Utility operational experience on the NASA/DOE MOD-0A 200-kW wind turbine

NASA Technical Reports Server (NTRS)

Glasgow, J. C.; Robbins, W. H.

1979-01-01

The Mod-0A 200 wind turbine was designed and fabricated as part of the Federal Wind Energy Program. Early wind turbine operation and performance data were obtained while gaining initial experience in the operation of large, horizontal axis wind turbines in typical utility environments. The Mod-0A wind turbine was turned over to the Town of Clayton Light and Water Plant, Clayton, NM, for utility operation and on December 31, 1978, the machine had completed ten months of utility operation. The machine is described and the recent operational experience at Clayton, NMis documented.

LIQUID CRYSTAL POLYMERS (LCP) USED AS A MACHINING FLUID CD

EPA Science Inventory

This interactive CD was produced to present the science, research activities, and beneficial environmental and machining advantages for utilizing Liquid Crystal Polymers (LCPs) as a machine fluid in the manufacturing industry.

In 1995, the USEPA funded a project to cut flu...

Technology of machine tools. Volume 2. Machine tool systems management and utilization

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

Thomson, A.R.

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.

Latin-square three-dimensional gage master

DOEpatents

Jones, L.

1981-05-12

A gage master for coordinate measuring machines has an nxn array of objects distributed in the Z coordinate utilizing the concept of a Latin square experimental design. Using analysis of variance techniques, the invention may be used to identify sources of error in machine geometry and quantify machine accuracy.

Latin square three dimensional gage master

DOEpatents

Jones, Lynn L.

1982-01-01

A gage master for coordinate measuring machines has an nxn array of objects distributed in the Z coordinate utilizing the concept of a Latin square experimental design. Using analysis of variance techniques, the invention may be used to identify sources of error in machine geometry and quantify machine accuracy.

High slot utilization systems for electric machines

DOEpatents

Hsu, John S

2009-06-23

Two new High Slot Utilization (HSU) Systems for electric machines enable the use of form wound coils that have the highest fill factor and the best use of magnetic materials. The epoxy/resin/curing treatment ensures the mechanical strength of the assembly of teeth, core, and coils. In addition, the first HSU system allows the coil layers to be moved inside the slots for the assembly purpose. The second system uses the slided-in teeth instead of the plugged-in teeth. The power density of the electric machine that uses either system can reach its highest limit.

General Theory of the Double Fed Synchronous Machine. Ph.D. Thesis - Swiss Technological Univ., 1950

NASA Technical Reports Server (NTRS)

El-Magrabi, M. G.

1982-01-01

Motor and generator operation of a double-fed synchronous machine were studied and physically and mathematically treated. Experiments with different connections, voltages, etc. were carried out. It was concluded that a certain degree of asymmetry is necessary for the best utilization of the machine.

Man Machine Systems in Education.

ERIC Educational Resources Information Center

Sall, Malkit S.

This review of the research literature on the interaction between humans and computers discusses how man machine systems can be utilized effectively in the learning-teaching process, especially in secondary education. Beginning with a definition of man machine systems and comments on the poor quality of much of the computer-based learning material…

Scheduling job shop - A case study

NASA Astrophysics Data System (ADS)

Abas, M.; Abbas, A.; Khan, W. A.

2016-08-01

The scheduling in job shop is important for efficient utilization of machines in the manufacturing industry. There are number of algorithms available for scheduling of jobs which depend on machines tools, indirect consumables and jobs which are to be processed. In this paper a case study is presented for scheduling of jobs when parts are treated on available machines. Through time and motion study setup time and operation time are measured as total processing time for variety of products having different manufacturing processes. Based on due dates different level of priority are assigned to the jobs and the jobs are scheduled on the basis of priority. In view of the measured processing time, the times for processing of some new jobs are estimated and for efficient utilization of the machines available an algorithm is proposed and validated.

Software Tools for Emittance Measurement and Matching for 12 GeV CEBAF

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

Turner, Dennis L.

2016-05-01

This paper discusses model-driven setup of the Continuous Electron Beam Accelerator Facility (CEBAF) for the 12GeV era, focusing on qsUtility. qsUtility is a set of software tools created to perform emittance measurements, analyze those measurements, and compute optics corrections based upon the measurements.qsUtility was developed as a toolset to facilitate reducing machine configuration time and reproducibility by way of an accurate accelerator model, and to provide Operations staff with tools to measure and correct machine optics with little or no assistance from optics experts.

Support vector machine for the diagnosis of malignant mesothelioma

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Methods, systems and apparatus for optimization of third harmonic current injection in a multi-phase machine

DOEpatents

Gallegos-Lopez, Gabriel

2012-10-02

Methods, system and apparatus are provided for increasing voltage utilization in a five-phase vector controlled machine drive system that employs third harmonic current injection to increase torque and power output by a five-phase machine. To do so, a fundamental current angle of a fundamental current vector is optimized for each particular torque-speed of operating point of the five-phase machine.

An Analysis of Methods for Maximizing the Utilization of Space in USAF Facilities.

DTIC Science & Technology

1987-09-01

vegetable . peeling machines and dish washing machines- i. Fixed barracks equipment including sinks, troughs and washing machines of all types: .4. Fixed...Prentice-Hall, 1977. 52. Spillars, W.R. and S. Al- Banna . "An Interactive Computer Graphics Space Allocation System," DAW Nine. 229-237. Association for

"Pack[superscript2]": VM Resource Scheduling for Fine-Grained Application SLAs in Highly Consolidated Environment

ERIC Educational Resources Information Center

Sukwong, Orathai

2013-01-01

Virtualization enables the ability to consolidate multiple servers on a single physical machine, increasing the infrastructure utilization. Maximizing the ratio of server virtual machines (VMs) to physical machines, namely the consolidation ratio, becomes an important goal toward infrastructure cost saving in a cloud. However, the consolidation…

175Hp contrarotating homopolar motor design report

NASA Astrophysics Data System (ADS)

Cannell, Michael J.; Drake, John L.; McConnell, Richard A.; Martino, William R.

1994-06-01

A normally conducting contrarotating homopolar motor has been designed and constructed. The reaction torque, in the outer rotor, from the inner rotor is utilized to produce true contrarotation. The machine utilizes liquid cooled conductors, high performance liquid metal current collectors, and ferrous conductors in the active region. The basic machine output is 175 hp at + or - 1,200 rpm with an input of 4 volts and 35,000 amps.

Construction machine control guidance implementation strategy.

DOT National Transportation Integrated Search

2010-07-01

Machine Controlled Guidance (MCG) technology may be used in roadway and bridge construction to improve construction efficiencies, potentially resulting in reduced project costs and accelerated schedules. The technology utilizes a Global Positioning S...

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.

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

None

This factsheet describes a project that developed and demonstrated a new manufacturing-informed design framework that utilizes advanced multi-scale, physics-based process modeling to dramatically improve manufacturing productivity and quality in machining operations while reducing the cost of machined components.

Machine learning in the string landscape

NASA Astrophysics Data System (ADS)

Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

2017-09-01

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

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 to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine.

Assessment and evaluation of noise controls on roof bolting equipment and a method for predicting sound pressure levels in underground coal mining

NASA Astrophysics Data System (ADS)

Matetic, Rudy J.

Over-exposure to noise remains a widespread and serious health hazard in the U.S. mining industries despite 25 years of regulation. Every day, 80% of the nation's miners go to work in an environment where the time weighted average (TWA) noise level exceeds 85 dBA and more than 25% of the miners are exposed to a TWA noise level that exceeds 90 dBA, the permissible exposure limit (PEL). Additionally, MSHA coal noise sample data collected from 2000 to 2002 show that 65% of the equipment whose operators exceeded 100% noise dosage comprise only seven different types of machines; auger miners, bulldozers, continuous miners, front end loaders, roof bolters, shuttle cars (electric), and trucks. In addition, the MSHA data indicate that the roof bolter is third among all the equipment and second among equipment in underground coal whose operators exceed 100% dosage. A research program was implemented to: (1) determine, characterize and to measure sound power levels radiated by a roof bolting machine during differing drilling configurations (thrust, rotational speed, penetration rate, etc.) and utilizing differing types of drilling methods in high compressive strength rock media (>20,000 psi). The research approach characterized the sound power level results from laboratory testing and provided the mining industry with empirical data relative to utilizing differing noise control technologies (drilling configurations and types of drilling methods) in reducing sound power level emissions on a roof bolting machine; (2) distinguish and correlate the empirical data into one, statistically valid, equation, in which, provided the mining industry with a tool to predict overall sound power levels of a roof bolting machine given any type of drilling configuration and drilling method utilized in industry; (3) provided the mining industry with several approaches to predict or determine sound pressure levels in an underground coal mine utilizing laboratory test results from a roof bolting machine and (4) described a method for determining an operators' noise dosage of a roof bolting machine utilizing predicted or determined sound pressure levels.

Novachip surface treatment : technical assistance report.

DOT National Transportation Integrated Search

1997-09-01

Novachip was a French process utilizing a unique paving machine manufactured in Germany. This machine simultaneously applied an evenly distributed asphaltic emulsion and a thin lift of hot mix to the existing roadway surface. : The project extended f...

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.

Quick-Turn Finite Element Analysis for Plug-and-Play Satellite Structures

DTIC Science & Technology

2007-03-01

produced from 0.375 inch round stock and turned on a machine lathe to achieve the shoulder feature and drilled to make it hollow. Figure 3.1...component, a linear taper was machined from the connection shoulder to the solar panel connecting fork. The part was then turned using the machine lathe ...utilizing a modern five-axis Computer Numerical Code ( CNC ) machine mill, the process time could be reduced by as much as seventy-five percent and the

78 FR 73883 - Notice Pursuant to the National Cooperative Research and Production Act of 1993; Members of SGIP...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-09

... Utilities System, Lafayette, LA; Machine-to- Machine Intelligence Corporation (M2Mi), Moffett Field, CA; Inman Technology, Cambridge, MA; Kkrish Energy LLC, Colorado Springs, CO; Smarthome Laboratories, Ltd...

Tunnel Boring Machine Performance Study. Final Report

DOT National Transportation Integrated Search

1984-06-01

Full face tunnel boring machine "TBM" performance during the excavation of 6 tunnels in sedimentary rock is considered in terms of utilization, penetration rates and cutter wear. The construction records are analyzed and the results are used to inves...

Controlling the type and the form of chip when machining steel

NASA Astrophysics Data System (ADS)

Gruby, S. V.; Lasukov, A. A.; Nekrasov, R. Yu; Politsinsky, E. V.; Arkhipova, D. A.

2016-08-01

The type of the chip produced in the process of machining influences many factors of production process. Controlling the type of chip when cutting metals is important for producing swarf chips and for easing its utilization as well as for protecting the machined surface, cutting tool and the worker. In the given work we provide the experimental data on machining structural steel with implanted tool. The authors show that it is possible to control the chip formation process to produce the required type of chip by selecting the material for machining the tool surface.

The in-situ 3D measurement system combined with CNC machine tools

NASA Astrophysics Data System (ADS)

Zhao, Huijie; Jiang, Hongzhi; Li, Xudong; Sui, Shaochun; Tang, Limin; Liang, Xiaoyue; Diao, Xiaochun; Dai, Jiliang

2013-06-01

With the development of manufacturing industry, the in-situ 3D measurement for the machining workpieces in CNC machine tools is regarded as the new trend of efficient measurement. We introduce a 3D measurement system based on the stereovision and phase-shifting method combined with CNC machine tools, which can measure 3D profile of the machining workpieces between the key machining processes. The measurement system utilizes the method of high dynamic range fringe acquisition to solve the problem of saturation induced by specular lights reflected from shiny surfaces such as aluminum alloy workpiece or titanium alloy workpiece. We measured two workpieces of aluminum alloy on the CNC machine tools to demonstrate the effectiveness of the developed measurement system.

Design of a Modular E-Core Flux Concentrating Axial Flux Machine

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

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal

2015-09-02

In this paper a novel E-Core axial flux machine is proposed. The machine has a double stator-single rotor configuration with flux concentrating ferrite magnets, and pole windings across each leg of an E-Core stator. E-Core stators with the proposed flux-concentrating rotor arrangement result in better magnet utilization and higher torque density. The machine also has a modular structure facilitating simpler construction. This paper presents a single phase and a three-phase version of the E-Core machine. Case study for a 1.1 kW, 400 rpm machine for both the single phase and three-phase axial flux machine is presented. The results are verifiedmore » through 3D finite element analysis.« less

On the relationship between parallel computation and graph embedding

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

Gupta, A.K.

1989-01-01

The problem of efficiently simulating an algorithm designed for an n-processor parallel machine G on an m-processor parallel machine H with n > m arises when parallel algorithms designed for an ideal size machine are simulated on existing machines which are of a fixed size. The author studies this problem when every processor of H takes over the function of a number of processors in G, and he phrases the simulation problem as a graph embedding problem. New embeddings presented address relevant issues arising from the parallel computation environment. The main focus centers around embedding complete binary trees into smaller-sizedmore » binary trees, butterflies, and hypercubes. He also considers simultaneous embeddings of r source machines into a single hypercube. Constant factors play a crucial role in his embeddings since they are not only important in practice but also lead to interesting theoretical problems. All of his embeddings minimize dilation and load, which are the conventional cost measures in graph embeddings and determine the maximum amount of time required to simulate one step of G on H. His embeddings also optimize a new cost measure called ({alpha},{beta})-utilization which characterizes how evenly the processors of H are used by the processors of G. Ideally, the utilization should be balanced (i.e., every processor of H simulates at most (n/m) processors of G) and the ({alpha},{beta})-utilization measures how far off from a balanced utilization the embedding is. He presents embeddings for the situation when some processors of G have different capabilities (e.g. memory or I/O) than others and the processors with different capabilities are to be distributed uniformly among the processors of H. Placing such conditions on an embedding results in an increase in some of the cost measures.« less

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.

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

PubMed

Thabtah, Fadi

2018-02-13

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

Utility operational experience on the NASA/DOE Mod-OA 200 kW Wind Turbine

NASA Technical Reports Server (NTRS)

Glasgow, J. C.; Robbins, W. H.

1979-01-01

The Mod-OA 200 kW Wind Turbine was designed and fabricated by the Lewis Research Center of the NASA under the direction of the U.S. Department of Energy. The project is a part of the Federal Wind Energy Program and is designed to obtain early wind turbine operation and performance data while gaining initial experience in the operation of large, horizontal axis wind turbines in typical utility environments. On March 6, 1978, the Mod-OA wind turbine was turned over to the Town of Clayton Light and Water Plant, Clayton, NM, for utility operation and on December 31, 1978 the machine had completed ten months of utility operation. This paper describes the machine and documents the recent operational experience at Clayton, NM.

A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)

DTIC Science & Technology

2008-11-01

retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for...estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity...and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach

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.

Electromechanical converters for electric vehicles

NASA Astrophysics Data System (ADS)

Ambros, T.; Burduniuc, M.; Deaconu, S. I.; Rujanschi, N.

2018-01-01

The paper presents the analysis of various constructive schemes of synchronous electromechanical converters with permanent magnets fixed on the rotor and asynchronous with the short-circuit rotor. Various electrical stator winding schemes have also been compared, demonstrating the efficiency of copper utilization in toroidal windings. The electromagnetic calculus of the axial machine has particularities compared to the cylindrical machine, in the paper is presented the method of correlating the geometry of the cylindrical and axial machines. In this case the method and recommendations used in the design of such machines may be used.

An intelligent CNC machine control system architecture

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

Miller, D.J.; Loucks, C.S.

1996-10-01

Intelligent, agile manufacturing relies on automated programming of digitally controlled processes. Currently, processes such as Computer Numerically Controlled (CNC) machining are difficult to automate because of highly restrictive controllers and poor software environments. It is also difficult to utilize sensors and process models for adaptive control, or to integrate machining processes with other tasks within a factory floor setting. As part of a Laboratory Directed Research and Development (LDRD) program, a CNC machine control system architecture based on object-oriented design and graphical programming has been developed to address some of these problems and to demonstrate automated agile machining applications usingmore » platform-independent software.« less

Machine Aids to Translation.

ERIC Educational Resources Information Center

Brinkmann, Karl-Heinz

1981-01-01

Describes the TEAM Program System of the Siemens Language Services Department, particularly the main features of its terminology data bank. Discusses criteria to which stored terminology must conform and methods of data bank utilization. Concludes by summarizing the consequences that machine-aided translation development has had for the…

Teardrop and heart orbits of a swinging Atwood's machine

NASA Astrophysics Data System (ADS)

Tufillaro, Nicholas B.

1994-03-01

An exact solution is presented for a swinging Atwood's machine. This teardrop-heart orbit is constructed using Hamilton-Jacobi theory. The example nicely illustrates the utility of the Hamilton-Jacobi method for finding solutions to nonlinear mechanical systems when more elementary techniques fail.

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.

Mounting arrangement for the drive system of an air-bearing spindle on a machine tool

DOEpatents

Lunsford, J.S.; Crisp, D.W.; Petrowski, P.L.

1987-12-07

The present invention is directed to a mounting arrangement for the drive system of an air-bearing spindle utilized on a machine tool such as a lathe. The mounting arrangement of the present invention comprises a housing which is secured to the casing of the air bearing in such a manner that the housing position can be selectively adjusted to provide alignment of the air-bearing drive shaft supported by the housing and the air-bearing spindle. Once this alignment is achieved the air between spindle and the drive arrangement is maintained in permanent alignment so as to overcome misalignment problems encountered in the operation of the machine tool between the air-bearing spindle and the shaft utilized for driving the air-bearing spindle.

Novel target fabrication using 3D printing developed at University of Michigan

DOE PAGES

Klein, Sallee R.; Deininger, Michael; Gillespie, Robb S.; ...

2016-05-24

The University of Michigan has been fabricating targets for high-energy-density experiments for the past decade. We utilize the technique of machined acrylic bodies and mating components acting as constraints to build repeatable targets. Combining 3D printing with traditional machining, we are able to take advantage of the very best part of both aspects of manufacturing. Furthermore, we present several recent campaigns to act as showcase and introduction of our techniques and our experience with 3D printing, effecting how we utilize 3D printing in our target builds.

Novel target fabrication using 3D printing developed at University of Michigan

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

Klein, Sallee R.; Deininger, Michael; Gillespie, Robb S.

The University of Michigan has been fabricating targets for high-energy-density experiments for the past decade. We utilize the technique of machined acrylic bodies and mating components acting as constraints to build repeatable targets. Combining 3D printing with traditional machining, we are able to take advantage of the very best part of both aspects of manufacturing. Furthermore, we present several recent campaigns to act as showcase and introduction of our techniques and our experience with 3D printing, effecting how we utilize 3D printing in our target builds.

Human factors - Man-machine symbiosis in space

NASA Technical Reports Server (NTRS)

Brown, Jeri W.

1987-01-01

The relation between man and machine in space is studied. Early spaceflight and the goal of establishing a permanent space presence are described. The need to consider the physiological, psychological, and social integration of humans for each space mission is examined. Human factors must also be considered in the design of spacecraft. The effective utilization of man and machine capabilities, and research in anthropometry and biomechanics aimed at determining the limitations of spacecrews are discussed.

Quantum Machine Learning over Infinite Dimensions

DOE PAGES

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George; ...

2017-02-21

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

Quantum Machine Learning over Infinite Dimensions

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

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

[Invert transformer design for high frequency X-ray machine based on PWM controller SG 3525].

PubMed

Yu, Xue-fei; Li, Zhe

2005-07-01

This paper introduces the principle of invert transformer of high frequency X-ray machine, and analyzes its main constitution. Meanwhile, a scheme based on SG3525 for closed loop voltage regulation is given. The experimental result testifies its efficiency and utility.

ClearTK 2.0: Design Patterns for Machine Learning in UIMA

PubMed Central

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework. PMID:29104966

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

PubMed

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

Studying depression using imaging and machine learning methods.

PubMed

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

2016-01-01

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

Block-Module Electric Machines of Alternating Current

NASA Astrophysics Data System (ADS)

Zabora, I.

2018-03-01

The paper deals with electric machines having active zone based on uniform elements. It presents data on disk-type asynchronous electric motors with short-circuited rotors, where active elements are made by integrated technique that forms modular elements. Photolithography, spraying, stamping of windings, pressing of core and combined methods are utilized as the basic technological approaches of production. The constructions and features of operation for new electric machine - compatible electric machines-transformers are considered. Induction motors are intended for operation in hermetic plants with extreme conditions surrounding gas, steam-to-gas and liquid environment at a high temperature (to several hundred of degrees).

Recent R&D status for 70 MW class superconducting generators in the Super-GM project

NASA Astrophysics Data System (ADS)

Ageta, Takasuke

2000-05-01

Three types of 70 MW class superconducting generators called model machines have been developed to establish basic technologies for a pilot machine. The series of on-site verification tests was completed in June 1999. The world's highest generator output (79 MW), the world's longest continuous operation (1500 hours) and other excellent results were obtained. The model machine was connected to a commercial power grid and fundamental data were collected for future utilization. It is expected that fundamental technologies on design and manufacture required for a 200 MW class pilot machine are established.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Productivity improvement using discrete events simulation

NASA Astrophysics Data System (ADS)

Hazza, M. H. F. Al; Elbishari, E. M. Y.; Ismail, M. Y. Bin; Adesta, E. Y. T.; Rahman, Nur Salihah Binti Abdul

2018-01-01

The increasing in complexity of the manufacturing systems has increased the cost of investment in many industries. Furthermore, the theoretical feasibility studies are not enough to take the decision in investing for that particular area. Therefore, the development of the new advanced software is protecting the manufacturer from investing money in production lines that may not be sufficient and effective with their requirement in terms of machine utilization and productivity issue. By conducting a simulation, using accurate model will reduce and eliminate the risk associated with their new investment. The aim of this research is to prove and highlight the importance of simulation in decision-making process. Delmia quest software was used as a simulation program to run a simulation for the production line. A simulation was first done for the existing production line and show that the estimated production rate is 261 units/day. The results have been analysed based on utilization percentage and idle time. Two different scenarios have been proposed based on different objectives. The first scenario is by focusing on low utilization machines and their idle time, this was resulted in minimizing the number of machines used by three with the addition of the works who maintain them without having an effect on the production rate. The second scenario is to increase the production rate by upgrading the curing machine which lead to the increase in the daily productivity by 7% from 261 units to 281 units.

Development of large, horizontal-axis wind turbines

NASA Technical Reports Server (NTRS)

Baldwin, D. H.; Kennard, J.

1985-01-01

A program to develop large, horizontal-axis wind turbines is discussed. The program is directed toward developing the technology for safe, reliable, environmentally acceptable large wind turbines that can generate a significant amount of electricity at costs competitive with those of conventional electricity-generating systems. In addition, these large wind turbines must be fully compatible with electric utility operations and interface requirements. Several ongoing projects in large-wind-turbine development are directed toward meeting the technology requirements for utility applications. The machines based on first-generation technology (Mod-OA and Mod-1) successfully completed their planned periods of experimental operation in June, 1982. The second-generation machines (Mod-2) are in operation at selected utility sites. A third-generation machine (Mod-5) is under contract. Erection and initial operation of the Mod-5 in Hawaii should take place in 1986. Each successive generation of technology increased reliability and energy capture while reducing the cost of electricity. These advances are being made by gaining a better understanding of the system-design drivers, improving the analytical design tools, verifying design methods with operating field data, and incorporating new technology and innovative designs. Information is given on the results from the first- and second-generation machines (Mod-OA, - 1, and -2), the status of the Department of Interior, and the status of the third-generation wind turbine (Mod-5).

Design of a Modular E-Core Flux Concentrating Axial Flux Machine: Preprint

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

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal

2015-08-24

In this paper a novel E-Core axial flux machine is proposed. The machine has a double-stator, single-rotor configuration with flux-concentrating ferrite magnets and pole windings across each leg of an E-Core stator. E-Core stators with the proposed flux-concentrating rotor arrangement result in better magnet utilization and higher torque density. The machine also has a modular structure facilitating simpler construction. This paper presents a single-phase and a three-phase version of the E-Core machine. Case studies for a 1.1-kW, 400-rpm machine for both the single-phase and three-phase axial flux machines are presented. The results are verified through 3D finite element analysis. facilitatingmore » simpler construction. This paper presents a single-phase and a three-phase version of the E-Core machine. Case studies for a 1.1-kW, 400-rpm machine for both the single-phase and three-phase axial flux machines are presented. The results are verified through 3D finite element analysis.« less

Utilization and cost for animal logging operations

Treesearch

Suraj P. Shrestha; Bobby L. Lanford

2001-01-01

Forest harvesting with animals is a labor-intensive operation. Due to the development of efficient machines and high volume demands from the forest products industry, mechanization of logging developed very fast, leaving behind the traditional horse and mule logging. It is expensive to use machines on smaller woodlots, which require frequent moves if mechanically...

A SYSTEMS APPROACH UTILIZING GENERAL-PURPOSE AND SPECIAL-PURPOSE TEACHING MACHINES.

ERIC Educational Resources Information Center

SILVERN, LEONARD C.

IN ORDER TO IMPROVE THE EMPLOYEE TRAINING-EVALUATION METHOD, TEACHING MACHINES AND PERFORMANCE AIDS MUST BE PHYSICALLY AND OPERATIONALLY INTEGRATED INTO THE SYSTEM, THUS RETURNING TRAINING TO THE ACTUAL JOB ENVIRONMENT. GIVEN THESE CONDITIONS, TRAINING CAN BE MEASURED, CALIBRATED, AND CONTROLLED WITH RESPECT TO ACTUAL JOB PERFORMANCE STANDARDS AND…

Molecular Symmetry in Ab Initio Calculations

NASA Astrophysics Data System (ADS)

Madhavan, P. V.; Written, J. L.

1987-05-01

A scheme is presented for the construction of the Fock matrix in LCAO-SCF calculations and for the transformation of basis integrals to LCAO-MO integrals that can utilize several symmetry unique lists of integrals corresponding to different symmetry groups. The algorithm is fully compatible with vector processing machines and is especially suited for parallel processing machines.

The Factory of the Future

NASA Technical Reports Server (NTRS)

Byman, J. E.

1985-01-01

A brief history of aircraft production techniques is given. A flexible machining cell is then described. It is a computer controlled system capable of performing 4-axis machining part cleaning, dimensional inspection and materials handling functions in an unmanned environment. The cell was designed to: allow processing of similar and dissimilar parts in random order without disrupting production; allow serial (one-shipset-at-a-time) manufacturing; reduce work-in-process inventory; maximize machine utilization through remote set-up; maximize throughput and minimize labor.

Simulation model of a single-stage lithium bromide-water absorption cooling unit

NASA Technical Reports Server (NTRS)

Miao, D.

1978-01-01

A computer model of a LiBr-H2O single-stage absorption machine was developed. The model, utilizing a given set of design data such as water-flow rates and inlet or outlet temperatures of these flow rates but without knowing the interior characteristics of the machine (heat transfer rates and surface areas), can be used to predict or simulate off-design performance. Results from 130 off-design cases for a given commercial machine agree with the published data within 2 percent.

Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

NASA Astrophysics Data System (ADS)

Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

2017-07-01

This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

Tool setting device

DOEpatents

Brown, Raymond J.

1977-01-01

The present invention relates to a tool setting device for use with numerically controlled machine tools, such as lathes and milling machines. A reference position of the machine tool relative to the workpiece along both the X and Y axes is utilized by the control circuit for driving the tool through its program. This reference position is determined for both axes by displacing a single linear variable displacement transducer (LVDT) with the machine tool through a T-shaped pivotal bar. The use of the T-shaped bar allows the cutting tool to be moved sequentially in the X or Y direction for indicating the actual position of the machine tool relative to the predetermined desired position in the numerical control circuit by using a single LVDT.

Technology Utilization Conference Series, volume 2

NASA Technical Reports Server (NTRS)

1975-01-01

Proceedings of a series of technology utilization conferences are presented. Commercial applications of space technology, machine tool and metal fabrication, energy and pollution, and mechanical design are among the topics discussed. Emphasis is placed on technology transfer and the minority businessman.

Absorption machine with desorber-resorber

DOEpatents

Biermann, Wendell J.

1985-01-01

An absorption refrigeration system utilizing a low temperature desorber and intermediate temperature resorber. The system operates at three temperatures and three pressures to increase the efficiency of the system and is capable of utilizing a lower generator temperature than previously used.

