS WERE PRESENTED 61 LESSONS BY MEANS OF SEMIAUTOMATIC TEACHING MACHINES. LESSONS WERE ARRANGED SO THAT EACH PARTICIPATING STUDENT STUDIED PART OF THE COURSE MATERIAL WITH A SINGLE REPETITION AND PART WITHOUT REPETITION. DATA WERE OBTAINED FROM TWO TESTS SHOWING TEACHING-MACHINE RESULTS AND ONE FINAL COURSE EXAMINATION. NO SIGNIFICANT…

Application of Machine Learning to Rotorcraft Health Monitoring

NASA Technical Reports Server (NTRS)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

Experimental investigation of the tip based micro/nano machining

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

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

PubMed

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

2018-01-01

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

Your Sewing Machine.

ERIC Educational Resources Information Center

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

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

Impact of machining on the flexural fatigue strength of glass and polycrystalline CAD/CAM ceramics.

PubMed

Fraga, Sara; Amaral, Marina; Bottino, Marco Antônio; Valandro, Luiz Felipe; Kleverlaan, Cornelis Johannes; May, Liliana Gressler

2017-11-01

To assess the effect of machining on the flexural fatigue strength and on the surface roughness of different computer-aided design, computer-aided manufacturing (CAD/CAM) ceramics by comparing machined and polished after machining specimens. Disc-shaped specimens of yttria-stabilized polycrystalline tetragonal zirconia (Y-TZP), leucite-, and lithium disilicate-based glass ceramics were prepared by CAD/CAM machining, and divided into two groups: machining (M) and machining followed by polishing (MP). The surface roughness was measured and the flexural fatigue strength was evaluated by the step-test method (n=20). The initial load and the load increment for each ceramic material were based on a monotonic test (n=5). A maximum of 10,000 cycles was applied in each load step, at 1.4Hz. Weibull probability statistics was used for the analysis of the flexural fatigue strength, and Mann-Whitney test (α=5%) to compare roughness between the M and MP conditions. Machining resulted in lower values of characteristic flexural fatigue strength than machining followed by polishing. The greatest reduction in flexural fatigue strength from MP to M was observed for Y-TZP (40%; M=536.48MPa; MP=894.50MPa), followed by lithium disilicate (33%; M=187.71MPa; MP=278.93MPa) and leucite (29%; M=72.61MPa; MP=102.55MPa). Significantly higher values of roughness (Ra) were observed for M compared to MP (leucite: M=1.59μm and MP=0.08μm; lithium disilicate: M=1.84μm and MP=0.13μm; Y-TZP: M=1.79μm and MP=0.18μm). Machining negatively affected the flexural fatigue strength of CAD/CAM ceramics, indicating that machining of partially or fully sintered ceramics is deleterious to fatigue strength. Copyright © 2017 The Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Machine Learning with Distances

DTIC Science & Technology

2015-02-16

of training class-wise densities p(x|y) to test input density p′(x). For the fitting of qπ to p ′, we may use the Kullback - Leibler (KL...Problems of Information Transmission, 23(9):95–101, 1987. [103] S. Kullback and R. A. Leibler . On information and sufficiency. The Annals of ...distributions. The Kullback - Leibler (KL) distance is the de-facto standard distance measure in statis- tics and machine learning, because

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

PubMed Central

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

A Real-Time Tool Positioning Sensor for Machine-Tools

PubMed Central

Ruiz, Antonio Ramon Jimenez; Rosas, Jorge Guevara; Granja, Fernando Seco; Honorato, Jose Carlos Prieto; Taboada, Jose Juan Esteve; Serrano, Vicente Mico; Jimenez, Teresa Molina

2009-01-01

In machining, natural oscillations, and elastic, gravitational or temperature deformations, are still a problem to guarantee the quality of fabricated parts. In this paper we present an optical measurement system designed to track and localize in 3D a reference retro-reflector close to the machine-tool's drill. The complete system and its components are described in detail. Several tests, some static (including impacts and rotations) and others dynamic (by executing linear and circular trajectories), were performed on two different machine tools. It has been integrated, for the first time, a laser tracking system into the position control loop of a machine-tool. Results indicate that oscillations and deformations close to the tool can be estimated with micrometric resolution and a bandwidth from 0 to more than 100 Hz. Therefore this sensor opens the possibility for on-line compensation of oscillations and deformations. PMID:22408472

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

PubMed

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

2015-02-01

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

The prototype of high stiffness load cell for Rockwell hardness testing machine calibration according to ISO 6508-2:2015

NASA Astrophysics Data System (ADS)

Pakkratoke, M.; Sanponpute, T.

2017-09-01

The penetrated depth of the Rockwell hardness testing machine is normally not more than 0.260 mm. Using commercial load cell cannot achieve the proposed force calibration according to ISO 6508-2[1]. For these reason, the high stiffness load cell (HSL) was fabricated. Its obvious advantage is deformation less than 0.020 mm at 150 kgf maximum load applied. The HSL prototype was designed in concept of direct compression and then confirmed with finite element analysis, FEA. The results showed that the maximum deformation was lower than 0.012 mm at capacity.

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1990-01-01

A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine using the principle of increasing precision with decreasing intelligence from a hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution can be found by minimizing the total entropy in the system. The focus is on the design of the organization level as a Boltzmann machine. Since this level is responsible for planning the actions of the machine, the Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence and illustrated with numerical examples.

Dictionary Based Machine Translation from Kannada to Telugu

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Experimental Investigation of Superconducting Synchronous Machines

DTIC Science & Technology

The report details the design and testing of a synchronous motor with superconducting field and armature windings. Data are furnished on the...as a generator with its armature in LN2 and in the superconducting state are given. Data are given on the machine operated as a synchronous motor. The

A comparison of muscle activation between a Smith machine and free weight bench press.

PubMed

Schick, Evan E; Coburn, Jared W; Brown, Lee E; Judelson, Daniel A; Khamoui, Andy V; Tran, Tai T; Uribe, Brandon P

2010-03-01

The bench press exercise exists in multiple forms including the machine and free weight bench press. It is not clear though how each mode differs in its effect on muscle activation. The purpose of this study was to compare muscle activation of the anterior deltoid, medial deltoid, and pectoralis major during a Smith machine and free weight bench press at lower (70% 1 repetition maximum [1RM]) and higher (90% 1RM) intensities. Normalized electromyography amplitude values were used during the concentric phase of the bench press to compare muscle activity between a free weight and Smith machine bench press. Participants were classified as either experienced or inexperienced bench pressers. Two testing sessions were used, each of which entailed either all free weight or all Smith machine testing. In each testing session, each participant's 1RM was established followed by 2 repetitions at 70% of 1RM and 2 repetitions at 90% of 1RM. Results indicated greater activation of the medial deltoid on the free weight bench press than on the Smith machine bench press. Also, there was greater muscle activation at the 90% 1RM load than at the 70% 1RM load. The results of this study suggest that strength coaches should consider choosing the free weight bench press over the Smith machine bench press because of its potential for greater upper-body muscular development.

Standardized Curriculum for Machine Tool Operation/Machine Shop.

ERIC Educational Resources Information Center

Mississippi State Dept. of Education, Jackson. Office of Vocational, Technical and Adult Education.

Standardized vocational education course titles and core contents for two courses in Mississippi are provided: machine tool operation/machine shop I and II. The first course contains the following units: (1) orientation; (2) shop safety; (3) shop math; (4) measuring tools and instruments; (5) hand and bench tools; (6) blueprint reading; (7)…

MachineProse: an Ontological Framework for Scientific Assertions

PubMed Central

Dinakarpandian, Deendayal; Lee, Yugyung; Vishwanath, Kartik; Lingambhotla, Rohini

2006-01-01

Objective: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge. Design: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically. Measurements: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles. Results: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions. Conclusion: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches. PMID:16357355

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

PubMed

Lane, Thomas; Russo, Daniel P; Zorn, Kimberley M; Clark, Alex M; Korotcov, Alexandru; Tkachenko, Valery; Reynolds, Robert C; Perryman, Alexander L; Freundlich, Joel S; Ekins, Sean

2018-04-26

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

Design and application of the falling vertical sorting machine

NASA Astrophysics Data System (ADS)

Zuo, Ping; Peng, Tao; Yang, Hai

2018-04-01

In the process of tobacco production, it is necessary to pack the smoke according to the needs of different customers. A sorting machine is used to pick up the cigarette at present, there is a launch channel machine, a percussible vertical machine, But in the sorting process, the rolling channel machine is different in terms of the quality of smoke and the frictional force. It is difficult to ensure the location and posture of the belt sorting line, which causes the manipulator to not grasp. The strike type vertical machine is difficult to control the parallelism of the smoke. Now this team has developed a falling sorting machine, which has solved the smoke drop of a cigarette to the transmission belt. There will not be no code, can satisfy most of the different types of smoke sorting and no damage to smoke status. The dynamic characteristics such as the angular error of the opening and closing mechanism are carried out by ADAMS software. The simulation results show that the maximum angular error is 0.016rad. Through the test of the device, the goods falling speed is 7031/hour, the good of the falling position error within 2mm, meet the crawl accuracy requirements of the palletizing robot.

Machine Learning.

ERIC Educational Resources Information Center

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)

Machine tool task force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1980-10-22

The Machine Tool Task Force (MTTF) is a multidisciplined team of international experts, whose mission was to investigate the state of the art of machine tool technology, to identify promising future directions of that technology for both the US government and private industry, and to disseminate the findings of its research in a conference and through the publication of a final report. MTTF was a two and one-half year effort that involved the participation of 122 experts in the specialized technologies of machine tools and in the management of machine tool operations. The scope of the MTTF was limited tomore » cutting-type or material-removal-type machine tools, because they represent the major share and value of all machine tools now installed or being built. The activities of the MTTF and the technical, commercial and economic signifiance of recommended activities for improving machine tool technology are discussed. (LCL)« less

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2011 CFR

2011-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2012 CFR

2012-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2013 CFR

2013-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2014 CFR

2014-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2010 CFR

2010-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

Cooperating reduction machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kluge, W.E.

1983-11-01

This paper presents a concept and a system architecture for the concurrent execution of program expressions of a concrete reduction language based on lamda-expressions. If formulated appropriately, these expressions are well-suited for concurrent execution, following a demand-driven model of computation. In particular, recursive program expressions with nonlinear expansion may, at run time, recursively be partitioned into a hierarchy of independent subexpressions which can be reduced by a corresponding hierarchy of virtual reduction machines. This hierarchy unfolds and collapses dynamically, with virtual machines recursively assuming the role of masters that create and eventually terminate, or synchronize with, slaves. The paper alsomore » proposes a nonhierarchically organized system of reduction machines, each featuring a stack architecture, that effectively supports the allocation of virtual machines to the real machines of the system in compliance with their hierarchical order of creation and termination. 25 references.« less

Compensation strategy for machining optical freeform surfaces by the combined on- and off-machine measurement.

PubMed

Zhang, Xiaodong; Zeng, Zhen; Liu, Xianlei; Fang, Fengzhou

2015-09-21

Freeform surface is promising to be the next generation optics, however it needs high form accuracy for excellent performance. The closed-loop of fabrication-measurement-compensation is necessary for the improvement of the form accuracy. It is difficult to do an off-machine measurement during the freeform machining because the remounting inaccuracy can result in significant form deviations. On the other side, on-machine measurement may hides the systematic errors of the machine because the measuring device is placed in situ on the machine. This study proposes a new compensation strategy based on the combination of on-machine and off-machine measurement. The freeform surface is measured in off-machine mode with nanometric accuracy, and the on-machine probe achieves accurate relative position between the workpiece and machine after remounting. The compensation cutting path is generated according to the calculated relative position and shape errors to avoid employing extra manual adjustment or highly accurate reference-feature fixture. Experimental results verified the effectiveness of the proposed method.

Ship localization in Santa Barbara Channel using machine learning classifiers.

PubMed

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

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

Improved Tensile Test for Ceramics

NASA Technical Reports Server (NTRS)

Osiecki, R. A.

1982-01-01

For almost-nondestructive tensile testing of ceramics, steel rod is bonded to sample of ceramic. Assembly is then pulled apart in conventional tensile-test machine. Test destroys only shallow surface layer which can be machined away making specimen ready for other uses. Method should be useful as manufacturing inspection procedure for low-strength brittle materials.

Designing Anticancer Peptides by Constructive Machine Learning.

PubMed

Grisoni, Francesca; Neuhaus, Claudia S; Gabernet, Gisela; Müller, Alex T; Hiss, Jan A; Schneider, Gisbert

2018-04-21

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on α-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fault Tolerant State Machines

NASA Technical Reports Server (NTRS)

Burke, Gary R.; Taft, Stephanie

2004-01-01

State machines are commonly used to control sequential logic in FPGAs and ASKS. An errant state machine can cause considerable damage to the device it is controlling. For example in space applications, the FPGA might be controlling Pyros, which when fired at the wrong time will cause a mission failure. Even a well designed state machine can be subject to random errors us a result of SEUs from the radiation environment in space. There are various ways to encode the states of a state machine, and the type of encoding makes a large difference in the susceptibility of the state machine to radiation. In this paper we compare 4 methods of state machine encoding and find which method gives the best fault tolerance, as well as determining the resources needed for each method.

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

The high frequency light load fatigue testing machine based on giant magnetostrictive material and stroke multiplier

NASA Astrophysics Data System (ADS)

Wang, M. D.; Li, D. S.; Huang, Y.; Zhang, C.; Zhong, K. M.; Sun, L. N.

2013-08-01

In the notebook and clamshell mobile phone, data communication wire often requires repeated bending. Generally, communication wire with the actual application conditions, the test data cannot assess bending resistance performance of data communication wire is tested conventionally using wires with weights of 90 degree to test bending number, this test method and device is not fully reflect the fatigue performance in high frequency and light load application condition, at the same time it has a large difference between the test data of the long-term reliability of high frequency and low load conditions. In this paper, high frequency light load fatigue testing machine based on the giant magnetostrictive material and stroke multiplier is put forward, in which internal reflux stroke multiplier is driven by giant magnetostrictive material to realize the rapid movement of light load. This fatigue testing device has the following advantages: (1) When the load is far less than the friction, reducing friction is very effective to improve the device performance. Because the body is symmetrical, the friction loss of radial does not exist in theory, so the stress situation of mechanism is good with high transmission efficiency and long service life. (2) The installation position of the output hydraulic cylinder, can be arranged conveniently as ordinary cylinder. (3) Reciprocating frequency, displacement and speed of high frequency movement can be programmed easily to change with higher position precision. (4)Hydraulic oil in this device is closed to transmit, which does not produce any environment pollution. The device has no hydraulic pump and tank, and less energy conversion processes, so it is with the trend of green manufacturing.

Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers

PubMed Central

Racette, Lyne; Chiou, Christine Y.; Hao, Jiucang; Bowd, Christopher; Goldbaum, Michael H.; Zangwill, Linda M.; Lee, Te-Won; Weinreb, Robert N.; Sample, Pamela A.

2009-01-01

Purpose To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared to using each test alone. Methods One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation (PD); total deviation (TD)) and on Heidelberg Retina Tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared to two HRT linear discriminant functions (LDF) and to SWAP mean deviation (MD) and pattern standard deviation (PSD). Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90% and 96%. Results RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (MD: 0.68; PSD: 0.72) offered no advantage over SWAP thresholds plus age, while the LDF AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set. Conclusions Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared to training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy. PMID:19528827

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

NASA Astrophysics Data System (ADS)

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.

14. Machine in north 1922 section of Building 59. Machine ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. Machine in north 1922 section of Building 59. Machine is 24' Jointer made by Oliver Machinery Co. Camera pointed E. - Puget Sound Naval Shipyard, Pattern Shop, Farragut Avenue, Bremerton, Kitsap County, WA

15. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

15. Interior, Machine Shop, Roundhouse Machine Shop Extension, Southern Pacific Railroad Carlin Shops, view to northeast (90mm lens). The arched cutouts in the bottom chords of the roof trusses were necessary to provide clearance for the smokestacks of steam locomotives, and also mark the location of the former inspection pit in the floor (now filled in and covered by a new concrete floor). - Southern Pacific Railroad, Carlin Shops, Roundhouse Machine Shop Extension, Foot of Sixth Street, Carlin, Elko County, NV

Distribution of man-machine controls in space teleoperation

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1982-01-01

The distribution of control between man and machine is dependent on the tasks, available technology, human performance characteristics and control goals. This dependency has very specific projections on systems designed for teleoperation in space. This paper gives a brief outline of the space-related issues and presents the results of advanced teleoperator research and development at the Jet Propulsion Laboratory (JPL). The research and development work includes smart sensors, flexible computer controls and intelligent man-machine interface devices in the area of visual displays and kinesthetic man-machine coupling in remote control of manipulators. Some of the development results have been tested at the Johnson Space Center (JSC) using the simulated full-scale Shuttle Remote Manipulator System (RMS). The research and development work for advanced space teleoperation is far from complete and poses many interdisciplinary challenges.