Tomography and generative training with quantum Boltzmann machines

NASA Astrophysics Data System (ADS)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

Design of a line-VISAR interferometer system for the Sandia Z Machine

NASA Astrophysics Data System (ADS)

Galbraith, J.; Austin, K.; Baker, J.; Bettencourt, R.; Bliss, E.; Celeste, J.; Clancy, T.; Cohen, S.; Crosley, M.; Datte, P.; Fratanduono, D.; Frieders, G.; Hammer, J.; Jackson, J.; Johnson, D.; Jones, M.; Koen, D.; Lusk, J.; Martinez, A.; Massey, W.; McCarville, T.; McLean, H.; Raman, K.; Rodriguez, S.; Spencer, D.; Springer, P.; Wong, J.

2017-08-01

A joint team comprised of Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratory (SNL) personnel is designing a line-VISAR (Velocity Interferometer System for Any Reflector) for the Sandia Z Machine, Z Line-VISAR. The diagnostic utilizes interferometry to assess current delivery as a function of radius during a magnetically-driven implosion. The Z Line-VISAR system is comprised of the following: a two-leg line-VISAR interferometer, an eight-channel Gated Optical Imager (GOI), and a fifty-meter transport beampath to/from the target of interest. The Z Machine presents unique optomechanical design challenges. The machine utilizes magnetically driven pulsed power to drive a target to elevated temperatures and pressures useful for high energy density science. Shock accelerations exceeding 30g and a strong electromagnetic pulse (EMP) are generated during the shot event as the machine discharges currents of over 25 million amps. Sensitive optical components must be protected from shock loading, and electrical equipment must be adequately shielded from the EMP. The optical design must accommodate temperature and humidity fluctuations in the facility as well as airborne hydrocarbons from the pulsed power components. We will describe the engineering design and concept of operations of the Z Line-VISAR system. Focus will be on optomechanical design.

Determinants of Pediatric Echocardiography Laboratory Productivity: Analysis from the Second Survey of the American Society of Echocardiography Committee on Echocardiography Laboratory Productivity.

PubMed

Srivastava, Shubhika; Allada, Vivekanand; Younoszai, Adel; Lopez, Leo; Soriano, Brian D; Fleishman, Craig E; Van Hoever, Andrea M; Lai, Wyman W

2016-10-01

The American Society of Echocardiography Committee on Pediatric Echocardiography Laboratory Productivity aimed to study factors that could influence the clinical productivity of physicians and sonographers and assess longitudinal trends for the same. The first survey results indicated that productivity correlated with the total volume of echocardiograms. Survey questions were designed to assess productivity for (1) physician full-time equivalent (FTE) allocated to echocardiography reading (echocardiograms per physician FTE per day), (2) sonographer FTE (echocardiograms per sonographer FTE per year), and (3) machine utilization (echocardiograms per machine per year). Questions were also posed to assess work flow and workforce. For fiscal year 2013 or academic year 2012-2013, the mean number of total echocardiograms-including outreach, transthoracic, fetal, and transesophageal echocardiograms-per physician FTE per day was 14.3 ± 5.9, the mean number of echocardiograms per sonographer FTE per year was 1,056 ± 441, and the mean number of echocardiograms per machine per year was 778 ± 303. Both physician and sonographer productivity was higher at high-volume surgical centers and with echocardiography slots scheduled concordantly with clinic visits. Having an advanced imaging fellow and outpatient sedation correlated negatively with clinical laboratory productivity. Machine utilization was greater in laboratories with higher sonographer and physician productivity and lower for machines obtained before 2009. Measures of pediatric echocardiography laboratory staff productivity and machine utilization were shown to correlate positively with surgical volume, total echocardiography volumes, and concordant echocardiography scheduling; the same measures correlated negatively with having an advanced imaging fellow and outpatient sedation. There has been no significant change in staff productivity noted over two Committee on Pediatric Echocardiography Laboratory Productivity survey cycles, suggesting that hiring practices have matched laboratory volume increases. Copyright © 2016 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

Micro-machined thermo-conductivity detector

DOEpatents

Yu, Conrad

2003-01-01

A micro-machined thermal conductivity detector for a portable gas chromatograph. The detector is highly sensitive and has fast response time to enable detection of the small size gas samples in a portable gas chromatograph which are in the order of nanoliters. The high sensitivity and fast response time are achieved through micro-machined devices composed of a nickel wire, for example, on a silicon nitride window formed in a silicon member and about a millimeter square in size. In addition to operating as a thermal conductivity detector, the silicon nitride window with a micro-machined wire therein of the device can be utilized for a fast response heater for PCR applications.

Clock Agreement Among Parallel Supercomputer Nodes

DOE Data Explorer

Jones, Terry R.; Koenig, Gregory A.

2014-04-30

This dataset presents measurements that quantify the clock synchronization time-agreement characteristics among several high performance computers including the current world's most powerful machine for open science, the U.S. Department of Energy's Titan machine sited at Oak Ridge National Laboratory. These ultra-fast machines derive much of their computational capability from extreme node counts (over 18000 nodes in the case of the Titan machine). Time-agreement is commonly utilized by parallel programming applications and tools, distributed programming application and tools, and system software. Our time-agreement measurements detail the degree of time variance between nodes and how that variance changes over time. The dataset includes empirical measurements and the accompanying spreadsheets.

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

PubMed

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Simultaneous Scheduling of Jobs, AGVs and Tools Considering Tool Transfer Times in Multi Machine FMS By SOS Algorithm

NASA Astrophysics Data System (ADS)

Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.

2017-08-01

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

Application of high speed machining technology in aviation

NASA Astrophysics Data System (ADS)

Bałon, Paweł; Szostak, Janusz; Kiełbasa, Bartłomiej; Rejman, Edward; Smusz, Robert

2018-05-01

Aircraft structures are exposed to many loads during their working lifespan. Every particular action made during a flight is composed of a series of air movements which generate various aircraft loads. The most rigorous requirement which modern aircraft structures must fulfill is to maintain their high durability and reliability. This requirement involves taking many restrictions into account during the aircraft design process. The most important factor is the structure's overall mass, which has a crucial impact on both utility properties and cost-effectiveness. This makes aircraft one of the most complex results of modern technology. Additionally, there is currently an increasing utilization of high strength aluminum alloys, which requires the implementation of new manufacturing processes. High Speed Machining technology (HSM) is currently one of the most important machining technologies used in the aviation industry, especially in the machining of aluminium alloys. The primary difference between HSM and other milling techniques is the ability to select cutting parameters - depth of the cut layer, feed rate, and cutting speed in order to simultaneously ensure high quality, precision of the machined surface, and high machining efficiency, all of which shorten the manufacturing process of the integral components. In this paper, the authors explain the implementation of the HSM method in integral aircraft constructions. It presents the method of the airframe manufacturing method, and the final results. The HSM method is compared to the previous method where all subcomponents were manufactured by bending and forming processes, and then, they were joined by riveting.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Alumina-zirconia machinable abutments for implant-supported single-tooth anterior crowns.

PubMed

Sadoun, M; Perelmuter, S

1997-01-01

Innovative materials and application techniques are constantly being developed in the ongoing search for improved restorations. This article describes a new material and the fabrication process of aesthetic machinable ceramic anterior implant abutments. The ceramic material utilized is a mixture of alumina (aluminum oxide) and ceria (cerium oxide) with partially stabilized zirconia (zirconium oxide). The initial core material is a cylinder with a 9-mm diameter and a 15-mm height, obtained by ceramic injection and presintering processes. The resultant alumina-zirconia core is porous and readily machinable. It is secured to the analog, and its design is customized by machining the abutment to suit the particular clinical circumstances. The machining is followed by glass infiltration, and the crown is finalized. The learning objective of this article is to gain a basic knowledge of the fabrication and clinical application of the custom machinable abutments.

MOD-2 wind turbine farm stability study

NASA Technical Reports Server (NTRS)

Hinrichsen, E. N.

1980-01-01

The dynamics of single and multiple 2.5 ME, Boeing MOD-2 wind turbine generators (WTGs) connected to utility power systems were investigated. The analysis was based on digital simulation. Both time response and frequency response methods were used. The dynamics of this type of WTG are characterized by two torsional modes, a low frequency 'shaft' mode below 1 Hz and an 'electrical' mode at 3-5 Hz. High turbine inertia and low torsional stiffness between turbine and generator are inherent features. Turbine control is based on electrical power, not turbine speed as in conventional utility turbine generators. Multi-machine dynamics differ very little from single machine dynamics.

Electric vehicle traction motors - The development of an advanced motor concept

NASA Technical Reports Server (NTRS)

Campbell, P.

1980-01-01

An axial-field permanent magnet traction motor is described, similar to several advanced motors that are being developed in the United States. This type of machine has several advantages over conventional dc motors, particularly in the electric vehicle application. The rapidly changing cost of magnetic materials, particularly cobalt, makes it important to study the utilization of permanent magnet materials in such machines. The impact of different magnets on machine design is evaluated, and the advantages of using iron powder composites in the armature are assessed.

Machine learning and computer vision approaches for phenotypic profiling.

PubMed

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

3D Visualization of Machine Learning Algorithms with Astronomical Data

NASA Astrophysics Data System (ADS)

Kent, Brian R.

2016-01-01

We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

Machine learning and computer vision approaches for phenotypic profiling

PubMed Central

Morris, Quaid

2017-01-01

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. PMID:27940887

Methods, systems and apparatus for adjusting duty cycle of pulse width modulated (PWM) waveforms

DOEpatents

Gallegos-Lopez, Gabriel; Kinoshita, Michael H; Ransom, Ray M; Perisic, Milun

2013-05-21

Embodiments of the present invention relate to methods, systems and apparatus for controlling operation of a multi-phase machine in a vector controlled motor drive system when the multi-phase machine operates in an overmodulation region. The disclosed embodiments provide a mechanism for adjusting a duty cycle of PWM waveforms so that the correct phase voltage command signals are applied at the angle transitions. This can reduce variations/errors in the phase voltage command signals applied to the multi-phase machine so that phase current may be properly regulated thus reducing current/torque oscillation, which can in turn improve machine efficiency and performance, as well as utilization of the DC voltage source.

The Body-Machine Interface: A new perspective on an old theme

PubMed Central

Casadio, Maura; Ranganathan, Rajiv; Mussa-Ivaldi, Ferdinando A.

2012-01-01

Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer-interfaces to bionic limbs, have had important consequences for people with movement disorders. In this article, we provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. We outline the steps involved in building such an interface and we highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation. PMID:23237465

Wind energy utilization: A bibliography

NASA Technical Reports Server (NTRS)

1975-01-01

Bibliography cites documents published to and including 1974 with abstracts and references, and is indexed by topic, author, organization, title, and keywords. Topics include: Wind Energy Potential and Economic Feasibility, Utilization, Wind Power Plants and Generators, Wind Machines, Wind Data and Properties, Energy Storage, and related topics.

Shift level analysis of cable yarder availability, utilization, and productive time

Treesearch

James R. Sherar; Chris B. LeDoux

1989-01-01

Decision makers, loggers, managers, and planners need to understand and have methods for estimating utilization and productive time of cable logging systems. In making an accurate prediction of how much area and volume a machine will log per unit time and the associated cable yarding costs, a reliable estimate of the availability, utilization, and productive time of...

Evaluating the Advisory Flags and Machine Scoring Difficulty in the "e-rater"® Automated Scoring Engine. Research Report. ETS RR-16-30

ERIC Educational Resources Information Center

Zhang, Mo; Chen, Jing; Ruan, Chunyi

2016-01-01

Successful detection of unusual responses is critical for using machine scoring in the assessment context. This study evaluated the utility of approaches to detecting unusual responses in automated essay scoring. Two research questions were pursued. One question concerned the performance of various prescreening advisory flags, and the other…

Units of Instruction for Vocational Office Education. Volume 1. Filing, Office Machines, and General Office Clerical Occupations. Teacher's Guide.

ERIC Educational Resources Information Center

East Texas State Univ., Commerce. Occupational Curriculum Lab.

Nineteen units on filing, office machines, and general office clerical occupations are presented in this teacher's guide. The unit topics include indexing, alphabetizing, and filing (e.g., business names); labeling and positioning file folders and guides; establishing a correspondence filing system; utilizing charge-out and follow-up file systems;…

Modelling machine ensembles with discrete event dynamical system theory

NASA Technical Reports Server (NTRS)

Hunter, Dan

1990-01-01

Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

Horizontal-axis clothes washer market poised for expansion

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

George, K.L.

1994-12-31

The availability of energy- and water-efficient horizontal-axis washing machines in the North American market is growing, as US and European manufacturers position for an expected long-term market shift toward horizontal-axis (H-axis) technology. Four of the five major producers of washing machines in the US are developing or considering new H-axis models. New entrants, including US-based Staber Industries and several European manufacturers, are also expected to compete in this market. The intensified interest in H-axis technology is partly driven by speculation that new US energy efficiency standards, to be proposed in 1996 and implemented in 1999, will effectively mandate H-axis machines.more » H-axis washers typically use one-third to two-thirds less energy, water, and detergent than vertical-axis machines. Some models also reduce the energy needed to dry the laundry, since their higher spin speeds extract more water than is typical with vertical-axis designs. H-axis washing machines are the focus of two broadly-based efforts to support coordinated research and incentive programs by electric, gas, and water utilities: The High-Efficiency Laundry Metering/Marketing Analysis (THELMA), and the Consortium for Energy Efficiency (CEE) High-Efficiency Clothes Washer Initiative. These efforts may help to pave the way for new types of marketing partnerships among utilities and other parties that could help to speed adoption of H-axis washers.« less

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

Analysis of a display and control system man-machine interface concept. Volume 1: Final technical report

NASA Technical Reports Server (NTRS)

Karl, D. R.

1972-01-01

An evaluation was made of the feasibility of utilizing a simplified man machine interface concept to manage and control a complex space system involving multiple redundant computers that control multiple redundant subsystems. The concept involves the use of a CRT for display and a simple keyboard for control, with a tree-type control logic for accessing and controlling mission, systems, and subsystem elements. The concept was evaluated in terms of the Phase B space shuttle orbiter, to utilize the wide scope of data management and subsystem control inherent in the central data management subsystem provided by the Phase B design philosophy. Results of these investigations are reported in four volumes.

WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell

NASA Technical Reports Server (NTRS)

Pagni, A.; Poluzzi, R.; Rizzotto, G. G.

1992-01-01

During the last five years Fuzzy Logic has gained enormous popularity in the academic and industrial worlds. The success of this new methodology has led the microelectronics industry to create a new class of machines, called Fuzzy Machines, to overcome the limitations of traditional computing systems when utilized as Fuzzy Systems. This paper gives an overview of the methods by which Fuzzy Logic data structures are represented in the machines (each with its own advantages and inefficiencies). Next, the paper introduces WARP (Weight Associative Rule Processor) which is a dedicated VLSI megacell allowing the realization of a fuzzy controller suitable for a wide range of applications. WARP represents an innovative approach to VLSI Fuzzy controllers by utilizing different types of data structures for characterizing the membership functions during the various stages of the Fuzzy processing. WARP dedicated architecture has been designed in order to achieve high performance by exploiting the computational advantages offered by the different data representations.

Micro-machined resonator oscillator

DOEpatents

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

1994-01-01

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

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Scaling Support Vector Machines On Modern HPC Platforms

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

You, Yang; Fu, Haohuan; Song, Shuaiwen

2015-02-01

We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.

Operational results for the experimental DOE/NASA Mod-OA wind turbine project

NASA Astrophysics Data System (ADS)

Shaltens, R. K.; Birchenough, A. G.

The Mod-OA wind turbine project which was to gain early experience in the operation of large wind turbines in a utility environment is discussed. The Mod-OA wind turbines were a first generation design, and even though not cost effective, the operating experience and performance characteristics had a significant effect on the design and development of the second and third generation machines. The Mod-OA machines were modified as a result of the operational experience, particularly the blade development and control system strategy. The results of study to investigate the interaction of a Mod-OA wind turbine with an isolated diesel generation system are discussed. The machine configuration, its advantages and disadvantages and the machine performance and availability are discussed.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Operational results for the experimental DOE/NASA Mod-OA wind turbine project

NASA Technical Reports Server (NTRS)

Shaltens, R. K.; Birchenough, A. G.

1983-01-01

The Mod-OA wind turbine project which was to gain early experience in the operation of large wind turbines in a utility environment is discussed. The Mod-OA wind turbines were a first generation design, and even though not cost effective, the operating experience and performance characteristics had a significant effect on the design and development of the second and third generation machines. The Mod-OA machines were modified as a result of the operational experience, particularly the blade development and control system strategy. The results of study to investigate the interaction of a Mod-OA wind turbine with an isolated diesel generation system are discussed. The machine configuration, its advantages and disadvantages and the machine performance and availability are discussed.

Sample preparation of metal alloys by electric discharge machining

NASA Technical Reports Server (NTRS)

Chapman, G. B., II; Gordon, W. A.

1976-01-01

Electric discharge machining was investigated as a noncontaminating method of comminuting alloys for subsequent chemical analysis. Particulate dispersions in water were produced from bulk alloys at a rate of about 5 mg/min by using a commercially available machining instrument. The utility of this approach was demonstrated by results obtained when acidified dispersions were substituted for true acid solutions in an established spectrochemical method. The analysis results were not significantly different for the two sample forms. Particle size measurements and preliminary results from other spectrochemical methods which require direct aspiration of liquid into flame or plasma sources are reported.

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.

Equivalence Between Squirrel Cage and Sheet Rotor Induction Motor

NASA Astrophysics Data System (ADS)

Dwivedi, Ankita; Singh, S. K.; Srivastava, R. K.

2016-06-01

Due to topological changes in dual stator induction motor and high cost of its fabrication, it is convenient to replace the squirrel cage rotor with a composite sheet rotor. For an experimental machine, the inner and outer stator stampings are normally available whereas the procurement of rotor stampings is quite cumbersome and is not always cost effective. In this paper, the equivalence between sheet/solid rotor induction motor and squirrel cage induction motor has been investigated using layer theory of electrical machines, so as to enable one to utilize sheet/solid rotor in dual port experimental machines.

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

NASA Technical Reports Server (NTRS)

Ray, R. B.

1994-01-01

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

Machine vision 1992-1996: technology program to promote research and its utilization in industry

NASA Astrophysics Data System (ADS)

Soini, Antti J.

1994-10-01

Machine vision technology has got a strong interest in Finnish research organizations, which is resulting in many innovative products to industry. Despite this end users were very skeptical towards machine vision and its robustness for harsh industrial environments. Therefore Technology Development Centre, TEKES, who funds technology related research and development projects in universities and individual companies, decided to start a national technology program, Machine Vision 1992 - 1996. Led by industry the program boosts research in machine vision technology and seeks to put the research results to work in practical industrial applications. The emphasis is in nationally important, demanding applications. The program will create new industry and business for machine vision producers and encourage the process and manufacturing industry to take advantage of this new technology. So far 60 companies and all major universities and research centers are working on our forty different projects. The key themes that we have are process control, robot vision and quality control.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing.

PubMed

Angiuoli, Samuel V; Matalka, Malcolm; Gussman, Aaron; Galens, Kevin; Vangala, Mahesh; Riley, David R; Arze, Cesar; White, James R; White, Owen; Fricke, W Florian

2011-08-30

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

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

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

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.

Preliminary Evaluation of an Aviation Safety Thesaurus' Utility for Enhancing Automated Processing of Incident Reports

NASA Technical Reports Server (NTRS)

Barrientos, Francesca; Castle, Joseph; McIntosh, Dawn; Srivastava, Ashok

2007-01-01

This document presents a preliminary evaluation the utility of the FAA Safety Analytics Thesaurus (SAT) utility in enhancing automated document processing applications under development at NASA Ames Research Center (ARC). Current development efforts at ARC are described, including overviews of the statistical machine learning techniques that have been investigated. An analysis of opportunities for applying thesaurus knowledge to improving algorithm performance is then presented.

Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.

PubMed

Roysden, Nathaniel; Wright, Adam

2015-01-01

Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.

Summary of REAC Experience

DTIC Science & Technology

1949-09-08

error from this source can be substantially reduced by the use of polystyrene insulating materials in the plugboard system of problem patching (Section...present at some point in the machine (see Section 5b). -10- ( 1 d ~ PLUGBOARD Our experience ·with the operation of the REAC indicates that...utilization of the machine could be very significantly increased by a drastic revision of the patch bay. We propose to install a separable plugboard which

A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data

DTIC Science & Technology

2014-10-02

condition monitoring , and equipment time to failure prediction in manufacturing 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 589...Component Level Health Monitoring and Prediction One of the characteristics of a self-aware machine is to be able to detect its components...the annual conference of the prognostics and health management society. Filzmoser, P., Garrett, R. G., & Reimann, C . (2005). Mul- tivariate outlier

Induction machine

DOEpatents

Owen, Whitney H.

1980-01-01

A polyphase rotary induction machine for use as a motor or generator utilizing a single rotor assembly having two series connected sets of rotor windings, a first stator winding disposed around the first rotor winding and means for controlling the current induced in one set of the rotor windings compared to the current induced in the other set of the rotor windings. The rotor windings may be wound rotor windings or squirrel cage windings.

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

Trujillo, Angelina Michelle

Strategy, Planning, Acquiring- very large scale computing platforms come and go and planning for immensely scalable machines often precedes actual procurement by 3 years. Procurement can be another year or more. Integration- After Acquisition, machines must be integrated into the computing environments at LANL. Connection to scalable storage via large scale storage networking, assuring correct and secure operations. Management and Utilization – Ongoing operations, maintenance, and trouble shooting of the hardware and systems software at massive scale is required.

Successful Outflow Reconstruction to Salvage Traumatic Hepatic Vein-Caval Avulsion of a Normothermic Machine Ex-Situ Perfused Liver Graft

PubMed Central

Athanasopoulos, Panagiotis G.; Hadjittofi, Christopher; Dharmapala, Arinda Dinesh; Orti-Rodriguez, Rafael Jose; Ferro, Alessandra; Nasralla, David; Konstantinidou, Sofia K.; Malagó, Massimo

2016-01-01

Abstract Donor organ shortage continues to limit the availability of liver transplantation, a successful and established therapy of end-stage liver diseases. Strategies to mitigate graft shortage include the utilization of marginal livers and recently ex-situ normothermic machine perfusion devices. A 59-year-old woman with cirrhosis due to primary sclerosing cholangitis was offered an ex-situ machine perfused graft with unnoticed severe injury of the suprahepatic vasculature due to road traffic accident. Following a complex avulsion, repair and reconstruction of all donor hepatic veins as well as the suprahepatic inferior vena cava, the patient underwent a face-to-face piggy-back orthotopic liver transplantation and was discharged on the 11th postoperative day after an uncomplicated recovery. This report illustrates the operative technique to utilize an otherwise unusable organ, in the current environment of donor shortage and declining graft quality. Normothermic machine perfusion can definitely play a role in increasing the graft pool, without compromising the quality of livers who had vascular or other damage before being ex-situ perfused. Furthermore, it emphasizes the importance of promptly and thoroughly communicating organ injuries, as well as considering all reconstructive options within the level of expertise at the recipient center. PMID:27082550

The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population.

PubMed

Mateen, Bilal Akhter; Bussas, Matthias; Doogan, Catherine; Waller, Denise; Saverino, Alessia; Király, Franz J; Playford, E Diane

2018-05-01

To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Prospective cohort study. Tertiary neurological and neurosurgical center. In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.

Successful Outflow Reconstruction to Salvage Traumatic Hepatic Vein-Caval Avulsion of a Normothermic Machine Ex-Situ Perfused Liver Graft: Case Report and Management of Organ Pool Challenges.

PubMed

Athanasopoulos, Panagiotis G; Hadjittofi, Christopher; Dharmapala, Arinda Dinesh; Orti-Rodriguez, Rafael Jose; Ferro, Alessandra; Nasralla, David; Konstantinidou, Sofia K; Malagó, Massimo

2016-04-01

Donor organ shortage continues to limit the availability of liver transplantation, a successful and established therapy of end-stage liver diseases. Strategies to mitigate graft shortage include the utilization of marginal livers and recently ex-situ normothermic machine perfusion devices. A 59-year-old woman with cirrhosis due to primary sclerosing cholangitis was offered an ex-situ machine perfused graft with unnoticed severe injury of the suprahepatic vasculature due to road traffic accident. Following a complex avulsion, repair and reconstruction of all donor hepatic veins as well as the suprahepatic inferior vena cava, the patient underwent a face-to-face piggy-back orthotopic liver transplantation and was discharged on the 11th postoperative day after an uncomplicated recovery. This report illustrates the operative technique to utilize an otherwise unusable organ, in the current environment of donor shortage and declining graft quality. Normothermic machine perfusion can definitely play a role in increasing the graft pool, without compromising the quality of livers who had vascular or other damage before being ex-situ perfused. Furthermore, it emphasizes the importance of promptly and thoroughly communicating organ injuries, as well as considering all reconstructive options within the level of expertise at the recipient center.

SLURM: Simple Linux Utility for Resource Management

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

Jette, M; Grondona, M

2002-12-19

Simple Linux Utility for Resource Management (SLURM) is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for Linux clusters of thousands of nodes. Components include machine status, partition management, job management, scheduling and stream copy modules. This paper presents an overview of the SLURM architecture and functionality.

SLURM: Simplex Linux Utility for Resource Management

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

Jette, M; Grondona, M

2003-04-22

Simple Linux Utility for Resource Management (SLURM) is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for Linux clusters of thousands of nodes. Components include machine status, partition management, job management, scheduling, and stream copy modules. This paper presents an overview of the SLURM architecture and functionality.

Functional and Software Considerations for Bibliographic Data Base Utilization.

ERIC Educational Resources Information Center

Cadwallader, Gouverneur

This is the fourth in a series of eight reports of a research study for the National Agricultural Library (NAL) on the effective utilization of bibliographic data bases in machine-readable form. It describes the general functional and software requirements of an NAL system using external sources of bibliographic data. Various system design…

Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

PubMed

Garcia-Garcia, Martha G; Bergquist, Austin J; Vargas-Perez, Hector; Nagai, Mary K; Zariffa, Jose; Marquez-Chin, Cesar; Popovic, Milos R

2017-11-01

Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.

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

Curry, Bennett

The Arizona Commerce Authority (ACA) conducted an Innovation in Advanced Manufacturing Grant Competition to support and grow southern and central Arizona’s Aerospace and Defense (A&D) industry and its supply chain. The problem statement for this grant challenge was that many A&D machining processes utilize older generation CNC machine tool technologies that can result an inefficient use of resources – energy, time and materials – compared to the latest state-of-the-art CNC machines. Competitive awards funded projects to develop innovative new tools and technologies that reduce energy consumption for older generation machine tools and foster working relationships between industry small to medium-sizedmore » manufacturing enterprises and third-party solution providers. During the 42-month term of this grant, 12 competitive awards were made. Final reports have been included with this submission.« less

Parameter monitoring compensation system and method

DOEpatents

Barkman, William E.; Babelay, Edwin F.; DeMint, Paul D.; Hebble, Thomas L.; Igou, Richard E.; Williams, Richard R.; Klages, Edward J.; Rasnick, William H.

1995-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along preprogrammed path during a machining operation utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer.

Automated inspection and precision grinding of spiral bevel gears

NASA Technical Reports Server (NTRS)

Frint, Harold

1987-01-01

The results are presented of a four phase MM&T program to define, develop, and evaluate an improved inspection system for spiral bevel gears. The improved method utilizes a multi-axis coordinate measuring machine which maps the working flank of the tooth and compares it to nominal reference values stored in the machine's computer. A unique feature of the system is that corrective grinding machine settings can be automatically calculated and printed out when necessary to correct an errant tooth profile. This new method eliminates most of the subjective decision making involved in the present method, which compares contact patterns obtained when the gear set is run under light load in a rolling test machine. It produces a higher quality gear with significant inspection time and cost savings.

A Novel Transverse Flux Machine for Vehicle Traction Applications

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

Wan, Zhao; Ahmed, Adeeb; Husain, Iqbal

2015-10-05

A novel transverse flux machine topology for electric vehicle traction application using ferrite magnets is presented in this paper. The proposed transverse flux topology utilizes novel magnet arrangements in the rotor that are similar to Halbach-array to boost flux linkage; on the stator side, cores are alternately arranged around a pair of ring windings in each phase to make use of the entire rotor flux that eliminates end windings. Analytical design considerations and finite element methods are used for an optimized design of a scooter in-wheel motor. Simulation results from Finite Element Analysis (FEA) show the motor achieved comparable torquemore » density to conventional rare-earth permanent magnet machines. This machine is a viable candidate for direct drive applications with low cost and high torque density.« less

Grumman WS33 wind system: prototype construction and testing, Phase II technical report

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

Adler, F.M.; Henton, P.; King, P.W.