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

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

The electrical discharge machining of ceramics

NASA Astrophysics Data System (ADS)

Trueman, Christopher Stuart

This study introduces the concept of developing a novel and rapid rough-machining methodology for spark eroding suitable ceramic compositions based on material removal by thermal shock induced spalling, as opposed to conventional melting mechanisms. The principal materials studied were TiB2 dispersion toughened SiC, and Syalon501 - a commercially available TiN toughened sialon ceramic specifically designed for spark erosion. A preliminary study was also carried out on a range of SiC:B4C composites. Machinability and material performance were assessed where appropriate using machining parameters, material removal rate tests, surface analysis, four-point flexure testing, and tool wear. The machining technologies which supported the different mechanisms of material removal were identified, and each mechanism investigated by analysis of captured debris and sectioning of the workpiece. The SiC:B4C composites were found to be spark erodible only with B4C levels above 50% (by mass), and material removal was found to be solely by melting mechanisms. A SiC:TiB2 composition with the addition of 26.5% TiB2 (by mass) was found to be more machinable than a composition with 10% TiB2 (by mass), achieving greater material removal rates owing to its higher electrical conductivity. An in-depth study of the latter (10%TiB2) SiC composition and Syalon501 revealed surprisingly robust materials. Under conventional sparking (no arcing), material was removed by combined dissociation, melting and evaporation. Syalon501 in particular behaved with a high degree of predictability, and neither material could be made to spall under conventional sparking. However, by imposing conditions which deliberately induced arcing, both compositions spalled large flakes of material (up to several hundred microns across) in the localised region of the arc-strike. Examination of captured debris and fracture facets of the spall interface revealed the existence of small "penny cracks", each characterised by

Advanced warfighter machine interface (Invited Paper)

NASA Astrophysics Data System (ADS)

Franks, Erin

2005-05-01

Future military crewmen may have more individual and shared tasks to complete throughout a mission as a result of smaller crew sizes and an increased number of technology interactions. To maintain reasonable workload levels, the Warfighter Machine Interface (WMI) must provide information in a consistent, logical manner, tailored to the environment in which the soldier will be completing their mission. This paper addresses design criteria for creating an advanced, multi-modal warfighter machine interface for on-the-move mounted operations. The Vetronics Technology Integration (VTI) WMI currently provides capabilities such as mission planning and rehearsal, voice and data communications, and manned/unmanned vehicle payload and mobility control. A history of the crewstation and more importantly, the WMI software will be provided with an overview of requirements and criteria used for completing the design. Multiple phases of field and laboratory testing provide the opportunity to evaluate the design and hardware in stationary and motion environments. Lessons learned related to system usability and user performance are presented with mitigation strategies to be tested in the future.

Prediction of skin sensitization potency using machine learning approaches.

PubMed

Zang, Qingda; Paris, Michael; Lehmann, David M; Bell, Shannon; Kleinstreuer, Nicole; Allen, David; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Strickland, Judy

2017-07-01

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Topical Report Tantalum – 2.5% Tungsten Machinability Testing

DOE Office of Scientific and Technical Information (OSTI.GOV)

L. J. Lazarus

2009-09-02

Protection Association (NFPA). NFPA 484, Standard for Combustible Metals, Chapter 9 Tantalum and Annex E, supplemental Information on Tantalum require cutting oil be used when machining tantalum because it burns at such a high temperature that it breaks down the water in a water-based metalworking fluid (MWF). The NFPA guide devotes approximately 20 pages to this material. The Kansas City Plant (KCP) uses Fuchs Lubricants Ecocut Base 44 LVC as a MWF. This is a highly chlorinated oil with a high flash point (above 200° F). The chlorine is very helpful in preventing BUE (Built Up Edge) that occurs frequentlymore » with this very gummy material. The Ecocut is really a MWF additive that Fuchs uses to add chlorinated fats to other non-chlorinated MWF.« less

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

PubMed

Brown, Andrew D; Marotta, Thomas R

2018-05-01

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest - to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Multi-parameter monitoring of electrical machines using integrated fibre Bragg gratings

NASA Astrophysics Data System (ADS)

Fabian, Matthias; Hind, David; Gerada, Chris; Sun, Tong; Grattan, Kenneth T. V.

2017-04-01

In this paper a sensor system for multi-parameter electrical machine condition monitoring is reported. The proposed FBG-based system allows for the simultaneous monitoring of machine vibration, rotor speed and position, torque, spinning direction, temperature distribution along the stator windings and on the rotor surface as well as the stator wave frequency. This all-optical sensing solution reduces the component count of conventional sensor systems, i.e., all 48 sensing elements are contained within the machine operated by a single sensing interrogation unit. In this work, the sensing system has been successfully integrated into and tested on a permanent magnet motor prototype.

Diamond machine tool face lapping machine

DOEpatents

Yetter, H.H.

1985-05-06

An apparatus for shaping, sharpening and polishing diamond-tipped single-point machine tools. The isolation of a rotating grinding wheel from its driving apparatus using an air bearing and causing the tool to be shaped, polished or sharpened to be moved across the surface of the grinding wheel so that it does not remain at one radius for more than a single rotation of the grinding wheel has been found to readily result in machine tools of a quality which can only be obtained by the most tedious and costly processing procedures, and previously unattainable by simple lapping techniques.

Machine Learning Principles Can Improve Hip Fracture Prediction.

PubMed

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-04-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates. The best models were retrained with restricted predictor subsets to estimate the best subsets. For women, bootstrap aggregated flexible discriminant analysis ("bagFDA") performed best with a test AUC of 0.92 [0.89; 0.94] and well-calibrated probabilities following Naïve Bayes adjustments. A "bagFDA" model limited to 11 predictors (among them bone mineral densities (BMD), biochemical glucose measurements, general practitioner and dentist use) achieved a test AUC of 0.91 [0.88; 0.93]. For men, eXtreme Gradient Boosting ("xgbTree") performed best with a test AUC of 0.89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an "xgbTree" model. Machine learning can improve hip fracture prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration.

Quantification of uncertainty in machining operations for on-machine acceptance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Claudet, Andre A.; Tran, Hy D.; Su, Jiann-Chemg

2008-09-01

Manufactured parts are designed with acceptance tolerances, i.e. deviations from ideal design conditions, due to unavoidable errors in the manufacturing process. It is necessary to measure and evaluate the manufactured part, compared to the nominal design, to determine whether the part meets design specifications. The scope of this research project is dimensional acceptance of machined parts; specifically, parts machined using numerically controlled (NC, or also CNC for Computer Numerically Controlled) machines. In the design/build/accept cycle, the designer will specify both a nominal value, and an acceptable tolerance. As part of the typical design/build/accept business practice, it is required to verifymore » that the part did meet acceptable values prior to acceptance. Manufacturing cost must include not only raw materials and added labor, but also the cost of ensuring conformance to specifications. Ensuring conformance is a substantial portion of the cost of manufacturing. In this project, the costs of measurements were approximately 50% of the cost of the machined part. In production, cost of measurement would be smaller, but still a substantial proportion of manufacturing cost. The results of this research project will point to a science-based approach to reducing the cost of ensuring conformance to specifications. The approach that we take is to determine, a priori, how well a CNC machine can manufacture a particular geometry from stock. Based on the knowledge of the manufacturing process, we are then able to decide features which need further measurements from features which can be accepted 'as is' from the CNC. By calibration of the machine tool, and establishing a machining accuracy ratio, we can validate the ability of CNC to fabricate to a particular level of tolerance. This will eliminate the costs of checking for conformance for relatively large tolerances.« less

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

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

2012-01-01

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. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. 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. 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. 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.

Investigations on high speed machining of EN-353 steel alloy under different machining environments

NASA Astrophysics Data System (ADS)

Venkata Vishnu, A.; Jamaleswara Kumar, P.

2018-03-01

The addition of Nano Particles into conventional cutting fluids enhances its cooling capabilities; in the present paper an attempt is made by adding nano sized particles into conventional cutting fluids. Taguchi Robust Design Methodology is employed in order to study the performance characteristics of different turning parameters i.e. cutting speed, feed rate, depth of cut and type of tool under different machining environments i.e. dry machining, machining with lubricant - SAE 40 and machining with mixture of nano sized particles of Boric acid and base fluid SAE 40. A series of turning operations were performed using L27 (3)13 orthogonal array, considering high cutting speeds and the other machining parameters to measure hardness. The results are compared among the different machining environments, and it is concluded that there is considerable improvement in the machining performance using lubricant SAE 40 and mixture of SAE 40 + boric acid compared with dry machining. The ANOVA suggests that the selected parameters and the interactions are significant and cutting speed has most significant effect on hardness.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

Mobile Landing Platform with Core Capability Set (MLP w/CCS): Combined Initial Operational Test and Evaluation and Live Fire Test and Evaluation Report

DTIC Science & Technology

2015-07-01

annex.   iii Self-defense testing was limited to structural test firing from each machine gun mount and an ammunition resupply drill. Robust self...provided in the classified annex. Self-   8 defense testing was limited to structural test firing from each machine gun mount and a single...Caliber Machine Gun Mount Structural Test Fire November 2014 San Diego, Offshore Ship Weapons Range Operating Independently       9 Section Three

Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.

PubMed

Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav

2014-01-01

Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.

Electric machine

DOEpatents

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.

ERECTING/MACHINE SHOP, CRANE ACCESS GANGWAY BETWEEN ERECTING (L) AND MACHINE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ERECTING/MACHINE SHOP, CRANE ACCESS GANGWAY BETWEEN ERECTING (L) AND MACHINE (R) SHOPS, LOOKING NORTH. - Southern Pacific, Sacramento Shops, Erecting Shop, 111 I Street, Sacramento, Sacramento County, CA

The simplest chronoscope II: reaction time measured by meterstick versus machine.

PubMed

Montare, Alberto

2010-12-01

Visual simple reaction time (SRT) scores measured in 31 college students of both sexes by use of the simplest chronoscope methodology (meterstick SRT) were compared to scores obtained by use of an electromechanical multi-choice reaction timer (machine SRT). Four hypotheses were tested. Results indicated that the previous mean value of meterstick SRT was replicated; meterstick SRT was significantly faster than long-standing population estimates of mean SRT; and machine SRT was significantly slower than the same long-standing mean SRT estimates for the population. Also, the mean meterstick SRT of 181 msec. was significantly faster than the mean machine SRT of 294 msec. It was theorized that differential visual information processing occurred such that the dorsal visual stream subserved meterstick SRT; whereas the ventral visual stream subserved machine SRT.

Correct machine learning on protein sequences: a peer-reviewing perspective.

PubMed

Walsh, Ian; Pollastri, Gianluca; Tosatto, Silvio C E

2016-09-01

Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

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

Machine Learning and Radiology

PubMed Central

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

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

PubMed

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

2016-06-21

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

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

PubMed Central

Choi, Woong-Kirl; Kim, Seong-Hyun; Choi, Seung-Geon; Lee, Eun-Sang

2018-01-01

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. PMID:29351235

On the Stability of Jump-Linear Systems Driven by Finite-State Machines with Markovian Inputs

NASA Technical Reports Server (NTRS)

Patilkulkarni, Sudarshan; Herencia-Zapana, Heber; Gray, W. Steven; Gonzalez, Oscar R.

2004-01-01

This paper presents two mean-square stability tests for a jump-linear system driven by a finite-state machine with a first-order Markovian input process. The first test is based on conventional Markov jump-linear theory and avoids the use of any higher-order statistics. The second test is developed directly using the higher-order statistics of the machine s output process. The two approaches are illustrated with a simple model for a recoverable computer control system.

Apprentice Machine Theory Outline.

ERIC Educational Resources Information Center

Connecticut State Dept. of Education, Hartford. Div. of Vocational-Technical Schools.

This volume contains outlines for 16 courses in machine theory that are designed for machine tool apprentices. Addressed in the individual course outlines are the following topics: basic concepts; lathes; milling machines; drills, saws, and shapers; heat treatment and metallurgy; grinders; quality control; hydraulics and pneumatics;…

A support vector machine based test for incongruence between sets of trees in tree space

PubMed Central

2012-01-01

Background The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer. Results Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributions of gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approach maps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) to measure the separation between two sets of pre-defined trees. We use a permutation test to assess the significance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to the comparison of gene trees simulated within different species trees across a range of species tree depths. Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect very small differences between two set of gene trees generated under different species trees. Our statistical test can also include tree reconstruction into its test framework through a variety of phylogenetic optimality criteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the form of receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detection of differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, it controlled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it an applicable test for any scenario where evolutionary or other factors can lead to trees with different multi-dimensional distributions. The

Wire connector classification with machine vision and a novel hybrid SVM

NASA Astrophysics Data System (ADS)

Chauhan, Vedang; Joshi, Keyur D.; Surgenor, Brian W.

2018-04-01

A machine vision-based system has been developed and tested that uses a novel hybrid Support Vector Machine (SVM) in a part inspection application with clear plastic wire connectors. The application required the system to differentiate between 4 different known styles of connectors plus one unknown style, for a total of 5 classes. The requirement to handle an unknown class is what necessitated the hybrid approach. The system was trained with the 4 known classes and tested with 5 classes (the 4 known plus the 1 unknown). The hybrid classification approach used two layers of SVMs: one layer was semi-supervised and the other layer was supervised. The semi-supervised SVM was a special case of unsupervised machine learning that classified test images as one of the 4 known classes (to accept) or as the unknown class (to reject). The supervised SVM classified test images as one of the 4 known classes and consequently would give false positives (FPs). Two methods were tested. The difference between the methods was that the order of the layers was switched. The method with the semi-supervised layer first gave an accuracy of 80% with 20% FPs. The method with the supervised layer first gave an accuracy of 98% with 0% FPs. Further work is being conducted to see if the hybrid approach works with other applications that have an unknown class requirement.

Modeling Electronic Quantum Transport with Machine Learning

DOE PAGES

Lopez Bezanilla, Alejandro; von Lilienfeld Toal, Otto A.

2014-06-11

We present a machine learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test data sets to the machine. The system’s representation encodes energetic as well as geometrical information to characterize similarities between disordered configurations, while the Euclidean norm is used as a measure of similarity. Errors for out-of-sample predictions systematically decrease with training set size, enabling the accurate and fast prediction of new transmission coefficients. The remarkable performance of our model to capture the complexity of interference phenomena lends further support to its viability inmore » dealing with transport problems of undulatory nature.« less

Reducing the uncertainty in robotic machining by modal analysis

NASA Astrophysics Data System (ADS)

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.

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions

Effect of heat treatments on machinability of gold alloy with age-hardenability at intraoral temperature.

PubMed

Watanabe, I; Baba, N; Watanabe, E; Atsuta, M; Okabe, T

2004-01-01

This study investigated the effect of heat treatment on the machinability of heat-treated cast gold alloy with age-hardenability at intraoral temperature using a handpiece engine with SiC wheels and an air-turbine handpiece with carbide burs and diamond points. Cast gold alloy specimens underwent various heat treatments [As-cast (AC); Solution treatment (ST); High-temperature aging (HA), Intraoral aging (IA)] before machinability testing. The machinability test was conducted at a constant machining force of 0.784N. The three circumferential speeds used for the handpiece engine were 500, 1,000 and 1,500 m/min. The machinability index (M-index) was determined as the amount of metal removed by machining (volume loss, mm(3)). The results were analyzed by ANOVA and Scheffé's test. When an air-turbine handpiece was used, there was no difference in the M-index of the gold alloy among the heat treatments. The air-turbine carbide burs showed significantly (p<0.05) higher M-indexes than the diamond points after any heat treatments. With the SiC wheels, increasing the circumferential speed increased the M-index values for each heat treatment. The specimens heat-treated with AC, HA and IA had similar M-indexes at the lower speeds (500 and 1,000 m/min). The ST specimens exhibited the lowest M-index at the lower speeds. However, at the highest speed (1,500 m/min), there were no significant differences in the M-indexes among the heat treatments except for HA, which showed the highest M-index. There was no effect of heat treatment on the machinability of the gold alloy using the air-turbine handpiece. The heat treatments had a small effect on the M-index of the gold alloy machined with a SiC wheel for a handpiece engine.

Technology of machine tools. Volume 4. Machine tool controls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 3. Machine tool mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tlusty, J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 5. Machine tool accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hocken, R.J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Analysis and optimization of machining parameters of laser cutting for polypropylene composite

NASA Astrophysics Data System (ADS)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

Present works explains about machining of self-reinforced Polypropylene composite fabricated using hot compaction method. The objective of the experiment is to find optimum machining parameters for Polypropylene (PP). Laser power and Machining speed were the parameters considered in response to tensile test and Flexure test. Taguchi method is used for experimentation. Grey Relational Analysis (GRA) is used for multiple process parameter optimization. ANOVA (Analysis of Variance) is used to find impact for process parameter. Polypropylene has got the great application in various fields like, it is used in the form of foam in model aircraft and other radio-controlled vehicles, thin sheets (∼2-20μm) used as a dielectric, PP is also used in piping system, it is also been used in hernia and pelvic organ repair or protect new herrnis in the same location.

Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

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 Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

ERA 1103 UNIVAC 2 Calculating Machine

NASA Image and Video Library

1955-09-21

The new 10-by 10-Foot Supersonic Wind Tunnel at the Lewis Flight Propulsion Laboratory included high tech data acquisition and analysis systems. The reliable gathering of pressure, speed, temperature, and other data from test runs in the facilities was critical to the research process. Throughout the 1940s and early 1950s female employees, known as computers, recorded all test data and performed initial calculations by hand. The introduction of punch card computers in the late 1940s gradually reduced the number of hands-on calculations. In the mid-1950s new computational machines were installed in the office building of the 10-by 10-Foot tunnel. The new systems included this UNIVAC 1103 vacuum tube computer—the lab’s first centralized computer system. The programming was done on paper tape and fed into the machine. The 10-by 10 computer center also included the Lewis-designed Computer Automated Digital Encoder (CADDE) and Digital Automated Multiple Pressure Recorder (DAMPR) systems which converted test data to binary-coded decimal numbers and recorded test pressures automatically, respectively. The systems primarily served the 10-by 10, but were also applied to the other large facilities. Engineering Research Associates (ERA) developed the initial UNIVAC computer for the Navy in the late 1940s. In 1952 the company designed a commercial version, the UNIVAC 1103. The 1103 was the first computer designed by Seymour Cray and the first commercially successful computer.

Experimental evaluation of high performance base course and road base asphalt concrete with electric arc furnace steel slags.

PubMed

Pasetto, Marco; Baldo, Nicola

2010-09-15

The paper presents the results of a laboratory study aimed at verifying the use of two types of electric arc furnace (EAF) steel slags as substitutes for natural aggregates, in the composition of base course and road base asphalt concrete (BBAC) for flexible pavements. The trial was composed of a preliminary study of the chemical, physical, mechanical and leaching properties of the EAF steel slags, followed by the mix design and performance characterization of the bituminous mixes, through gyratory compaction tests, permanent deformation tests, stiffness modulus tests at various temperatures, fatigue tests and indirect tensile strength tests. All the mixtures with EAF slags presented better mechanical characteristics than those of the corresponding asphalts with natural aggregate and satisfied the requisites for acceptance in the Italian road sector technical standards, thus resulting as suitable for use in road construction. Copyright 2010 Elsevier B.V. All rights reserved.

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

Machine vision systems using machine learning for industrial product inspection

NASA Astrophysics Data System (ADS)

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.

Machine learning, social learning and the governance of self-driving cars.

PubMed

Stilgoe, Jack

2018-02-01

Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. 'Self-driving' or 'autonomous' cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.

Walking Machine Control Programming

DTIC Science & Technology

1983-08-31

configuration is useful for two reasons: first, the machine won’t fit through the garage door unless it is in the tuck position, and second, a principal way...machine out of its garage . ’We call the garage a "laboratory" even though the shorter term is more apt.- We regularly run the machine in the parking...comes down from a high push-up. The natural position for the feet as the machine comes out of the garage is the "tuck" in which each knee is bent in as

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

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.

Improving diagnostic recognition of primary hyperparathyroidism with machine learning.

PubMed

Somnay, Yash R; Craven, Mark; McCoy, Kelly L; Carty, Sally E; Wang, Tracy S; Greenberg, Caprice C; Schneider, David F

2017-04-01

Parathyroidectomy offers the only cure for primary hyperparathyroidism, but today only 50% of primary hyperparathyroidism patients are referred for operation, in large part, because the condition is widely under-recognized. The diagnosis of primary hyperparathyroidism can be especially challenging with mild biochemical indices. Machine learning is a collection of methods in which computers build predictive algorithms based on labeled examples. With the aim of facilitating diagnosis, we tested the ability of machine learning to distinguish primary hyperparathyroidism from normal physiology using clinical and laboratory data. This retrospective cohort study used a labeled training set and 10-fold cross-validation to evaluate accuracy of the algorithm. Measures of accuracy included area under the receiver operating characteristic curve, precision (sensitivity), and positive and negative predictive value. Several different algorithms and ensembles of algorithms were tested using the Weka platform. Among 11,830 patients managed operatively at 3 high-volume endocrine surgery programs from March 2001 to August 2013, 6,777 underwent parathyroidectomy for confirmed primary hyperparathyroidism, and 5,053 control patients without primary hyperparathyroidism underwent thyroidectomy. Test-set accuracies for machine learning models were determined using 10-fold cross-validation. Age, sex, and serum levels of preoperative calcium, phosphate, parathyroid hormone, vitamin D, and creatinine were defined as potential predictors of primary hyperparathyroidism. Mild primary hyperparathyroidism was defined as primary hyperparathyroidism with normal preoperative calcium or parathyroid hormone levels. After testing a variety of machine learning algorithms, Bayesian network models proved most accurate, classifying correctly 95.2% of all primary hyperparathyroidism patients (area under receiver operating characteristic = 0.989). Omitting parathyroid hormone from the model did not

The machinability of cast titanium and Ti-6Al-4V.

PubMed

Ohkubo, C; Watanabe, I; Ford, J P; Nakajima, H; Hosoi, T; Okabe, T

2000-02-01

This study investigated the machinability (ease of metal removal) of commercially pure (CP) titanium and Ti-6Al-4V alloy. Both CP Ti and Ti-6Al-4V were cast into magnesia molds. Two types of specimens (with alpha-case and without alpha-case) were made for CP Ti and Ti-6Al-4V. Machinability (n = 5) was evaluated as volume loss (mm3) by cutting/grinding the 3.0 mm surface using fissure burs and silicon carbide (SiC) under two machining conditions: (1) two machining forces (100 or 300 gf) at two rotational speeds (15000 or 30000 rpm) for 1 min, and (2) constant machining force of 100 gf and rotational speed of 15000 rpm for 1, 2, 5, 10, and 30 min. As controls, conventionally cast Co-Cr and Type IV gold alloys were evaluated in the same manner as the titanium. When fissure burs were used, there was a significant difference in the machinability between CP titanium with alpha-case and without alpha-case. On the other hand, there was no appreciable difference in the amount of metal removed for each tested metal when using the SiC points.

Machine learning and radiology.

PubMed

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.

Vane Pump Casing Machining of Dumpling Machine Based on CAD/CAM

NASA Astrophysics Data System (ADS)

Huang, Yusen; Li, Shilong; Li, Chengcheng; Yang, Zhen

Automatic dumpling forming machine is also called dumpling machine, which makes dumplings through mechanical motions. This paper adopts the stuffing delivery mechanism featuring the improved and specially-designed vane pump casing, which can contribute to the formation of dumplings. Its 3D modeling in Pro/E software, machining process planning, milling path optimization, simulation based on UG and compiling post program were introduced and verified. The results indicated that adoption of CAD/CAM offers firms the potential to pursue new innovative strategies.

Operator-coached machine vision for space telerobotics

NASA Technical Reports Server (NTRS)

Bon, Bruce; Wilcox, Brian; Litwin, Todd; Gennery, Donald B.

1991-01-01

A prototype system for interactive object modeling has been developed and tested. The goal of this effort has been to create a system which would demonstrate the feasibility of high interactive operator-coached machine vision in a realistic task environment, and to provide a testbed for experimentation with various modes of operator interaction. The purpose for such a system is to use human perception where machine vision is difficult, i.e., to segment the scene into objects and to designate their features, and to use machine vision to overcome limitations of human perception, i.e., for accurate measurement of object geometry. The system captures and displays video images from a number of cameras, allows the operator to designate a polyhedral object one edge at a time by moving a 3-D cursor within these images, performs a least-squares fit of the designated edges to edge data detected with a modified Sobel operator, and combines the edges thus detected to form a wire-frame object model that matches the Sobel data.

A Simple Universal Turing Machine for the Game of Life Turing Machine

NASA Astrophysics Data System (ADS)

Rendell, Paul

In this chapter we present a simple universal Turing machine which is small enough to fit into the design limits of the Turing machine build in Conway's Game of Life by the author. That limit is 8 symbols and 16 states. By way of comparison we also describe one of the smallest known universal Turing machines due to Rogozhin which has 6 symbols and 4 states.

The Knife Machine. Module 15.

ERIC Educational Resources Information Center

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,…

Study of Man-Machine Communications Systems for the Handicapped. Volume III. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

The report describes a series of studies conducted to determine the extent to which severly handicapped students who were able to comprehend language and language structure but who were not able to write or type could communicate using various man-machine systems. Included among the systems tested were specialized electric typewriting machines, a…

[Cyclic fatigue of Vita mark II machinable ceramics under Hertzian's contact].

PubMed

Liu, Wei-Cai; Zhang, Zhi-Shen; Huang, Cheng-Min; Chao, Yong-Lie; Wan, Qian-Bing

2006-08-01

To investigate the cyclic fatigue modes of Vita mark II machinable ceramics under Hertzian's contact. Hertzian's contact technique (WC spheres r = 3.18 mm) was used to investigate the cyclic fatigue of Vita mark II machinable ceramic. All specimens were fatigued by cyclic loading in moist environment, furthermore, surviving strength was examined by three point test and morphology damage observation. In homogeneous Vita mark II machinable ceramics, two fatigue damage modes existed after cyclic loading with spheres under moist environment, including conventional tensile-driven cone cracking (brittle mode) and shear-driven microdamage accumulation (quasi-plastic mode). The latter generated radial cracks and deeply penetrating secondary cone crack. Initial strength degradation were caused by the cone cracks, subsequent and much more deleterious loss was caused by radial cracks. Cyclic fatigue modes of Vita mark II machinable ceramics includes brittle and quasi-plastic mode.

Sample Holder for Cryogenic Adhesive Shear Test

NASA Technical Reports Server (NTRS)

Ledbetter, F. E.; Clemons, J. M.; White, W. T.; Penn, B.; Semmel, M. L.

1983-01-01

Five samples tested in one cooldown. Holder mounted in testing machine. Submerged in cryogenic liquid held in cryostat. Movable crosshead of testing machine moves gradually downward. Samples placed under tension, one after another, starting with top one; each sample fails in turn before next is stressed.

Effects of pole flux distribution in a homopolar linear synchronous machine

NASA Astrophysics Data System (ADS)

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

1994-05-01

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

Theoretical and experimental research on machine tool servo system for ultra-precision position compensation on CNC lathe

NASA Astrophysics Data System (ADS)

Ma, Zhichao; Hu, Leilei; Zhao, Hongwei; Wu, Boda; Peng, Zhenxing; Zhou, Xiaoqin; Zhang, Hongguo; Zhu, Shuai; Xing, Lifeng; Hu, Huang

2010-08-01

The theories and techniques for improving machining accuracy via position control of diamond tool's tip and raising resolution of cutting depth on precise CNC lathes have been extremely focused on. A new piezo-driven ultra-precision machine tool servo system is designed and tested to improve manufacturing accuracy of workpiece. The mathematical model of machine tool servo system is established and the finite element analysis is carried out on parallel plate flexure hinges. The output position of diamond tool's tip driven by the machine tool servo system is tested via a contact capacitive displacement sensor. Proportional, integral, derivative (PID) feedback is also implemented to accommodate and compensate dynamical change owing cutting forces as well as the inherent non-linearity factors of the piezoelectric stack during cutting process. By closed loop feedback controlling strategy, the tracking error is limited to 0.8 μm. Experimental results have shown the proposed machine tool servo system could provide a tool positioning resolution of 12 nm, which is much accurate than the inherent CNC resolution magnitude. The stepped shaft of aluminum specimen with a step increment of cutting depth of 1 μm is tested, and the obtained contour illustrates the displacement command output from controller is accurately and real-time reflected on the machined part.

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Machine for use in monitoring fatigue life for a plurality of elastomeric specimens

NASA Technical Reports Server (NTRS)

Fitzer, G. E. (Inventor)

1977-01-01

An improved machine is described for use in determining the fatigue life for elastomeric specimens. The machine is characterized by a plurality of juxtaposed test stations, specimen support means located at each of the test stations for supporting a plurality of specimens of elastomeric material, and means for subjecting the specimens at each of said stations to sinusoidal strain at a strain rate unique with respect to the strain rate at which the specimens at each of the other stations is subjected to sinusoidal strain.

An Electronic Cigarette Vaping Machine for the Characterization of Aerosol Delivery and Composition.

PubMed

Havel, Christopher M; Benowitz, Neal L; Jacob, Peyton; St Helen, Gideon

2017-10-01

Characterization of aerosols generated by electronic cigarettes (e-cigarettes) is one method used to evaluate the safety of e-cigarettes. While some researchers have modified smoking machines for e-cigarette aerosol generation, these machines are either not readily available, not automated for e-cigarette testing or have not been adequately described. The objective of this study was to build an e-cigarette vaping machine that can be used to test, under standard conditions, e-liquid aerosolization and nicotine and toxicant delivery. The vaping machine was assembled from commercially available parts, including a puff controller, vacuum pump, power supply, switch to control current flow to the atomizer, three-way value to direct air flow to the atomizer, and three gas dispersion tubes for aerosol trapping. To validate and illustrate its use, the variation in aerosol generation was assessed within and between KangerTech Mini ProTank 3 clearomizers, and the effect of voltage on aerosolization and toxic aldehyde generation were assessed. When using one ProTank 3 clearomizer and different e-liquid flavors, the coefficient of variation (CV) of aerosol generated ranged between 11.5% and 19.3%. The variation in aerosol generated between ProTank 3 clearomizers with different e-liquid flavors and voltage settings ranged between 8.3% and 16.3% CV. Aerosol generation increased linearly at 3-6V across e-liquids and clearomizer brands. Acetaldehyde, acrolein, and formaldehyde generation increased markedly at voltages at or above 5V. The vaping machine that we describe reproducibly aerosolizes e-liquids from e-cigarette atomizers under controlled conditions and is useful for testing of nicotine and toxicant delivery. This study describes an electronic cigarette vaping machine that was assembled from commercially available parts. The vaping machine can be replicated by researchers and used under standard conditions to generate e-cigarette aerosols and characterize nicotine and

Laser-machined piezoelectric cantilevers for mechanical energy harvesting.

PubMed

Kim, HyunUk; Bedekar, Vishwas; Islam, Rashed Adnan; Lee, Woo-Ho; Leo, Don; Priya, Shashank

2008-09-01

In this study, we report results on a piezoelectric- material-based mechanical energy-harvesting device that was fabricated by combining laser machining with microelectronics packaging technology. It was found that the laser-machining process did not have significant effect on the electrical properties of piezoelectric material. The fabricated device was tested in the low-frequency regime of 50 to 1000 Hz at constant force of 8 g (where g = 9.8 m/s(2)). The device was found to generate continuous power of 1.13 microW at 870 Hz across a 288.5 kOmega load with a power density of 301.3 microW/cm(3).

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

PubMed

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

2018-05-01

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

Investigation of Machine-ability of Inconel 800 in EDM with Coated Electrode

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-03-01

The Inconel 800 is a high temperature application alloy which is classified as a nickel based super alloy. It has wide scope in aerospace engineering, gas Turbine etc. The machine-ability studies were found limited on this material. Hence This research focuses on machine-ability studies on EDM of Inconel 800 with Silver Coated Electrolyte Copper Electrode. The purpose of coating on electrode is to reduce tool wear. The factors pulse on Time, Pulse off Time and Peck Current were considered to observe the responses of surface roughness, material removal rate, tool wear rate. Taguchi Full Factorial Design is employed for Design the experiment. Some specific findings were reported and the percentage of contribution of each parameter was furnished

A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

The Universal Anaesthesia Machine (UAM): assessment of a new anaesthesia workstation built with global health in mind.

PubMed

de Beer, D A H; Nesbitt, F D; Bell, G T; Rapuleng, A

2017-04-01

The Universal Anaesthesia Machine has been developed as a complete anaesthesia workstation for use in low- and middle-income countries, where the provision of safe general anaesthesia is often compromised by unreliable supply of electricity and anaesthetic gases. We performed a functional and clinical assessment of this anaesthetic machine, with particular reference to novel features and functioning in the intended environment. The Universal Anaesthesia Machine was found to be reliable, safe and consistent across a range of tests during targeted functional testing. © 2016 The Association of Anaesthetists of Great Britain and Ireland.

A Novel Application of Machine Learning Methods to Model Microcontroller Upset Due to Intentional Electromagnetic Interference

NASA Astrophysics Data System (ADS)

Bilalic, Rusmir

A novel application of support vector machines (SVMs), artificial neural networks (ANNs), and Gaussian processes (GPs) for machine learning (GPML) to model microcontroller unit (MCU) upset due to intentional electromagnetic interference (IEMI) is presented. In this approach, an MCU performs a counting operation (0-7) while electromagnetic interference in the form of a radio frequency (RF) pulse is direct-injected into the MCU clock line. Injection times with respect to the clock signal are the clock low, clock rising edge, clock high, and the clock falling edge periods in the clock window during which the MCU is performing initialization and executing the counting procedure. The intent is to cause disruption in the counting operation and model the probability of effect (PoE) using machine learning tools. Five experiments were executed as part of this research, each of which contained a set of 38,300 training points and 38,300 test points, for a total of 383,000 total points with the following experiment variables: injection times with respect to the clock signal, injected RF power, injected RF pulse width, and injected RF frequency. For the 191,500 training points, the average training error was 12.47%, while for the 191,500 test points the average test error was 14.85%, meaning that on average, the machine was able to predict MCU upset with an 85.15% accuracy. Leaving out the results for the worst-performing model (SVM with a linear kernel), the test prediction accuracy for the remaining machines is almost 89%. All three machine learning methods (ANNs, SVMs, and GPML) showed excellent and consistent results in their ability to model and predict the PoE on an MCU due to IEMI. The GP approach performed best during training with a 7.43% average training error, while the ANN technique was most accurate during the test with a 10.80% error.

An imperialist competitive algorithm for virtual machine placement in cloud computing

NASA Astrophysics Data System (ADS)

Jamali, Shahram; Malektaji, Sepideh; Analoui, Morteza

2017-05-01

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user's applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

NASA Astrophysics Data System (ADS)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

Young’s modulus and Poisson’s ratio changes due to machining in porous microcracked cordierite

DOE PAGES

Cooper, R. C.; Bruno, Giovanni; Onel, Yener; ...

2016-07-25

Microstructural changes in porous cordierite caused by machining were characterized using microtensile testing, X-ray computed tomography and scanning electron microscopy. Young s moduli and Poisson s ratios were determined on ~215-380 um thick machined samples by combining digital image correlation and microtensile loading. The results provide evidence for an increase in microcrack density due to machining of the thin samples extracted from diesel particulate filter honeycombs.

Multisensor systems today and tomorrow: Machine control, diagnosis and thermal compensation

NASA Astrophysics Data System (ADS)

Nunzio, D'Addea

2000-05-01

Multisensor techniques that deal with control of tribology test rig and with diagnosis and thermal error compensation of machine tools are the starting point for some consideration about the use of these techniques as in fuzzy and neural net systems. The author comes to conclusion that anticipatory systems and multisensor techniques will have in the next future a great improvement and a great development mainly in the thermal error compensation of machine tools.

High frequency testing of rubber mounts.

PubMed

Vahdati, Nader; Saunders, L Ken Lauderbaugh

2002-04-01

Rubber and fluid-filled rubber engine mounts are commonly used in automotive and aerospace applications to provide reduced cabin noise and vibration, and/or motion accommodations. In certain applications, the rubber mount may operate at frequencies as high as 5000 Hz. Therefore, dynamic stiffness of the mount needs to be known in this frequency range. Commercial high frequency test machines are practically nonexistent, and the best high frequency test machine on the market is only capable of frequencies as high as 1000 Hz. In this paper, a high frequency test machine is described that allows test engineers to study the high frequency performance of rubber mounts at frequencies up to 5000 Hz.

Automatic soldering machine

NASA Technical Reports Server (NTRS)

Stein, J. A.

1974-01-01

Fully-automatic tube-joint soldering machine can be used to make leakproof joints in aluminum tubes of 3/16 to 2 in. in diameter. Machine consists of temperature-control unit, heater transformer and heater head, vibrator, and associated circuitry controls, and indicators.

Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B–induced local skin hypersensitization in healthy subjects: a machine-learned analysis

PubMed Central

Lötsch, Jörn; Geisslinger, Gerd; Heinemann, Sarah; Lerch, Florian; Oertel, Bruno G.; Ultsch, Alfred

2018-01-01

Abstract The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models. PMID:28700537

Study on magnetic force of electromagnetic levitation circular knitting machine

NASA Astrophysics Data System (ADS)

Wu, X. G.; Zhang, C.; Xu, X. S.; Zhang, J. G.; Yan, N.; Zhang, G. Z.

2018-06-01

The structure of the driving coil and the electromagnetic force of the test prototype of electromagnetic-levitation (EL) circular knitting machine are studied. In this paper, the driving coil’s structure and working principle of the EL circular knitting machine are firstly introduced, then the mathematical modelling analysis of the driving electromagnetic force is carried out, and through the Ansoft Maxwell finite element simulation software the coil’s magnetic induction intensity and the needle’s electromagnetic force is simulated, finally an experimental platform is built to measure the coil’s magnetic induction intensity and the needle’s electromagnetic force. The results show that the theoretical analysis, the simulation analysis and the results of the test are very close, which proves the correctness of the proposed model.

Detection of longitudinal visual field progression in glaucoma using machine learning.

PubMed

Yousefi, Siamak; Kiwaki, Taichi; Zheng, Yuhui; Suigara, Hiroki; Asaoka, Ryo; Murata, Hiroshi; Lemij, Hans; Yamanishi, Kenji

2018-06-16

Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine-learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices. Development and comparison of a prognostic index. Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were utilized to compare methods. The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 years (95% confidence interval, 4.1 - 6.5 years); 4.5 years (4.0 - 5.5) using region-wise, 3.9 years (3.5 - 4.6) using point-wise, and 3.5 years (3.1 - 4.0) using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after two additional visits were included were 6.6 years (5.6 - 7.4 years), 5.7 years (4.8 - 6.7), 5.6 years (4.7 - 6.5), and 5.1 years (4.5 - 6.0) for global, region-wise, point-wise, and machine learning analyses, respectively. Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods. Copyright © 2018 Elsevier Inc. All rights reserved.

The Security of Machine Learning

DTIC Science & Technology

2008-04-24

Machine learning has become a fundamental tool for computer security, since it can rapidly evolve to changing and complex situations. That...adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine ...We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing

Classification of Strawberry Fruit Shape by Machine Learning

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

Kurzweil Reading Machine: A Partial Evaluation of Its Optical Character Recognition Error Rate.

ERIC Educational Resources Information Center

Goodrich, Gregory L.; And Others

1979-01-01

A study designed to assess the ability of the Kurzweil reading machine (a speech reading device for the visually handicapped) to read three different type styles produced by five different means indicated that the machines tested had different error rates depending upon the means of producing the copy and upon the type style used. (Author/CL)

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

PubMed Central

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

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

PubMed

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 < 0.0001) and a 33% point 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.

Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.

PubMed

Zhang, Bin; He, Xin; Ouyang, Fusheng; Gu, Dongsheng; Dong, Yuhao; Zhang, Lu; Mo, Xiaokai; Huang, Wenhui; Tian, Jie; Zhang, Shuixing

2017-09-10

We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

Over the past few decades the induction machine has been chosen for many applications due to its structural simplicity and low manufacturing cost. However, modest torque density and control challenges have motivated researchers to find alternative machines. The permanent magnet synchronous machine has been viewed as one of the alternatives because it features higher torque density for a given loss than the induction machine. However, the assembly and permanent magnet material cost, along with safety under fault conditions, have been concerns for this class of machine. An alternative machine type, namely the asymmetrical reluctance machine, is proposed in this work. Since the proposed machine is of the reluctance machine type, it possesses desirable feature, such as near absence of rotor losses, low assembly cost, low no-load rotational losses, modest torque ripple, and rather benign fault conditions. Through theoretical analysis performed herein, it is shown that this machine has a higher torque density for a given loss than typical reluctance machines, although not as high as the permanent magnet machines. Thus, the asymmetrical reluctance machine is a viable and advantageous machine alternative where the use of permanent magnet machines are undesirable.

Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Tellaeche, A.; Arana, R.; Ibarguren, A.; Martínez-Otzeta, J. M.

The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

Mississippi Curriculum Framework for Machine Tool Operation/Machine Shop (Program CIP: 48.0503--Machine Shop Assistant). Secondary Programs.

ERIC Educational Resources Information Center

Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

This document, which reflects Mississippi's statutory requirement that instructional programs be based on core curricula and performance-based assessment, contains outlines of the instructional units required in local instructional management plans and daily lesson plans for machine tool operation/machine shop I and II. Presented first are a…

30 CFR 18.49 - Connection boxes on machines.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Connection boxes on machines. 18.49 Section 18.49 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and...

10 CFR 431.295 - Units to be tested.

Code of Federal Regulations, 2011 CFR

2011-01-01

... EQUIPMENT Refrigerated Bottled or Canned Beverage Vending Machines Test Procedures § 431.295 Units to be tested. For each basic model of refrigerated bottled or canned beverage vending machine selected for...

Machine Tool Software

NASA Technical Reports Server (NTRS)

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.

Nanocomposites for Machining Tools

PubMed Central

Loginov, Pavel; Mishnaevsky, Leon; Levashov, Evgeny

2017-01-01

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. PMID:29027926

Progress in machine consciousness.

PubMed

Gamez, David

2008-09-01

This paper is a review of the work that has been carried out on machine consciousness. A clear overview of this diverse field is achieved by breaking machine consciousness down into four different areas, which are used to understand its aims, discuss its relationship with other subjects and outline the work that has been carried out so far. The criticisms that have been made against machine consciousness are also covered, along with its potential benefits, and the work that has been done on analysing systems for signs of consciousness. Some of the social and ethical issues raised by machine consciousness are examined at the end of the paper.

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.

PubMed

Wu, Mon-Ju; Passos, Ives Cavalcante; Bauer, Isabelle E; Lavagnino, Luca; Cao, Bo; Zunta-Soares, Giovana B; Kapczinski, Flávio; Mwangi, Benson; Soares, Jair C

2016-03-01

Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC). A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC. The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC. Our study sample was small as we only considered euthymic BD patients and demographically matched HC. Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-12-12

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

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

PubMed Central

Segreto, Tiziana; Karam, Sara; Teti, Roberto

2017-01-01

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions. PMID:29231864

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Machining process influence on the chip form and surface roughness by neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Anicic, Obrad; Jović, Srđan; Aksić, Danilo; Skulić, Aleksandar; Nedić, Bogdan

2017-04-01

The main aim of the study was to analyze the influence of six machining parameters on the chip shape formation and surface roughness as well during turning of Steel 30CrNiMo8. Three components of cutting forces were used as inputs together with cutting speed, feed rate, and depth of cut. It is crucial for the engineers to use optimal machining parameters to get the best results or to high control of the machining process. Therefore, there is need to find the machining parameters for the optimal procedure of the machining process. Adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the inputs influence on the chip shape formation and surface roughness. According to the results, the cutting force in direction of the depth of cut has the highest influence on the chip form. The testing error for the cutting force in direction of the depth of cut has testing error 0.2562. This cutting force determines the depth of cut. According to the results, the depth of cut has the highest influence on the surface roughness. Also the depth of cut has the highest influence on the surface roughness. The testing error for the cutting force in direction of the depth of cut has testing error 5.2753. Generally the depth of cut and the cutting force which provides the depth of cut are the most dominant factors for chip forms and surface roughness. Any small changes in depth of cut or in cutting force which provide the depth of cut could drastically affect the chip form or surface roughness of the working material.

Machine Learning of Fault Friction

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Osteoporosis risk prediction using machine learning and conventional methods.

PubMed

Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

2013-01-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

The Hooey Machine.

ERIC Educational Resources Information Center

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)

Electrical machines with superconducting windings. Part 3: Homopolar dc machines

NASA Astrophysics Data System (ADS)

Kullman, D.; Henninger, P.

1981-01-01

The losses in rotating liquid metal contacts and the problems in including liquid metals were theoretically and experimentally studied. These machines are shown realiable. For electric ship propulsion, they are a more efficient method of power transmission than mechanical gearboxes. However, weight reduction as compared to mechanical gearboxes can hardly be achieved with machines fully shielded by magnetic iron.

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

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-02

... Machines (IBM), Software Group Business Unit, Quality Assurance Group, San Jose, California; Notice of... workers of International Business Machines (IBM), Software Group Business Unit, Optim Data Studio Tools QA... February 2, 2011 (76 FR 5832). The subject worker group supplies acceptance testing services, design...

Trends and developments in industrial machine vision: 2013

NASA Astrophysics Data System (ADS)

Niel, Kurt; Heinzl, Christoph

2014-03-01

When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own

[Study on high strength mica-based machinable glass-ceramic].

PubMed

Li, Hong; Ran, Junguo; Gou, Li; Wang, Fanghu

2004-02-01

The phase constitution, microstructure and properties of a new type of machinable glass-ceramics containing fluorophlogopite-type (FPT) Ca-mica for used in restorative dentistry were investigated. According to the results of X-ray diffraction (XRD) and energy-dispersive spectrometry(EDS), its main crystalline phases were FPT Ca-mica and t-ZrO2, together with few KxCa(1-x)/2Mg2Si4O10F2, m-ZrO2. The flexible strength was 235 MPa, which was nearly two times larger than that of the present mica-based dental materials, and the highest fracture toughness was 2.17 MPa.m1/2. The microstructure had a great effect on properties, the glass-ceramics contained a large volume, and the fine crystals showed higher strength. The material possessed typical microstructure of machinable glass-ceramics and displayed excellent machinability during drilling test and CAD/CAM.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries.

PubMed

Han, Bucong; Ma, Xiaohua; Zhao, Ruiying; Zhang, Jingxian; Wei, Xiaona; Liu, Xianghui; Liu, Xin; Zhang, Cunlong; Tan, Chunyan; Jiang, Yuyang; Chen, Yuzong

2012-11-23

Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries

PubMed Central

2012-01-01

Background Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. Results We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. Conclusions SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates. PMID:23173901

Manufacturing Methods for High Speed Machining of Aluminum

DTIC Science & Technology

1978-02-01

Tests 53 4.4.3 Intergrmnular Corrosion Tests. ........... 53 4.4.4 Cost Analysis . .. ............... . .. .... 60 4.5 Conclusions...Corporat~ion and Others to equuip an existing Uwidstvahd, five-axes, Modal as-i, oidail with a 20,000 rVIL 20 hOW~pse spindle, Based anresults obtained...economic analysis for high-speed machining wan also conducted by Metout, and the results are given in Section 11.0. Xn Section 12.0, conclusions and

Test - Apollo-Soyuz Test Project (ASTP)

NASA Image and Video Library

1974-07-01

S74-24671 (10 July 1974) --- Three Apollo-Soyuz Test Project (ASTP) engineers look over a Soyuz spacecraft docking system prior to an ASTP docking mechanism fitness test conducted in Building 13 at the Johnson Space Center (JSC). They are (left to right) Robert White, Vladimir Syromyatnikov and Yevgeniy Bobrov. White is the American chairman of ASTP Working Group Number 3, and Syromyatnikov is his Soviet counterpart. This working group is concerned with ASTP docking problems and procedures. White is with JSC's Spacecraft Design Division. Syromyatnikov is senior researcher of the Soviet State Research Institute of Machine Building. Bobrov is a junior researcher with the Institute of Machine Building. The joint United States - USSR ASTP docking mission in Earth orbit is scheduled for the summer of 1975.

Simplest chronoscope. III. Further comparisons between reaction times obtained by meterstick versus machine.

PubMed

Montare, Alberto

2013-06-01

The three classical Donders' reaction time (RT) tasks (simple, choice, and discriminative RTs) were employed to compare reaction time scores from college students obtained by use of Montare's simplest chronoscope (meterstick) methodology to scores obtained by use of a digital-readout multi-choice reaction timer (machine). Five hypotheses were tested. Simple RT, choice RT, and discriminative RT were faster when obtained by meterstick than by machine. The meterstick method showed higher reliability than the machine method and was less variable. The meterstick method of the simplest chronoscope may help to alleviate the longstanding problems of low reliability and high variability of reaction time performances; while at the same time producing faster performance on Donders' simple, choice and discriminative RT tasks than the machine method.

Langmuir probes for SPIDER (source for the production of ions of deuterium extracted from radio frequency plasma) experiment: Tests in BATMAN (Bavarian test machine for negative ions)

NASA Astrophysics Data System (ADS)

Brombin, M.; Spolaore, M.; Serianni, G.; Pomaro, N.; Taliercio, C.; Palma, M. Dalla; Pasqualotto, R.; Schiesko, L.

2014-11-01

A prototype system of the Langmuir probes for SPIDER (Source for the production of Ions of Deuterium Extracted from RF plasma) was manufactured and experimentally qualified. The diagnostic was operated in RF (Radio Frequency) plasmas with cesium evaporation on the BATMAN (BAvarian Test MAchine for Negative ions) test facility, which can provide plasma conditions as expected in the SPIDER source. A RF passive compensation circuit was realised to operate the Langmuir probes in RF plasmas. The sensors' holder, designed to better simulate the bias plate conditions in SPIDER, was exposed to a severe experimental campaign in BATMAN with cesium evaporation. No detrimental effect on the diagnostic due to cesium evaporation was found during the exposure to the BATMAN plasma and in particular the insulation of the electrodes was preserved. The paper presents the system prototype, the RF compensation circuit, the acquisition system (as foreseen in SPIDER), and the results obtained during the experimental campaigns.

Langmuir probes for SPIDER (Source for the production of Ions of Deuterium Extracted from Radio Frequency plasma) experiment: tests in BATMAN (BAvarian Test Machine for Negative ions).

PubMed

Brombin, M; Spolaore, M; Serianni, G; Pomaro, N; Taliercio, C; Dalla Palma, M; Pasqualotto, R; Schiesko, L

2014-11-01

A prototype system of the Langmuir probes for SPIDER (Source for the production of Ions of Deuterium Extracted from RF plasma) was manufactured and experimentally qualified. The diagnostic was operated in RF (Radio Frequency) plasmas with cesium evaporation on the BATMAN (BAvarian Test MAchine for Negative ions) test facility, which can provide plasma conditions as expected in the SPIDER source. A RF passive compensation circuit was realised to operate the Langmuir probes in RF plasmas. The sensors' holder, designed to better simulate the bias plate conditions in SPIDER, was exposed to a severe experimental campaign in BATMAN with cesium evaporation. No detrimental effect on the diagnostic due to cesium evaporation was found during the exposure to the BATMAN plasma and in particular the insulation of the electrodes was preserved. The paper presents the system prototype, the RF compensation circuit, the acquisition system (as foreseen in SPIDER), and the results obtained during the experimental campaigns.