1980-11-01

The prototype fabrication and testing of the 8 kW small wind energy conversion system are reported. The turbine is a three-bladed, down-wind machine designed to interface directly with an electrical utility network. The machine as finally fabricated is rated at 15 kW at 24 mpH and peak power of 18 kW at 35 mph. Utility compatible electrical power is generated in winds between a cut-in speed of 9 mph and a cut-out speed of 35 mph by using the torque characteristics of the unit's induction generator combined with the rotor aerodynamics to maintain essentially constant speed. Inspection procedures, pre-delivery testing,more » and a cost analysis are included.« less

Fabrication of Flex Joint Utilizing Additively Manufactured Parts

NASA Technical Reports Server (NTRS)

Eddleman, David; Richard, Jim

2015-01-01

The Selective Laser Melting (SLM) manufacturing technique has been utilized in the manufacture of a flex joint typical of those found in rocket engine and main propulsion system ducting. The SLM process allowed for the combination of parts that are typically machined separately and welded together. This resulted in roughly a 65% reduction of the total number of parts, roughly 70% reduction in the total number of welds, and an estimated 60% reduction in the number of machining operations. The majority of the new design was in three SLM pieces. These pieces, as well as a few traditionally fabricated parts, were assembled into a complete unit, which has been pressure tested. The design and planned cryogenic testing of the unit will be presented.

Comparative implementation of Handwritten and Machine written Gurmukhi text utilizing appropriate parameters

NASA Astrophysics Data System (ADS)

Kaur, Jaswinder; Jagdev, Gagandeep, Dr.

2018-01-01

Optical character recognition is concerned with the recognition of optically processed characters. The recognition is done offline after the writing or printing has been completed, unlike online recognition where the computer has to recognize the characters instantly as they are drawn. The performance of character recognition depends upon the quality of scanned documents. The preprocessing steps are used for removing low-frequency background noise and normalizing the intensity of individual scanned documents. Several filters are used for reducing certain image details and enabling an easier or faster evaluation. The primary aim of the research work is to recognize handwritten and machine written characters and differentiate them. The language opted for the research work is Punjabi Gurmukhi and tool utilized is Matlab.

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.

Sequence invariant state machines

NASA Technical Reports Server (NTRS)

Whitaker, S.; Manjunath, S.

1990-01-01

A synthesis method and new VLSI architecture are introduced to realize sequential circuits that have the ability to implement any state machine having N states and m inputs, regardless of the actual sequence specified in the flow table. A design method is proposed that utilizes BTS logic to implement regular and dense circuits. A given state sequence can be programmed with power supply connections or dynamically reallocated if stored in a register. Arbitrary flow table sequences can be modified or programmed to dynamically alter the function of the machine. This allows VLSI controllers to be designed with the programmability of a general purpose processor but with the compact size and performance of dedicated logic.

Sequence-invariant state machines

NASA Technical Reports Server (NTRS)

Whitaker, Sterling R.; Manjunath, Shamanna K.; Maki, Gary K.

1991-01-01

A synthesis method and an MOS VLSI architecture are presented to realize sequential circuits that have the ability to implement any state machine having N states and m inputs, regardless of the actual sequence specified in the flow table. The design method utilizes binary tree structured (BTS) logic to implement regular and dense circuits. The desired state sequence can be hardwired with power supply connections or can be dynamically reallocated if stored in a register. This allows programmable VLSI controllers to be designed with a compact size and performance approaching that of dedicated logic. Results of ICV implementations are reported and an example sequence-invariant state machine is contrasted with implementations based on traditional methods.

Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

PubMed Central

Yu, Yang; Niederleithinger, Ernst; Li, Jianchun; Wiggenhauser, Herbert

2017-01-01

This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%. PMID:29258274

Direct Digital Manufacturing of Integrated Naval Systems Using Ultrasonic Consolidation, Support Material Deposition and Direct Write Technologies

DTIC Science & Technology

2012-02-17

tool should be combined with a user-friendly Windows-based software interface that utilizes the best practices for process planning developed by us and...best practices developed through this project, resulting in the commercial availability of machines for the Navy and others. These machines will...research 2011 Outstanding Paper Award, VRAP 2011, for paper "Some Studies on Dislocation Density based Finite Element Modeling of Ultrasonic

18 CFR 125.2 - General instructions.

Code of Federal Regulations, 2011 CFR

2011-04-01

... stored on machine readable media. Internal control procedures must be documented by a responsible... associated companies. Public utilities and licensees must assure the availability of records of services...

An experimental investigation on orthogonal cutting of hybrid CFRP/Ti stacks

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2016-10-01

Hybrid CFRP/Ti stack has been widely used in the modern aerospace industry owing to its superior mechanical/physical properties and excellent structural functions. Several applications require mechanical machining of these hybrid composite stacks in order to achieve dimensional accuracy and assembly performance. However, machining of such composite-to-metal alliance is usually an extremely challenging task in the manufacturing sectors due to the disparate natures of each stacked constituent and their respective poor machinability. Special issues may arise from the high force/heat generation, severe subsurface damage and rapid tool wear. To study the fundamental mechanisms controlling the bi-material machining, this paper presented an experimental study on orthogonal cutting of hybrid CFRP/Ti stack by using superior polycrystalline diamond (PCD) tipped tools. The utilized cutting parameters for hybrid CFRP/Ti machining were rigorously adopted through a compromise selection due to the disparate machinability behaviors of the CFRP laminate and Ti alloy. The key cutting responses in terms of cutting force generation, machined surface quality and tool wear mechanism were precisely addressed. The experimental results highlighted the involved five stages of CFRP/Ti cutting and the predominant crater wear and edge fracture failure governing the PCD cutting process.

Dynamic Resource Allocation and Access Class Barring Scheme for Delay-Sensitive Devices in Machine to Machine (M2M) Communications.

PubMed

Li, Ning; Cao, Chao; Wang, Cong

2017-06-15

Supporting simultaneous access of machine-type devices is a critical challenge in machine-to-machine (M2M) communications. In this paper, we propose an optimal scheme to dynamically adjust the Access Class Barring (ACB) factor and the number of random access channel (RACH) resources for clustered machine-to-machine (M2M) communications, in which Delay-Sensitive (DS) devices coexist with Delay-Tolerant (DT) ones. In M2M communications, since delay-sensitive devices share random access resources with delay-tolerant devices, reducing the resources consumed by delay-sensitive devices means that there will be more resources available to delay-tolerant ones. Our goal is to optimize the random access scheme, which can not only satisfy the requirements of delay-sensitive devices, but also take the communication quality of delay-tolerant ones into consideration. We discuss this problem from the perspective of delay-sensitive services by adjusting the resource allocation and ACB scheme for these devices dynamically. Simulation results show that our proposed scheme realizes good performance in satisfying the delay-sensitive services as well as increasing the utilization rate of the random access resources allocated to them.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Cutting tool form compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F. Jr.; Klages, E.J.

1993-10-19

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed. 9 figures.

Micro-machined resonator oscillator

DOEpatents

Koehler, D.R.; Sniegowski, J.J.; Bivens, H.M.; Wessendorf, K.O.

1994-08-16

A micro-miniature resonator-oscillator is disclosed. Due to the miniaturization of the resonator-oscillator, oscillation frequencies of one MHz and higher are utilized. A thickness-mode quartz resonator housed in a micro-machined silicon package and operated as a telemetered sensor beacon'' that is, a digital, self-powered, remote, parameter measuring-transmitter in the FM-band. The resonator design uses trapped energy principles and temperature dependence methodology through crystal orientation control, with operation in the 20--100 MHz range. High volume batch-processing manufacturing is utilized, with package and resonator assembly at the wafer level. Unique design features include squeeze-film damping for robust vibration and shock performance, capacitive coupling through micro-machined diaphragms allowing resonator excitation at the package exterior, circuit integration and extremely small (0.1 in. square) dimensioning. A family of micro-miniature sensor beacons is also disclosed with widespread applications as bio-medical sensors, vehicle status monitors and high-volume animal identification and health sensors. The sensor family allows measurement of temperatures, chemicals, acceleration and pressure. A microphone and clock realization is also available. 21 figs.

Cutting tool form compensaton system and method

DOEpatents

Barkman, William E.; Babelay, Jr., Edwin F.; Klages, Edward J.

1993-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed.

Classifying the Indication for Colonoscopy Procedures: A Comparison of NLP Approaches in a Diverse National Healthcare System.

PubMed

Patterson, Olga V; Forbush, Tyler B; Saini, Sameer D; Moser, Stephanie E; DuVall, Scott L

2015-01-01

In order to measure the level of utilization of colonoscopy procedures, identifying the primary indication for the procedure is required. Colonoscopies may be utilized not only for screening, but also for diagnostic or therapeutic purposes. To determine whether a colonoscopy was performed for screening, we created a natural language processing system to identify colonoscopy reports in the electronic medical record system and extract indications for the procedure. A rule-based model and three machine-learning models were created using 2,000 manually annotated clinical notes of patients cared for in the Department of Veterans Affairs. Performance of the models was measured and compared. Analysis of the models on a test set of 1,000 documents indicates that the rule-based system performance stays fairly constant as evaluated on training and testing sets. However, the machine learning model without feature selection showed significant decrease in performance. Therefore, rule-based classification system appears to be more robust than a machine-learning system in cases when no feature selection is performed.

Exploring the influence of constitutive models and associated parameters for the orthogonal machining of Ti6Al4V

NASA Astrophysics Data System (ADS)

Pervaiz, S.; Anwar, S.; Kannan, S.; Almarfadi, A.

2018-04-01

Ti6Al4V is known as difficult-to-cut material due to its inherent properties such as high hot hardness, low thermal conductivity and high chemical reactivity. Though, Ti6Al4V is utilized by industrial sectors such as aeronautics, energy generation, petrochemical and bio-medical etc. For the metal cutting community, competent and cost-effective machining of Ti6Al4V is a challenging task. To optimize cost and machining performance for the machining of Ti6Al4V, finite element based cutting simulation can be a very useful tool. The aim of this paper is to develop a finite element machining model for the simulation of Ti6Al4V machining process. The study incorporates material constitutive models namely Power Law (PL) and Johnson – Cook (JC) material models to mimic the mechanical behaviour of Ti6Al4V. The study investigates cutting temperatures, cutting forces, stresses, and plastic strains with respect to different PL and JC material models with associated parameters. In addition, the numerical study also integrates different cutting tool rake angles in the machining simulations. The simulated results will be beneficial to draw conclusions for improving the overall machining performance of Ti6Al4V.

COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.

ERIC Educational Resources Information Center

Gratch, Jonathan; DeJong, Gerald

In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge.…

Design Tools for Evaluating Multiprocessor Programs

DTIC Science & Technology

1976-07-01

than large uniprocessing machines, and 2. economies of scale in manufacturing. Perhaps the most compelling reason (possibly a consequence of the...speed, redundancy, (inefficiency, resource utilization, and economies of the components. [Browne 73, Lehman 66] 6. How can the system be scheduled...mejsures are interesting about the computation? Somn may be: speed, redundancy, (inefficiency, resource utilization, and economies of the components

An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud

NASA Astrophysics Data System (ADS)

Shenbaga Moorthy, Rajalakshmi; Fareentaj, U.; Divya, T. K.

2017-08-01

Cloud computing provides an effective way to dynamically provide numerous resources to meet customer demands. A major challenging problem for cloud providers is designing efficient mechanisms for optimal virtual machine Placement (OVMP). Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. In order to provide appropriate resources to the clients an optimal virtual machine placement algorithm is proposed. Virtual machine placement is NP-Hard problem. Such NP-Hard problem can be solved using heuristic algorithm. In this paper, Ant Colony Optimization based virtual machine placement is proposed. Our proposed system focuses on minimizing the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment and the response time of each cloud provider is monitored periodically, in such a way to minimize delay in providing the resources to the users. The performance of the proposed algorithm is compared with greedy mechanism. The proposed algorithm is simulated in Eclipse IDE. The results clearly show that the proposed algorithm minimizes the cost, response time and also number of migrations.

Mechanical design of walking machines.

PubMed

Arikawa, Keisuke; Hirose, Shigeo

2007-01-15

The performance of existing actuators, such as electric motors, is very limited, be it power-weight ratio or energy efficiency. In this paper, we discuss the method to design a practical walking machine under this severe constraint with focus on two concepts, the gravitationally decoupled actuation (GDA) and the coupled drive. The GDA decouples the driving system against the gravitational field to suppress generation of negative power and improve energy efficiency. On the other hand, the coupled drive couples the driving system to distribute the output power equally among actuators and maximize the utilization of installed actuator power. First, we depict the GDA and coupled drive in detail. Then, we present actual machines, TITAN-III and VIII, quadruped walking machines designed on the basis of the GDA, and NINJA-I and II, quadruped wall walking machines designed on the basis of the coupled drive. Finally, we discuss walking machines that travel on three-dimensional terrain (3D terrain), which includes the ground, walls and ceiling. Then, we demonstrate with computer simulation that we can selectively leverage GDA and coupled drive by walking posture control.

Hardware assisted hypervisor introspection.

PubMed

Shi, Jiangyong; Yang, Yuexiang; Tang, Chuan

2016-01-01

In this paper, we introduce hypervisor introspection, an out-of-box way to monitor the execution of hypervisors. Similar to virtual machine introspection which has been proposed to protect virtual machines in an out-of-box way over the past decade, hypervisor introspection can be used to protect hypervisors which are the basis of cloud security. Virtual machine introspection tools are usually deployed either in hypervisor or in privileged virtual machines, which might also be compromised. By utilizing hardware support including nested virtualization, EPT protection and #BP, we are able to monitor all hypercalls belongs to the virtual machines of one hypervisor, include that of privileged virtual machine and even when the hypervisor is compromised. What's more, hypercall injection method is used to simulate hypercall-based attacks and evaluate the performance of our method. Experiment results show that our method can effectively detect hypercall-based attacks with some performance cost. Lastly, we discuss our furture approaches of reducing the performance cost and preventing the compromised hypervisor from detecting the existence of our introspector, in addition with some new scenarios to apply our hypervisor introspection system.

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

PubMed Central

2011-01-01

Background Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. Results We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. Conclusion The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing. PMID:21878105

17 CFR 256.308 - Office furniture and equipment.

Code of Federal Regulations, 2011 CFR

2011-04-01

... UTILITY HOLDING COMPANY ACT OF 1935 Service Company Property Accounts § 256.308 Office furniture and... equipment, accounting machines, electronic claculators, typewriters and other mechanical office equipment. ...

17 CFR 256.308 - Office furniture and equipment.

Code of Federal Regulations, 2010 CFR

2010-04-01

... UTILITY HOLDING COMPANY ACT OF 1935 Service Company Property Accounts § 256.308 Office furniture and... equipment, accounting machines, electronic claculators, typewriters and other mechanical office equipment. ...

Novel Transverse Flux Machine for Vehicle Traction Applications: Preprint

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

Wan, Z.; Ahmed, A.; Husain, I.

2015-04-02

A novel transverse flux machine topology for electric vehicle traction applications using ferrite magnets is presented in this paper. The proposed transverse flux topology utilizes novel magnet arrangements in the rotor that are similar to the Halbach array to boost flux linkage; on the stator side, cores are alternately arranged around a pair of ring windings in each phase to make use of the entire rotor flux that eliminates end windings. Analytical design considerations and finite-element methods are used for an optimized design of a scooter in-wheel motor. Simulation results from finite element analysis (FEA) show that the motor achievedmore » comparable torque density to conventional rare-earth permanent magnet (PM) machines. This machine is a viable candidate for direct-drive applications with low cost and high torque density.« less

Parameter monitoring compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F.; DeMint, P.D.; Hebble, T.L.; Igou, R.E.; Williams, R.R.; Klages, E.J.; Rasnick, W.H.

1995-02-07

A compensation system is described for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation. It utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer. 7 figs.

Induction motor control

NASA Technical Reports Server (NTRS)

Hansen, Irving G.

1990-01-01

Electromechanical actuators developed to date have commonly utilized permanent magnet (PM) synchronous motors. More recently switched reluctance (SR) motors have been advocated due to their robust characteristics. Implications of work which utilizes induction motors and advanced control techniques are discussed. When induction motors are operated from an energy source capable of controlling voltages and frequencies independently, drive characteristics are obtained which are superior to either PM or SR motors. By synthesizing the machine frequency from a high frequency carrier (nominally 20 kHz), high efficiencies, low distortion, and rapid torque response are available. At this time multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of aerospace actuators. This effort is based upon high frequency power distribution and management techniques developed by NASA for Space Station Freedom.

Manufacturing and Machining Challenges of Hybrid Aluminium Metal Matix Composites

NASA Astrophysics Data System (ADS)

Baburaja, Kammuluri; Sainadh Teja, S.; Karthik Sri, D.; Kuldeep, J.; Gowtham, V.

2017-08-01

Manufacturing which involves material removal processes or material addition processes or material transformation processes. One or all the processes to obtain the final desired properties for a material with desired shape which meets the required precision and accuracy values for the expected service life of a material in working conditions. Researchers found the utility of aluminium to be the second largest after steel. Aluminium and its metal matrix composite possess wide applications in various applications in aerospace industry, automobile industry, Constructions and even in kitchen utensils. Hybrid Al-MMCconsist of two different materials, and one will be from organic origin along with the base material. In this paper an attempt is made to bring out the importance of utilization of aluminium and the challenges concerned in manufacturing and machining of hybrid aluminium MMC.

Survey of beam instrumentation used in SLC

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

Ecklund, S.D.

A survey of beam instruments used at SLAC in the SLC machine is presented. The basic utility and operation of each device is briefly described. The various beam instruments used at the Stanford Linear Collider (SLC), can be classified by the function they perform. Beam intensity, position and size are typical of the parameters of beam which are measured. Each type of parameter is important for adjusting or tuning the machine in order to achieve optimum performance. 39 refs.

Lathe tool bit and holder for machining fiberglass materials

NASA Technical Reports Server (NTRS)

Winn, L. E. (Inventor)

1972-01-01

A lathe tool and holder combination for machining resin impregnated fiberglass cloth laminates is described. The tool holder and tool bit combination is designed to accommodate a conventional carbide-tipped, round shank router bit as the cutting medium, and provides an infinite number of cutting angles in order to produce a true and smooth surface in the fiberglass material workpiece with every pass of the tool bit. The technique utilizes damaged router bits which ordinarily would be discarded.

Passenger baggage object database (PBOD)

NASA Astrophysics Data System (ADS)

Gittinger, Jaxon M.; Suknot, April N.; Jimenez, Edward S.; Spaulding, Terry W.; Wenrich, Steve A.

2018-04-01

Detection of anomalies of interest in x-ray images is an ever-evolving problem that requires the rapid development of automatic detection algorithms. Automatic detection algorithms are developed using machine learning techniques, which would require developers to obtain the x-ray machine that was used to create the images being trained on, and compile all associated metadata for those images by hand. The Passenger Baggage Object Database (PBOD) and data acquisition application were designed and developed for acquiring and persisting 2-D and 3-D x-ray image data and associated metadata. PBOD was specifically created to capture simulated airline passenger "stream of commerce" luggage data, but could be applied to other areas of x-ray imaging to utilize machine-learning methods.

Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations

NASA Astrophysics Data System (ADS)

Miyazato, Itsuki; Tanaka, Yuzuru; Takahashi, Keisuke

2018-02-01

Two-dimensional (2D) magnets are explored in terms of data science and first principle calculations. Machine learning determines four descriptors for predicting the magnetic moments of 2D materials within reported 216 2D materials data. With the trained machine, 254 2D materials are predicted to have high magnetic moments. First principle calculations are performed to evaluate the predicted 254 2D materials where eight undiscovered stable 2D materials with high magnetic moments are revealed. The approach taken in this work indicates that undiscovered materials can be surfaced by utilizing data science and materials data, leading to an innovative way of discovering hidden materials.

SLURM: Simple Linux Utility for Resource Management

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

Jette, M; Dunlap, C; Garlick, J

2002-07-08

Simple Linux Utility for Resource Management (SLURM) is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for Linux clusters of thousands of nodes. Components include machine status, partition management, job management, scheduling and stream copy modules. The design also includes a scalable, general-purpose communication infrastructure. This paper presents a overview of the SLURM architecture and functionality.

Soldier-Machine Interface for the Army Future Combat System: Literature Review, Requirements, and Emerging Design Principles

DTIC Science & Technology

2003-04-01

Development vs . Iterative Design ............................ II-7 3. Getting to Know the User: Designing for Usability, Utility, and Pleasure...III-1 2. Terrain Focus .................................................................................... III-1 3. Display vs . Control...heterogeneous, and it diverged into broad philosophical issues, such as “design as engineering” vs . “design as art” and the utility of controlled

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

PubMed

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

2018-02-01

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

Using PVM to host CLIPS in distributed environments

NASA Technical Reports Server (NTRS)

Myers, Leonard; Pohl, Kym

1994-01-01

It is relatively easy to enhance CLIPS (C Language Integrated Production System) to support multiple expert systems running in a distributed environment with heterogeneous machines. The task is minimized by using the PVM (Parallel Virtual Machine) code from Oak Ridge Labs to provide the distributed utility. PVM is a library of C and FORTRAN subprograms that supports distributive computing on many different UNIX platforms. A PVM deamon is easily installed on each CPU that enters the virtual machine environment. Any user with rsh or rexec access to a machine can use the one PVM deamon to obtain a generous set of distributed facilities. The ready availability of both CLIPS and PVM makes the combination of software particularly attractive for budget conscious experimentation of heterogeneous distributive computing with multiple CLIPS executables. This paper presents a design that is sufficient to provide essential message passing functions in CLIPS and enable the full range of PVM facilities.

Liquid lens: advances in adaptive optics

NASA Astrophysics Data System (ADS)

Casey, Shawn Patrick

2010-12-01

'Liquid lens' technologies promise significant advancements in machine vision and optical communications systems. Adaptations for machine vision, human vision correction, and optical communications are used to exemplify the versatile nature of this technology. Utilization of liquid lens elements allows the cost effective implementation of optical velocity measurement. The project consists of a custom image processor, camera, and interface. The images are passed into customized pattern recognition and optical character recognition algorithms. A single camera would be used for both speed detection and object recognition.

Phenomenology tools on cloud infrastructures using OpenStack

NASA Astrophysics Data System (ADS)

Campos, I.; Fernández-del-Castillo, E.; Heinemeyer, S.; Lopez-Garcia, A.; Pahlen, F.; Borges, G.

2013-04-01

We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage "virtual" machines on which the phenomenology codes/tools can be deployed easily in an automated way. We analyze the performance of this environment based on "virtual" machines versus the utilization of physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.

A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

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

Hamann, Hendrik F.

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

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.

A bio-inspired approach for the design of a multifunctional robotic end-effector customized for automated maintenance of a reconfigurable vibrating screen.

PubMed

Makinde, O A; Mpofu, K; Vrabic, R; Ramatsetse, B I

2017-01-01

The development of a robotic-driven maintenance solution capable of automatically maintaining reconfigurable vibrating screen (RVS) machine when utilized in dangerous and hazardous underground mining environment has called for the design of a multifunctional robotic end-effector capable of carrying out all the maintenance tasks on the RVS machine. In view of this, the paper presents a bio-inspired approach which unfolds the design of a novel multifunctional robotic end-effector embedded with mechanical and control mechanisms capable of automatically maintaining the RVS machine. To achieve this, therblig and morphological methodologies (which classifies the motions as well as the actions required by the robotic end-effector in carrying out RVS machine maintenance tasks), obtained from a detailed analogy of how human being (i.e. a machine maintenance manager) will carry out different maintenance tasks on the RVS machine, were used to obtain the maintenance objective functions or goals of the multifunctional robotic end-effector as well as the maintenance activity constraints of the RVS machine that must be adhered to by the multifunctional robotic end-effector during the machine maintenance. The results of the therblig and morphological analyses of five (5) different maintenance tasks capture and classify one hundred and thirty-four (134) repetitive motions and fifty-four (54) functions required in automating the maintenance tasks of the RVS machine. Based on these findings, a worm-gear mechanism embedded with fingers extruded with a hexagonal shaped heads capable of carrying out the "gripping and ungrasping" and "loosening and bolting" functions of the robotic end-effector and an electric cylinder actuator module capable of carrying out "unpinning and hammering" functions of the robotic end-effector were integrated together to produce the customized multifunctional robotic end-effector capable of automatically maintaining the RVS machine. The axial forces ([Formula: see text] and [Formula: see text]), normal forces ([Formula: see text]) and total load [Formula: see text] acting on the teeth of the worm-gear module of the multifunctional robotic end-effector during the gripping of worn-out or new RVS machine subsystems, which are 978.547, 1245.06 and 1016.406 N, respectively, were satisfactory. The nominal bending and torsional stresses acting on the shoulder of the socket module of the multifunctional robotic end-effector during the loosing and tightening of bolts, which are 1450.72 and 179.523 MPa, respectively, were satisfactory. The hammering and unpinning forces utilized by the electric cylinder actuator module of the multifunctional robotic end-effector during the unpinning and hammering of screen panel pins out of and into the screen panels were satisfactory.

Comparison of a pocket-size ultrasound device with a premium ultrasound machine: diagnostic value and time required in bedside ultrasound examination.

PubMed

Stock, Konrad Friedrich; Klein, Bettina; Steubl, Dominik; Lersch, Christian; Heemann, Uwe; Wagenpfeil, Stefan; Eyer, Florian; Clevert, Dir-Andre

2015-10-01

Time savings and clinical accuracy of a new miniature ultrasound device was investigated utilizing comparison with conventional high-end ultrasound instruments. Our objective was to determine appropriate usage and limitations of this diagnostic tool in internal medicine. We investigated 28 patients from the internal-medicine department. Patients were examined with the Acuson P10 portable device and a Sonoline Antares instrument in a cross-over design. All investigations were carried out at the bedside; the results were entered on a standardized report form. The time for the ultrasound examination (transfer time, setting up and disassembly, switching on and off, and complete investigation time) was recorded separately. Mean time for overall examination per patient with the portable ultrasound device was shorter (25.0 ± 4.5 min) than with the high-end machine (29.4 ± 4.4 min; p < 0.001). When measuring the size of liver, spleen, and kidneys, the values obtained differed significantly between portable device and the high-end instrument. In our study, we identified 113 pathological ultrasound findings with the high-end ultrasound machine, while 82 pathological findings (73%) were concordantly detected with the portable ultrasound device. The main diagnostic strengths of the portable device were in the detection of ascites (sensitivity 80%), diagnosis of fatty liver, and identification of severe parenchymal liver damage. The clinical utility of portable ultrasound machines is limited. There will be clinical roles for distinct clinical questions such as detection of ascites or pleural effusion when used by experienced examiners. However, sensitivity in detecting multiple pathologies is not comparable to high-end ultrasound machines.

Progress on big data publication and documentation for machine-to-machine discovery, access, and processing

NASA Astrophysics Data System (ADS)

Walker, J. I.; Blodgett, D. L.; Suftin, I.; Kunicki, T.

2013-12-01

High-resolution data for use in environmental modeling is increasingly becoming available at broad spatial and temporal scales. Downscaled climate projections, remotely sensed landscape parameters, and land-use/land-cover projections are examples of datasets that may exceed an individual investigation's data management and analysis capacity. To allow projects on limited budgets to work with many of these data sets, the burden of working with them must be reduced. The approach being pursued at the U.S. Geological Survey Center for Integrated Data Analytics uses standard self-describing web services that allow machine to machine data access and manipulation. These techniques have been implemented and deployed in production level server-based Web Processing Services that can be accessed from a web application or scripted workflow. Data publication techniques that allow machine-interpretation of large collections of data have also been implemented for numerous datasets at U.S. Geological Survey data centers as well as partner agencies and academic institutions. Discovery of data services is accomplished using a method in which a machine-generated metadata record holds content--derived from the data's source web service--that is intended for human interpretation as well as machine interpretation. A distributed search application has been developed that demonstrates the utility of a decentralized search of data-owner metadata catalogs from multiple agencies. The integrated but decentralized system of metadata, data, and server-based processing capabilities will be presented. The design, utility, and value of these solutions will be illustrated with applied science examples and success stories. Datasets such as the EPA's Integrated Climate and Land Use Scenarios, USGS/NASA MODIS derived land cover attributes, and downscaled climate projections from several sources are examples of data this system includes. These and other datasets, have been published as standard, self-describing, web services that provide the ability to inspect and subset the data. This presentation will demonstrate this file-to-web service concept and how it can be used from script-based workflows or web applications.

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

NASA Astrophysics Data System (ADS)

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

2009-10-01

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

Nanometric edge profile measurement of cutting tools on a diamond turning machine

NASA Astrophysics Data System (ADS)

Asai, Takemi; Arai, Yoshikazu; Cui, Yuguo; Gao, Wei

2008-10-01

Single crystal diamond tools are used for fabrication of precision parts [1-5]. Although there are many types of tools that are supplied, the tools with round nose are popular for machining very smooth surfaces. Tools with small nose radii, small wedge angles and included angles are also being utilized for fabrication of micro structured surfaces such as microlens arrays [6], diffractive optical elements and so on. In ultra precision machining, tools are very important as a part of the machining equipment. The roughness or profile of machined surface may become out of desired tolerance. It is thus necessary to know the state of the tool edge accurately. To meet these requirements, an atomic force microscope (AFM) for measuring the 3D edge profiles of tools having nanometer-scale cutting edge radii with high resolution has been developed [7-8]. Although the AFM probe unit is combined with an optical sensor for aligning the measurement probe with the tools edge top to be measured in short time in this system, this time only the AFM probe unit was used. During the measurement time, that was attached onto the ultra precision turning machine to confirm the possibility of profile measurement system.

Automated Ordering System.

ERIC Educational Resources Information Center

Jones, Richard M.

1981-01-01

A computer program that utilizes an optical scanning machine is used for ordering supplies in a Louisiana school system. The program provides savings in time and labor, more accurate data, and easy-to-use reports. (Author/MLF)

Assessment of multi-wildfire occurrence data for machine learning based risk modelling

NASA Astrophysics Data System (ADS)

Lim, C. H.; Kim, M.; Kim, S. J.; Yoo, S.; Lee, W. K.

2017-12-01

The occurrence of East Asian wildfires is mainly caused by human-activities, but the extreme drought increased due to the climate change caused wildfires and they spread to large-scale fires. Accurate occurrence location data is required for modelling wildfire probability and risk. In South Korea, occurrence data surveyed through KFS (Korea Forest Service) and MODIS (MODerate-resolution Imaging Spectroradiometer) satellite-based active fire data can be utilized. In this study, two sorts of wildfire occurrence data were applied to select suitable occurrence data for machine learning based wildfire risk modelling. MaxEnt (Maximum Entropy) model based on machine learning is used for wildfire risk modelling, and two types of occurrence data and socio-economic and climate-environment data are applied to modelling. In the results with KFS survey based data, the low relationship was shown with climate-environmental factors, and the uncertainty of coordinate information appeared. The MODIS-based active fire data were found outside the forests, and there were a lot of spots that did not match the actual wildfires. In order to utilize MODIS-based active fire data, it was necessary to extract forest area and utilize only high-confidence level data. In KFS data, it was necessary to separate the analysis according to the damage scale to improve the modelling accuracy. Ultimately, it is considered to be the best way to simulate the wildfire risk by constructing more accurate information by combining two sorts of wildfire occurrence data.

THE COMPUTER AS A MANAGEMENT TOOL--PHYSICAL FACILITIES INVENTORIES, UTILIZATION, AND PROJECTIONS. 11TH ANNUAL MACHINE RECORDS CONFERENCE PROCEEDINGS (UNIVERSITY OF TENNESSEE, KNOXVILLE, APRIL 25-27, 1966).

ERIC Educational Resources Information Center

WITMER, DAVID R.

WISCONSIN STATE UNIVERSITIES HAVE BEEN USING THE COMPUTER AS A MANAGEMENT TOOL TO STUDY PHYSICAL FACILITIES INVENTORIES, SPACE UTILIZATION, AND ENROLLMENT AND PLANT PROJECTIONS. EXAMPLES ARE SHOWN GRAPHICALLY AND DESCRIBED FOR DIFFERENT TYPES OF ANALYSIS, SHOWING THE CARD FORMAT, CODING SYSTEMS, AND PRINTOUT. EQUATIONS ARE PROVIDED FOR DETERMINING…

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

Werner, Mike

Why this utility? After years of upgrading the Java Runtime Environment (JRE) or the Java Software Development Kit (JDK/SDK), a Windows computer becomes littered with so many old versions that the machine may become a security risk due to exploits targeted at those older versions. This utility helps mitigate those vulnerabilities by searching for, and removing, versions 1.3.x thru 1.7.x of the Java JRE and/or JDK/SDK.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system

NASA Astrophysics Data System (ADS)

Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.

2012-05-01

The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

Design and fabrication of a freeform phase plate for high-order ocular aberration correction

NASA Astrophysics Data System (ADS)

Yi, Allen Y.; Raasch, Thomas W.

2005-11-01

In recent years it has become possible to measure and in some instances to correct the high-order aberrations of human eyes. We have investigated the correction of wavefront error of human eyes by using phase plates designed to compensate for that error. The wavefront aberrations of the four eyes of two subjects were experimentally determined, and compensating phase plates were machined with an ultraprecision diamond-turning machine equipped with four independent axes. A slow-tool servo freeform trajectory was developed for the machine tool path. The machined phase-correction plates were measured and compared with the original design values to validate the process. The position of the phase-plate relative to the pupil is discussed. The practical utility of this mode of aberration correction was investigated with visual acuity testing. The results are consistent with the potential benefit of aberration correction but also underscore the critical positioning requirements of this mode of aberration correction. This process is described in detail from optical measurements, through machining process design and development, to final results.

RIP-REMOTE INTERACTIVE PARTICLE-TRACER

NASA Technical Reports Server (NTRS)

Rogers, S. E.

1994-01-01

Remote Interactive Particle-tracing (RIP) is a distributed-graphics program which computes particle traces for computational fluid dynamics (CFD) solution data sets. A particle trace is a line which shows the path a massless particle in a fluid will take; it is a visual image of where the fluid is going. The program is able to compute and display particle traces at a speed of about one trace per second because it runs on two machines concurrently. The data used by the program is contained in two files. The solution file contains data on density, momentum and energy quantities of a flow field at discrete points in three-dimensional space, while the grid file contains the physical coordinates of each of the discrete points. RIP requires two computers. A local graphics workstation interfaces with the user for program control and graphics manipulation, and a remote machine interfaces with the solution data set and performs time-intensive computations. The program utilizes two machines in a distributed mode for two reasons. First, the data to be used by the program is usually generated on the supercomputer. RIP avoids having to convert and transfer the data, eliminating any memory limitations of the local machine. Second, as computing the particle traces can be computationally expensive, RIP utilizes the power of the supercomputer for this task. Although the remote site code was developed on a CRAY, it is possible to port this to any supercomputer class machine with a UNIX-like operating system. Integration of a velocity field from a starting physical location produces the particle trace. The remote machine computes the particle traces using the particle-tracing subroutines from PLOT3D/AMES, a CFD post-processing graphics program available from COSMIC (ARC-12779). These routines use a second-order predictor-corrector method to integrate the velocity field. Then the remote program sends graphics tokens to the local machine via a remote-graphics library. The local machine interprets the graphics tokens and draws the particle traces. The program is menu driven. RIP is implemented on the silicon graphics IRIS 3000 (local workstation) with an IRIX operating system and on the CRAY2 (remote station) with a UNICOS 1.0 or 2.0 operating system. The IRIS 4D can be used in place of the IRIS 3000. The program is written in C (67%) and FORTRAN 77 (43%) and has an IRIS memory requirement of 4 MB. The remote and local stations must use the same user ID. PLOT3D/AMES unformatted data sets are required for the remote machine. The program was developed in 1988.

A comparative analysis of dynamic grids vs. virtual grids using the A3pviGrid framework.

PubMed

Shankaranarayanan, Avinas; Amaldas, Christine

2010-11-01

With the proliferation of Quad/Multi-core micro-processors in mainstream platforms such as desktops and workstations; a large number of unused CPU cycles can be utilized for running virtual machines (VMs) as dynamic nodes in distributed environments. Grid services and its service oriented business broker now termed cloud computing could deploy image based virtualization platforms enabling agent based resource management and dynamic fault management. In this paper we present an efficient way of utilizing heterogeneous virtual machines on idle desktops as an environment for consumption of high performance grid services. Spurious and exponential increases in the size of the datasets are constant concerns in medical and pharmaceutical industries due to the constant discovery and publication of large sequence databases. Traditional algorithms are not modeled at handing large data sizes under sudden and dynamic changes in the execution environment as previously discussed. This research was undertaken to compare our previous results with running the same test dataset with that of a virtual Grid platform using virtual machines (Virtualization). The implemented architecture, A3pviGrid utilizes game theoretic optimization and agent based team formation (Coalition) algorithms to improve upon scalability with respect to team formation. Due to the dynamic nature of distributed systems (as discussed in our previous work) all interactions were made local within a team transparently. This paper is a proof of concept of an experimental mini-Grid test-bed compared to running the platform on local virtual machines on a local test cluster. This was done to give every agent its own execution platform enabling anonymity and better control of the dynamic environmental parameters. We also analyze performance and scalability of Blast in a multiple virtual node setup and present our findings. This paper is an extension of our previous research on improving the BLAST application framework using dynamic Grids on virtualization platforms such as the virtual box.

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

PubMed

Churpek, Matthew M; Yuen, Trevor C; Winslow, Christopher; Meltzer, David O; Kattan, Michael W; Edelson, Dana P

2016-02-01

Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Observational cohort study. Five hospitals, from November 2008 until January 2013. Hospitalized ward patients None Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]). In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

Plan for conducting an international machine tool task force

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

Sutton, G.P.; McClure, E.R.; Schuman, J.F.

1978-08-28

The basic objectives of the Machine Tool Task Force (MTTF) are to characterize and summarize the state of the art of cutting machine tool technology and to identify promising areas of future R and D. These goals will be accomplished with a series of multidisciplinary teams of prominent experts and individuals experienced in the specialized technologies of machine tools or in the management of machine tool operations. Experts will be drawn from all areas of the machine tool community: machine tool users or buyer organizations, builders, and R and D establishments including universities and government laboratories, both domestic and foreign.more » A plan for accomplishing this task is presented. The area of machine tool technology has been divided into about two dozen technology subjects on which teams of one or more experts will work. These teams are, in turn, organized into four principal working groups dealing, respectively, with machine tool accuracy, mechanics, control, and management systems/utilization. Details are presented on specific subjects to be covered, the organization of the Task Force and its four working groups, and the basic approach to determining the state of the art of technology and the future directions of this technology. The planned review procedure, the potential benefits, our management approach, and the schedule, as well as the key participating personnel and their background are discussed. The initial meeting of MTTF members will be held at a plenary session on October 16 and 17, 1978, in Scottsdale, AZ. The MTTF study will culminate in a conference on September 1, 1980, in Chicago, IL, immediately preceeding the 1980 International Machine Tool Show. At this time, our results will be released to the public; a series of reports will be published in late 1980.« less

Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems.

PubMed

Nishimoto, Ryu; Tani, Jun

2004-09-01

This study shows how sensory-action sequences of imitating finite state machines (FSMs) can be learned by utilizing the deterministic dynamics of recurrent neural networks (RNNs). Our experiments indicated that each possible combinatorial sequence can be recalled by specifying its respective initial state value and also that fractal structures appear in this initial state mapping after the learning converges. We also observed that the sequences of mimicking FSMs are encoded utilizing the transient regions rather than the invariant sets of the evolved dynamical systems of the RNNs.

Results of a utility survey of the status of large wind turbine development

NASA Technical Reports Server (NTRS)

Watts, A.; Quraeshi, S.; Rowley, L. P.

1979-01-01

Wind energy conversion systems were surveyed from a utility viewpoint to establish the state of the art with regard to: (1) availability of the type of machines; (2) quality of power generation; (3) suitability for electrical grid; (4) reliability; and (5) economics. Of the 23 designs discussed, 7 have vertical axis wind turbines, 9 have upwind horizontal axis turbines, and 7 have downwind horizontal axis turbines.

Homopolar machine for reversible energy storage and transfer systems

DOEpatents

Stillwagon, Roy E.

1978-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermo-nuclear reactor. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals. A stator concentrically disposed around the sleeves consists of a hollow cylinder having a number of excitation coils each located radially outward from the ends of adjacent sleeves. Current collected at an end of each sleeve by sleeve slip rings and brushes is transferred through terminals to the magnetic load coil. Thereafter, electrical energy returned from the coil then flows through the machine which causes the sleeves to motor up to the desired speed in preparation for repetition of the cycle. To eliminate drag on the rotor between current pulses, the brush rigging is designed to lift brushes from all slip rings in the machine.

Homopolar machine for reversible energy storage and transfer systems

DOEpatents

Stillwagon, Roy E.

1981-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermo-nuclear reactor. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals. A stator concentrically disposed around the sleeves consists of a hollow cylinder having a number of excitation coils each located radially outward from the ends of adjacent sleeves. Current collected at an end of each sleeve by sleeve slip rings and brushes is transferred through terminals to the magnetic load coil. Thereafter, electrical energy returned from the coil then flows through the machine which causes the sleeves to motor up to the desired speed in preparation for repetition of the cycle. To eliminate drag on the rotor between current pulses, the brush rigging is designed to lift brushes from all slip rings in the machine.

AV-Based Mechanics

ERIC Educational Resources Information Center

Training in Business and Industry, 1974

1974-01-01

An individualized study approach to learning the occupation of sewing machine mechanic was developed by Union Special Corporation. The approach utilizes audiovisual aids to a great extent. The time spent in training has been cut from two years to ten weeks. (AG)

Solar pv fed stand-alone excitation system of a synchronous machine for reactive power generation

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Jain, Siddhartha; Jyotheeswara Reddy, K.

2017-11-01

This paper presents a model of a stand-alone solar energy conversion system based on synchronous machine working as a synchronous condenser in overexcited state. The proposed model consists of a Synchronous Condenser, a DC/DC boost converter whose output is fed to the field of the SC. The boost converter is supplied by the modelled solar panel and a day time variable irradiance is fed to the panel during the simulation time. The model also has one alternate source of rechargeable batteries for the time when irradiance falls below a threshold value. Also the excess power produced when there is ample irradiance is divided in two parts and one is fed to the boost converter while other is utilized to recharge the batteries. A simulation is done in MATLAB-SIMULINK and the obtained results show the utility of such modelling for supplying reactive power is feasible.

Comparative hybrid and digital simulation studies of the behaviour of a wind generator equipped with a static frequency converter

NASA Astrophysics Data System (ADS)

Dube, B.; Lefebvre, S.; Perocheau, A.; Nakra, H. L.

1988-01-01

This paper describes the comparative results obtained from digital and hybrid simulation studies on a variable speed wind generator interconnected to the utility grid. The wind generator is a vertical-axis Darrieus type coupled to a synchronous machine by a gear-box; the synchronous machine is connected to the AC utility grid through a static frequency converter. Digital simulation results have been obtained using CSMP software; these results are compared with those obtained from a real-time hybrid simulator that in turn uses a part of the IREQ HVDC simulator. The agreement between hybrid and digital simulation results is generally good. The results demonstrate that the digital simulation reproduces the dynamic behavior of the system in a satisfactory manner and thus constitutes a valid tool for the design of the control systems of the wind generator.

Computational Nanotechnology of Materials, Devices, and Machines: Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Kwak, Dolhan (Technical Monitor)

2000-01-01

The mechanics and chemistry of carbon nanotubes have relevance for their numerous electronic applications. Mechanical deformations such as bending and twisting affect the nanotube's conductive properties, and at the same time they possess high strength and elasticity. Two principal techniques were utilized including the analysis of large scale classical molecular dynamics on a shared memory architecture machine and a quantum molecular dynamics methodology. In carbon based electronics, nanotubes are used as molecular wires with topological defects which are mediated through various means. Nanotubes can be connected to form junctions.

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

Yang, C.

Almost every computer architect dreams of achieving high system performance with low implementation costs. A multigauge machine can reconfigure its data-path width, provide parallelism, achieve better resource utilization, and sometimes can trade computational precision for increased speed. A simple experimental method is used here to capture the main characteristics of multigauging. The measurements indicate evidence of near-optimal speedups. Adapting these ideas in designing parallel processors incurs low costs and provides flexibility. Several operational aspects of designing a multigauge machine are discussed as well. Thus, this research reports the technical, economical, and operational feasibility studies of multigauging.

Enterprise Cloud Architecture for Chinese Ministry of Railway

NASA Astrophysics Data System (ADS)

Shan, Xumei; Liu, Hefeng

Enterprise like PRC Ministry of Railways (MOR), is facing various challenges ranging from highly distributed computing environment and low legacy system utilization, Cloud Computing is increasingly regarded as one workable solution to address this. This article describes full scale cloud solution with Intel Tashi as virtual machine infrastructure layer, Hadoop HDFS as computing platform, and self developed SaaS interface, gluing virtual machine and HDFS with Xen hypervisor. As a result, on demand computing task application and deployment have been tackled per MOR real working scenarios at the end of article.

Micro Machining of Injection Mold Inserts for Fluidic Channel of Polymeric Biochips

PubMed Central

Jung, Woo-Chul; Heo, Young-Moo; Yoon, Gil-Sang; Shin, Kwang-Ho; Chang, Sung-Ho; Kim, Gun-Hee; Cho, Myeong-Woo

2007-01-01

Recently, the polymeric micro-fluidic biochip, often called LOC (lab-on-a-chip), has been focused as a cheap, rapid and simplified method to replace the existing biochemical laboratory works. It becomes possible to form miniaturized lab functionalities on a chip with the development of MEMS technologies. The micro-fluidic chips contain many micro-channels for the flow of sample and reagents, mixing, and detection tasks. Typical substrate materials for the chip are glass and polymers. Typical techniques for microfluidic chip fabrication are utilizing various micro pattern forming methods, such as wet-etching, micro-contact printing, and hot-embossing, micro injection molding, LIGA, and micro powder blasting processes, etc. In this study, to establish the basis of the micro pattern fabrication and mass production of polymeric micro-fluidic chips using injection molding process, micro machining method was applied to form micro-channels on the LOC molds. In the research, a series of machining experiments using micro end-mills were performed to determine optimum machining conditions to improve surface roughness and shape accuracy of designed simplified micro-channels. Obtained conditions were used to machine required mold inserts for micro-channels using micro end-mills. Test injection processes using machined molds and COC polymer were performed, and then the results were investigated.

Statistical analysis and machine learning algorithms for optical biopsy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R.

2018-02-01

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

Support Vector Machine-Based Endmember Extraction

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

Filippi, Anthony M; Archibald, Richard K

Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmembermore » Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.« less

Study on Damage Evaluation and Machinability of UD-CFRP for the Orthogonal Cutting Operation Using Scanning Acoustic Microscopy and the Finite Element Method.

PubMed

Wang, Dongyao; He, Xiaodong; Xu, Zhonghai; Jiao, Weicheng; Yang, Fan; Jiang, Long; Li, Linlin; Liu, Wenbo; Wang, Rongguo

2017-02-20

Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP) has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM) was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin's criteria a numerical model was further proposed in terms of the finite element method (FEM). A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP.

Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

DOE PAGES

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

2014-12-18

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

NASA Astrophysics Data System (ADS)

Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

2012-01-01

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

Study on Damage Evaluation and Machinability of UD-CFRP for the Orthogonal Cutting Operation Using Scanning Acoustic Microscopy and the Finite Element Method

PubMed Central

Wang, Dongyao; He, Xiaodong; Xu, Zhonghai; Jiao, Weicheng; Yang, Fan; Jiang, Long; Li, Linlin; Liu, Wenbo; Wang, Rongguo

2017-01-01

Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP) has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM) was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin’s criteria a numerical model was further proposed in terms of the finite element method (FEM). A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP. PMID:28772565

Design and fabrication of metal briquette machine for shop floor

NASA Astrophysics Data System (ADS)

Pramod, R.; Kumar, G. B. Veeresh; Prashanth B., N.

2017-07-01

Efforts have to be taken to ensure efficient waste management system in shop floors, with minimum utilization of space and energy when it comes to disposing metal chips formed during machining processes. The salvaging of junk metallic chips and the us e of scrap are important for the economic production of a steelworks. For this purpose, we have fabricated a metal chip compaction machine, which can compact the metal chips into small briquettes. The project started with the survey of chips formed in shop floors and the practices involved in waste management. Study was done on the requirements for a better compaction. The heating chamber was designed taking into consideration the temperature required for an easy compaction of the metal chips. The power source for compaction and the pneumatic design for mechanism was done following the appropriate calculations regarding the air pressure provided and thrust required. The processes were tested under different conditions and found effective. The fabrication of the machine has been explained in detail and the results have been discussed.

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

PubMed

Gerovitch, Slava

2002-06-01

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

A review of machine learning in obesity.

PubMed

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

2018-05-01

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

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

PubMed

Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W

2018-03-01

The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.

Harnessing Pavement Power : Developing Renewable Energy Technology in the Public Right-of-Way

DOT National Transportation Integrated Search

2013-09-18

Intelligent Compaction (IC) of soil and asphalt mixes is an innovative approach that has been utilized to achieve uniform, adequate compaction of pavement layers during construction. Commercially available IC products provide machine specific compact...

25 CFR 542.13 - What are the minimum internal control standards for gaming machines?

Code of Federal Regulations, 2014 CFR

2014-04-01

.... (j) Player tracking system. (1) The following standards apply if a player tracking system is utilized... image on the computer screen; (B) Comparing the customer to image on customer's picture ID; or (C...

25 CFR 542.13 - What are the minimum internal control standards for gaming machines?

Code of Federal Regulations, 2012 CFR

2012-04-01

.... (j) Player tracking system. (1) The following standards apply if a player tracking system is utilized... image on the computer screen; (B) Comparing the customer to image on customer's picture ID; or (C...

25 CFR 542.13 - What are the minimum internal control standards for gaming machines?

Code of Federal Regulations, 2013 CFR

2013-04-01

.... (j) Player tracking system. (1) The following standards apply if a player tracking system is utilized... image on the computer screen; (B) Comparing the customer to image on customer's picture ID; or (C...

25 CFR 542.13 - What are the minimum internal control standards for gaming machines?

Code of Federal Regulations, 2010 CFR

2010-04-01

.... (j) Player tracking system. (1) The following standards apply if a player tracking system is utilized... image on the computer screen; (B) Comparing the customer to image on customer's picture ID; or (C...

25 CFR 542.13 - What are the minimum internal control standards for gaming machines?

Code of Federal Regulations, 2011 CFR

2011-04-01

.... (j) Player tracking system. (1) The following standards apply if a player tracking system is utilized... image on the computer screen; (B) Comparing the customer to image on customer's picture ID; or (C...

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

PubMed

Passos, Ives Cavalcante; Mwangi, Benson; Cao, Bo; Hamilton, Jane E; Wu, Mon-Ju; Zhang, Xiang Yang; Zunta-Soares, Giovana B; Quevedo, Joao; Kauer-Sant'Anna, Marcia; Kapczinski, Flávio; Soares, Jair C

2016-03-15

A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity. Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide. Copyright © 2015 Elsevier B.V. All rights reserved.

Identifying a clinical signature of suicidality among patients with mood disorders: a pilot study using a machine learning approach

PubMed Central

Passos, Ives Cavalcante; Mwangi, Benson; Cao, Bo; Hamilton, Jane E; Wu, Mon-Ju; Zhang, Xiang Yang; Zunta-Soares, Giovana B.; Quevedo, Joao; Kauer-Sant'Anna, Marcia; Kapczinski, Flávio; Soares, Jair C.

2016-01-01

Objective A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. Method A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to ‘train’ a machine learning algorithm. The resulting algorithm was utilized in identifying novel or ‘unseen’ individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. Results All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65%-72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity. Conclusion Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide. PMID:26773901

Performance Analysis of the NAS Y-MP Workload

NASA Technical Reports Server (NTRS)

Bergeron, Robert J.; Kutler, Paul (Technical Monitor)

1997-01-01

This paper describes the performance characteristics of the computational workloads on the NAS Cray Y-MP machines, a Y-MP 832 and later a Y-MP 8128. Hardware measurements indicated that the Y-MP workload performance matured over time, ultimately sustaining an average throughput of 0.8 GFLOPS and a vector operation fraction of 87%. The measurements also revealed an operation rate exceeding 1 per clock period, a well-balanced architecture featuring a strong utilization of vector functional units, and an efficient memory organization. Introduction of the larger memory 8128 increased throughput by allowing a more efficient utilization of CPUs. Throughput also depended on the metering of the batch queues; low-idle Saturday workloads required a buffer of small jobs to prevent memory starvation of the CPU. UNICOS required about 7% of total CPU time to service the 832 workloads; this overhead decreased to 5% for the 8128 workloads. While most of the system time went to service I/O requests, efficient scheduling prevented excessive idle due to I/O wait. System measurements disclosed no obvious bottlenecks in the response of the machine and UNICOS to the workloads. In most cases, Cray-provided software tools were- quite sufficient for measuring the performance of both the machine and operating, system.

Performance of an MPI-only semiconductor device simulator on a quad socket/quad core InfiniBand platform.

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

Shadid, John Nicolas; Lin, Paul Tinphone

2009-01-01

This preliminary study considers the scaling and performance of a finite element (FE) semiconductor device simulator on a capacity cluster with 272 compute nodes based on a homogeneous multicore node architecture utilizing 16 cores. The inter-node communication backbone for this Tri-Lab Linux Capacity Cluster (TLCC) machine is comprised of an InfiniBand interconnect. The nonuniform memory access (NUMA) nodes consist of 2.2 GHz quad socket/quad core AMD Opteron processors. The performance results for this study are obtained with a FE semiconductor device simulation code (Charon) that is based on a fully-coupled Newton-Krylov solver with domain decomposition and multilevel preconditioners. Scaling andmore » multicore performance results are presented for large-scale problems of 100+ million unknowns on up to 4096 cores. A parallel scaling comparison is also presented with the Cray XT3/4 Red Storm capability platform. The results indicate that an MPI-only programming model for utilizing the multicore nodes is reasonably efficient on all 16 cores per compute node. However, the results also indicated that the multilevel preconditioner, which is critical for large-scale capability type simulations, scales better on the Red Storm machine than the TLCC machine.« less

Concurrent Image Processing Executive (CIPE). Volume 1: Design overview

NASA Technical Reports Server (NTRS)

Lee, Meemong; Groom, Steven L.; Mazer, Alan S.; Williams, Winifred I.

1990-01-01

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are described. The target machine for this software is a JPL/Caltech Mark 3fp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules: user interface, host-resident executive, hypercube-resident executive, and application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube, a data management method which distributes, redistributes, and tracks data set information was implemented. The data management also allows data sharing among application programs. The CIPE software architecture provides a flexible environment for scientific analysis of complex remote sensing image data, such as planetary data and imaging spectrometry, utilizing state-of-the-art concurrent computation capabilities.

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.

Mechanization for Optimal Landscape Reclamation

NASA Astrophysics Data System (ADS)

Vondráčková, Terezie; Voštová, Věra; Kraus, Michal

2017-12-01

Reclamation is a method of ultimate utilization of land adversely affected by mining or other industrial activity. The paper explains the types of reclamation and the term “optimal reclamation”. Technological options of the long-lasting process of mine dumps reclamation starting with the removal of overlying rocks, transport and backfilling up to the follow-up remodelling of the mine dumps terrain. Technological units and equipment for stripping flow division. Stripping flow solution with respect to optimal reclamation. We recommend that the application of logistic chains and mining simulation with follow-up reclamation to open-pit mines be used for the implementation of optimal reclamation. In addition to a database of local heterogeneities of the stripped soil and reclaimed land, the flow of earths should be resolved in a manner allowing the most suitable soil substrate to be created for the restoration of agricultural and forest land on mine dumps. The methodology under development for the solution of a number of problems, including the geological survey of overlying rocks, extraction of stripping, their transport and backfilling in specified locations with the follow-up deployment of goal-directed reclamation. It will make possible to reduce the financial resources needed for the complex process chain by utilizing GIS, GPS and DGPS technologies, logistic tools and synergistic effects. When selecting machines for transport, moving and spreading of earths, various points of view and aspects must be taken into account. Among such aspects are e.g. the kind of earth to be operated by the respective construction machine, the kind of work activities to be performed, the machine’s capacity, the option to control the machine’s implement and economic aspects and clients’ requirements. All these points of view must be considered in the decision-making process so that the selected machine is capable of executing the required activity and that the use of an unsuitable machine is eliminated as it would result in a delay and increase in the project costs. Therefore, reclamation always includes extensive earth-moving work activities restoring the required relief of the land being reclaimed. Using the earth-moving machine capacity, the kind of soil in mine dumps, the kind of the work activity performed and the machine design, a SW application has been developed that allows the most suitable machine for the respective work technology to be selected with a view to preparing the land intended for reclamation.