Langmuir probes for SPIDER (source for the production of ions of deuterium extracted from radio frequency plasma) experiment: Tests in BATMAN (Bavarian test machine for negative ions)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brombin, M., E-mail: matteo.brombin@igi.cnr.it; Spolaore, M.; Serianni, G.

2014-11-15

A prototype system of the Langmuir probes for SPIDER (Source for the production of Ions of Deuterium Extracted from RF plasma) was manufactured and experimentally qualified. The diagnostic was operated in RF (Radio Frequency) plasmas with cesium evaporation on the BATMAN (BAvarian Test MAchine for Negative ions) test facility, which can provide plasma conditions as expected in the SPIDER source. A RF passive compensation circuit was realised to operate the Langmuir probes in RF plasmas. The sensors’ holder, designed to better simulate the bias plate conditions in SPIDER, was exposed to a severe experimental campaign in BATMAN with cesium evaporation.more » No detrimental effect on the diagnostic due to cesium evaporation was found during the exposure to the BATMAN plasma and in particular the insulation of the electrodes was preserved. The paper presents the system prototype, the RF compensation circuit, the acquisition system (as foreseen in SPIDER), and the results obtained during the experimental campaigns.« less

Control of discrete event systems modeled as hierarchical state machines

NASA Technical Reports Server (NTRS)

Brave, Y.; Heymann, M.

1991-01-01

The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

Force supplementary comparison at SIM (SIM.M.F-S5), compression testing machines calibration, up to 200 kN

NASA Astrophysics Data System (ADS)

Cárdenas Moctezuma, A.; Torres Guzmán, J. C.

2016-01-01

CENAM, through the Force and Pressure Division, organized a comparison on testing machines calibration, in compression mode. The participating laboratories were SIM National Institutes of Metrology from Colombia, Peru and Costa Rica, where CENAM, Mexico was the pilot and reference laboratory. The results obtained by the laboratories are presented in this paper as well as the analysis of compatibility. Main text To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/. The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Beam Loss Monitoring for LHC Machine Protection

NASA Astrophysics Data System (ADS)

Holzer, Eva Barbara; Dehning, Bernd; Effnger, Ewald; Emery, Jonathan; Grishin, Viatcheslav; Hajdu, Csaba; Jackson, Stephen; Kurfuerst, Christoph; Marsili, Aurelien; Misiowiec, Marek; Nagel, Markus; Busto, Eduardo Nebot Del; Nordt, Annika; Roderick, Chris; Sapinski, Mariusz; Zamantzas, Christos

The energy stored in the nominal LHC beams is two times 362 MJ, 100 times the energy of the Tevatron. As little as 1 mJ/cm3 deposited energy quenches a magnet at 7 TeV and 1 J/cm3 causes magnet damage. The beam dumps are the only places to safely dispose of this beam. One of the key systems for machine protection is the beam loss monitoring (BLM) system. About 3600 ionization chambers are installed at likely or critical loss locations around the LHC ring. The losses are integrated in 12 time intervals ranging from 40 μs to 84 s and compared to threshold values defined in 32 energy ranges. A beam abort is requested when potentially dangerous losses are detected or when any of the numerous internal system validation tests fails. In addition, loss data are used for machine set-up and operational verifications. The collimation system for example uses the loss data for set-up and regular performance verification. Commissioning and operational experience of the BLM are presented: The machine protection functionality of the BLM system has been fully reliable; the LHC availability has not been compromised by false beam aborts.

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

NASA Astrophysics Data System (ADS)

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.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Multiple man-machine interfaces

NASA Technical Reports Server (NTRS)

Stanton, L.; Cook, C. W.

1981-01-01

The multiple man machine interfaces inherent in military pilot training, their social implications, and the issue of possible negative feedback were explored. Modern technology has produced machines which can see, hear, and touch with greater accuracy and precision than human beings. Consequently, the military pilot is more a systems manager, often doing battle against a target he never sees. It is concluded that unquantifiable human activity requires motivation that is not intrinsic in a machine.

Long-range nanopositioning and nanomeasuring machine for application to micro- and nanotechnology

NASA Astrophysics Data System (ADS)

Jäger, Gerd; Hausotte, Tino; Büchner, Hans-Joachim; Manske, Eberhard; Schmidt, Ingomar; Mastylo, Rostyslav

2006-03-01

The paper describes the operation of a high-precision long range three-dimensional nanopositioning and nanomeasuring machine (NPM-Machine). The NPM-Machine has been developed by the Institute of Process Measurement and Sensor Technology of the Technische Universität Ilmenau. The machine was successfully tested and continually improved in the last few years. The machines are operating successfully in several German and foreign research institutes including the Physikalisch-Technische Bundesanstalt (PTB). Three plane mirror miniature interferometers are installed into the NPM-machine having a resolution of less than 0,1 nm over the entire positioning and measuring range of 25 mm x 25 mm x 5 mm. An Abbe offset-free design of the three miniature plane mirror interferometers and applying a new concept for compensating systematic errors resulting from mechanical guide systems provide extraordinary accuracy with an expanded uncertainty of only 5 - 10 nm. The integration of several, optical and tactile probe systems and nanotools makes the NPM-Machine suitable for various tasks, such as large-area scanning probe microscopy, mask and wafer inspection, nanostructuring, biotechnology and genetic engineering as well as measuring mechanical precision workpieces, precision treatment and for engineering new material. Various developed probe systems have been integrated into the NPM-Machine. The measurement results of a focus sensor, metrological AFM, white light sensor, tactile stylus probe and of a 3D-micro-touch-probe are presented. Single beam-, double beam- and triple beam interferometers built in the NPM-Machine for six degrees of freedom measurements are described.

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

Anaesthesia is one of the few sub-specialties of medicine, which has quickly adapted technology to improve patient safety. This application of technology can be seen in patient monitoring, advances in anaesthesia machines, intubating devices, ultrasound for visualisation of nerves and vessels, etc., Anaesthesia machines have come a long way in the last 100 years, the improvements being driven both by patient safety as well as functionality and economy of use. Incorporation of safety features in anaesthesia machines and ensuring that a proper check of the machine is done before use on a patient ensures patient safety. This review will trace all the present safety features in the machine and their evolution. PMID:24249880

CD process control through machine learning

NASA Astrophysics Data System (ADS)

Utzny, Clemens

2016-10-01

For the specific requirements of the 14nm and 20nm site applications a new CD map approach was developed at the AMTC. This approach relies on a well established machine learning technique called recursive partitioning. Recursive partitioning is a powerful technique which creates a decision tree by successively testing whether the quantity of interest can be explained by one of the supplied covariates. The test performed is generally a statistical test with a pre-supplied significance level. Once the test indicates significant association between the variable of interest and a covariate a split performed at a threshold value which minimizes the variation within the newly attained groups. This partitioning is recurred until either no significant association can be detected or the resulting sub group size falls below a pre-supplied level.

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

PubMed

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.

30 CFR 18.97 - Inspection of machines; minimum requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.97 Inspection of machines; minimum... shall be conducted by an electrical representative and such inspection shall include: (1) Examination of...

30 CFR 18.97 - Inspection of machines; minimum requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.97 Inspection of machines; minimum... shall be conducted by an electrical representative and such inspection shall include: (1) Examination of...

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

Monel Machining

NASA Technical Reports Server (NTRS)

1983-01-01

Castle Industries, Inc. is a small machine shop manufacturing replacement plumbing repair parts, such as faucet, tub and ballcock seats. Therese Castley, president of Castle decided to introduce Monel because it offered a chance to improve competitiveness and expand the product line. Before expanding, Castley sought NERAC assistance on Monel technology. NERAC (New England Research Application Center) provided an information package which proved very helpful. The NASA database was included in NERAC's search and yielded a wealth of information on machining Monel.

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

PubMed Central

Moomen, Abdelniser; Ali, Abdulbaset; Ramahi, Omar M.

2016-01-01

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms. PMID:27104533

Gloved Human-Machine Interface

NASA Technical Reports Server (NTRS)

Adams, Richard (Inventor); Hannaford, Blake (Inventor); Olowin, Aaron (Inventor)

2015-01-01

Certain exemplary embodiments can provide a system, machine, device, manufacture, circuit, composition of matter, and/or user interface adapted for and/or resulting from, and/or a method and/or machine-readable medium comprising machine-implementable instructions for, activities that can comprise and/or relate to: tracking movement of a gloved hand of a human; interpreting a gloved finger movement of the human; and/or in response to interpreting the gloved finger movement, providing feedback to the human.

THE TEACHING MACHINE.

ERIC Educational Resources Information Center

KLEIN, CHARLES; WAYNE, ELLIS

THE ROLE OF THE TEACHING MACHINE IS COMPARED WITH THE ROLE OF THE PROGRAMED TEXTBOOK. THE TEACHING MACHINE IS USED FOR INDIVIDUAL INSTRUCTION, CONTAINS AND PRESENTS PROGRAM CONTENT IN STEPS, PROVIDES A MEANS WHEREBY THE STUDENT MAY RESPOND TO THE PROGRAM, PROVIDES THE STUDENT WITH IMMEDIATE INFORMATION OF SOME KIND CONCERNING HIS RESPONSE THAT CAN…

Machine Translation Project

NASA Technical Reports Server (NTRS)

Bajis, Katie

1993-01-01

The characteristics and capabilities of existing machine translation systems were examined and procurement recommendations were developed. Four systems, SYSTRAN, GLOBALINK, PC TRANSLATOR, and STYLUS, were determined to meet the NASA requirements for a machine translation system. Initially, four language pairs were selected for implementation. These are Russian-English, French-English, German-English, and Japanese-English.

OptiCentric lathe centering machine

NASA Astrophysics Data System (ADS)

Buß, C.; Heinisch, J.

2013-09-01

High precision optics depend on precisely aligned lenses. The shift and tilt of individual lenses as well as the air gap between elements require accuracies in the single micron regime. These accuracies are hard to meet with traditional assembly methods. Instead, lathe centering can be used to machine the mount with respect to the optical axis. Using a diamond turning process, all relevant errors of single mounted lenses can be corrected in one post-machining step. Building on the OptiCentric® and OptiSurf® measurement systems, Trioptics has developed their first lathe centering machines. The machine and specific design elements of the setup will be shown. For example, the machine can be used to turn optics for i-line steppers with highest precision.

Machinability of Stellite 6 hardfacing

NASA Astrophysics Data System (ADS)

Benghersallah, M.; Boulanouar, L.; Le Coz, G.; Devillez, A.; Dudzinski, D.

2010-06-01

This paper reports some experimental findings concerning the machinability at high cutting speed of nickel-base weld-deposited hardfacings for the manufacture of hot tooling. The forging work involves extreme impacts, forces, stresses and temperatures. Thus, mould dies must be extremely resistant. The aim of the project is to create a rapid prototyping process answering to forging conditions integrating a Stellite 6 hardfacing deposed PTA process. This study talks about the dry machining of the hardfacing, using a two tips machining tool and a high speed milling machine equipped by a power consumption recorder Wattpilote. The aim is to show the machinability of the hardfacing, measuring the power and the tip wear by optical microscope and white light interferometer, using different strategies and cutting conditions.

Design features and results from fatigue reliability research machines.

NASA Technical Reports Server (NTRS)

Lalli, V. R.; Kececioglu, D.; Mcconnell, J. B.

1971-01-01

The design, fabrication, development, operation, calibration and results from reversed bending combined with steady torque fatigue research machines are presented. Fifteen-centimeter long, notched, SAE 4340 steel specimens are subjected to various combinations of these stresses and cycled to failure. Failure occurs when the crack in the notch passes through the specimen automatically shutting down the test machine. These cycles-to-failure data are statistically analyzed to develop a probabilistic S-N diagram. These diagrams have many uses; a rotating component design example given in the literature shows that minimum size and weight for a specified number of cycles and reliability can be calculated using these diagrams.

34 CFR 395.32 - Collection and distribution of vending machine income from vending machines on Federal property.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 34 Education 2 2011-07-01 2010-07-01 true Collection and distribution of vending machine income from vending machines on Federal property. 395.32 Section 395.32 Education Regulations of the Offices... Management § 395.32 Collection and distribution of vending machine income from vending machines on Federal...

34 CFR 395.32 - Collection and distribution of vending machine income from vending machines on Federal property.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 34 Education 2 2012-07-01 2012-07-01 false Collection and distribution of vending machine income from vending machines on Federal property. 395.32 Section 395.32 Education Regulations of the Offices... Management § 395.32 Collection and distribution of vending machine income from vending machines on Federal...

34 CFR 395.32 - Collection and distribution of vending machine income from vending machines on Federal property.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 34 Education 2 2014-07-01 2013-07-01 true Collection and distribution of vending machine income from vending machines on Federal property. 395.32 Section 395.32 Education Regulations of the Offices... Management § 395.32 Collection and distribution of vending machine income from vending machines on Federal...

34 CFR 395.32 - Collection and distribution of vending machine income from vending machines on Federal property.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 34 Education 2 2013-07-01 2013-07-01 false Collection and distribution of vending machine income from vending machines on Federal property. 395.32 Section 395.32 Education Regulations of the Offices... Management § 395.32 Collection and distribution of vending machine income from vending machines on Federal...

Machine Shop Grinding Machines.

ERIC Educational Resources Information Center

Dunn, James

This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

``Diagonalization'' of a compound Atwood machine

NASA Astrophysics Data System (ADS)

Crawford, Frank S.

1987-06-01

We consider a simple Atwood machine consisting of a massless frictionless pulley no. 0 supporting two masses m1 and m2 connected by a massless flexible string. We show that the string that supports massless pulley no. 0 ``thinks'' it is simply supporting a mass m0, with m0=4m1m2/(m1+m2). This result, together with Einstein's equivalence principle, allows us to solve easily those compound Atwood machines created by replacing one or both of m1 and m2 in machine no. 0 by an Atwood machine. We may then replacing the masses in these new machines by machines, etc. The complete solution can be written down immediately, without solving simultaneous equations. Finally we give the effective mass of an Atwood machine whose pulley has nonzero mass and moment of inertia.

Reactive power generation in high speed induction machines by continuously occurring space-transients

NASA Astrophysics Data System (ADS)

Laithwaite, E. R.; Kuznetsov, S. B.

1980-09-01

A new technique of continuously generating reactive power from the stator of a brushless induction machine is conceived and tested on a 10-kw linear machine and on 35 and 150 rotary cage motors. An auxiliary magnetic wave traveling at rotor speed is artificially created by the space-transient attributable to the asymmetrical stator winding. At least two distinct windings of different pole-pitch must be incorporated. This rotor wave drifts in and out of phase repeatedly with the stator MMF wave proper and the resulting modulation of the airgap flux is used to generate reactive VA apart from that required for magnetization or leakage flux. The VAR generation effect increases with machine size, and leading power factor operation of the entire machine is viable for large industrial motors and power system induction generators.

Vibration Damping Analysis of Lightweight Structures in Machine Tools

PubMed Central

Aggogeri, Francesco; Borboni, Alberto; Merlo, Angelo; Pellegrini, Nicola; Ricatto, Raffaele

2017-01-01

The dynamic behaviour of a machine tool (MT) directly influences the machining performance. The adoption of lightweight structures may reduce the effects of undesired vibrations and increase the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidates to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to dampen mechanical vibrations. In this way, starting from the kinematic model of a milling machine, this study evaluates a number of prototypes made of Al foam sandwiches (AFS), Al corrugated sandwiches (ACS) and composite materials reinforced by carbon fibres (CFRP). These prototypes represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been analysed by using both finite element (FE) simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performance. In particular, the AFS prototype highlighted a damping ratio that is 20 times greater than a conventional ram (e.g., steel). Its application is particularly suitable to minimize unwanted oscillations during high-speed finishing operations. The results also show that the CFRP structure guarantees high stiffness with a weight reduced by 48.5%, suggesting effective applications in roughing operations, saving MT energy consumption. The ACS structure has a good trade-off between stiffness and damping and may represent a further alternative, if correctly evaluated. PMID:28772653

Altering the near-miss effect in slot machine gamblers.

PubMed

Dixon, Mark R; Nastally, Becky L; Jackson, James E; Habib, Reza

2009-01-01

This study investigated the potential for recreational gamblers to respond as if certain types of losing slot machine outcomes were actually closer to a win than others (termed the near-miss effect). Exposure to conditional discrimination training and testing disrupted this effect for 10 of the 16 participants. These 10 participants demonstrated high percentages of conditional discrimination testing performance, and the remaining 6 participants failed the discrimination tests. The implications for a verbally based behavioral explanation of gambling are presented.

Relative Performance of Hardwood Sawing Machines

Treesearch

Philip H. Steele; Michael W. Wade; Steven H. Bullard; Philip A. Araman

1991-01-01

Only limited information has been available to hardwood sawmillers on the performance of their sawing machines. This study analyzes a large database of individual machine studies to provide detailed information on 6 machine types. These machine types were band headrig, circular headrig, band linebar resaw, vertical band splitter resaw, single arbor gang resaw and...