Machine learning in cardiovascular medicine: are we there yet?

PubMed

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

2018-01-19

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

Automated Rapid Prototyping of 3D Ceramic Parts

NASA Technical Reports Server (NTRS)

McMillin, Scott G.; Griffin, Eugene A.; Griffin, Curtis W.; Coles, Peter W. H.; Engle, James D.

2005-01-01

An automated system of manufacturing equipment produces three-dimensional (3D) ceramic parts specified by computational models of the parts. The system implements an advanced, automated version of a generic rapid-prototyping process in which the fabrication of an object having a possibly complex 3D shape includes stacking of thin sheets, the outlines of which closely approximate the horizontal cross sections of the object at their respective heights. In this process, the thin sheets are made of a ceramic precursor material, and the stack is subsequently heated to transform it into a unitary ceramic object. In addition to the computer used to generate the computational model of the part to be fabricated, the equipment used in this process includes: 1) A commercially available laminated-object-manufacturing machine that was originally designed for building woodlike 3D objects from paper and was modified to accept sheets of ceramic precursor material, and 2) A machine designed specifically to feed single sheets of ceramic precursor material to the laminated-object-manufacturing machine. Like other rapid-prototyping processes that utilize stacking of thin sheets, this process begins with generation of the computational model of the part to be fabricated, followed by computational sectioning of the part into layers of predetermined thickness that collectively define the shape of the part. Information about each layer is transmitted to rapid-prototyping equipment, where the part is built layer by layer. What distinguishes this process from other rapid-prototyping processes that utilize stacking of thin sheets are the details of the machines and the actions that they perform. In this process, flexible sheets of ceramic precursor material (called "green" ceramic sheets) suitable for lamination are produced by tape casting. The binder used in the tape casting is specially formulated to enable lamination of layers with little or no applied heat or pressure. The tape is cut into individual sheets, which are stacked in the sheet-feeding machine until used. The sheet-feeding machine can hold enough sheets for about 8 hours of continuous operation.

Contributions a l'etude et a l'application industrielle de la machine asynchrone

NASA Astrophysics Data System (ADS)

Ouhrouche, Mohand-Ameziane

The work presented in this thesis, done in the Electrical Drives Laboratory of Electrical and Computer Engineering Department, deals with the industrial applications of a three-phase induction machine (electrical drives and electricity generation). This thesis, characterized by its multidisciplinary content, has two major parts. The first one deals with the on-line and off-line parametric identification of the induction machine model necessary to achieve accurate vector control strategy. The second part, which is a resume of a research work sponsored by Hydro-Quebec, deals with the application of an induction machine in Asynchronous Non Utility Generators units (ANUG). As it is shown in the following, major scientific contributions are made in both two parts. In the first part of our research work, we propose a new speed sensorless vector control strategy for an induction machine, which is adaptive to the rotor resistance variations. The proposed control strategy is based on the Extended Kalman Filter approach and a decoupling controller which takes into account the rotor resistance variations. The consideration of coupled electrical and mechanical modes leads to a fifth order nonlinear model of the induction machine. The load torque is taken as a function of the rotor angular speed. The Extended Kalman Filter, based on the process's nonlinear (bilinear) model, estimate simultaneously the rotor resistance, angular speed and the flux vector from the startup to the steady state equilibrium point. The machine-converter-control system is implemented in MATLAB/SIMULINK environment and the obtained results confirm the robustness of the proposed scheme. As in the electrical drives erea, the induction machine is now widely used by small to medium power Non Utility Generator units (NUG) to produce electricity. In Quebec, these NUGs units are integrated into the Hydro-Quebec 25 kV distribution system via transformer which exhibit nonlinear characteristics. We have shown by using the ElectroMagnetic Program (EMTP) that, in some islanding scenarios, i.e. that the NUG unit is disconnected from the power grid, in addition to frequency variations, appearence of high an abnormal overvoltages, ferroresonance should occur. As a consequence, normal protective devices could fail to securely operate, which could cause serious damages to the equipment and the maintenance staff. This result, established for the first time , can be useful to improve the reliability of the NUGs units and is considered important by the power engineering community. This has led to a publication in the John Wiley & Sons Encyclopedia of Electrical and Electronics Engineering which will be available in February 1999 ( http://www.engr.wisc.edu/ ece/ece).

A variable-mode stator consequent pole memory machine

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Machine tools error characterization and compensation by on-line measurement of artifact

NASA Astrophysics Data System (ADS)

Wahid Khan, Abdul; Chen, Wuyi; Wu, Lili

2009-11-01

Most manufacturing machine tools are utilized for mass production or batch production with high accuracy at a deterministic manufacturing principle. Volumetric accuracy of machine tools depends on the positional accuracy of the cutting tool, probe or end effector related to the workpiece in the workspace volume. In this research paper, a methodology is presented for volumetric calibration of machine tools by on-line measurement of an artifact or an object of a similar type. The machine tool geometric error characterization was carried out through a standard or an artifact, having similar geometry to the mass production or batch production product. The artifact was measured at an arbitrary position in the volumetric workspace with a calibrated Renishaw touch trigger probe system. Positional errors were stored into a computer for compensation purpose, to further run the manufacturing batch through compensated codes. This methodology was found quite effective to manufacture high precision components with more dimensional accuracy and reliability. Calibration by on-line measurement gives the advantage to improve the manufacturing process by use of deterministic manufacturing principle and found efficient and economical but limited to the workspace or envelop surface of the measured artifact's geometry or the profile.

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Performance analysis of a new radial-axial flux machine with SMC cores and ferrite magnets

NASA Astrophysics Data System (ADS)

Liu, Chengcheng; Wang, Youhua; Lei, Gang; Guo, Youguang; Zhu, Jianguo

2017-05-01

Soft magnetic composite (SMC) is a popular material in designing of new 3D flux electrical machines nowadays for it has the merits of isotropic magnetic characteristic, low eddy current loss and high design flexibility over the electric steel. The axial flux machine (AFM) with the extended stator tooth tip both in the radial and circumferential direction is a good example, which has been investigated in the last years. Based on the 3D flux AFM and radial flux machine, this paper proposes a new radial-axial flux machine (RAFM) with SMC cores and ferrite magnets, which has very high torque density though the low cost low magnetic energy ferrite magnet is utilized. Moreover, the cost of RAFM is quite low since the manufacturing cost can be reduced by using the SMC cores and the material cost will be decreased due to the adoption of the ferrite magnets. The 3D finite element method (FEM) is used to calculate the magnetic flux density distribution and electromagnetic parameters. For the core loss calculation, the rotational core loss computation method is used based on the experiment results from previous 3D magnetic tester.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Comparison of two cut-to-length harvesting systems operating in eastern hardwoods

Treesearch

Chris B. LeDoux; Niel K. Huyler

2001-01-01

We compared production rates, operating costs, and break-even points (BEP) for small and large cut-to-length (CTL) harvesting systems operating at several machine utilization rates (MUR) in mixed hardwood and softwood stands in Vermont.

Wind energy utilization: A bibliography with abstracts - Cumulative volume 1944/1974

NASA Technical Reports Server (NTRS)

1975-01-01

Bibliography, up to 1974 inclusive, of articles and books on utilization of wind power in energy generation. Worldwide literature is surveyed, and short abstracts are provided in many cases. The citations are grouped by subject: (1) general; (2) utilization; (3) wind power plants; (4) wind power generators (rural, synchronous, remote station); (5) wind machines (motors, pumps, turbines, windmills, home-built); (6) wind data and properties; (7) energy storage; and (8) related topics (control and regulation devices, wind measuring devices, blade design and rotors, wind tunnel simulation, aerodynamics). Gross-referencing is aided by indexes of authors, corporate sources, titles, and keywords.

Utilizing semantic networks to database and retrieve generalized stochastic colored Petri nets

NASA Technical Reports Server (NTRS)

Farah, Jeffrey J.; Kelley, Robert B.

1992-01-01

Previous work has introduced the Planning Coordinator (PCOORD), a coordinator functioning within the hierarchy of the Intelligent Machine Mode. Within the structure of the Planning Coordinator resides the Primitive Structure Database (PSDB) functioning to provide the primitive structures utilized by the Planning Coordinator in the establishing of error recovery or on-line path plans. This report further explores the Primitive Structure Database and establishes the potential of utilizing semantic networks as a means of efficiently storing and retrieving the Generalized Stochastic Colored Petri Nets from which the error recovery plans are derived.

Decomposition method for fast computation of gigapixel-sized Fresnel holograms on a graphics processing unit cluster.

PubMed

Jackin, Boaz Jessie; Watanabe, Shinpei; Ootsu, Kanemitsu; Ohkawa, Takeshi; Yokota, Takashi; Hayasaki, Yoshio; Yatagai, Toyohiko; Baba, Takanobu

2018-04-20

A parallel computation method for large-size Fresnel computer-generated hologram (CGH) is reported. The method was introduced by us in an earlier report as a technique for calculating Fourier CGH from 2D object data. In this paper we extend the method to compute Fresnel CGH from 3D object data. The scale of the computation problem is also expanded to 2 gigapixels, making it closer to real application requirements. The significant feature of the reported method is its ability to avoid communication overhead and thereby fully utilize the computing power of parallel devices. The method exhibits three layers of parallelism that favor small to large scale parallel computing machines. Simulation and optical experiments were conducted to demonstrate the workability and to evaluate the efficiency of the proposed technique. A two-times improvement in computation speed has been achieved compared to the conventional method, on a 16-node cluster (one GPU per node) utilizing only one layer of parallelism. A 20-times improvement in computation speed has been estimated utilizing two layers of parallelism on a very large-scale parallel machine with 16 nodes, where each node has 16 GPUs.

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.

Vending machine assessment methodology. A systematic review.

PubMed

Matthews, Melissa A; Horacek, Tanya M

2015-07-01

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

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

Deriving the expected utility of a predictive model when the utilities are uncertain.

PubMed

Cooper, Gregory F; Visweswaran, Shyam

2005-01-01

Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

Motion Estimation System Utilizing Point Cloud Registration

NASA Technical Reports Server (NTRS)

Chen, Qi (Inventor)

2016-01-01

A system and method of estimation motion of a machine is disclosed. The method may include determining a first point cloud and a second point cloud corresponding to an environment in a vicinity of the machine. The method may further include generating a first extended gaussian image (EGI) for the first point cloud and a second EGI for the second point cloud. The method may further include determining a first EGI segment based on the first EGI and a second EGI segment based on the second EGI. The method may further include determining a first two dimensional distribution for points in the first EGI segment and a second two dimensional distribution for points in the second EGI segment. The method may further include estimating motion of the machine based on the first and second two dimensional distributions.

What is the machine learning?

NASA Astrophysics Data System (ADS)

Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

2018-03-01

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

Utilization of rotor kinetic energy storage for hybrid vehicles

DOEpatents

Hsu, John S [Oak Ridge, TN

2011-05-03

A power system for a motor vehicle having an internal combustion engine, the power system comprises an electric machine (12) further comprising a first excitation source (47), a permanent magnet rotor (28) and a magnetic coupling rotor (26) spaced from the permanent magnet rotor and at least one second excitation source (43), the magnetic coupling rotor (26) also including a flywheel having an inertial mass to store kinetic energy during an initial acceleration to an operating speed; and wherein the first excitation source is electrically connected to the second excitation source for power cycling such that the flywheel rotor (26) exerts torque on the permanent magnet rotor (28) to assist braking and acceleration of the permanent magnet rotor (28) and consequently, the vehicle. An axial gap machine and a radial gap machine are disclosed and methods of the invention are also disclosed.

Ultrasonic drilling apparatus

DOEpatents

Duran, Edward L.; Lundin, Ralph L.

1989-01-01

Apparatus attachable to an ultrasonic drilling machine for drilling deep holes in very hard materials, such as boron carbide, is provided. The apparatus utilizes a hollow spindle attached to the output horn of the ultrasonic drilling machine. The spindle has a hollow drill bit attached at the opposite end. A housing surrounds the spindle, forming a cavity for holding slurry. In operation, slurry is provided into the housing, and into the spindle through inlets while the spindle is rotating and ultrasonically reciprocating. Slurry flows through the spindle and through the hollow drill bit to cleanse the cutting edge of the bit during a drilling operation.

Reliability and quality assurance on the MOD 2 wind system

NASA Technical Reports Server (NTRS)

Mason, W. E. B.; Jones, B. G.

1981-01-01

The Safety, Reliability, and Quality Assurance (R&QA) approach developed for the largest wind turbine generator, the Mod 2, is described. The R&QA approach assures that the machine is not hazardous to the public or to the operating personnel, is operated unattended on a utility grid, demonstrates reliable operation, and helps establish the quality assurance and maintainability requirements for future wind turbine projects. The significant guideline consisted of a failure modes and effects analysis (FMEA) during the design phase, hardware inspections during parts fabrication, and three simple documents to control activities during machine construction and operation.

Experience with modified aerospace reliability and quality assurance method for wind turbines

NASA Technical Reports Server (NTRS)

Klein, W. E.

1982-01-01

The SR&QA approach assures that the machine is not hazardous to the public or operating personnel, can operate unattended on a utility grid, demonstrates reliability operation, and helps establish the quality assurance and maintainability requirements for future wind turbine projects. The approach consisted of modified failure modes and effects analysis (FMEA) during the design phase, minimal hardware inspection during parts fabrication, and three simple documents to control activities during machine construction and operation. Five years experience shows that this low cost approach works well enough that it should be considered by others for similar projects.

Improving the Prediction of Mortality and the Need for Life-Saving Interventions in Trauma Patients Using Standard Vital Signs With Heart-Rate Variability and Complexity

DTIC Science & Technology

2015-06-01

Trauma 69:S10YS13, 2010. 2. Liu NT, Holcomb JB, Wade CE, Darrah MI, Salinas J: Utility of vital signs, heart-rate variability and complexity, and machine ... learning for identifying the need for life-saving interventions in trauma patients. Shock 42:108Y114, 2014. 3. Pickering TG, Shimbo D, Hass D...Ann Emerg Med 45:68Y76, 2005. 8. Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, Salinas J: Development and validation of a machine

Machine learning methods for credibility assessment of interviewees based on posturographic data.

PubMed

Saripalle, Sashi K; Vemulapalli, Spandana; King, Gregory W; Burgoon, Judee K; Derakhshani, Reza

2015-01-01

This paper discusses the advantages of using posturographic signals from force plates for non-invasive credibility assessment. The contributions of our work are two fold: first, the proposed method is highly efficient and non invasive. Second, feasibility for creating an autonomous credibility assessment system using machine-learning algorithms is studied. This study employs an interview paradigm that includes subjects responding with truthful and deceptive intent while their center of pressure (COP) signal is being recorded. Classification models utilizing sets of COP features for deceptive responses are derived and best accuracy of 93.5% for test interval is reported.

Development of the Corporal: The Embryo of the Army Missile Program, Volume II: Supporting Data. Documents 1 through 31

DTIC Science & Technology

1961-04-01

heads, were used. Flights E-4 through E-6 utilized forged cylinders of SAE 4140 , machined to 0.25- inch wall thickness with machined skirts. The heads...cylindrical portion of the air tank in E-1 was welded of SAE 4130 steel plates 0.50 inch thick. The heads were 0.375 inch thick, whereas the internal...Edward S. Forman, and Weld Arnold. The early phases of the research were financed by a fund of $1,000 from Mr. Weld Arnold. "The first activities of

Ultrasonic drilling apparatus

DOEpatents

Duran, E.L.; Lundin, R.L.

1988-06-20

Apparatus attachable to an ultrasonic drilling machine for drilling deep holes in very hard materials, such as boron carbide, is provided. The apparatus utilizes a hollow spindle attached to the output horn of the ultrasonic drilling machine. The spindle has a hollow drill bit attached at the opposite end. A housing surrounds the spindle, forming a cavity for holding slurry. In operation, slurry is provided into the housing, and into the spindle through inlets while the spindle is rotating and ultrasonically reciprocating. Slurry flows through the spindle and through the hollow drill bit to cleanse the cutting edge of the bit during a drilling operation. 3 figs.

Wind utilization in remote regions: An economic study. [for comparison with diesel engines

NASA Technical Reports Server (NTRS)

Vansant, J. H.

1973-01-01

A wind driven generator was considered as a supplement to a diesel group, for the purpose of economizing fuel when wind power is available. A specific location on Hudson's Bay, Povognituk, was selected. Technical and economic data available for a wind machine of 10-kilowatt nominal capacity and available wind data for that region were used for the study. After subtracting the yearly wind machine costs from savings in fuel costs, a net savings of $1400 per year is realized. These values are approximate, but are though to be highly conservative.

Explanation-Based Knowledge Acquisition of Schemas in Practical Electronics: A Machine Learning Approach

DTIC Science & Technology

1990-09-12

electronics reading to the next. To test this hypothesis and the suitability of EBL to acquiring schemas, I have implemented an automated reader/learner as...used. For example, testing the utility of a kidnapping schema using several readings about kidnapping can only go so far toward establishing the...the cost of carrying the new rules while processing unrelated material will be underestimated. The present research tests the utility of new schemas in

MSUSTAT.

ERIC Educational Resources Information Center

Mauriello, David

1984-01-01

Reviews an interactive statistical analysis package (designed to run on 8- and 16-bit machines that utilize CP/M 80 and MS-DOS operating systems), considering its features and uses, documentation, operation, and performance. The package consists of 40 general purpose statistical procedures derived from the classic textbook "Statistical…

Revision of Import and Export Requirements for Controlled Substances, Listed Chemicals, and Tableting and Encapsulating Machines, Including Changes To Implement the International Trade Data System (ITDS); Revision of Reporting Requirements for Domestic Transactions in Listed Chemicals and Tableting and Encapsulating Machines; and Technical Amendments. Final rule.

PubMed

2016-12-30

The Drug Enforcement Administration is updating its regulations for the import and export of tableting and encapsulating machines, controlled substances, and listed chemicals, and its regulations relating to reports required for domestic transactions in listed chemicals, gamma-hydroxybutyric acid, and tableting and encapsulating machines. In accordance with Executive Order 13563, the Drug Enforcement Administration has reviewed its import and export regulations and reporting requirements for domestic transactions in listed chemicals (and gamma-hydroxybutyric acid) and tableting and encapsulating machines, and evaluated them for clarity, consistency, continued accuracy, and effectiveness. The amendments clarify certain policies and reflect current procedures and technological advancements. The amendments also allow for the implementation, as applicable to tableting and encapsulating machines, controlled substances, and listed chemicals, of the President's Executive Order 13659 on streamlining the export/import process and requiring the government-wide utilization of the International Trade Data System (ITDS). This rule additionally contains amendments that implement recent changes to the Controlled Substances Import and Export Act (CSIEA) for reexportation of controlled substances among members of the European Economic Area made by the Improving Regulatory Transparency for New Medical Therapies Act. The rule also includes additional substantive and technical and stylistic amendments.

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

PubMed

Go, Taesik; Byeon, Hyeokjun; Lee, Sang Joon

2018-04-30

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic. Copyright © 2017 Elsevier B.V. All rights reserved.

An initiative to minimize amount of contrast media utilizing a novel rotational coronary sinus occlusive venography technique with ordinary cath-lab X-ray machine during CRT implantation.

PubMed

Al Fagih, Ahmed; Al Ghamdi, Saleh; El Tayeb, Areeg; Dagriri, Khaled

2010-09-01

Rotational angiography is one of the latest angiographic modalities to map the coronary venous tree anatomy. It provides a significant reduction in both contrast agent usage and radiation dose (up to 30%), without compromising the clinical utility of images. Hence, the present study was conducted to describe a new technique to minimize the amount of contrast media used during cardiac resynchronization therapy (CRT) implantation. The SL3 sheath was inserted into the right atrium via the femoral vein followed by withdrawal of the dilator. The tip of the sheath was manipulated to the vicinity of the coronary sinus (CS) ostium (OS). The CS was entered using a deflated balloon catheter. The sheath was then advanced gently beyond the CS OS. Occlusive venography was performed using 5-8 ml of contrast media in a rotational view starting from 45 degrees LAO to 0 degrees AP while holding the inflated balloon for a few seconds. Data from 30 consecutive patients who underwent CRT implantation were analyzed. The feasibility of rotational angiography, while occluding the CS with a specialized long, preshaped sheath and using an ordinary cath-lab imaging machine, was supported by the correctly delineated CS anatomy of all patients without any complications and death related to the placement of the CS catheters or sheaths. The mean contrast dose used for the entire procedure in all patients undergoing CRT was 14.76 +/- 6.8 ml. Use of rotational CS occlusive venography utilizing an ordinary cath-lab X-ray machine minimizes the use of contrast media during CRT implantation without compromising the visualized anatomy.

CANFAR + Skytree: Mining Massive Datasets as an Essential Part of the Future of Astronomy

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.

2013-01-01

The future study of large astronomical datasets, consisting of hundreds of millions to billions of objects, will be dominated by large computing resources, and by analysis tools of the necessary scalability and sophistication to extract useful information. Significant effort will be required to fulfil their potential as a provider of the next generation of science results. To-date, computing systems have allowed either sophisticated analysis of small datasets, e.g., most astronomy software, or simple analysis of large datasets, e.g., database queries. At the Canadian Astronomy Data Centre, we have combined our cloud computing system, the Canadian Advanced Network for Astronomical Research (CANFAR), with the world's most advanced machine learning software, Skytree, to create the world's first cloud computing system for data mining in astronomy. This allows the full sophistication of the huge fields of data mining and machine learning to be applied to the hundreds of millions of objects that make up current large datasets. CANFAR works by utilizing virtual machines, which appear to the user as equivalent to a desktop. Each machine is replicated as desired to perform large-scale parallel processing. Such an arrangement carries far more flexibility than other cloud systems, because it enables the user to immediately install and run the same code that they already utilize for science on their desktop. We demonstrate the utility of the CANFAR + Skytree system by showing science results obtained, including assigning photometric redshifts with full probability density functions (PDFs) to a catalog of approximately 133 million galaxies from the MegaPipe reductions of the Canada-France-Hawaii Telescope Legacy Wide and Deep surveys. Each PDF is produced nonparametrically from 100 instances of the photometric parameters for each galaxy, generated by perturbing within the errors on the measurements. Hence, we produce, store, and assign redshifts to, a catalog of over 13 billion object instances. This catalog is comparable in size to those expected from next-generation surveys, such as Large Synoptic Survey Telescope. The CANFAR+Skytree system is open for use by any interested member of the astronomical community.

Application of Elements of TPM Strategy for Operation Analysis of Mining Machine

NASA Astrophysics Data System (ADS)

Brodny, Jaroslaw; Tutak, Magdalena

2017-12-01

Total Productive Maintenance (TPM) strategy includes group of activities and actions in order to maintenance machines in failure-free state and without breakdowns thanks to tending limitation of failures, non-planned shutdowns, lacks and non-planned service of machines. These actions are ordered to increase effectiveness of utilization of possessed devices and machines in company. Very significant element of this strategy is connection of technical actions with changes in their perception by employees. Whereas fundamental aim of introduction this strategy is improvement of economic efficiency of enterprise. Increasing competition and necessity of reduction of production costs causes that also mining enterprises are forced to introduce this strategy. In the paper examples of use of OEE model for quantitative evaluation of selected mining devices were presented. OEE model is quantitative tool of TPM strategy and can be the base for further works connected with its introduction. OEE indicator is the product of three components which include availability and performance of the studied machine and the quality of the obtained product. The paper presents the results of the effectiveness analysis of the use of a set of mining machines included in the longwall system, which is the first and most important link in the technological line of coal production. The set of analyzed machines included the longwall shearer, armored face conveyor and cruscher. From a reliability point of view, the analyzed set of machines is a system that is characterized by the serial structure. The analysis was based on data recorded by the industrial automation system used in the mines. This method of data acquisition ensured their high credibility and a full time synchronization. Conclusions from the research and analyses should be used to reduce breakdowns, failures and unplanned downtime, increase performance and improve production quality.

Synthesis of actual knowledge on machine-tool monitoring methods and equipment

NASA Astrophysics Data System (ADS)

Tanguy, J. C.

1988-06-01

Problems connected with the automatic supervision of production were studied. Many different automatic control devices are now able to identify defects in the tools, but the solutions proposed to detect optimal limits in the utilization of a tool are not satisfactory.

Vibration monitoring via nano-composite piezoelectric foam bushings

NASA Astrophysics Data System (ADS)

Bird, Evan T.; Merrell, A. Jake; Anderson, Brady K.; Newton, Cory N.; Rosquist, Parker G.; Fullwood, David T.; Bowden, Anton E.; Seeley, Matthew K.

2016-11-01

Most mechanical systems produce vibrations as an inherent side effect of operation. Though some vibrations are acceptable in operation, others can cause damage or signal a machine’s imminent failure. These vibrations would optimally be monitored in real-time, without human supervision to prevent failure and excessive wear in machinery. This paper explores a new alternative to currently-used machine-monitoring equipment, namely a piezoelectric foam sensor system. These sensors are made of a silicone-based foam embedded with nano- and micro-scale conductive particles. Upon impact, they emit an electric response that is directly correlated with impact energy, with no electrical power input. In the present work, we investigated their utility as self-sensing bushings on machinery. These sensors were found to accurately detect both the amplitude and frequency of typical machine vibrations. The bushings could potentially save time and money over other vibration sensing mechanisms, while simultaneously providing a potential control input that could be utilized for correcting vibrational imbalance.

Two Capacitive Micro-Machined Ultrasonic Transducers for Wind Speed Measurement

PubMed Central

Bui, Gia Thinh; Jiang, Yu-Tsung; Pang, Da-Chen

2016-01-01

This paper presents a new wind speed measurement method using a single capacitive micro-machined ultrasonic transducer (CMUT). The CMUT was arranged perpendicular to the direction of the wind flow, and a reflector was set up a short distance away, facing the CMUT. To reduce the size, weight, cost, and power consumption of conventional ultrasonic anemometers this study proposes two CMUT designs for the measurement of wind speed using either the amplitude of the signal or the time of flight (TOF). Each CMUT with a double array element design can transmit and receive signals in five different operation modes. Experiments showed that the two CMUT designs utilizing the TOF were better than those utilizing the amplitude of the signal for wind speed measurements ranging from 1 m/s to 10 m/s, providing a measurement error of less than 0.2 m/s. These results indicate that the sensitivity of the TOF is independent of the five operation modes. PMID:27271625

Two Capacitive Micro-Machined Ultrasonic Transducers for Wind Speed Measurement.

PubMed

Bui, Gia Thinh; Jiang, Yu-Tsung; Pang, Da-Chen

2016-06-02

This paper presents a new wind speed measurement method using a single capacitive micro-machined ultrasonic transducer (CMUT). The CMUT was arranged perpendicular to the direction of the wind flow, and a reflector was set up a short distance away, facing the CMUT. To reduce the size, weight, cost, and power consumption of conventional ultrasonic anemometers this study proposes two CMUT designs for the measurement of wind speed using either the amplitude of the signal or the time of flight (TOF). Each CMUT with a double array element design can transmit and receive signals in five different operation modes. Experiments showed that the two CMUT designs utilizing the TOF were better than those utilizing the amplitude of the signal for wind speed measurements ranging from 1 m/s to 10 m/s, providing a measurement error of less than 0.2 m/s. These results indicate that the sensitivity of the TOF is independent of the five operation modes.

Analysis of an Environmental Exposure Health Questionnaire in a Metropolitan Minority Population Utilizing Logistic Regression and Support Vector Machines

PubMed Central

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler

2014-01-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953

Replacement of seam welded hot reheat pipe using narrow groove GTA machine welding

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

Richardson, R.R.; Yanes, J.; Bryant, R.

1995-12-31

Southern California Edison, recognizing a potential safety concern, scrutinized its existing seam welded hot reheat pipe manufactured by the same supplier as that which failed. Alternatives were narrowed to two in dealing with the installed seam welded pipe. The overriding consideration, however, was one of safety. With this in mind, the utility company evaluated replacement of the seam welded hot reheat pipe with seamless pipe or increasing the frequency of its inspection program. Although increased inspection was much costly, pipe replacement was chosen due to potential safety concerns with seam welded pipe even with more frequent inspection. The utility companymore » then proceeded to determine the most effective method to complete this work. Analysis showed machine-made (automatic) gas tungsten arc welds (GTAW) as the method of choice due to cleanliness and superior mechanical properties. In conjunction with this method, the narrow groove (3{degree} bevel) weld joint as opposed to the traditional groove (37 1/2{degree} bevel) was shown to provide significant technical advantages.« less

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

PubMed

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells

NASA Astrophysics Data System (ADS)

Bielecki, Christiane; Bocklitz, Thomas W.; Schmitt, Michael; Krafft, Christoph; Marquardt, Claudio; Gharbi, Akram; Knösel, Thomas; Stallmach, Andreas; Popp, Juergen

2012-07-01

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Life cycle costing as a decision making tool for technology acquisition in radio-diagnosis

PubMed Central

Chakravarty, Abhijit; Debnath, Jyotindu

2014-01-01

Background Life cycle costing analysis is an emerging conceptual tool to validate capital investment in healthcare. Methods A preliminary study was done to analyze the long-term cost impact of acquiring a new 3 T MRI system when compared to technological upgradation of the existing 1.5 T MRI system with a view to evolve a decision matrix for correct investment planning and technology management. Operating costing method was utilized to estimate cost per unit MRI scan, costing inputs were considered for the existing 1.5 T and the proposed 3 T machine. Cost for each expected year in the life span of both 1.5 T and 3 T MRI scan options were then discounted to its Net Present Value. Net Present Value thus calculated for both the alternative options of 1.5 T and 3 T MRI machine was charted along with various intangible but critical Figures of Merit (FOM) to create a decision matrix for capital investment planning. Result Considering all fixed and variable costs contributing towards assumed operation, unit cost per MRI procedure was found to be Rs. 4244.58 for the 1.5 T upgrade and Rs. 6059.37 for the new 3 T MRI machine. Life Cycle Cost Analysis of the proposed 1.5 T upgrade and new 3 T machine showed a Net Present Value of Rs. 42,148,587.80 and Rs. 27,587,842.38 respectively. Conclusion The utility of life cycle costing as a strategic decision making tool towards evaluating alternative options for capital investment planning in health care environment is reiterated. PMID:25609862

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

Dynamic cellular manufacturing system considering machine failure and workload balance

NASA Astrophysics Data System (ADS)

Rabbani, Masoud; Farrokhi-Asl, Hamed; Ravanbakhsh, Mohammad

2018-02-01

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators' assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

Installation of the Ignitor Machine at the Caorso Site

NASA Astrophysics Data System (ADS)

Migliori, S.; Pierattini, S.; Bombarda, F.; Faelli, G.; Zucchetti, M.; Coppi, B.

2008-11-01

The actual cost of building a new experiment can be considerably contained if infrastructures are already available on its envisioned site. The facilities of the Caorso site (near Piacenza, Italy) that, at present, houses a spent nuclear power station, have been analyzed in view of their utilization for the operation of the Ignitor machine. The main feature of the site is its robust connection to the electrical national power grid that can take the disturbance caused by Ignitor discharges with the highest magnetic fields and plasma currents, avoiding the need for rotating flywheels generators. Other assets include a vast building that can be modified to house the machine core and the associated diagnostic systems. A layout of the Ignitor plant, including the tritium laboratory and other service areas, the distribution of the components of the electrical power supply system and of the He gas cooling sytem are presented. Relevant safety issues have been analyzed, based on the in depth activation analysis of the machine components carried out by means of the FISPAC code. Waste management and environment impact issues, including risk to the population assessments, have also been addressed.

Modeling and Analysis Compute Environments, Utilizing Virtualization Technology in the Climate and Earth Systems Science domain

NASA Astrophysics Data System (ADS)

Michaelis, A.; Nemani, R. R.; Wang, W.; Votava, P.; Hashimoto, H.

2010-12-01

Given the increasing complexity of climate modeling and analysis tools, it is often difficult and expensive to build or recreate an exact replica of the software compute environment used in past experiments. With the recent development of new technologies for hardware virtualization, an opportunity exists to create full modeling, analysis and compute environments that are “archiveable”, transferable and may be easily shared amongst a scientific community or presented to a bureaucratic body if the need arises. By encapsulating and entire modeling and analysis environment in a virtual machine image, others may quickly gain access to the fully built system used in past experiments, potentially easing the task and reducing the costs of reproducing and verify past results produced by other researchers. Moreover, these virtual machine images may be used as a pedagogical tool for others that are interested in performing an academic exercise but don't yet possess the broad expertise required. We built two virtual machine images, one with the Community Earth System Model (CESM) and one with Weather Research Forecast Model (WRF), then ran several small experiments to assess the feasibility, performance overheads costs, reusability, and transferability. We present a list of the pros and cons as well as lessoned learned from utilizing virtualization technology in the climate and earth systems modeling domain.

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition.

PubMed

Zhang, Yifan; Gao, Xunzhang; Peng, Xuan; Ye, Jiaqi; Li, Xiang

2018-05-16

The High Resolution Range Profile (HRRP) recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). However, traditional HRRP recognition methods failed to model high dimensional sequential data efficiently and have a poor anti-noise ability. To deal with these problems, a novel stochastic neural network model named Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed in this paper. RTRBM is utilized to extract discriminative features and the attention mechanism is adopted to select major features. RTRBM is efficient to model high dimensional HRRP sequences because it can extract the information of temporal and spatial correlation between adjacent HRRPs. The attention mechanism is used in sequential data recognition tasks including machine translation and relation classification, which makes the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and the attention mechanism makes our model effective for extracting more internal related features and choose the important parts of the extracted features. Additionally, the model performs well with the noise corrupted HRRP data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, which indicates that ARTRBM extracts, selects, and utilizes the correlation information between adjacent HRRPs effectively and is suitable for high dimensional data or noise corrupted data.

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

Tool geometry and damage mechanisms influencing CNC turning efficiency of Ti6Al4V

NASA Astrophysics Data System (ADS)

Suresh, Sangeeth; Hamid, Darulihsan Abdul; Yazid, M. Z. A.; Nasuha, Nurdiyanah; Ain, Siti Nurul

2017-12-01

Ti6Al4V or Grade 5 titanium alloy is widely used in the aerospace, medical, automotive and fabrication industries, due to its distinctive combination of mechanical and physical properties. Ti6Al4V has always been perverse during its machining, strangely due to the same mix of properties mentioned earlier. Ti6Al4V machining has resulted in shorter cutting tool life which has led to objectionable surface integrity and rapid failure of the parts machined. However, the proven functional relevance of this material has prompted extensive research in the optimization of machine parameters and cutting tool characteristics. Cutting tool geometry plays a vital role in ensuring dimensional and geometric accuracy in machined parts. In this study, an experimental investigation is actualized to optimize the nose radius and relief angles of the cutting tools and their interaction to different levels of machining parameters. Low elastic modulus and thermal conductivity of Ti6Al4V contribute to the rapid tool damage. The impact of these properties over the tool tips damage is studied. An experimental design approach is utilized in the CNC turning process of Ti6Al4V to statistically analyze and propose optimum levels of input parameters to lengthen the tool life and enhance surface characteristics of the machined parts. A greater tool nose radius with a straight flank, combined with low feed rates have resulted in a desirable surface integrity. The presence of relief angle has proven to aggravate tool damage and also dimensional instability in the CNC turning of Ti6Al4V.

Automated Composites Processing Technology: Film Module

NASA Technical Reports Server (NTRS)

Hulcher, A. Bruce

2004-01-01

NASA's Marshall Space Flight Center (MSFC) has developed a technology that combines a film/adhesive laydown module with fiber placement technology to enable the processing of composite prepreg tow/tape and films, foils or adhesives on the same placement machine. The development of this technology grew out of NASA's need for lightweight, permeation-resistant cryogenic propellant tanks. Autoclave processing of high performance composites results in thermally-induced stresses due to differences in the coefficients of thermal expansion of the fiber and matrix resin components. These stresses, together with the reduction in temperature due to cryogen storage, tend to initiate microcracking within the composite tank wall. One way in which to mitigate this problem is to introduce a thin, crack-resistant polymer film or foil into the tank wall. Investigation into methods to automate the processing of thin film or foil materials into composites led to the development of this technology. The concept employs an automated film supply and feed module that may be designed to fit existing fiber placement machines, or may be designed as integral equipment to new machines. This patent-pending technology can be designed such that both film and foil materials may be processed simultaneously, leading to a decrease in part build cycle time. The module may be designed having a compaction device independent of the host machine, or may utilize the host machine's compactor. The film module functions are controlled by a dedicated system independent of the fiber placement machine controls. The film, foil, or adhesive is processed via pre-existing placement machine run programs, further reducing operational expense.

Performance testing of a high frequency link converter for Space Station power distribution system

NASA Technical Reports Server (NTRS)

Sul, S. K.; Alan, I.; Lipo, T. A.

1989-01-01

The testing of a brassboard version of a 20-kHz high-frequency ac voltage link prototype converter dynamics for Space Station application is presented. The converter is based on a three-phase six-pulse bridge concept. The testing includes details of the operation of the converter when it is driving an induction machine source/load. By adapting a field orientation controller (FOC) to the converter, four-quadrant operation of the induction machine from the converter has been achieved. Circuit modifications carried out to improve the performance of the converter are described. The performance of two 400-Hz induction machines powered by the converter with simple V/f regulation mode is reported. The testing and performance results for the converter utilizing the FOC, which provides the capability for rapid torque changes, speed reversal, and four-quadrant operation, are reported.

Methods for the Precise Locating and Forming of Arrays of Curved Features into a Workpiece

DOEpatents

Gill, David Dennis; Keeler, Gordon A.; Serkland, Darwin K.; Mukherjee, Sayan D.

2008-10-14

Methods for manufacturing high precision arrays of curved features (e.g. lenses) in the surface of a workpiece are described utilizing orthogonal sets of inter-fitting locating grooves to mate a workpiece to a workpiece holder mounted to the spindle face of a rotating machine tool. The matching inter-fitting groove sets in the workpiece and the chuck allow precisely and non-kinematically indexing the workpiece to locations defined in two orthogonal directions perpendicular to the turning axis of the machine tool. At each location on the workpiece a curved feature can then be on-center machined to create arrays of curved features on the workpiece. The averaging effect of the corresponding sets of inter-fitting grooves provide for precise repeatability in determining, the relative locations of the centers of each of the curved features in an array of curved features.

Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter

PubMed Central

Babiker, Areej; Faye, Ibrahima; Prehn, Kristin; Malik, Aamir

2015-01-01

Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual’s emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions. PMID:26733912

Potential application of machine learning in health outcomes research and some statistical cautions.

PubMed

Crown, William H

2015-03-01

Traditional analytic methods are often ill-suited to the evolving world of health care big data characterized by massive volume, complexity, and velocity. In particular, methods are needed that can estimate models efficiently using very large datasets containing healthcare utilization data, clinical data, data from personal devices, and many other sources. Although very large, such datasets can also be quite sparse (e.g., device data may only be available for a small subset of individuals), which creates problems for traditional regression models. Many machine learning methods address such limitations effectively but are still subject to the usual sources of bias that commonly arise in observational studies. Researchers using machine learning methods such as lasso or ridge regression should assess these models using conventional specification tests. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Superior memory efficiency of quantum devices for the simulation of continuous-time stochastic processes

NASA Astrophysics Data System (ADS)

Elliott, Thomas J.; Gu, Mile

2018-03-01

Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of information about past behaviour, even for relatively simple models, enforcing limits on precision due to the finite memory of the machine. However, quantum machines can require less information about the past than even their optimal classical counterparts to simulate the future of discrete-time processes, and we demonstrate that this advantage extends to the continuous-time regime. Moreover, we show that this reduction in the memory requirement can be unboundedly large, allowing for arbitrary precision even with a finite quantum memory. We provide a systematic method for finding superior quantum constructions, and a protocol for analogue simulation of continuous-time renewal processes with a quantum machine.

Generic decoding of seen and imagined objects using hierarchical visual features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-05-22

Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

Transducer-actuator systems and methods for performing on-machine measurements and automatic part alignment

DOEpatents

Barkman, William E.; Dow, Thomas A.; Garrard, Kenneth P.; Marston, Zachary

2016-07-12

Systems and methods for performing on-machine measurements and automatic part alignment, including: a measurement component operable for determining the position of a part on a machine; and an actuation component operable for adjusting the position of the part by contacting the part with a predetermined force responsive to the determined position of the part. The measurement component consists of a transducer. The actuation component consists of a linear actuator. Optionally, the measurement component and the actuation component consist of a single linear actuator operable for contacting the part with a first lighter force for determining the position of the part and with a second harder force for adjusting the position of the part. The actuation component is utilized in a substantially horizontal configuration and the effects of gravitational drop of the part are accounted for in the force applied and the timing of the contact.

Wind turbines for electric utilities: Development status and economics

NASA Technical Reports Server (NTRS)

Ramler, J. R.; Donovan, R. M.

1979-01-01

The technology and economics of the large, horizontal-axis wind turbines currently in the Federal Wind Energy Program are presented. Wind turbine technology advancements made in the last several years are discussed. It is shown that, based on current projections of the costs of these machines when produced in quantity, they should be attractive for utility application. The cost of electricity (COE) produced at the busbar is shown to be a strong function of the mean wind speed at the installation site. The breakeven COE as a fuel saver is discussed and the COE range that would be generally attractive to utilities is indicated.

Wind turbines for electric utilities - Development status and economics

NASA Technical Reports Server (NTRS)

Ramler, J. R.; Donovan, R. M.

1979-01-01

The technology and economics of the large, horizontal-axis wind turbines currently in the Federal Wind Energy Program are presented. Wind turbine technology advancements made in the last several years are discussed. It is shown that, based on current projections of the costs of these machines when produced in quantity, they should be attractive for utility application. The cost of electricity (COE) produced at the busbar is shown to be a strong function of the mean wind speed at the installation site. The breakeven COE as a 'fuel saver' is discussed and the COE range that would be generally attractive to utilities is indicated.

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

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

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

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

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

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

PubMed

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

2017-03-01

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

A novel flux-switching permanent magnet machine with v-shaped magnets

NASA Astrophysics Data System (ADS)

Zhao, Guishu; Hua, Wei

2017-05-01

In this paper, firstly a novel 6-stator-coil/17-rotor-pole (6/17) flux-switching permanent magnet (FSPM) machine with V-shaped magnets, deduced from conventional 12/17 FSPM machines is proposed to achieve more symmetrical phase back-electromotive force (back-EMF), and smaller torque ripple by comparing with an existing 6/10 V-shaped FSPM machine. Then, to obtain larger electromagnetic torque, less torque ripple, and easier mechanical processing, two improved variants based on the original 6/17 V-shaped topology are proposed. For the first variant, the separate stator-core segments located on the stator yoke are connected into a united stator yoke, while for the second variant the stator core is a whole entity by adding magnetic bridges at the ends of permanent magnets (PMs). Consequently, the performances of the three 6/17 V-shaped FSPM machines, namely, the original one and the two variants, are conducted by finite element analysis (FEA). The results reveal that the first variant exhibits significantly larger torque and considerably improved torque per magnet volume, i.e., the magnet utilization ratio than the original one, and the second variant exhibits the smallest torque ripple, least total harmonic distribution (THD) of phase back-EMF, and easiest mechanical processing for manufacturing.

MARTI: man-machine animation real-time interface

NASA Astrophysics Data System (ADS)

Jones, Christian M.; Dlay, Satnam S.

1997-05-01

The research introduces MARTI (man-machine animation real-time interface) for the realization of natural human-machine interfacing. The system uses simple vocal sound-tracks of human speakers to provide lip synchronization of computer graphical facial models. We present novel research in a number of engineering disciplines, which include speech recognition, facial modeling, and computer animation. This interdisciplinary research utilizes the latest, hybrid connectionist/hidden Markov model, speech recognition system to provide very accurate phone recognition and timing for speaker independent continuous speech, and expands on knowledge from the animation industry in the development of accurate facial models and automated animation. The research has many real-world applications which include the provision of a highly accurate and 'natural' man-machine interface to assist user interactions with computer systems and communication with one other using human idiosyncrasies; a complete special effects and animation toolbox providing automatic lip synchronization without the normal constraints of head-sets, joysticks, and skilled animators; compression of video data to well below standard telecommunication channel bandwidth for video communications and multi-media systems; assisting speech training and aids for the handicapped; and facilitating player interaction for 'video gaming' and 'virtual worlds.' MARTI has introduced a new level of realism to man-machine interfacing and special effect animation which has been previously unseen.

Investigation of roughing machining simulation by using visual basic programming in NX CAM system

NASA Astrophysics Data System (ADS)

Hafiz Mohamad, Mohamad; Nafis Osman Zahid, Muhammed

2018-03-01

This paper outlines a simulation study to investigate the characteristic of roughing machining simulation in 4th axis milling processes by utilizing visual basic programming in NX CAM systems. The selection and optimization of cutting orientation in rough milling operation is critical in 4th axis machining. The main purpose of roughing operation is to approximately shape the machined parts into finished form by removing the bulk of material from workpieces. In this paper, the simulations are executed by manipulating a set of different cutting orientation to generate estimated volume removed from the machine parts. The cutting orientation with high volume removal is denoted as an optimum value and chosen to execute a roughing operation. In order to run the simulation, customized software is developed to assist the routines. Operations build-up instructions in NX CAM interface are translated into programming codes via advanced tool available in the Visual Basic Studio. The codes is customized and equipped with decision making tools to run and control the simulations. It permits the integration with any independent program files to execute specific operations. This paper aims to discuss about the simulation program and identifies optimum cutting orientations for roughing processes. The output of this study will broaden up the simulation routines performed in NX CAM systems.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

NASA Technical Reports Server (NTRS)

Ambur, Manjula; Schwartz, Katherine G.; Mavris, Dimitri N.

2016-01-01

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Expanding the utility of the Agricultural Research Service (ARS) process bleaching

USDA-ARS?s Scientific Manuscript database

The ARS Process for bleaching, biopolishing, and shrinkproofing wool is a novel alternative to chlorination and conventional bleaching. Consumer acceptance of domestic machine-washable, comfortable wool which can be worn next to the skin will lead to niche-market- potential and competitive, increas...

School Community Relations and Resources in Effective Schools.

ERIC Educational Resources Information Center

Michel, George J.

1985-01-01

Discusses resources available to schools operating as open and closed systems. Examines school/community relations and school effectiveness, schools as resource machines, and resources offered by teachers and parents. Stresses that broad concepts of community, good communication, and citizen involvement can utilize resources at high levels of…

Probability Simulations by Non-Lipschitz Chaos

NASA Technical Reports Server (NTRS)

Zak, Michail

1996-01-01

It has been demonstrated that classical probabilities, and in particular, probabilistic Turing machine, can be simulated by combining chaos and non-Lipschitz dynamics, without utilization of any man-made devices. Self-organizing properties of systems coupling simulated and calculated probabilities and their link to quantum computations are discussed.

Laughing Bear.

ERIC Educational Resources Information Center

Seeds, Michael A.; Seeds, Kathryn Anne

1983-01-01

Provided is a complete listing (Applesoft Basic) for a children's spelling program. The listing includes a machine language music utility that plays short tunes and uses the Apple's two hi-res screens for animation. Also included is a program that allows pictures to be drawn and saved to animate other programs. (JN)

TECHNOLOGY AND THE INSTRUCTIONAL PROCESS.

ERIC Educational Resources Information Center

FINN, JAMES D.

A TEACHER SHORTAGE, LARGE CLASSES, AND NEED FOR QUALITY INSTRUCTION FORCED EDUCATION INTO MASS INSTRUCTIONAL TECHNOLOGY. INSTRUCTIONAL TECHNOLOGY IS GOVERNED BY SUCH SYSTEMS AS TELEVISION AND FILMS WHICH CAN REACH MORE STUDENTS WITH FEWER TEACHERS. THERE IS A TREND TOWARD INDIVIDUAL INSTRUCTION UTILIZING TEACHING MACHINES. IF A COMBINATION OF…

A Photographic Profile Recorder for Airscrews and Wing Models

NASA Technical Reports Server (NTRS)

Kuhl, R.; Raab, K.

1946-01-01

This report describes an apparatus enabling measurements of bodies to be made photographically, where other methods would be difficult. It is especially useful in the case of airscrews and wing models. The utility of the machine is shown by a few examples of profile records.

Efficient Conduct of Individual Flights and Air Traffic or Optimum Utilization of Modern Technology for the Overall Benefit of Civil and Military Airspace Users. Conference Proceedings of the Symposium of the Guidance and Control Panel (42nd) Held in Brussels, Belgium on 10-13 June 1986.

DTIC Science & Technology

1986-12-01

subjects such as: - the need to have reliable systems which will be "fault-tolerant’ - the man/machine relationship ; - compatibility between systems. 8. THE...be worked out and that acceptable solutions can be found as regards the man/ machine relationship . It will also be necessary to resolve the problems...management functions of the system should be essentially ground-based. 9. Capacity for coping with demands. 10. ATIM capability and relationship with

Cognitive Foundry v. 3.0 (OSS)

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

Basilico, Justin; Dixon, Kevin; McClain, Jonathan

2009-11-18

The Cognitive Foundry is a unified collection of tools designed for research and applications that use cognitive modeling, machine learning, or pattern recognition. The software library contains design patterns, interface definitions, and default implementations of reusable software components and algorithms designed to support a wide variety of research and development needs. The library contains three main software packages: the Common package that contains basic utilities and linear algebraic methods, the Cognitive Framework package that contains tools to assist in implementing and analyzing theories of cognition, and the Machine Learning package that provides general algorithms and methods for populating Cognitive Frameworkmore » components from domain-relevant data.« less

Social and Economic Impact of Solar Electricity at Schuchuli Village

NASA Technical Reports Server (NTRS)

Bifano, W. J.; Ratajczak, A. F.; Bahr, D. M.; Garrett, B. G.

1979-01-01

Schuchuli, a small remote village on the Papago Indian Reservation in southwest Arizona, is 27 kilometers (17 miles) from the nearest available utility power. Its lack of conventional power is due to the prohibitive cost of supplying a small electrical load with a long-distance distribution line. Furthermore, alternate energy sources are expensive and place a burden on the resources of the villagers. On December 16, 1978, as part of a federally funded project, a solar cell power system was put into operation at Schuchuli. The system powers the village water pump, lighting for homes and other village buildings, family refrigerators and a communal washing machine and sewing machine.

Auction dynamics: A volume constrained MBO scheme

NASA Astrophysics Data System (ADS)

Jacobs, Matt; Merkurjev, Ekaterina; Esedoǧlu, Selim

2018-02-01

We show how auction algorithms, originally developed for the assignment problem, can be utilized in Merriman, Bence, and Osher's threshold dynamics scheme to simulate multi-phase motion by mean curvature in the presence of equality and inequality volume constraints on the individual phases. The resulting algorithms are highly efficient and robust, and can be used in simulations ranging from minimal partition problems in Euclidean space to semi-supervised machine learning via clustering on graphs. In the case of the latter application, numerous experimental results on benchmark machine learning datasets show that our approach exceeds the performance of current state-of-the-art methods, while requiring a fraction of the computation time.

Various Recrystallizations of CL-20 (HNIW hexanitrohexaazaisowurtzitane).

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

Phillips, Jason Joe

Impact sensitivity testing was performed using a modified Bureau of Mines (MBOM) impactor manufactured by Safety Management Services, Inc., shown in Figure 1. Type-12 tooling was utilized on this machine with a 2.5kg impactor and matching intermediate mass. This particular machine is capable of a maximum drop height of 115cm with 0.1cm increments, though 1cm increments are typically used. Sample material was placed (35 ± 2mg) onto 1 inch squares of Norton brand 180A Garnet sandpaper. Positive results were detected visually or audibly by the operator as smoke, flash, report, charring/tearing of the sandpaper, etc.

Space Weather in the Machine Learning Era: A Multidisciplinary Approach

NASA Astrophysics Data System (ADS)

Camporeale, E.; Wing, S.; Johnson, J.; Jackman, C. M.; McGranaghan, R.

2018-01-01

The workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25-29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun-Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather-related problems.

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

NASA Technical Reports Server (NTRS)

Kedar, Smadar T.; McKusick, Kathleen B.

1992-01-01

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.

An overview of large wind turbine tests by electric utilities

NASA Technical Reports Server (NTRS)

Vachon, W. A.; Schiff, D.

1982-01-01

A summary of recent plants and experiences on current large wind turbine (WT) tests being conducted by electric utilities is provided. The test programs discussed do not include federal research and development (R&D) programs, many of which are also being conducted in conjunction with electric utilities. The information presented is being assembled in a project, funded by the Electric Power Research Institute (EPRI), the objective of which is to provide electric utilities with timely summaries of test performance on key large wind turbines. A summary of key tests, test instrumentation, and recent results and plans is given. During the past year, many of the utility test programs initiated have encountered test difficulties that required specific WT design changes. However, test results to date continue to indicate that long-term machine performance and cost-effectiveness are achievable.

Optimizing cutting conditions on sustainable machining of aluminum alloy to minimize power consumption

NASA Astrophysics Data System (ADS)

Nur, Rusdi; Suyuti, Muhammad Arsyad; Susanto, Tri Agus

2017-06-01

Aluminum is widely utilized in the industrial sector. There are several advantages of aluminum, i.e. good flexibility and formability, high corrosion resistance and electrical conductivity, and high heat. Despite of these characteristics, however, pure aluminum is rarely used because of its lacks of strength. Thus, most of the aluminum used in the industrial sectors was in the form of alloy form. Sustainable machining can be considered to link with the transformation of input materials and energy/power demand into finished goods. Machining processes are responsible for environmental effects accepting to their power consumption. The cutting conditions have been optimized to minimize the cutting power, which is the power consumed for cutting. This paper presents an experimental study of sustainable machining of Al-11%Si base alloy that was operated without any cooling system to assess the capacity in reducing power consumption. The cutting force was measured and the cutting power was calculated. Both of cutting force and cutting power were analyzed and modeled by using the central composite design (CCD). The result of this study indicated that the cutting speed has an effect on machining performance and that optimum cutting conditions have to be determined, while sustainable machining can be followed in terms of minimizing power consumption and cutting force. The model developed from this study can be used for evaluation process and optimization to determine optimal cutting conditions for the performance of the whole process.

Wireless tracking of cotton modules Part II: automatic machine identification and system testing

USDA-ARS?s Scientific Manuscript database

Mapping the harvest location of cotton modules is essential to practical understanding and utilization of spatial-variability information in fiber quality. A wireless module-tracking system was recently developed, but automation of the system is required before it will find practical use on the far...

A system for the automatic measurement and digital display of systolic and diastolic blood pressures

NASA Technical Reports Server (NTRS)

Schulze, A. E.

1971-01-01

Basic components of system are - occluding cuff with mounted cuff microscope, cuff pump deflator, pressure transducer, preamplifier unit, electrocardiograph machine, an analog to digital convertor unit, and digital display unit. System utilizes indirect auscultatory method, based on Korotkoff sounds, for measurement.

Electromagnetic Machines Which Utilize Microgeometry Field Structures

DTIC Science & Technology

1990-04-01

34Phenomenological Theory of Electret Discharge Including Incomplete Recombination of Ions". Acta Physica Polonica , A50(1)1 1. Mehendru, P.C. (177)(: "Electret...Resonance of Stabilized Electrons in PTFE Coronoelectrets". Physica Status Solidi, (a)90:K71-K74. Lagues, M. (1976): "Les Electrets: des Transducteurs

Machine-Aided Translation: From Terminology Banks to Interactive Translation Systems.

ERIC Educational Resources Information Center

Greenfield, Concetta C.; Serain, Daniel

The rapid growth of the need for technical translations in recent years has led specialists to utilize computer technology to improve the efficiency and quality of translation. The two approaches considered were automatic translation and terminology banks. Since the results of fully automatic translation were considered unsatisfactory by various…

Mechanics and Inventors.

ERIC Educational Resources Information Center

Lutton, Louise Pietsch

1998-01-01

Presents an integrated science and art lesson for first-grade students. Explains that first the students examined various machines by taking them apart and then they utilized that knowledge to draw their own inventions. States that this lesson provides students with a chance to develop their problem-solving and critical thinking skills. (CMK)

Simulations of Probabilities for Quantum Computing

NASA Technical Reports Server (NTRS)

Zak, M.

1996-01-01

It has been demonstrated that classical probabilities, and in particular, probabilistic Turing machine, can be simulated by combining chaos and non-LIpschitz dynamics, without utilization of any man-made devices (such as random number generators). Self-organizing properties of systems coupling simulated and calculated probabilities and their link to quantum computations are discussed.