Tube Alinement for Machining

NASA Technical Reports Server (NTRS)

Garcia, J.

1984-01-01

Tool with stepped shoulders alines tubes for machining in preparation for welding. Alinement with machine tool axis accurate to within 5 mils (0.13mm) and completed much faster than visual setup by machinist.

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

PubMed

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

2018-05-04

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

Machine Learning

NASA Astrophysics Data System (ADS)

Hoffmann, Achim; Mahidadia, Ashesh

The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules - a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for

Fifth Graders' Learning About Simple Machines Through Engineering Design-Based Instruction Using LEGO™ Materials

NASA Astrophysics Data System (ADS)

Marulcu, Ismail; Barnett, Mike

2013-10-01

This study is part of a 5-year National Science Foundation-funded project, Transforming Elementary Science Learning Through LEGO™ Engineering Design. In this study, we report on the successes and challenges of implementing an engineering design-based and LEGO™-oriented unit in an urban classroom setting and we focus on the impact of the unit on students' content understanding of simple machines. The LEGO™ engineering-based simple machines module, which was developed for fifth graders by our research team, was implemented in an urban school in a large city in the Northeastern region of the USA. Thirty-three fifth grade students participated in the study, and they showed significant growth in content understanding. We measured students' content knowledge by using identical paper tests and semistructured interviews before and after instruction. Our paired t test analysis results showed that students significantly improved their test and interview scores (t = -3.62, p < 0.001 for multiple-choice items and t = -9.06, p < 0.000 for the open-ended items in the test and t = -12.11, p < 0.000 for the items in interviews). We also identified several alternative conceptions that are held by students on simple machines.

15 CFR 5.5 - Vending machines.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 1 2011-01-01 2011-01-01 false Vending machines. 5.5 Section 5.5... machines. (a) The income from any vending machines which are located within reasonable proximity to and are... shall be assigned to the operator of such stand. (b) If a vending machine vends articles of a type...

15 CFR 5.5 - Vending machines.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Vending machines. 5.5 Section 5.5... machines. (a) The income from any vending machines which are located within reasonable proximity to and are... shall be assigned to the operator of such stand. (b) If a vending machine vends articles of a type...

Machinability evaluation of titanium alloys (Part 2)--Analyses of cutting force and spindle motor current.

PubMed

Kikuchi, Masafumi; Okuno, Osamu

2004-12-01

To establish a method of determining the machinability of dental materials for CAD/CAM systems, the machinability of titanium, two titanium alloys (Ti-6Al-4V and Ti-6Al-7Nb), and free-cutting brass was evaluated through cutting force and spindle motor current. The metals were slotted using a milling machine and square end mills at four cutting conditions. Both the static and dynamic components of the cutting force represented well the machinability of the metals tested: the machinability of Ti-6Al-4V and Ti-6Al-7Nb was worse than that of titanium, while that of free-cutting brass was better. On the other hand, the results indicated that the spindle motor current was not sensitive enough to detect the material difference among the titanium and its alloys.

Decomposition of the compound Atwood machine

NASA Astrophysics Data System (ADS)

Lopes Coelho, R.

2017-11-01

Non-standard solving strategies for the compound Atwood machine problem have been proposed. The present strategy is based on a very simple idea. Taking an Atwood machine and replacing one of its bodies by another Atwood machine, we have a compound machine. As this operation can be repeated, we can construct any compound Atwood machine. This rule of construction is transferred to a mathematical model, whereby the equations of motion are obtained. The only difference between the machine and its model is that instead of pulleys and bodies, we have reference frames that move solidarily with these objects. This model provides us with the accelerations in the non-inertial frames of the bodies, which we will use to obtain the equations of motion. This approach to the problem will be justified by the Lagrange method and exemplified by machines with six and eight bodies.

Development of plasma chemical vaporization machining

NASA Astrophysics Data System (ADS)

Mori, Yuzo; Yamauchi, Kazuto; Yamamura, Kazuya; Sano, Yasuhisa

2000-12-01

Conventional machining processes, such as turning, grinding, or lapping are still applied for many materials including functional ones. But those processes are accompanied with the formation of a deformed layer, so that machined surfaces cannot perform their original functions. In order to avoid such points, plasma chemical vaporization machining (CVM) has been developed. Plasma CVM is a chemical machining method using neutral radicals, which are generated by the atmospheric pressure plasma. By using a rotary electrode for generation of plasma, a high density of neutral radicals was formed, and we succeeded in obtaining high removal rate of several microns to several hundred microns per minute for various functional materials such as fused silica, single crystal silicon, molybdenum, tungsten, silicon carbide, and diamond. Especially, a high removal rate equal to lapping in the mechanical machining of fused silica and silicon was realized. 1.4 nm (p-v) was obtained as a surface roughness in the case of machining a silicon wafer. The defect density of a silicon wafer surface polished by various machining method was evaluated by the surface photo voltage spectroscopy. As a result, the defect density of the surface machined by plasma CVM was under 1/100 in comparison with the surface machined by mechanical polishing and argon ion sputtering, and very low defect density which was equivalent to the chemical etched surface was realized. A numerically controlled CVM machine for x-ray mirror fabrication is detailed in the accompanying article in this issue.

Methodologies for Combined Loads Tests Using a Multi-Actuator Test Machine

NASA Technical Reports Server (NTRS)

Rouse, Marshall

2013-01-01

The NASA Langley COmbined Loads Test System (COLTS) Facility was designed to accommodate a range of fuselage structures and wing sections and subject them to both quasistatic and cyclic loading conditions. Structural tests have been conducted in COLTS that address structural integrity issues of metallic and fiber reinforced composite aerospace structures in support of NASA Programs (i.e. the Aircraft Structural Integrity (ASIP) Program, High-Speed-Research program and the Supersonic Project, NASA Engineering and Safety Center (NESC) Composite Crew Module Project, and the Environmentally Responsible Aviation Program),. This paper presents experimental results for curved panels subjected to mechanical and internal pressure loads using a D-box test fixture. Also, results are presented that describe use of a checkout beam for development of testing procedures for a combined mechanical and pressure loading test of a Multi-bay box. The Multi-bay box test will be used to experimentally verify the structural performance of the Multi-bay box in support of the Environmentally Responsible Aviation Project at NASA Langley.

Workout Machine

NASA Technical Reports Server (NTRS)

1995-01-01

The Orbotron is a tri-axle exercise machine patterned after a NASA training simulator for astronaut orientation in the microgravity of space. It has three orbiting rings corresponding to roll, pitch and yaw. The user is in the middle of the inner ring with the stomach remaining in the center of all axes, eliminating dizziness. Human power starts the rings spinning, unlike the NASA air-powered system. Marketed by Fantasy Factory (formerly Orbotron, Inc.), the machine can improve aerobic capacity, strength and endurance in five to seven minute workouts.

TEACHING MACHINES AND PROGRAMMED LEARNING, A SOURCE BOOK.

ERIC Educational Resources Information Center

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,…

An asymptotical machine

NASA Astrophysics Data System (ADS)

Cristallini, Achille

2016-07-01

A new and intriguing machine may be obtained replacing the moving pulley of a gun tackle with a fixed point in the rope. Its most important feature is the asymptotic efficiency. Here we obtain a satisfactory description of this machine by means of vector calculus and elementary trigonometry. The mathematical model has been compared with experimental data and briefly discussed.

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer's Disease Tests: A Multivariate and Machine Learning Study.

PubMed

Tunvirachaisakul, Chavit; Supasitthumrong, Thitiporn; Tangwongchai, Sookjareon; Hemrunroj, Solaphat; Chuchuen, Phenphichcha; Tawankanjanachot, Itthipol; Likitchareon, Yuthachai; Phanthumchinda, Kamman; Sriswasdi, Sira; Maes, Michael

2018-04-04

The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer's dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI. © 2018 S. Karger AG, Basel.

30 CFR 18.61 - Final inspection of complete machine.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Final inspection of complete machine. 18.61 Section 18.61 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Inspections...

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

PubMed

Rashidi, Mohammad; Wolkow, Robert A

2018-05-23

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

Evaluation of Fatigue Behavior and Surface Characteristics of Aluminum Alloy 2024 T6 After Electric Discharge Machining

NASA Astrophysics Data System (ADS)

Mehmood, Shahid; Shah, Masood; Pasha, Riffat Asim; Sultan, Amir

2017-10-01

The effect of electric discharge machining (EDM) on surface quality and consequently on the fatigue performance of Al 2024 T6 is investigated. Five levels of discharge current are analyzed, while all other electrical and nonelectrical parameters are kept constant. At each discharge current level, dog-bone specimens are machined by generating a peripheral notch at the center. The fatigue tests are performed on four-point rotating bending machine at room temperature. For comparison purposes, fatigue tests are also performed on the conventionally machined specimens. Linearized SN curves for 95% failure probability and with four different confidence levels (75, 90, 95 and 99%) are plotted for each discharge current level as well as for conventionally machined specimens. These plots show that the electric discharge machined (EDMed) specimens give inferior fatigue behavior as compared to conventionally machined specimen. Moreover, discharge current inversely affects the fatigue life, and this influence is highly pronounced at lower stresses. The EDMed surfaces are characterized by surface properties that could be responsible for change in fatigue life such as surface morphology, surface roughness, white layer thickness, microhardness and residual stresses. It is found that all these surface properties are affected by changing discharge current level. However, change in fatigue life by discharge current could not be associated independently to any single surface property.

Properties of Free-Machining Aluminum Alloys at Elevated Temperatures

NASA Astrophysics Data System (ADS)

Faltus, Jiří; Karlík, Miroslav; Haušild, Petr

In areas close to the cutting tool the workpieces being dry machined could be heated up to 350°C and they may be impact loaded. Therefore it is of interest to study mechanical properties of corresponding materials at elevated temperatures. Free-machining alloys of Al-Cu and Al-Mg-Si systems containing Pb, Bi and Sn additions (AA2011, AA2111B, AA6262, and AA6023) were subjected to Charpy U notch impact test at the temperatures ranging from 20 to 350°C. The tested alloys show a sharp drop in notch impact strength KU at different temperatures. This drop of KU is caused by liquid metal embrittlement due to the melting of low-melting point dispersed phases which is documented by differential scanning calorimetry. Fracture surfaces of the specimens were observed using a scanning electron microscope. At room temperature, the fractures of all studied alloys exhibited similar ductile dimple fracture micromorphology, at elevated temperatures, numerous secondary intergranular cracks were observed.

Effect of Ceramic Surface Treatments After Machine Grinding on the Biaxial Flexural Strength of Different CAD/CAM Dental Ceramics.

PubMed

Bagheri, Hossein; Hooshmand, Tabassom; Aghajani, Farzaneh

2015-09-01

This study aimed to evaluate the effect of different ceramic surface treatments after machining grinding on the biaxial flexural strength (BFS) of machinable dental ceramics with different crystalline phases. Disk-shape specimens (10mm in diameter and 1.3mm in thickness) of machinable ceramic cores (two silica-based and one zirconia-based ceramics) were prepared. Each type of the ceramic surfaces was then randomly treated (n=15) with different treatments as follows: 1) machined finish as control, 2) machined finish and sandblasting with alumina, and 3) machined finish and hydrofluoric acid etching for the leucite and lithium disilicate-based ceramics, and for the zirconia; 1) machined finish and post-sintered as control, 2) machined finish, post-sintered, and sandblasting, and 3) machined finish, post-sintered, and Nd;YAG laser irradiation. The BFS were measured in a universal testing machine. Data based were analyzed by ANOVA and Tukey's multiple comparisons post-hoc test (α=0.05). The mean BFS of machined finish only surfaces for leucite ceramic was significantly higher than that of sandblasted (P=0.001) and acid etched surfaces (P=0.005). A significantly lower BFS was found after sandblasting for lithium disilicate compared with that of other groups (P<0.05). Sandblasting significantly increased the BFS for the zirconia (P<0.05), but the BFS was significantly decreased after laser irradiation (P<0.05). The BFS of the machinable ceramics was affected by the type of ceramic material and surface treatment method. Sandblasting with alumina was detrimental to the strength of only silica-based ceramics. Nd:YAG laser irradiation may lead to substantial strength degradation of zirconia.

Effect of Ceramic Surface Treatments After Machine Grinding on the Biaxial Flexural Strength of Different CAD/CAM Dental Ceramics

PubMed Central

Bagheri, Hossein; Aghajani, Farzaneh

2015-01-01

Objectives: This study aimed to evaluate the effect of different ceramic surface treatments after machining grinding on the biaxial flexural strength (BFS) of machinable dental ceramics with different crystalline phases. Materials and Methods: Disk-shape specimens (10mm in diameter and 1.3mm in thickness) of machinable ceramic cores (two silica-based and one zirconia-based ceramics) were prepared. Each type of the ceramic surfaces was then randomly treated (n=15) with different treatments as follows: 1) machined finish as control, 2) machined finish and sandblasting with alumina, and 3) machined finish and hydrofluoric acid etching for the leucite and lithium disilicate-based ceramics, and for the zirconia; 1) machined finish and post-sintered as control, 2) machined finish, post-sintered, and sandblasting, and 3) machined finish, post-sintered, and Nd;YAG laser irradiation. The BFS were measured in a universal testing machine. Data based were analyzed by ANOVA and Tukey’s multiple comparisons post-hoc test (α=0.05). Results: The mean BFS of machined finish only surfaces for leucite ceramic was significantly higher than that of sandblasted (P=0.001) and acid etched surfaces (P=0.005). A significantly lower BFS was found after sandblasting for lithium disilicate compared with that of other groups (P<0.05). Sandblasting significantly increased the BFS for the zirconia (P<0.05), but the BFS was significantly decreased after laser irradiation (P<0.05). Conclusions: The BFS of the machinable ceramics was affected by the type of ceramic material and surface treatment method. Sandblasting with alumina was detrimental to the strength of only silica-based ceramics. Nd:YAG laser irradiation may lead to substantial strength degradation of zirconia. PMID:27148372

Mistaking minds and machines: How speech affects dehumanization and anthropomorphism.

PubMed

Schroeder, Juliana; Epley, Nicholas

2016-11-01

Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person's actual mental experience-a humanlike voice-affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text's creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text's creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual cues to text (i.e., seeing a person perform a script in a subtitled video clip), did not increase the likelihood of inferring a human creator compared with only reading text, suggesting that defining features of personhood may be conveyed more clearly in speech (Experiments 1 and 2). Removing the naturalistic paralinguistic cues that convey humanlike capacity for thinking and feeling, such as varied pace and intonation, eliminates the humanizing effect of speech (Experiment 4). We discuss implications for dehumanizing others through text-based media, and for anthropomorphizing machines through speech-based media. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

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

Testing filamentary composites

NASA Technical Reports Server (NTRS)

Dow, N. F.; Rosen, B. W.

1970-01-01

NOL ring split-dee tensile test has the advantages that the specimen is readily fabricated by winding and the test is performed in a conventional testing machine without special fixtures. Strain gages cannot be mounted, however, and substantial bending moments are introduced.

Model-based machine learning.

PubMed

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.

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

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. PMID:23277612

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

PubMed Central

Gao, Junfeng; Wang, Zhao; Yang, Yong; Zhang, Wenjia; Tao, Chunyi; Guan, Jinan; Rao, Nini

2013-01-01

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time. PMID:23755136

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

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

Comparison of Test Procedures and Energy Efficiency Criteria in Selected International Standards & Labeling Programs for Copy Machines, External Power Supplies, LED Displays, Residential Gas Cooktops and Televisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zheng, Nina; Zhou, Nan; Fridley, David

2012-03-01

This report presents a technical review of international minimum energy performance standards (MEPS), voluntary and mandatory energy efficiency labels and test procedures for five products being considered for new or revised MEPS in China: copy machines, external power supply, LED displays, residential gas cooktops and flat-screen televisions. For each product, an overview of the scope of existing international standards and labeling programs, energy values and energy performance metrics and description and detailed summary table of criteria and procedures in major test standards are presented.

Achievement Test Program.

ERIC Educational Resources Information Center

Ohio State Dept. of Education, Columbus. Trade and Industrial Education Service.

The Ohio Trade and Industrial Education Achievement Test battery is comprised of seven basic achievement tests: Machine Trades, Automotive Mechanics, Basic Electricity, Basic Electronics, Mechanical Drafting, Printing, and Sheet Metal. The tests were developed by subject matter committees and specialists in testing and research. The Ohio Trade and…

MATC Machine Shop '84: Specific Skill Needs Assessment for Machine Shops in the Milwaukee Area.

ERIC Educational Resources Information Center

Roberts, Keith J.

Building on previous research on the future skill needs of workers in southeastern Wisconsin, a study was conducted at Milwaukee Area Technical College (MATC) to gather information on the machine tool industry in the Milwaukee area. Interviews were conducted by MATC Machine Shop and Tool and Die faculty with representatives from 135 machine shops,…

Efficacy of Cruise Control in controlling postocclusion surge with Legacy and Millennium venturi phacoemulsification machines.