Methods of Cost Reduction for United States Coast Guard Telephone Systems.

DTIC Science & Technology

1981-03-01

System (’FTS) for a single low utilization command is questionable. Small FAX machines such as EXXON’s QWIP /FAX can be purchased at approxi- mately...unsatisfactory operating condi- tion. Electrical faults, such as leakage or poor insulation, noise induction, crosstalk, or poor transmission

The Army word recognition system

NASA Technical Reports Server (NTRS)

Hadden, David R.; Haratz, David

1977-01-01

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

ARL Statement on Unlimited Use and Exchange of Bibliographic Records.

ERIC Educational Resources Information Center

Association of Research Libraries, Washington, DC.

The Association of Research Libraries is fully committed to the principle of unrestricted access to and dissemination of ideas, i.e., member libraries must have unlimited access to the machine-readable bibliographic records which are created by member libraries and maintained in bibliographic utilities. Coordinated collection development programs…

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

PubMed

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

2016-04-01

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

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

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.

Exploiting the Dynamics of Soft Materials for Machine Learning

PubMed Central

Hauser, Helmut; Li, Tao; Pfeifer, Rolf

2018-01-01

Abstract Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world. PMID:29708857

Exploiting the Dynamics of Soft Materials for Machine Learning.

PubMed

Nakajima, Kohei; Hauser, Helmut; Li, Tao; Pfeifer, Rolf

2018-06-01

Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world.

Novel jet observables from machine learning

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

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

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

Human-machine interface for a VR-based medical imaging environment

NASA Astrophysics Data System (ADS)

Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

1997-05-01

Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

Improving Overall Equipment Effectiveness Using CPM and MOST: A Case Study of an Indonesian Pharmaceutical Company

NASA Astrophysics Data System (ADS)

Omega, Dousmaris; Andika, Aditya

2017-12-01

This paper discusses the results of a research conducted on the production process of an Indonesian pharmaceutical company. The company is experiencing low performance in the Overall Equipment Effectiveness (OEE) metric. The OEE of the company machines are below world class standard. The machine that has the lowest OEE is the filler machine. Through observation and analysis, it is found that the cleaning process of the filler machine consumes significant amount of time. The long duration of the cleaning process happens because there is no structured division of jobs between cleaning operators, differences in operators’ ability, and operators’ inability in utilizing available cleaning equipment. The company needs to improve the cleaning process. Therefore, Critical Path Method (CPM) analysis is conducted to find out what activities are critical in order to shorten and simplify the cleaning process in the division of tasks. Afterwards, The Maynard Operation and Sequence Technique (MOST) method is used to reduce ineffective movement and specify the cleaning process standard time. From CPM and MOST, it is obtained the shortest time of the cleaning process is 1 hour 28 minutes and the standard time is 1 hour 38.826 minutes.

Automatic microseismic event picking via unsupervised machine learning

NASA Astrophysics Data System (ADS)

Chen, Yangkang

2018-01-01

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

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

PubMed Central

Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

2015-01-01

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

Human facial neural activities and gesture recognition for machine-interfacing applications.

PubMed

Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

2011-01-01

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

A novel device for head gesture measurement system in combination with eye-controlled human machine interface

NASA Astrophysics Data System (ADS)

Lin, Chern-Sheng; Ho, Chien-Wa; Chang, Kai-Chieh; Hung, San-Shan; Shei, Hung-Jung; Yeh, Mau-Shiun

2006-06-01

This study describes the design and combination of an eye-controlled and a head-controlled human-machine interface system. This system is a highly effective human-machine interface, detecting head movement by changing positions and numbers of light sources on the head. When the users utilize the head-mounted display to browse a computer screen, the system will catch the images of the user's eyes with CCD cameras, which can also measure the angle and position of the light sources. In the eye-tracking system, the program in the computer will locate each center point of the pupils in the images, and record the information on moving traces and pupil diameters. In the head gesture measurement system, the user wears a double-source eyeglass frame, so the system catches images of the user's head by using a CCD camera in front of the user. The computer program will locate the center point of the head, transferring it to the screen coordinates, and then the user can control the cursor by head motions. We combine the eye-controlled and head-controlled human-machine interface system for the virtual reality applications.

A comparative study on performance of CBN inserts when turning steel under dry and wet conditions

NASA Astrophysics Data System (ADS)

Abdullah Bagaber, Salem; Razlan Yusoff, Ahmad

2017-10-01

Cutting fluids is the most unsustainable components of machining processes, it is negatively impacting on the environmental and additional energy required. Due to its high strength and corrosion resistance, the machinability of stainless steel has attracted considerable interest. This study aims to evaluate performance of cubic boron nitride (CBN) inserts for the machining parameters includes the power consumption and surface roughness. Due to the high single cutting-edge cost of CBN, the performance of significant is importance for hard finish turning. The present work also deals with a comparative study on power consumption and surface roughness under dry and flood conditions. Turning process of the stainless steel 316 was performed. A response surface methodology based box-behnken design (BBD) was utilized for statistical analysis. The optimum process parameters are determined as the overall performance index. The comparison study has been done between dry and wet stainless-steel cut in terms of minimum value of energy and surface roughness. The result shows the stainless still can be machined under dry condition with 18.57% improvement of power consumption and acceptable quality compare to the wet cutting. The CBN tools under dry cutting stainless steel can be used to reduce the environment impacts in terms of no cutting fluid use and less energy required which is effected in machining productivity and profit.

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

PubMed Central

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

2017-01-01

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation. PMID:28277804

Soft robotics: a review and progress towards faster and higher torque actuators (presentation video)

NASA Astrophysics Data System (ADS)

Shepherd, Robert

2014-03-01

Last year, nearly 160,000 industrial robots were shipped worldwide—into a total market valued at 26 Bn (including hardware, software, and peripherals).[1] Service robots for professional (e.g., defense, medical, agriculture) and personal (e.g., household, handicap assistance, toys, and education) use accounted for 16,000 units, 3.4 Bn and 3,000,000 units, $1.2 Bn respectively.[1] The vast majority of these robotic systems use fully actuated, rigid components that take little advantage of passive dynamics. Soft robotics is a field that is taking advantage of compliant actuators and passive dynamics to achieve several goals: reduced design, manufacturing and control complexity, improved energy efficiency, more sophisticated motions, and safe human-machine interactions to name a few. The potential for societal impact is immense. In some instances, soft actuators have achieved commercial success; however, large scale adoption will require improved methods of controlling non-linear systems, greater reliability in their function, and increased utility from faster and more forceful actuation. In my talk, I will describe efforts from my work in the Whitesides group at Harvard to prove sophisticated motions in these machines using simple controls, as well capabilities unique to soft machines. I will also describe the potential for combinations of different classes of soft actuators (e.g., electrically and pneumatically actuated systems) to improve the utility of soft robots. 1. World Robotics - Industrial Robots 2013, 2013, International Federation of Robotics.

Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding.

PubMed

Gao, Wei; Kwong, Sam; Jia, Yuheng

2017-08-25

In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in High Efficiency Video Coding (HEVC). First, a support vector machine (SVM) based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level Rate-Distortion (R-D) model. The legacy "chicken-and-egg" dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model based utility function is proved, and Nash bargaining solution (NBS) is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level Quantization parameter (QP) change. Lastly, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

Inserts Automatically Lubricate Ball Bearings

NASA Technical Reports Server (NTRS)

Hager, J. A.

1983-01-01

Inserts on ball-separator ring of ball bearings provide continuous film of lubricant on ball surfaces. Inserts are machined or molded. Small inserts in ball pockets provide steady supply of lubricant. Technique is utilized on equipment for which maintenance is often poor and lubrication interval is uncertain, such as household appliances, automobiles, and marine engines.

A Framework for Structuring Learning Assessment in a Online Educational Game: Experiment Centered Design

ERIC Educational Resources Information Center

Conrad, Shawn; Clarke-Midura, Jody; Klopfer, Eric

2014-01-01

Educational games offer an opportunity to engage and inspire students to take interest in science, technology, engineering, and mathematical (STEM) subjects. Unobtrusive learning assessment techniques coupled with machine learning algorithms can be utilized to record students' in-game actions and formulate a model of the students' knowledge…

Classification of Word Levels with Usage Frequency, Expert Opinions and Machine Learning

ERIC Educational Resources Information Center

Sohsah, Gihad N.; Ünal, Muhammed Esad; Güzey, Onur

2015-01-01

Educational applications for language teaching can utilize the language levels of words to target proficiency levels of students. This paper and the accompanying data provide a methodology for making educational standard-aligned language-level predictions for all English words. The methodology involves expert opinions on language levels and…

Undergraduate Student Perceptions of the Use of Ultrasonography in the Study of "Living Anatomy"

ERIC Educational Resources Information Center

Ivanusic, Jason; Cowie, Brian; Barrington, Michael

2010-01-01

Ultrasonography is a noninvasive imaging modality, and modern ultrasound machines are portable, inexpensive (relative to other imaging modalities), and user friendly. The aim of this study was to explore student perceptions of the use of ultrasound to teach "living anatomy". A module utilizing transthoracic echocardiography was developed and…

Brain-Based Learning With Technological Support

ERIC Educational Resources Information Center

Miller, Anita

2004-01-01

Utilization of technology in secondary schools is varied and depends on the training and interest of the individual instructors. Even though technology has advanced way beyond its utilitarian roots of being viewed solely by educators as a useful machine for teachers to key exams and worksheets on, there are still many secondary educators who still…

Break-Even Point for a Proof Slip Operation

ERIC Educational Resources Information Center

Anderson, James F.

1972-01-01

Break-even analysis is applied to determine what magnitude of titles added per year is sufficient to utilize economically Library of Congress proof slips and a Xerox 914 copying machine in the cataloging operation of a library. A formula is derived, and an example of its use is given. (1 reference) (Author/SJ)

Wind Energy Systems.

ERIC Educational Resources Information Center

Conservation and Renewable Energy Inquiry and Referral Service (DOE), Silver Spring, MD.

During the 1920s and 1930s, millions of wind energy systems were used on farms and other locations far from utility lines. However, with passage of the Rural Electrification Act in 1939, cheap electricity was brought to rural areas. After that, the use of wind machines dramatically declined. Recently, the rapid rise in fuel prices has led to a…

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

An improved advertising CTR prediction approach based on the fuzzy deep neural network

PubMed Central

Gao, Shu; Li, Mingjiang

2018-01-01

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise. PMID:29727443

Motor proteins and molecular motors: how to operate machines at the nanoscale.

PubMed

Kolomeisky, Anatoly B

2013-11-20

Several classes of biological molecules that transform chemical energy into mechanical work are known as motor proteins or molecular motors. These nanometer-sized machines operate in noisy stochastic isothermal environments, strongly supporting fundamental cellular processes such as the transfer of genetic information, transport, organization and functioning. In the past two decades motor proteins have become a subject of intense research efforts, aimed at uncovering the fundamental principles and mechanisms of molecular motor dynamics. In this review, we critically discuss recent progress in experimental and theoretical studies on motor proteins. Our focus is on analyzing fundamental concepts and ideas that have been utilized to explain the non-equilibrium nature and mechanisms of molecular motors.

Prediction of laser cutting heat affected zone by extreme learning machine

NASA Astrophysics Data System (ADS)

Anicic, Obrad; Jović, Srđan; Skrijelj, Hivzo; Nedić, Bogdan

2017-01-01

Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.

Characterization of Subsurface Defects in Ceramic Rods by Laser Scattering and Fractography

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

Zhang, J. M.; Sun, J. G.; Andrews, M. J.

2006-03-06

Silicon nitride ceramics are leading materials being evaluated for valve train components in diesel engine applications. The surface and subsurface defects and damage induced by surface machining can significantly affect component strength and lifetime. In this study, a nondestructive evaluation (NDE) technique based upon laser scattering has been utilized to analyze eight transversely ground silicon nitride cylindrical rods before fracture tests. The fracture origins (machining cracks or material-inherent flaws) identified by fractography after fracture testing were correlated with laser scattering images. The results indicate that laser scattering is able to identify possible fracture origin in the silicon nitride subsurface withoutmore » the need for destructive fracture tests.« less

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Supervised Machine Learning for Population Genetics: A New Paradigm

PubMed Central

Schrider, Daniel R.; Kern, Andrew D.

2018-01-01

As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. PMID:29331490

The Mod-2 wind turbine development project

NASA Technical Reports Server (NTRS)

Linscott, B. S.; Dennett, J. T.; Gordon, L. H.

1981-01-01

A major phase of the Federal Wind Energy Program, the Mod-2 wind turbine, a second-generation machine developed by the Boeing Engineering and Construction Co. for the U.S. Department of Energy and the Lewis Research Center of the National Aeronautics and Space Administration, is described. The Mod-2 is a large (2.5-MW power rating) horizontal-axis wind turbine designed for the generation of electrical power on utility networks. Three machines were built and are located in a cluster at Goodnoe Hills, Washington. All technical aspects of the project are described: design approach, significant innovation features, the mechanical system, the electrical power system, the control system, and the safety system.

An improved advertising CTR prediction approach based on the fuzzy deep neural network.

PubMed

Jiang, Zilong; Gao, Shu; Li, Mingjiang

2018-01-01

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

PubMed Central

Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system. PMID:29687000

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

PubMed

Huang, Qinghua; Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Modeling the Virtual Machine Launching Overhead under Fermicloud

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

Garzoglio, Gabriele; Wu, Hao; Ren, Shangping

FermiCloud is a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows. The Cloud Bursting module of the FermiCloud enables the FermiCloud, when more computational resources are needed, to automatically launch virtual machines to available resources such as public clouds. One of the main challenges in developing the cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on FermiCloud’s system operational data, the VM launching overhead is not a constant. It varies with physical resourcemore » (CPU, memory, I/O device) utilization at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launch overhead reference model is needed. The paper is to develop a VM launch overhead reference model based on operational data we have obtained on FermiCloud and uses the reference model to guide the cloud bursting process.« less

Differences in Muscle Activation and Kinematics Between Cable-Based and Selectorized Weight Training.

PubMed

Signorile, Joseph F; Rendos, Nicole K; Heredia Vargas, Hector H; Alipio, Taislaine C; Regis, Rebecca C; Eltoukhy, Moataz M; Nargund, Renu S; Romero, Matthew A

2017-02-01

Signorile, JF, Rendos, NK, Heredia Vargas, HH, Alipio, TC, Regis, RC, Eltoukhy, MM, Nargund, RS, and Romero, MA. Differences in muscle activation and kinematics between cable-based and selectorized weight training. J Strength Cond Res 31(2): 313-322, 2017-Cable resistance training machines are showing resurgent popularity and allow greater number of degrees of freedom than typical selectorized equipment. Given that specific kinetic chains are used during distinct activities of daily living (ADL), cable machines may provide more effective interventions for some ADL, whereas others may be best addressed using selectorized equipment. This study examined differences in activity levels (root mean square of the EMG [rmsEMG]) of 6 major muscles (pectoralis major, PM; anterior deltoid, AD; biceps brachii, BB; rectus abdominis, RA; external obliques, EO; and triceps brachii, TB) and kinematics of multiple joints between a cable and standard selectorized machines during the biceps curl, the chest press, and the overhead press performed at 1.5 seconds per contractile stage. Fifteen individuals (9 men, 6 women; mean age ± SD, 24.33 ± 4.88 years) participated. Machine order was randomized. Significant differences favoring cable training were seen for PM and AD during biceps curl; BB, AD, and EO for chest press; and BB and EO during overhead press (p ≤ 0.05). Greater starting and ending angles were seen for the elbow and shoulder joints during selectorized biceps curl, whereas hip and knee starting and ending angles were greater for cable machine during chest and overhead presses (p < 0.0001). Greater range of motion (ROM) favoring the cable machine was also evident (p < 0.0001). These results indicate that utilization patterns of selected muscles, joint angles, and ROMs can be varied because of machine application even when similar exercises are used, and therefore, these machines can be used selectively in training programs requiring specific motor or biomechanical patterns.

Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

NASA Astrophysics Data System (ADS)

Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

2017-12-01

Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Multiple access to sterile syringes for injection drug users: vending machines, needle exchange programs and legal pharmacy sales in Marseille, France.

PubMed

Moatti, J P; Vlahov, D; Feroni, I; Perrin, V; Obadia, Y

2001-03-01

In Marseille, southeastern France, HIV prevention programs for injection drug users (IDUs) simultaneously include access to sterile syringes through needle exchange programs (NEPs), legal pharmacy sales and, since 1996, vending machines that mechanically exchange new syringes for used ones. The purpose of this study was to compare the characteristics of IDUs according to the site where they last obtained new syringes. During 3 days in September 1997, all IDUs who obtained syringes from 32 pharmacies, four NEPs and three vending machines were offered the opportunity to complete a self-administered questionnaire on demographics, drug use characteristics and program utilization. Of 485 individuals approached, the number who completed the questionnaire was 141 in pharmacies, 114 in NEPs and 88 at vending machines (response rate = 70.7%). Compared to NEP users, vending machine users were younger and less likely to be enrolled in a methadone program or to report being HIV infected, but more likely to misuse buprenorphine. They also had lower financial resources and were less likely to be heroin injectors than both pharmacy and NEP users. Our results suggest that vending machines attract a very different group of IDUs than NEPs, and that both programs are useful adjuncts to legal pharmacy sales for covering the needs of IDUs for sterile syringes in a single city. Assessment of the effectiveness and cost-effectiveness of combining such programs for the prevention of HIV and other infectious diseases among IDUs requires further comparative research. Copyright 2001 S. Karger AG, Basel

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

PubMed

Gaur, Pallavi; Chaturvedi, Anoop

2017-07-22

The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

PubMed

Barbosa, Eduardo J Mortani; Lanclus, Maarten; Vos, Wim; Van Holsbeke, Cedric; De Backer, William; De Backer, Jan; Lee, James

2018-02-19

Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV 1 ) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS. Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV 1 of >10% compared to baseline FEV 1 post LTx. Multifactor analysis correlated declining FEV 1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS. The FEV 1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV 1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P < .05), whereas no pulmonary function testing parameters could. Using ML methods (support vector machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters. ML utilizing qCT could discern distinct mechanisms driving FEV 1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Magnetic Flux Distribution of Linear Machines with Novel Three-Dimensional Hybrid Magnet Arrays

PubMed Central

Yao, Nan; Yan, Liang; Wang, Tianyi; Wang, Shaoping

2017-01-01

The objective of this paper is to propose a novel tubular linear machine with hybrid permanent magnet arrays and multiple movers, which could be employed for either actuation or sensing technology. The hybrid magnet array produces flux distribution on both sides of windings, and thus helps to increase the signal strength in the windings. The multiple movers are important for airspace technology, because they can improve the system’s redundancy and reliability. The proposed design concept is presented, and the governing equations are obtained based on source free property and Maxwell equations. The magnetic field distribution in the linear machine is thus analytically formulated by using Bessel functions and harmonic expansion of magnetization vector. Numerical simulation is then conducted to validate the analytical solutions of the magnetic flux field. It is proved that the analytical model agrees with the numerical results well. Therefore, it can be utilized for the formulation of signal or force output subsequently, depending on its particular implementation. PMID:29156577

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human-Machine Interaction.

PubMed

Cao, Ran; Pu, Xianjie; Du, Xinyu; Yang, Wei; Wang, Jiaona; Guo, Hengyu; Zhao, Shuyu; Yuan, Zuqing; Zhang, Chi; Li, Congju; Wang, Zhong Lin

2018-05-22

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human-machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human-machine interfacing.

Horizontal EDNA miner

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

Justice, J.C.; Delli-Gatti, F.A.

1985-12-03

A mining machine is utilized for making original generally horizontal bores in coal seams, and for enlarging preexisting bores. A single cutting head is mounted for rotation about a first horizontal axis generally perpendicular to the dimension of elongation of the horizontal bore, and is pivotal about a second horizontal axis, parallel to the first axis, to change its cutting, vertical position within the bore. A non-rotatable body member, with side wall supports, is mounted posteriorly of the cutting head, and includes a conveyor mechanism and a power mechanism operatively connected to it. The machine can be sumped into amore » bore and then the cutting head rotated about the second axis to change the vertical position thereof, and then moved rearwardly, any cut material being continuously conveyed to the bore mouth by the conveyor mechanism. The amount of vertical movement during the pivoting action about the second axis is controlled in response to the automatic sensing of the thickness of the coal seam in which the machine operates.« less

Autonomous Landmark Calibration Method for Indoor Localization

PubMed Central

Kim, Jae-Hoon; Kim, Byoung-Seop

2017-01-01

Machine-generated data expansion is a global phenomenon in recent Internet services. The proliferation of mobile communication and smart devices has increased the utilization of machine-generated data significantly. One of the most promising applications of machine-generated data is the estimation of the location of smart devices. The motion sensors integrated into smart devices generate continuous data that can be used to estimate the location of pedestrians in an indoor environment. We focus on the estimation of the accurate location of smart devices by determining the landmarks appropriately for location error calibration. In the motion sensor-based location estimation, the proposed threshold control method determines valid landmarks in real time to avoid the accumulation of errors. A statistical method analyzes the acquired motion sensor data and proposes a valid landmark for every movement of the smart devices. Motion sensor data used in the testbed are collected from the actual measurements taken throughout a commercial building to demonstrate the practical usefulness of the proposed method. PMID:28837071

Design consideration in constructing high performance embedded Knowledge-Based Systems (KBS)

NASA Technical Reports Server (NTRS)

Dalton, Shelly D.; Daley, Philip C.

1988-01-01

As the hardware trends for artificial intelligence (AI) involve more and more complexity, the process of optimizing the computer system design for a particular problem will also increase in complexity. Space applications of knowledge based systems (KBS) will often require an ability to perform both numerically intensive vector computations and real time symbolic computations. Although parallel machines can theoretically achieve the speeds necessary for most of these problems, if the application itself is not highly parallel, the machine's power cannot be utilized. A scheme is presented which will provide the computer systems engineer with a tool for analyzing machines with various configurations of array, symbolic, scaler, and multiprocessors. High speed networks and interconnections make customized, distributed, intelligent systems feasible for the application of AI in space. The method presented can be used to optimize such AI system configurations and to make comparisons between existing computer systems. It is an open question whether or not, for a given mission requirement, a suitable computer system design can be constructed for any amount of money.

Extraction of inland Nypa fruticans (Nipa Palm) using Support Vector Machine

NASA Astrophysics Data System (ADS)

Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Biagtan, A. R.; Panuyas, N. Z.; Quibuyen, J. S.

2017-09-01

Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

A machine learning approach to computer-aided molecular design

NASA Astrophysics Data System (ADS)

Bolis, Giorgio; Di Pace, Luigi; Fabrocini, Filippo

1991-12-01

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one — the specialization step — the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase — the generalization step — the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

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

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Magnetic Flux Distribution of Linear Machines with Novel Three-Dimensional Hybrid Magnet Arrays.

PubMed

Yao, Nan; Yan, Liang; Wang, Tianyi; Wang, Shaoping

2017-11-18

The objective of this paper is to propose a novel tubular linear machine with hybrid permanent magnet arrays and multiple movers, which could be employed for either actuation or sensing technology. The hybrid magnet array produces flux distribution on both sides of windings, and thus helps to increase the signal strength in the windings. The multiple movers are important for airspace technology, because they can improve the system's redundancy and reliability. The proposed design concept is presented, and the governing equations are obtained based on source free property and Maxwell equations. The magnetic field distribution in the linear machine is thus analytically formulated by using Bessel functions and harmonic expansion of magnetization vector. Numerical simulation is then conducted to validate the analytical solutions of the magnetic flux field. It is proved that the analytical model agrees with the numerical results well. Therefore, it can be utilized for the formulation of signal or force output subsequently, depending on its particular implementation.

Accurate airway segmentation based on intensity structure analysis and graph-cut

NASA Astrophysics Data System (ADS)

Meng, Qier; Kitsaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Mori, Kensaku

2016-03-01

This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the lung parenchyma areas.

Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST)

PubMed Central

Dowd, Scot E; Zaragoza, Joaquin; Rodriguez, Javier R; Oliver, Melvin J; Payton, Paxton R

2005-01-01

Background BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST), which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. Results W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN). W.ND-BLAST provides intuitive Graphic User Interfaces (GUI) for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours) on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV) and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. Conclusion W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is freely downloadable from . With registration the software is free, installation, networking, and usage instructions are provided as well as a support forum. PMID:15819992

Estimating radiotherapy demands in South East Asia countries in 2025 and 2035 using evidence-based optimal radiotherapy fractions.

PubMed

Yahya, Noorazrul; Roslan, Nurhaziqah

2018-01-08

As about 50% of cancer patients may require radiotherapy, the demand of radiotherapy as the main treatment to treat cancer is likely to rise due to rising cancer incidence. This study aims to quantify the radiotherapy demand in countries in Southeast Asia (SEA) in 2025 and 2035 using evidence-based optimal radiotherapy fractions. SEA country-specific cancer incidence by tumor site for 2015, 2025 and 2035 was extracted from the GLOBOCAN database. We utilized the optimal radiotherapy utilization rate model by Wong et al. (2016) to calculate the optimal number of fractions for all tumor sites in each SEA country. The available machines (LINAC & Co-60) were extracted from the IAEA's Directory of Radiotherapy Centres (DIRAC) from which the number of available fractions was calculated. The incidence of cancers in SEA countries are expected to be 1.1 mil cases (2025) and 1.4 mil (2035) compared to 0.9 mil (2015). The number of radiotherapy fractions needed in 2025 and 2035 are 11.1 and 14.1 mil, respectively, compared to 7.6 mil in 2015. In 2015, the radiotherapy fulfillment rate (RFR; required fractions/available fractions) varied between countries with Brunei, Singapore and Malaysia are highest (RFR > 1.0 - available fractions > required fractions), whereas Cambodia, Indonesia, Laos, Myanmar, Philippines, Timor-Leste and Vietnam have RFR < 0.5. RFR is correlated to GDP per capita (ρ = 0.73, P = 0.01). To allow RFR ≥1 in 2025 and 2035, another 866 and 1177 machines are required, respectively. The number are lower if longer running hours are implemented. With the optimal number of radiotherapy fractions, estimation for number of machines required can be obtained which will guide acquisition of machines in SEA countries. RFR is low with access varied based on the economic status. © 2018 John Wiley & Sons Australia, Ltd.

World of intelligence defense object detection-machine learning (artificial intelligence)

NASA Astrophysics Data System (ADS)

Gupta, Anitya; Kumar, Akhilesh; Bhushan, Vinayak

2018-04-01

This paper proposes a Quick Locale based Convolutional System strategy (Quick R-CNN) for question recognition. Quick R-CNN expands on past work to effectively characterize ob-ject recommendations utilizing profound convolutional systems. Com-pared to past work, Quick R-CNN utilizes a few in-novations to enhance preparing and testing speed while likewise expanding identification precision. Quick R-CNN trains the profound VGG16 arrange 9 quicker than R-CNN, is 213 speedier at test-time, and accomplishes a higher Guide on PASCAL VOC 2012. Contrasted with SPPnet, Quick R-CNN trains VGG16 3 quicker, tests 10 speedier, and is more exact. Quick R-CNN is actualized in Python and C++ (utilizing Caffe) and is accessible under the open-source MIT Permit.

Automated hardwood lumber grading utilizing a multiple sensor machine vision technology

Treesearch

D. Earl Kline; Chris Surak; Philip A. Araman

2003-01-01

Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical and Computer Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading...

Four-Year Summary, Educational and Commercial Utilization of a Chemical Information Center, Part II.

ERIC Educational Resources Information Center

Schipma, Peter B., Ed.

The major objective of the Illinois Institute of Technology Retrieval Institute (IITRI) Computer Search Center (CSC) is to educate and link industry, academia, and government institutions to chemical and other scientific information systems and sources. The CSC is in full operation providing services to users from a variety of machine-readable…

Educational and Commercial Utilization of a Chemical Information Center, Four Year Summary.