PubMed

Wade, Matthew; Isom, Ryan; Georgescu, Dan; Olson, Randall J

2007-06-01

To determine the efficacy of the Cruise Control surge-limiting device (Staar Surgical) with phacoemulsification machines known to have high levels of surge. John A. Moran Eye Center Clinical Laboratories. In an in vitro study, postocclusion anterior chamber depth changes were measured in fresh phakic human eye-bank eyes using the Alcon Legacy and Bausch & Lomb Millennium venturi machines in conjunction with the Staar Cruise Control device. Both machines were tested with 19-gauge non-Aspiration Bypass System tips at high-surge settings (500 mm Hg vacuum pressure, 75 cm bottle height, 40 mL/min flow rate for the Legacy) and low-surge settings (400 mm Hg vacuum pressure, 125 cm bottle height, 40 mL/min flow rate for the Legacy). Adjusted parameters of flow, vacuum, and irrigation were used based on previous studies to create identical conditions for each device tested. The effect of the Cruise Control device on aspiration rates was also tested with both machines at the low-surge settings. At the high setting with the addition of Cruise Control, surge decreased significantly with the Legacy but was too large to measure with the Millennium venturi. At the low setting with the addition of Cruise Control, surge decreased significantly with both machines. Surge with the Millennium decreased from more than 1.0 mm to a mean of 0.21 mm +/- 0.02 (SD) (P<.0001). Surge with the Legacy decreased from a mean of 0.09 +/- 0.02 mm to 0.05 +/- 0 mm, a 42.9% decrease (P<.0001). The Millennium had the highest surge and aspiration rate before Cruise Control and the greatest percentage decrease in the surge and aspiration rates as a result of the addition of Cruise Control. In the Legacy machine, the Cruise Control device had a statistically and clinically significant effect. Cruise Control had a large effect on fluidics as well as surge amplitude with the Millennium machine. The greater the flow or greater the initial surge, the greater the impact of the Cruise Control device.

EDM machinability of SiCw/Al composites

NASA Technical Reports Server (NTRS)

Ramulu, M.; Taya, M.

1989-01-01

Machinability of high temperature composites was investigated. Target materials, 15 and 25 vol pct SiC whisker-2124 aluminum composites, were machined by electrodischarge sinker machining and diamond saw. The machined surfaces of these metal matrix composites were examined by SEM and profilometry to determine the surface finish. Microhardness measurements were also performed on the as-machined composites.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Machine Learning for Medical Imaging

PubMed Central

Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054

Machine Learning for Medical Imaging.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Towards a molecular logic machine

NASA Astrophysics Data System (ADS)

Remacle, F.; Levine, R. D.

2001-06-01

Finite state logic machines can be realized by pump-probe spectroscopic experiments on an isolated molecule. The most elaborate setup, a Turing machine, can be programmed to carry out a specific computation. We argue that a molecule can be similarly programmed, and provide examples using two photon spectroscopies. The states of the molecule serve as the possible states of the head of the Turing machine and the physics of the problem determines the possible instructions of the program. The tape is written in an alphabet that allows the listing of the different pump and probe signals that are applied in a given experiment. Different experiments using the same set of molecular levels correspond to different tapes that can be read and processed by the same head and program. The analogy to a Turing machine is not a mechanical one and is not completely molecular because the tape is not part of the molecular machine. We therefore also discuss molecular finite state machines, such as sequential devices, for which the tape is not part of the machine. Nonmolecular tapes allow for quite long input sequences with a rich alphabet (at the level of 7 bits) and laser pulse shaping experiments provide concrete examples. Single molecule spectroscopies show that a single molecule can be repeatedly cycled through a logical operation.

Permutation parity machines for neural cryptography.

PubMed

Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

2010-06-01

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Permutation parity machines for neural cryptography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reyes, Oscar Mauricio; Escuela de Ingenieria Electrica, Electronica y Telecomunicaciones, Universidad Industrial de Santander, Bucaramanga; Zimmermann, Karl-Heinz

2010-06-15

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Improving Energy Efficiency in CNC Machining

NASA Astrophysics Data System (ADS)

Pavanaskar, Sushrut S.

We present our work on analyzing and improving the energy efficiency of multi-axis CNC milling process. Due to the differences in energy consumption behavior, we treat 3- and 5-axis CNC machines separately in our work. For 3-axis CNC machines, we first propose an energy model that estimates the energy requirement for machining a component on a specified 3-axis CNC milling machine. Our model makes machine-specific predictions of energy requirements while also considering the geometric aspects of the machining toolpath. Our model - and the associated software tool - facilitate direct comparison of various alternative toolpath strategies based on their energy-consumption performance. Further, we identify key factors in toolpath planning that affect energy consumption in CNC machining. We then use this knowledge to propose and demonstrate a novel toolpath planning strategy that may be used to generate new toolpaths that are inherently energy-efficient, inspired by research on digital micrography -- a form of computational art. For 5-axis CNC machines, the process planning problem consists of several sub-problems that researchers have traditionally solved separately to obtain an approximate solution. After illustrating the need to solve all sub-problems simultaneously for a truly optimal solution, we propose a unified formulation based on configuration space theory. We apply our formulation to solve a problem variant that retains key characteristics of the full problem but has lower dimensionality, allowing visualization in 2D. Given the complexity of the full 5-axis toolpath planning problem, our unified formulation represents an important step towards obtaining a truly optimal solution. With this work on the two types of CNC machines, we demonstrate that without changing the current infrastructure or business practices, machine-specific, geometry-based, customized toolpath planning can save energy in CNC machining.

Ada Compiler Validation Summary Report. Certificate Number: 891129W1. 10198 International Business Machines Corporation, the IBM Development System for the Ada Language AIX/RT Follow-on, Version 1.1 IBM RT Follow-on. Completion of On-Site Testing: 29 November 1989

DTIC Science & Technology

1989-11-29

nvmbe’j International Business Machines Corporation Wright-Patterson AFB, The IBM Development System for the Ada Language AIX/RT follow-on, Version 1.1...Certificate Number: 891129W1.10198 International Business Machines Corporation The IBM Development System for the Ada Language AIX/RT Follow-on, Version 1.1 IBM...scripts provided by International Business Machines Corporation and reviewed by the validation team. The compiler was tested using all the following

Availability of vending machines and school stores in California schools

PubMed Central

Liles, Sandy; Schmitz, Katharine E.; Kassem, Nada O.F; Irvin, Veronica L; Hovell, Melbourne F.

2015-01-01

Background This study examined the availability of foods sold in vending machines and school stores in US public and private schools, and associations of availability with students' food purchases and consumption. Methods Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected from 521 students aged 8 to15 years recruited from orthodontic offices in California. Results Vending machines were more common in private schools than in public schools, while school stores were common in both private and public schools. The food items most commonly available in both vending machines and school stores in all schools were predominately foods of minimal nutritional value (FMNV). Participant report of availability of food items in vending machines and/or school stores was significantly correlated with: (1) participant purchase of each item from those sources, except for energy drinks, milk, fruits, and vegetables; and (2) participants' friends' consumption of items at lunch, for two categories of FMNV (candy, cookies, or cake; soda or sports drinks). Conclusions Despite the Child Nutrition and WIC reauthorization Act of 2004, FMNV were still available in schools, and may be contributing to unhealthy dietary choices and ultimately to health risks. PMID:26645420

Smarter Instruments, Smarter Archives: Machine Learning for Tactical Science

NASA Astrophysics Data System (ADS)

Thompson, D. R.; Kiran, R.; Allwood, A.; Altinok, A.; Estlin, T.; Flannery, D.

2014-12-01

There has been a growing interest by Earth and Planetary Sciences in machine learning, visualization and cyberinfrastructure to interpret ever-increasing volumes of instrument data. Such tools are commonly used to analyze archival datasets, but they can also play a valuable real-time role during missions. Here we discuss ways that machine learning can benefit tactical science decisions during Earth and Planetary Exploration. Machine learning's potential begins at the instrument itself. Smart instruments endowed with pattern recognition can immediately recognize science features of interest. This allows robotic explorers to optimize their limited communications bandwidth, triaging science products and prioritizing the most relevant data. Smart instruments can also target their data collection on the fly, using principles of experimental design to reduce redundancy and generally improve sampling efficiency for time-limited operations. Moreover, smart instruments can respond immediately to transient or unexpected phenomena. Examples include detections of cometary plumes, terrestrial floods, or volcanism. We show recent examples of smart instruments from 2014 tests including: aircraft and spacecraft remote sensing instruments that recognize cloud contamination, field tests of a "smart camera" for robotic surface geology, and adaptive data collection by X-Ray fluorescence spectrometers. Machine learning can also assist human operators when tactical decision making is required. Terrestrial scenarios include airborne remote sensing, where the decision to re-fly a transect must be made immediately. Planetary scenarios include deep space encounters or planetary surface exploration, where the number of command cycles is limited and operators make rapid daily decisions about where next to collect measurements. Visualization and modeling can reveal trends, clusters, and outliers in new data. This can help operators recognize instrument artifacts or spot anomalies in real time

Multipurpose Prepregging Machine

NASA Technical Reports Server (NTRS)

Johnston, N. J.; Wilkinson, Steven; Marchello, J. M.; Dixon, D.

1995-01-01

Machine designed and built for variety of uses involving coating or impregnating ("prepregging") fibers, tows, yarns, or webs or tapes made of such fibrous materials with thermoplastic or thermosetting resins. Prepreg materials produced used to make matrix/fiber composite materials. Comprises modules operated individually, sequentially, or simultaneously, depending on nature of specific prepreg material and prepregging technique used. Machine incorporates number of safety features.

Machine tools and fixtures: A compilation

NASA Technical Reports Server (NTRS)

1971-01-01

As part of NASA's Technology Utilizations Program, a compilation was made of technological developments regarding machine tools, jigs, and fixtures that have been produced, modified, or adapted to meet requirements of the aerospace program. The compilation is divided into three sections that include: (1) a variety of machine tool applications that offer easier and more efficient production techniques; (2) methods, techniques, and hardware that aid in the setup, alignment, and control of machines and machine tools to further quality assurance in finished products: and (3) jigs, fixtures, and adapters that are ancillary to basic machine tools and aid in realizing their greatest potential.

Machine Learning Based Malware Detection

DTIC Science & Technology

2015-05-18

A TRIDENT SCHOLAR PROJECT REPORT NO. 440 Machine Learning Based Malware Detection by Midshipman 1/C Zane A. Markel, USN...COVERED (From - To) 4. TITLE AND SUBTITLE Machine Learning Based Malware Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...suitably be projected into realistic performance. This work explores several aspects of machine learning based malware detection . First, we

Factors Governing Surface Form Accuracy In Diamond Machined Components

NASA Astrophysics Data System (ADS)

Myler, J. K.; Page, D. A.

1988-10-01

Manufacturing methods for diamond machined optical surfaces, for application at infrared wavelengths, require that a new set of criteria must be recognised for the specification of surface form. Appropriate surface form parameters are discussed with particular reference to an XY cartesian geometry CNC machine. Methods for reducing surface form errors in diamond machining are discussed for certain areas such as tool wear, tool centring, and the fixturing of the workpiece. Examples of achievable surface form accuracy are presented. Traditionally, optical surfaces have been produced by use of random polishing techniques using polishing compounds and lapping tools. For lens manufacture, the simplest surface which could be created corresponded to a sphere. The sphere is a natural outcome of a random grinding and polishing process. The measurement of the surface form accuracy would most commonly be performed using a contact test gauge plate, polished to a sphere of known radius of curvature. QA would simply be achieved using a diffuse monochromatic source and looking for residual deviations between the polished surface and the test plate. The specifications governing the manufacture of surfaces using these techniques would call for the accuracy to which the generated surface should match the test plate as defined by a spherical deviations from the required curvature and a non spherical astigmatic error. Consequently, optical design software has tolerancing routines which specifically allow the designer to assess the influence of spherical error and astigmatic error on the optical performance. The creation of general aspheric surfaces is not so straightforward using conventional polishing techniques since the surface profile is non spherical and a good approximation to a power series. For infra red applications (X = 8-12p,m) numerically controlled single point diamond turning is an alternative manufacturing technology capable of creating aspheric profiles as well as

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

NASA Astrophysics Data System (ADS)

Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo

2017-05-01

Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

Manipulating Tabu List to Handle Machine Breakdowns in Job Shop Scheduling Problems

NASA Astrophysics Data System (ADS)

Nababan, Erna Budhiarti; SalimSitompul, Opim

2011-06-01

Machine breakdowns in a production schedule may occur on a random basis that make the well-known hard combinatorial problem of Job Shop Scheduling Problems (JSSP) becomes more complex. One of popular techniques used to solve the combinatorial problems is Tabu Search. In this technique, moves that will be not allowed to be revisited are retained in a tabu list in order to avoid in gaining solutions that have been obtained previously. In this paper, we propose an algorithm to employ a second tabu list to keep broken machines, in addition to the tabu list that keeps the moves. The period of how long the broken machines will be kept on the list is categorized using fuzzy membership function. Our technique are tested to the benchmark data of JSSP available on the OR library. From the experiment, we found that our algorithm is promising to help a decision maker to face the event of machine breakdowns.

Development and evaluation of amusement machine using autostereoscopic 3D display

NASA Astrophysics Data System (ADS)

Kawai, Takashi; Shibata, Takashi; Shimizu, Yoichi; Kawata, Mitsuhiro; Suto, Masahiro

2004-05-01

Pachinko is a pinball-like game peculiar to Japan, and is one of the most common pastimes around the country. Recently, with the videogame market contracting, various multimedia technologies have been introduced into Pachinko machines. The authors have developed a Pachinko machine incorporating an autostereoscopic 3D display, and evaluated its effect on the visual function. As of April 2003, the new Pachinko machine has been on sale in Japan. The stereoscopic 3D image is displayed using an LCD. Backlighting for the right and left images is separate, and passes through a polarizing filter before reaching the LCD, which is sandwiched with a micro polarizer. The content selected for display was ukiyoe pictures (Japanese traditional woodblocks). The authors intended to reduce visual fatigue by presenting 3D images with depth "behind" the display and switching between 3D and 2D images. For evaluation of the Pachinko machine, a 2D version with identical content was also prepared, and the effects were examined and compared by testing psycho-physiological responses.

Differences in liver stiffness values obtained with new ultrasound elastography machines and Fibroscan: A comparative study.

PubMed

Piscaglia, Fabio; Salvatore, Veronica; Mulazzani, Lorenzo; Cantisani, Vito; Colecchia, Antonio; Di Donato, Roberto; Felicani, Cristina; Ferrarini, Alessia; Gamal, Nesrine; Grasso, Valentina; Marasco, Giovanni; Mazzotta, Elena; Ravaioli, Federico; Ruggieri, Giacomo; Serio, Ilaria; Sitouok Nkamgho, Joules Fabrice; Serra, Carla; Festi, Davide; Schiavone, Cosima; Bolondi, Luigi

2017-07-01

Whether Fibroscan thresholds can be immediately adopted for none, some or all other shear wave elastography techniques has not been tested. The aim of the present study was to test the concordance of the findings obtained from 7 of the most recent ultrasound elastography machines with respect to Fibroscan. Sixteen hepatitis C virus-related patients with fibrosis ≥2 and having reliable results at Fibroscan were investigated in two intercostal spaces using 7 different elastography machines. Coefficients of both precision (an index of data dispersion) and accuracy (an index of bias correction factors expressing different magnitudes of changes in comparison to the reference) were calculated. Median stiffness values differed among the different machines as did coefficients of both precision (range 0.54-0.72) and accuracy (range 0.28-0.87). When the average of the measurements of two intercostal spaces was considered, coefficients of precision significantly increased with all machines (range 0.72-0.90) whereas of accuracy improved more scatteredly and by a smaller degree (range 0.40-0.99). The present results showed only moderate concordance of the majority of elastography machines with the Fibroscan results, preventing the possibility of the immediate universal adoption of Fibroscan thresholds for defining liver fibrosis staging for all new machines. Copyright © 2017 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Abstract quantum computing machines and quantum computational logics

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras

PubMed Central

Spinosa, Emanuele; Roberts, David A.

2017-01-01

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access. PMID:28757553

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras.

PubMed

Quinn, Mark Kenneth; Spinosa, Emanuele; Roberts, David A

2017-07-25

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access.

Machine learning algorithms for mode-of-action classification in toxicity assessment.

PubMed

Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

2016-01-01

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Use of IT platform in determination of efficiency of mining machines

NASA Astrophysics Data System (ADS)

Brodny, Jarosław; Tutak, Magdalena

2018-01-01

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

A Sensor-Based Method for Diagnostics of Machine Tool Linear Axes.

PubMed

Vogl, Gregory W; Weiss, Brian A; Donmez, M Alkan

2015-01-01

A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a 'sensor box' to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality.

Characterization of Machine Variability and Progressive Heat Treatment in Selective Laser Melting of Inconel 718

NASA Technical Reports Server (NTRS)

Prater, T.; Tilson, W.; Jones, Z.

2015-01-01

The absence of an economy of scale in spaceflight hardware makes additive manufacturing an immensely attractive option for propulsion components. As additive manufacturing techniques are increasingly adopted by government and industry to produce propulsion hardware in human-rated systems, significant development efforts are needed to establish these methods as reliable alternatives to conventional subtractive manufacturing. One of the critical challenges facing powder bed fusion techniques in this application is variability between machines used to perform builds. Even with implementation of robust process controls, it is possible for two machines operating at identical parameters with equivalent base materials to produce specimens with slightly different material properties. The machine variability study presented here evaluates 60 specimens of identical geometry built using the same parameters. 30 samples were produced on machine 1 (M1) and the other 30 samples were built on machine 2 (M2). Each of the 30-sample sets were further subdivided into three subsets (with 10 specimens in each subset) to assess the effect of progressive heat treatment on machine variability. The three categories for post-processing were: stress relief, stress relief followed by hot isostatic press (HIP), and stress relief followed by HIP followed by heat treatment per AMS 5664. Each specimen (a round, smooth tensile) was mechanically tested per ASTM E8. Two formal statistical techniques, hypothesis testing for equivalency of means and one-way analysis of variance (ANOVA), were applied to characterize the impact of machine variability and heat treatment on six material properties: tensile stress, yield stress, modulus of elasticity, fracture elongation, and reduction of area. This work represents the type of development effort that is critical as NASA, academia, and the industrial base work collaboratively to establish a path to certification for additively manufactured parts. For future

The influence of machining condition and cutting tool wear on surface roughness of AISI 4340 steel

NASA Astrophysics Data System (ADS)

Natasha, A. R.; Ghani, J. A.; Che Haron, C. H.; Syarif, J.

2018-01-01

Sustainable machining by using cryogenic coolant as the cutting fluid has been proven to enhance some machining outputs. The main objective of the current work was to investigate the influence of machining conditions; dry and cryogenic, as well as the cutting tool wear on the machined surface roughness of AISI 4340 steel. The experimental tests were performed using chemical vapor deposition (CVD) coated carbide inserts. The value of machined surface roughness were measured at 3 cutting intervals; beginning, middle, and end of the cutting based on the readings of the tool flank wear. The results revealed that cryogenic turning had the greatest influence on surface roughness when machined at lower cutting speed and higher feed rate. Meanwhile, the cutting tool wear was also found to influence the surface roughness, either improving it or deteriorating it, based on the severity and the mechanism of the flank wear.

Machine characterization and benchmark performance prediction

NASA Technical Reports Server (NTRS)

Saavedra-Barrera, Rafael H.

1988-01-01

From runs of standard benchmarks or benchmark suites, it is not possible to characterize the machine nor to predict the run time of other benchmarks which have not been run. A new approach to benchmarking and machine characterization is reported. The creation and use of a machine analyzer is described, which measures the performance of a given machine on FORTRAN source language constructs. The machine analyzer yields a set of parameters which characterize the machine and spotlight its strong and weak points. Also described is a program analyzer, which analyzes FORTRAN programs and determines the frequency of execution of each of the same set of source language operations. It is then shown that by combining a machine characterization and a program characterization, we are able to predict with good accuracy the run time of a given benchmark on a given machine. Characterizations are provided for the Cray-X-MP/48, Cyber 205, IBM 3090/200, Amdahl 5840, Convex C-1, VAX 8600, VAX 11/785, VAX 11/780, SUN 3/50, and IBM RT-PC/125, and for the following benchmark programs or suites: Los Alamos (BMK8A1), Baskett, Linpack, Livermore Loops, Madelbrot Set, NAS Kernels, Shell Sort, Smith, Whetstone and Sieve of Erathostenes.

MTR WING, TRA604. FIRST FLOOR PLAN. ENTRY LOBBY, MACHINE SHOP, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

MTR WING, TRA-604. FIRST FLOOR PLAN. ENTRY LOBBY, MACHINE SHOP, INSTRUMENT SHOP, COUNTING ROOM, HEALTH PHYSICS LAB, LABS AND OFFICES, STORAGE, SHIPPING AND RECEIVING. BLAW-KNOX 3150-4-2, 7/1950. INL INDEX NO. 053-604-00-099-100008, REV. 7. - Idaho National Engineering Laboratory, Test Reactor Area, Materials & Engineering Test Reactors, Scoville, Butte County, ID

Human-machine interactions

DOEpatents

Forsythe, J Chris [Sandia Park, NM; Xavier, Patrick G [Albuquerque, NM; Abbott, Robert G [Albuquerque, NM; Brannon, Nathan G [Albuquerque, NM; Bernard, Michael L [Tijeras, NM; Speed, Ann E [Albuquerque, NM

2009-04-28

Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

NASA Astrophysics Data System (ADS)

Paradis, Daniel; Lefebvre, René; Gloaguen, Erwan; Rivera, Alfonso

2015-01-01

The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.

Using a "time machine" to test for local adaptation of aquatic microbes to temporal and spatial environmental variation.

PubMed

Fox, Jeremy W; Harder, Lawrence D

2015-01-01

Local adaptation occurs when different environments are dominated by different specialist genotypes, each of which is relatively fit in its local conditions and relatively unfit under other conditions. Analogously, ecological species sorting occurs when different environments are dominated by different competing species, each of which is relatively fit in its local conditions. The simplest theory predicts that spatial, but not temporal, environmental variation selects for local adaptation (or generates species sorting), but this prediction is difficult to test. Although organisms can be reciprocally transplanted among sites, doing so among times seems implausible. Here, we describe a reciprocal transplant experiment testing for local adaptation or species sorting of lake bacteria in response to both temporal and spatial variation in water chemistry. The experiment used a -80°C freezer as a "time machine." Bacterial isolates and water samples were frozen for later use, allowing transplantation of older isolates "forward in time" and newer isolates "backward in time." Surprisingly, local maladaptation predominated over local adaptation in both space and time. Such local maladaptation may indicate that adaptation, or the analogous species sorting process, fails to keep pace with temporal fluctuations in water chemistry. This hypothesis could be tested with more finely resolved temporal data. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

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

Extreme ultraviolet lithography machine

DOEpatents

Tichenor, Daniel A.; Kubiak, Glenn D.; Haney, Steven J.; Sweeney, Donald W.

2000-01-01

An extreme ultraviolet lithography (EUVL) machine or system for producing integrated circuit (IC) components, such as transistors, formed on a substrate. The EUVL machine utilizes a laser plasma point source directed via an optical arrangement onto a mask or reticle which is reflected by a multiple mirror system onto the substrate or target. The EUVL machine operates in the 10-14 nm wavelength soft x-ray photon. Basically the EUV machine includes an evacuated source chamber, an evacuated main or project chamber interconnected by a transport tube arrangement, wherein a laser beam is directed into a plasma generator which produces an illumination beam which is directed by optics from the source chamber through the connecting tube, into the projection chamber, and onto the reticle or mask, from which a patterned beam is reflected by optics in a projection optics (PO) box mounted in the main or projection chamber onto the substrate. In one embodiment of a EUVL machine, nine optical components are utilized, with four of the optical components located in the PO box. The main or projection chamber includes vibration isolators for the PO box and a vibration isolator mounting for the substrate, with the main or projection chamber being mounted on a support structure and being isolated.

How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

PubMed

Ichikawa, Daisuke; Saito, Toki; Ujita, Waka; Oyama, Hiroshi

2016-12-01

Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared with administering an additional test. Data were collected during annual health check-ups performed in Japan between 2011 and 2013 (inclusive). We prepared training and test datasets from the health check-up data to build prediction models; these were composed of 43,524 and 17,789 persons, respectively. Gradient-boosting decision tree (GBDT), random forest (RF), and logistic regression (LR) approaches were trained using the training dataset and were then used to predict hyperuricemia in the test dataset. Undersampling was applied to build the prediction models to deal with the imbalanced class dataset. The results showed that the RF and GBDT approaches afforded the best performances in terms of sensitivity and specificity, respectively. The area under the curve (AUC) values of the models, which reflected the total discriminative ability of the classification, were 0.796 [95% confidence interval (CI): 0.766-0.825] for the GBDT, 0.784 [95% CI: 0.752-0.815] for the RF, and 0.785 [95% CI: 0.752-0.819] for the LR approaches. No significant differences were observed between pairs of each approach. Small changes occurred in the AUCs after applying undersampling to build the models. We developed a virtual health check-up that predicted the development of hyperuricemia using machine-learning methods. The GBDT, RF, and LR methods had similar predictive capability. Undersampling did not remarkably improve predictive power. Copyright © 2016 Elsevier Inc. All rights reserved.

Pulmonary Function Tests

MedlinePlus

... heavily for at least 30 minutes before the test. ■■ Do not wear tight clothing that makes it difficult for you ... be blowing into a tube connected to a machine (spirometer). To get the “best” test result, the test is repeated three times. You ...

Cylinder Test Specification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richard Catanach; Larry Hill; Herbert Harry

1999-10-01

The purpose of the cylinder testis two-fold: (1) to characterize the metal-pushing ability of an explosive relative to that of other explosives as evaluated by the E{sub 19} cylinder energy and the G{sub 19} Gurney energy and (2) to help establish the explosive product equation-of-state (historically, the Jones-Wilkins-Lee (JWL) equation). This specification details the material requirements and procedures necessary to assemble and fire a typical Los Alamos National Laboratory (LANL) cylinder test. Strict adherence to the cylinder. material properties, machining tolerances, material heat-treatment and etching processes, and high explosive machining tolerances is essential for test-to-test consistency and to maximize radialmore » wall expansions. Assembly and setup of the cylinder test require precise attention to detail, especially when placing intricate pin wires on the cylinder wall. The cylinder test is typically fired outdoors and at ambient temperature.« less

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Programming and machining of complex parts based on CATIA solid modeling

NASA Astrophysics Data System (ADS)

Zhu, Xiurong

2017-09-01

The complex parts of the use of CATIA solid modeling programming and simulation processing design, elaborated in the field of CNC machining, programming and the importance of processing technology. In parts of the design process, first make a deep analysis on the principle, and then the size of the design, the size of each chain, connected to each other. After the use of backstepping and a variety of methods to calculate the final size of the parts. In the selection of parts materials, careful study, repeated testing, the final choice of 6061 aluminum alloy. According to the actual situation of the processing site, it is necessary to make a comprehensive consideration of various factors in the machining process. The simulation process should be based on the actual processing, not only pay attention to shape. It can be used as reference for machining.

Machine safety: proper safeguarding techniques.

PubMed

Martin, K J

1992-06-01

1. OSHA mandates certain safeguarding of machinery to prevent accidents and protect machine operators. OSHA specifies moving parts that must be guarded and sets criteria for the guards. 2. A 1989 OSHA standard for lockout/tagout requires locking the energy source during maintenance, periodically inspecting for power transmission, and training maintenance workers. 3. In an amputation emergency, first aid for cardiopulmonary resuscitation, shock, and bleeding are the first considerations. The amputated part should be wrapped in moist gauze, placed in a sealed plastic bag, and placed in a container of 50% water and 50% ice for transport. 4. The role of the occupational health nurse in machine safety is to conduct worksite analyses to identify proper safeguarding and to communicate deficiencies to appropriate personnel; to train workers in safe work practices and observe compliance in the use of machine guards; to provide care to workers injured by machines; and to reinforce safe work practices among machine operators.

Mobile machine hazardous working zone warning system

DOEpatents

Schiffbauer, William H.; Ganoe, Carl W.

1999-01-01

A warning system is provided for a mobile working machine to alert an individual of a potentially dangerous condition in the event the individual strays into a hazardous working zone of the machine. The warning system includes a transmitter mounted on the machine and operable to generate a uniform magnetic field projecting beyond an outer periphery of the machine in defining a hazardous working zone around the machine during operation thereof. A receiver, carried by the individual and activated by the magnetic field, provides an alarm signal to alert the individual when he enters the hazardous working zone of the machine.

Mobile machine hazardous working zone warning system

DOEpatents

Schiffbauer, W.H.; Ganoe, C.W.

1999-08-17

A warning system is provided for a mobile working machine to alert an individual of a potentially dangerous condition in the event the individual strays into a hazardous working zone of the machine. The warning system includes a transmitter mounted on the machine and operable to generate a uniform magnetic field projecting beyond an outer periphery of the machine in defining a hazardous working zone around the machine during operation. A receiver, carried by the individual and activated by the magnetic field, provides an alarm signal to alert the individual when he enters the hazardous working zone of the machine. 3 figs.

Accuracy and Reliability of a New Tennis Ball Machine

PubMed Central

Brechbuhl, Cyril; Millet, Grégoire; Schmitt, Laurent

2016-01-01

The aim was to evaluate the reliability of a newly-developed ball machine named 'Hightof', on the field and to assess its accuracy. The experiment was conducted in the collaboration of the 'Hawk-Eye' technology. The accuracy and reliability of this ball machine were assessed during an incremental test, with 1 min of exercise and 30 sec of recovery, where the frequency of the balls increased from 10 to 30 balls·min-1. The initial frequency was 10 and increased by 2 until 22, then by 1 until 30 balls·min-1. The reference points for the impact were 8.39m from the net and 2.70m from lateral line for the right side and 2.83m for the left side. The precision of the machine was similar on the right and left sides (0.63 ± 0.39 vs 0.63 ± 0.34 m). The distances to the reference point were 0.52 ± 0.42, 0.26 ± 0.19, 0.52 ± 0.37, 0.28 ± 0.19 m for the Y-right, X-right, Y-left and X-left impacts. The precision was constant and did not increase with the intensity. (e.g ball frequency). The ball velocity was 86.3 ± 1.5 and 86.5 ± 1.3 km·h-1 for the right and the left side, respectively. The coefficient of variation for the velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min-1). Conclusion: both the accuracy and the reliability of this new ball machine appear satisfying enough for field testing and training. Key points The reliability and accuracy of a new ball machine named 'Hightof' were assessed. The impact point was reproducible and similar on the right and left sides (±0.63 m). The precision was constant and did not increase with the intensity (e.g ball frequency). The coefficient of variation of the ball velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min-1). PMID:27274663

Accuracy and Reliability of a New Tennis Ball Machine.

PubMed

Brechbuhl, Cyril; Millet, Grégoire; Schmitt, Laurent

2016-06-01

The aim was to evaluate the reliability of a newly-developed ball machine named 'Hightof', on the field and to assess its accuracy. The experiment was conducted in the collaboration of the 'Hawk-Eye' technology. The accuracy and reliability of this ball machine were assessed during an incremental test, with 1 min of exercise and 30 sec of recovery, where the frequency of the balls increased from 10 to 30 balls·min(-1). The initial frequency was 10 and increased by 2 until 22, then by 1 until 30 balls·min(-1). The reference points for the impact were 8.39m from the net and 2.70m from lateral line for the right side and 2.83m for the left side. The precision of the machine was similar on the right and left sides (0.63 ± 0.39 vs 0.63 ± 0.34 m). The distances to the reference point were 0.52 ± 0.42, 0.26 ± 0.19, 0.52 ± 0.37, 0.28 ± 0.19 m for the Y-right, X-right, Y-left and X-left impacts. The precision was constant and did not increase with the intensity. (e.g ball frequency). The ball velocity was 86.3 ± 1.5 and 86.5 ± 1.3 km·h(-1) for the right and the left side, respectively. The coefficient of variation for the velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min(-1)). both the accuracy and the reliability of this new ball machine appear satisfying enough for field testing and training. Key pointsThe reliability and accuracy of a new ball machine named 'Hightof' were assessed.The impact point was reproducible and similar on the right and left sides (±0.63 m).The precision was constant and did not increase with the intensity (e.g ball frequency).The coefficient of variation of the ball velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min(-1)).

[Effect of manual cleaning and machine cleaning for dental handpiece].

PubMed

Zhou, Xiaoli; Huang, Hao; He, Xiaoyan; Chen, Hui; Zhou, Xiaoying

2013-08-01

Comparing the dental handpiece' s cleaning effect between manual cleaning and machine cleaning. Eighty same contaminated dental handpieces were randomly divided into experimental group and control group, each group contains 40 pieces. The experimental group was treated by full automatic washing machine, and the control group was cleaned manually. The cleaning method was conducted according to the operations process standard, then ATP bioluminescence was used to test the cleaning results. Average relative light units (RLU) by ATP bioluminescence detection were as follows: Experimental group was 9, control group was 41. The two groups were less than the recommended RLU value provided by the instrument manufacturer (RLU < or = 45). There was significant difference between the two groups (P < 0.05). The cleaning quality of the experimental group was better than that of control group. It is recommended that the central sterile supply department should clean dental handpieces by machine to ensure the cleaning effect and maintain the quality.

Integrated Inverter For Driving Multiple Electric Machines

DOEpatents

Su, Gui-Jia [Knoxville, TN; Hsu, John S [Oak Ridge, TN

2006-04-04

An electric machine drive (50) has a plurality of inverters (50a, 50b) for controlling respective electric machines (57, 62), which may include a three-phase main traction machine (57) and two-phase accessory machines (62) in a hybrid or electric vehicle. The drive (50) has a common control section (53, 54) for controlling the plurality of inverters (50a, 50b) with only one microelectronic processor (54) for controlling the plurality of inverters (50a, 50b), only one gate driver circuit (53) for controlling conduction of semiconductor switches (S1-S10) in the plurality of inverters (50a, 50b), and also includes a common dc bus (70), a common dc bus filtering capacitor (C1) and a common dc bus voltage sensor (67). The electric machines (57, 62) may be synchronous machines, induction machines, or PM machines and may be operated in a motoring mode or a generating mode.