ERIC Educational Resources Information Center

Williams, Martha E.; And Others

The major objective of the IITRI Computer Search Center is to educate and link industry, academia, and government institutions to chemical and other scientific information systems and sources. The Center was developed to meet this objective and is in full operation providing services to users from a variety of machine-readable data bases with…

Make Your Classroom Run Like a Well-Oiled Machine

ERIC Educational Resources Information Center

Veverka, Joy Brunt

2011-01-01

At the beginning of each school term, bulletin boards sport fresh ideas and desks glisten, but will students entering the classroom have their expectations met? Will they be engaged in the learning? How can teachers utilize resources and enlist others to provide an even stronger and more effective learning environment? In this article, the author…

Relationship between Effective Application of Machine Learning and Malware Detection: A Quantitative Study

ERIC Educational Resources Information Center

Enfinger, Kerry Wayne

2016-01-01

The number of malicious files present in the public domain continues to rise at a substantial rate. Current anti-malware software utilizes a signature-based method to detect the presence of malicious software. Generating these pattern signatures is time consuming due to malicious code complexity and the need for expert analysis, however, by making…

Intersystem Compatibility and Convertibility of Subject Vocabularies.

ERIC Educational Resources Information Center

Wall, E.; Barnes, J.

This is the fifth in a series of eight reports of a research study for the National Agricultural Library (NAL) on the effective utilization of bibliographic data bases in machine readable form. NAL desires ultimately to develop techniques of interacting with other data bases so that queries put to NAL may be answered with documents or document…

SignMT: An Alternative Language Learning Tool

ERIC Educational Resources Information Center

Ditcharoen, Nadh; Naruedomkul, Kanlaya; Cercone, Nick

2010-01-01

Learning a second language is very difficult, especially, for the disabled; the disability may be a barrier to learn and to utilize information written in text form. We present the SignMT, Thai sign to Thai machine translation system, which is able to translate from Thai sign language into Thai text. In the translation process, SignMT takes into…

Four-Year Summary, Educational and Commercial Utilization of a Chemical Information Center. Part I.

ERIC Educational Resources Information Center

Schipma, Peter B., Ed.

The major objective of the Illinois Institute of Technology (IIT) Computer Search Center (CSC) is to educate and link industry, academia, and government institutions to chemical and other scientific information systems and sources. The CSC is in full operation providing services to users from a variety of machine-readable data bases with minimal…

The Shadow Space of Allegorical Machines: Situating Locative Media

ERIC Educational Resources Information Center

Ingersoll, Alex Monroe

2013-01-01

This dissertation utilizes a media archaeological approach to the analysis of locative media, which are technologies that organize an experience of spatial orientation. For instance, a user can use a mobile phone to connect to a cellular network and generate a visualization of the material space in which he or she is positioned with annotated or…

Modeling a Linear Generator for Energy Harvesting Applications

DTIC Science & Technology

2014-12-01

sensors where electrical power is not available (e.g., wireless sensors on train cars). While piezoelectric harvesters are primarily utilized in...Ship and the Future of Electricity Generation ............3 2. Unmanned Sensor Energy Needs .......................................................4...18 Figure 8. Example two-pole, three-phase salient-pole synchronous machine showing the general layout of windings and major axis

Utilization and cost of log production from animal loging operations

Treesearch

Suraj P. Shrestha; Bobby L. Lanford; Robert B. Rummer; Mark Dubois

2006-01-01

Forest harvesting with animals is a labor-intensive operation. It is expensive to use machines on smaller woodlots, which require frequent moves if mechanically logged. So, small logging systems using animals may be more cost effective. In this study, work sampling was used for five animal logging operations in Alabama to measure productive and non-productive time...

Amorphous Metals and Composites as Mirrors and Mirror Assemblies

NASA Technical Reports Server (NTRS)

Hofmann, Douglas C. (Inventor); Davis, Gregory L. (Inventor); Agnes, Gregory S. (Inventor); Shapiro, Andrew A. (Inventor)

2016-01-01

A mirror or mirror assembly fabricated by molding, pressing, assembling, or depositing one or more bulk metal glass (BMG), bulk metal glass composite (BMGMC), or amorphous metal (AM) parts and where the optical surface and backing of the mirror can be fabricated without machining or polishing by utilizing the unique molding capabilities of this class of materials.

Conformal anomaly of some 2-d Z (n) models

NASA Astrophysics Data System (ADS)

William, Peter

1991-01-01

We describe a numerical calculation of the conformal anomaly in the case of some two-dimensional statistical models undergoing a second-order phase transition, utilizing a recently developed method to compute the partition function exactly. This computation is carried out on a massively parallel CM2 machine, using the finite size scaling behaviour of the free energy.

STABILIZED PINCH MACHINE

DOEpatents

Anderson, O.A.

1962-04-24

A device for heating and confining a high temperature gas or plasma utilizing the linear pinch effect is described. The pinch discharge produced is the form of an elongated cylinder. The electrical discharge current is returned in parallel along an axial and a concentric conductor whereby the magnetic field of the conductors compresses and stabilizes the pinch discharge against lateral instability. (AEC)

Applied Math & Science Levels Utilized in Selected Trade & Industrial Vocational Education. Final Report.

ERIC Educational Resources Information Center

Gray, James R.

Research identified and evaluated the level of applied mathematics and science used in selected trade and industrial (T&I) subjects taught in the Kentucky Vocational Education System. The random sample was composed of 52 programs: 21 carpentry, 20 electricity/electronics, and 11 machine shop. The 96 math content items that were identified as…

Utility functions and resource management in an oversubscribed heterogeneous computing environment

DOE PAGES

Khemka, Bhavesh; Friese, Ryan; Briceno, Luis Diego; ...

2014-09-26

We model an oversubscribed heterogeneous computing system where tasks arrive dynamically and a scheduler maps the tasks to machines for execution. The environment and workloads are based on those being investigated by the Extreme Scale Systems Center at Oak Ridge National Laboratory. Utility functions that are designed based on specifications from the system owner and users are used to create a metric for the performance of resource allocation heuristics. Each task has a time-varying utility (importance) that the enterprise will earn based on when the task successfully completes execution. We design multiple heuristics, which include a technique to drop lowmore » utility-earning tasks, to maximize the total utility that can be earned by completing tasks. The heuristics are evaluated using simulation experiments with two levels of oversubscription. The results show the benefit of having fast heuristics that account for the importance of a task and the heterogeneity of the environment when making allocation decisions in an oversubscribed environment. Furthermore, the ability to drop low utility-earning tasks allow the heuristics to tolerate the high oversubscription as well as earn significant utility.« less

Feasibility Study of Thin Film Thermocouple Piles

NASA Technical Reports Server (NTRS)

Sisk, R. C.

2001-01-01

Historically, thermopile detectors, generators, and refrigerators based on bulk materials have been used to measure temperature, generate power for spacecraft, and cool sensors for scientific investigations. New potential uses of small, low-power, thin film thermopiles are in the area of microelectromechanical systems since power requirements decrease as electrical and mechanical machines shrink in size. In this research activity, thin film thermopile devices are fabricated utilizing radio frequency sputter coating and photoresist lift-off techniques. Electrical characterizations are performed on two designs in order to investigate the feasibility of generating small amounts of power, utilizing any available waste heat as the energy source.

User's guide for FRMOD, a zero dimensional FRM burn code

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

Driemeryer, D.; Miley, G.H.

1979-10-15

The zero-dimensional FRM plasma burn code, FRMOD is written in the FORTRAN language and is currently available on the Control Data Corporation (CDC) 7600 computer at the Magnetic Fusion Energy Computer Center (MFECC), sponsored by the US Department of Energy, in Livermore, CA. This guide assumes that the user is familiar with the system architecture and some of the utility programs available on the MFE-7600 machine, since online documentation is available for system routines through the use of the DOCUMENT utility. Users may therefore refer to it for answers to system related questions.

Drag Reduction of an Airfoil Using Deep Learning

NASA Astrophysics Data System (ADS)

Jiang, Chiyu; Sun, Anzhu; Marcus, Philip

2017-11-01

We reduced the drag of a 2D airfoil by starting with a NACA-0012 airfoil and used deep learning methods. We created a database which consists of simulations of 2D external flow over randomly generated shapes. We then developed a machine learning framework for external flow field inference given input shapes. Past work which utilized machine learning in Computational Fluid Dynamics focused on estimations of specific flow parameters, but this work is novel in the inference of entire flow fields. We further showed that learned flow patterns are transferable to cases that share certain similarities. This study illustrates the prospects of deeper integration of data-based modeling into current CFD simulation frameworks for faster flow inference and more accurate flow modeling.

Approximate message passing with restricted Boltzmann machine priors

NASA Astrophysics Data System (ADS)

Tramel, Eric W.; Drémeau, Angélique; Krzakala, Florent

2016-07-01

Approximate message passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problems. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernoulli prior which utilizes a restricted Boltzmann machine (RBM) trained on the signal support to push reconstruction performance beyond that of simple i.i.d. priors for signals whose support can be well represented by a trained binary RBM. We present and analyze two methods of RBM factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm. Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance.

Accuracy of radiographic caries diagnosis using different X-ray generators.

PubMed

Svenson, B; Petersson, A

1989-05-01

Dental X-ray machines utilizing five different combinations of X-ray generators and tube voltages (Philips Oralix 65 kV, Siemens Heliodent EC 60 kV, Siemens Heliodent 70 kV, Soredex Minray DC 60 kV and Soredex Minray DC 70 kV) were compared with respect to the accuracy of radiographic diagnosis of proximal caries. Nine observers diagnosed proximal caries in radiographs of extracted premolars. The findings of the observers were compared to the actual presence or absence of caries. The ROC-curve technique was used to evaluate differences in diagnostic accuracy between the X-ray machines. The results showed small differences in diagnostic accuracy between the different X-ray generators but they proved to be statistically non-significant.

Towards developing robust algorithms for solving partial differential equations on MIMD machines

NASA Technical Reports Server (NTRS)

Saltz, Joel H.; Naik, Vijay K.

1988-01-01

Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.

[Challenges to implementation of the ECG reading center in ELSA-Brasil].

PubMed

Ribeiro, Antonio Luiz; Pereira, Samuel Vianney da Cunha; Bergmann, Kaiser; Ladeira, Roberto Marini; Oliveira, Rackel Aguiar Mendes; Lotufo, Paulo A; Mill, José Geraldo; Barreto, Sandhi Maria

2013-06-01

Electrocardiography is an established low-cost method of cardiovascular assessment, utilized for decades large epidemiological studies. Nonetheless, its use in large epidemiological studies presents challenges, especially when seeking to develop a reading center. This article describes the process, difficulties and challenges of implementing an electrocardiogram reading center in Brazilian Longitudinal Study for Adult Health (ELSA-Brasil). Among the issues discussed, we have emphasized: the criteria for selection of the electrocardiography machine and the central for storage and management of the machines; the required personnel; the procedures for acquisition and transmission of electrocardiographs to the Reading Center; coding systems, with emphasis on the Minnesota code; ethical and practical issues regarding the delivery of reports to study participants; and aspects related to quality control.

Method for automated building of spindle thermal model with use of CAE system

NASA Astrophysics Data System (ADS)

Kamenev, S. V.

2018-03-01

The spindle is one of the most important units of the metal-cutting machine tool. Its performance is critical to minimize the machining error, especially the thermal error. Various methods are applied to improve the thermal behaviour of spindle units. One of the most important methods is mathematical modelling based on the finite element analysis. The most common approach for its realization is the use of CAE systems. This approach, however, is not capable to address the number of important effects that need to be taken into consideration for proper simulation. In the present article, the authors propose the solution to overcome these disadvantages using automated thermal model building for the spindle unit utilizing the CAE system ANSYS.

High temperature solar photon engines. [heat engines for terrestrial and space-based solar power plants

NASA Technical Reports Server (NTRS)

Hertzberg, A.; Decher, R.; Mattick, A. T.; Lau, C. V.

1978-01-01

High temperature heat engines designed to make maximum use of the thermodynamic potential of concentrated solar radiation are described. Plasmas between 2000 K and 4000 K can be achieved by volumetric absorption of radiation in alkali metal vapors, leading to thermal efficiencies up to 75% for terrestrial solar power plants and up to 50% for space power plants. Two machines capable of expanding hot plasmas using practical technology are discussed. A binary Rankine cycle uses fluid mechanical energy transfer in a device known as the 'Comprex' or 'energy exchanger.' The second machine utilizes magnetohydrodynamics in a Brayton cycle for space applications. Absorption of solar energy and plasma radiation losses are investigated for a solar superheater using potassium vapor.

Architecture and data processing alternatives for Tse computer. Volume 1: Tse logic design concepts and the development of image processing machine architectures

NASA Technical Reports Server (NTRS)

Rickard, D. A.; Bodenheimer, R. E.

1976-01-01

Digital computer components which perform two dimensional array logic operations (Tse logic) on binary data arrays are described. The properties of Golay transforms which make them useful in image processing are reviewed, and several architectures for Golay transform processors are presented with emphasis on the skeletonizing algorithm. Conventional logic control units developed for the Golay transform processors are described. One is a unique microprogrammable control unit that uses a microprocessor to control the Tse computer. The remaining control units are based on programmable logic arrays. Performance criteria are established and utilized to compare the various Golay transform machines developed. A critique of Tse logic is presented, and recommendations for additional research are included.

Inspection of wear particles in oils by using a fuzzy classifier

NASA Astrophysics Data System (ADS)

Hamalainen, Jari J.; Enwald, Petri

1994-11-01

The reliability of stand-alone machines and larger production units can be improved by automated condition monitoring. Analysis of wear particles in lubricating or hydraulic oils helps diagnosing the wear states of machine parts. This paper presents a computer vision system for automated classification of wear particles. Digitized images from experiments with a bearing test bench, a hydraulic system with an industrial company, and oil samples from different industrial sources were used for algorithm development and testing. The wear particles were divided into four classes indicating different wear mechanisms: cutting wear, fatigue wear, adhesive wear, and abrasive wear. The results showed that the fuzzy K-nearest neighbor classifier utilized gave the same distribution of wear particles as the classification by a human expert.

Towards developing robust algorithms for solving partial differential equations on MIMD machines

NASA Technical Reports Server (NTRS)

Saltz, J. H.; Naik, V. K.

1985-01-01

Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.

Prospects of a mathematical theory of human behavior in complex man-machine systems tasks. [time sharing computer analogy of automobile driving

NASA Technical Reports Server (NTRS)

Johannsen, G.; Rouse, W. B.

1978-01-01

A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued.

Atwood's Heavy Chain

NASA Astrophysics Data System (ADS)

Beeken, Paul

2011-11-01

While perusing various websites in search of a more challenging lab for my students, I came across a number of ideas where replacing the string in an Atwood's machine with a simple ball chain like the kind found in lamp pulls created an interesting system to investigate. The replacement of the string produced a nice nonuniform acceleration, but one that my AP® students found difficult to analyze given their current math background. As the year progressed, we began to explore the importance of work and its utility in making predictions on systems that did not lend themselves to easy analysis using Newtonian mechanics. The effort made it apparent that the heavy rope Atwood's machine would make a perfect system for investigation using the lessons gained from work and energy.

Experiments on PIM in Support of the Development of IVA Technology for Radiography at AWE

NASA Astrophysics Data System (ADS)

Clough, Stephen G.; Thomas, Kenneth J.; Williamson, Mark C.; Phillips, Martin J.; Smith, Ian D.; Bailey, Vernon L.; Kishi, Hiroshi J.; Maenchen, John E.; Johnson, David L.

2002-12-01

The PIM machine has been designed and constructed at AWE as part of a program to investigate IVA technology for radiographic applications. PIM, as originally constructed, was a prospective single module of a 14 MV, 100 kA, ten module machine. The design of such a machine is a primary goal of the program as several are required to provide multi-axis radiography in a new Hydrodynamics Research Facility (HRF). Another goal is to design lower voltage machines (ranging from 1 to 5 MV) utilizing PIM style components. The original PIM machine consisted of a single inductive cavity pulsed by a 10 ohm water dielectric Blumlein pulse forming line (PFL) charged by a Marx generator. These components successfully achieved their design voltages and data on the prepulse was obtained showing it to be worse than expected. This information provided a basis for design work on the 14 MV HRF IVA, carried out by Titan-PSD, resulting in a proposal for a prepulse switch, a prototype of which should be installed on PIM by the end of this year. The original single, coaxial switch used to initiate the Blumlein has been replaced by a prototype laser triggered switching arrangement, also designed by Titan-PSD, which it was desired to test prior to its eventual use in the HRF. Despite problems with the laser, which will necessitate further experiments, it was determined that laser triggering with low jitter was occurring. A split oil co-ax feed has now been used to install a second cavity, in parallel with the first, on the PIM Blumlein. This two cavity configuration provides a prototype for future radiographic machines operating at up to 3 MV and a test facility for diode research.

Use of machine-learning classifiers to predict requests for preoperative acute pain service consultation.

PubMed

Tighe, Patrick J; Lucas, Stephen D; Edwards, David A; Boezaart, André P; Aytug, Haldun; Bihorac, Azra

2012-10-01

  The purpose of this project was to determine whether machine-learning classifiers could predict which patients would require a preoperative acute pain service (APS) consultation.   Retrospective cohort.   University teaching hospital.   The records of 9,860 surgical patients posted between January 1 and June 30, 2010 were reviewed.   Request for APS consultation. A cohort of machine-learning classifiers was compared according to its ability or inability to classify surgical cases as requiring a request for a preoperative APS consultation. Classifiers were then optimized utilizing ensemble techniques. Computational efficiency was measured with the central processing unit processing times required for model training. Classifiers were tested using the full feature set, as well as the reduced feature set that was optimized using a merit-based dimensional reduction strategy.   Machine-learning classifiers correctly predicted preoperative requests for APS consultations in 92.3% (95% confidence intervals [CI], 91.8-92.8) of all surgical cases. Bayesian methods yielded the highest area under the receiver operating curve (0.87, 95% CI 0.84-0.89) and lowest training times (0.0018 seconds, 95% CI, 0.0017-0.0019 for the NaiveBayesUpdateable algorithm). An ensemble of high-performing machine-learning classifiers did not yield a higher area under the receiver operating curve than its component classifiers. Dimensional reduction decreased the computational requirements for multiple classifiers, but did not adversely affect classification performance.   Using historical data, machine-learning classifiers can predict which surgical cases should prompt a preoperative request for an APS consultation. Dimensional reduction improved computational efficiency and preserved predictive performance. Wiley Periodicals, Inc.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Gray, A.

2014-04-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Astronomy Data Centre, Canadian

2014-01-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine

NASA Astrophysics Data System (ADS)

Farda, N. M.

2017-12-01

Coastal wetlands provide ecosystem services essential to people and the environment. Changes in coastal wetlands, especially on land use, are important to monitor by utilizing multi-temporal imagery. The Google Earth Engine (GEE) provides many machine learning algorithms (10 algorithms) that are very useful for extracting land use from imagery. The research objective is to explore machine learning in Google Earth Engine and its accuracy for multi-temporal land use mapping of coastal wetland area. Landsat 3 MSS (1978), Landsat 5 TM (1991), Landsat 7 ETM+ (2001), and Landsat 8 OLI (2014) images located in Segara Anakan lagoon are selected to represent multi temporal images. The input for machine learning are visible and near infrared bands, PCA band, invers PCA bands, bare soil index, vegetation index, wetness index, elevation from ASTER GDEM, and GLCM (Harralick) texture, and also polygon samples in 140 locations. There are 10 machine learning algorithms applied to extract coastal wetlands land use from Landsat imagery. The algorithms are Fast Naive Bayes, CART (Classification and Regression Tree), Random Forests, GMO Max Entropy, Perceptron (Multi Class Perceptron), Winnow, Voting SVM, Margin SVM, Pegasos (Primal Estimated sub-GrAdient SOlver for Svm), IKPamir (Intersection Kernel Passive Aggressive Method for Information Retrieval, SVM). Machine learning in Google Earth Engine are very helpful in multi-temporal land use mapping, the highest accuracy for land use mapping of coastal wetland is CART with 96.98 % Overall Accuracy using K-Fold Cross Validation (K = 10). GEE is particularly useful for multi-temporal land use mapping with ready used image and classification algorithms, and also very challenging for other applications.

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

PubMed

Fang, Shih-Hau; Tsao, Yu; Hsiao, Min-Jing; Chen, Ji-Ying; Lai, Ying-Hui; Lin, Feng-Chuan; Wang, Chi-Te

2018-03-19

Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

PubMed

Goldstein, Stephanie P; Zhang, Fengqing; Thomas, John G; Butryn, Meghan L; Herbert, James D; Forman, Evan M

2018-05-01

Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction. The current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers. WEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance. The predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.

Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Fu, Yumei; Xiang, Shihan; Chen, Tianyi; Zhou, Ping; Huang, Weiyan

2015-10-01

The fetal electrocardiogram (FECG) signal has important clinical value for diagnosing the fetal heart diseases and choosing suitable therapeutics schemes to doctors. So, the noninvasive extraction of FECG from electrocardiogram (ECG) signals becomes a hot research point. A new method, the Support Vector Machine (SVM) is utilized for the extraction of FECG with limited size of data. Firstly, the theory of the SVM and the principle of the extraction based on the SVM are studied. Secondly, the transformation of maternal electrocardiogram (MECG) component in abdominal composite signal is verified to be nonlinear and fitted with the SVM. Then, the SVM is trained, and the training results are compared with the real data to ensure the effect of the training. Meanwhile, the parameters of the SVM are optimized to achieve the best performance so that the learning machine can be utilized to fit the unknown samples. Finally, the FECG is extracted by removing the optimal estimation of MECG component from the abdominal composite signal. In order to evaluate the performance of FECG extraction based on the SVM, the Signal-to-Noise Ratio (SNR) and the visual test are used. The experimental results show that the FECG with good quality can be extracted, its SNR ratio is significantly increased as high as 9.2349 dB and the time cost is significantly decreased as short as 0.802 seconds. Compared with the traditional method, the noninvasive extraction method based on the SVM has a simple realization, the shorter treatment time and the better extraction quality under the same conditions.

Static Measurements on HTS Coils of Fully Superconducting AC Electric Machines for Aircraft Electric Propulsion System

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.; Hunker, Keith R.; Hartwig, Jason; Brown, Gerald V.

2017-01-01

The NASA Glenn Research Center (GRC) has been developing the high efficiency and high-power density superconducting (SC) electric machines in full support of electrified aircraft propulsion (EAP) systems for a future electric aircraft. A SC coil test rig has been designed and built to perform static and AC measurements on BSCCO, (RE)BCO, and YBCO high temperature superconducting (HTS) wire and coils at liquid nitrogen (LN2) temperature. In this paper, DC measurements on five SC coil configurations of various geometry in zero external magnetic field are measured to develop good measurement technique and to determine the critical current (Ic) and the sharpness (n value) of the super-to-normal transition. Also, standard procedures for coil design, fabrication, coil mounting, micro-volt measurement, cryogenic testing, current control, and data acquisition technique were established. Experimentally measured critical currents are compared with theoretical predicted values based on an electric-field criterion (Ec). Data here are essential to quantify the SC electric machine operation limits where the SC begins to exhibit non-zero resistance. All test data will be utilized to assess the feasibility of using HTS coils for the fully superconducting AC electric machine development for an aircraft electric propulsion system.

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

PubMed Central

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. PMID:27213008

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography.

PubMed

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

High Performance Distributed Computing in a Supercomputer Environment: Computational Services and Applications Issues

NASA Technical Reports Server (NTRS)

Kramer, Williams T. C.; Simon, Horst D.

1994-01-01

This tutorial proposes to be a practical guide for the uninitiated to the main topics and themes of high-performance computing (HPC), with particular emphasis to distributed computing. The intent is first to provide some guidance and directions in the rapidly increasing field of scientific computing using both massively parallel and traditional supercomputers. Because of their considerable potential computational power, loosely or tightly coupled clusters of workstations are increasingly considered as a third alternative to both the more conventional supercomputers based on a small number of powerful vector processors, as well as high massively parallel processors. Even though many research issues concerning the effective use of workstation clusters and their integration into a large scale production facility are still unresolved, such clusters are already used for production computing. In this tutorial we will utilize the unique experience made at the NAS facility at NASA Ames Research Center. Over the last five years at NAS massively parallel supercomputers such as the Connection Machines CM-2 and CM-5 from Thinking Machines Corporation and the iPSC/860 (Touchstone Gamma Machine) and Paragon Machines from Intel were used in a production supercomputer center alongside with traditional vector supercomputers such as the Cray Y-MP and C90.

Efficient Checkpointing of Virtual Machines using Virtual Machine Introspection

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

Aderholdt, Ferrol; Han, Fang; Scott, Stephen L

Cloud Computing environments rely heavily on system-level virtualization. This is due to the inherent benefits of virtualization including fault tolerance through checkpoint/restart (C/R) mechanisms. Because clouds are the abstraction of large data centers and large data centers have a higher potential for failure, it is imperative that a C/R mechanism for such an environment provide minimal latency as well as a small checkpoint file size. Recently, there has been much research into C/R with respect to virtual machines (VM) providing excellent solutions to reduce either checkpoint latency or checkpoint file size. However, these approaches do not provide both. This papermore » presents a method of checkpointing VMs by utilizing virtual machine introspection (VMI). Through the usage of VMI, we are able to determine which pages of memory within the guest are used or free and are better able to reduce the amount of pages written to disk during a checkpoint. We have validated this work by using various benchmarks to measure the latency along with the checkpoint size. With respect to checkpoint file size, our approach results in file sizes within 24% or less of the actual used memory within the guest. Additionally, the checkpoint latency of our approach is up to 52% faster than KVM s default method.« less

Automatic detection of Martian dark slope streaks by machine learning using HiRISE images

NASA Astrophysics Data System (ADS)

Wang, Yexin; Di, Kaichang; Xin, Xin; Wan, Wenhui

2017-07-01

Dark slope streaks (DSSs) on the Martian surface are one of the active geologic features that can be observed on Mars nowadays. The detection of DSS is a prerequisite for studying its appearance, morphology, and distribution to reveal its underlying geological mechanisms. In addition, increasingly massive amounts of Mars high resolution data are now available. Hence, an automatic detection method for locating DSSs is highly desirable. In this research, we present an automatic DSS detection method by combining interest region extraction and machine learning techniques. The interest region extraction combines gradient and regional grayscale information. Moreover, a novel recognition strategy is proposed that takes the normalized minimum bounding rectangles (MBRs) of the extracted regions to calculate the Local Binary Pattern (LBP) feature and train a DSS classifier using the Adaboost machine learning algorithm. Comparative experiments using five different feature descriptors and three different machine learning algorithms show the superiority of the proposed method. Experimental results utilizing 888 extracted region samples from 28 HiRISE images show that the overall detection accuracy of our proposed method is 92.4%, with a true positive rate of 79.1% and false positive rate of 3.7%, which in particular indicates great performance of the method at eliminating non-DSS regions.

The Effect of Heat Treatment on Residual Stress and Machining Distortions in Advanced Nickel Base Disk Alloys

NASA Technical Reports Server (NTRS)

Gayda, John

2001-01-01

This paper describes an extension of NASA's AST and IDPAT Programs which sought to predict the effect of stabilization heat treatments on residual stress and subsequent machining distortions in the advanced disk alloy, ME-209. Simple "pancake" forgings of ME-209 were produced and given four heat treats: 2075F(SUBSOLVUS)/OIL QUENCH/NO AGE; 2075F/OIL QUENCH/1400F@8HR;2075F/OIL QUENCH/1550F@3HR/l400F@8HR; and 2160F(SUPERSOLVUS)/OIL QUENCH/1550F@3HR/ 1400F@8HR. The forgings were then measured to obtain surface profiles in the heat treated condition. A simple machining plan consisting of face cuts from the top surface followed by measurements of the surface profile opposite the cut were made. This data provided warpage maps which were compared with analytical results. The analysis followed the IDPAT methodology and utilized a 2-D axisymmetric, viscoplastic FEA code. The analytical results accurately tracked the experimental data for each of the four heat treatments. The 1550F stabilization heat treatment was found to significantly reduce residual stresses and subsequent machining distortions for fine grain (subsolvus) ME209, while coarse grain (supersolvus) ME209 would require additional time or higher stabilization temperatures to attain the same degree of stress relief.

Implementation of an agile maintenance mechanic assignment methodology

NASA Astrophysics Data System (ADS)

Jimenez, Jesus A.; Quintana, Rolando

2000-10-01

The objective of this research was to develop a decision support system (DSS) to study the impact of introducing new equipment into a medical apparel plant from a maintenance organizational structure perspective. This system will enable the company to determine if their capacity is sufficient to meet current maintenance challenges. The DSS contains two database sets that describe equipment and maintenance resource profiles. The equipment profile specifies data such as mean time to failures, mean time to repairs, and minimum mechanic skill level required to fix each machine group. Similarly, maintenance-resource profile reports information about the mechanic staff, such as number and type of certifications received, education level, and experience. The DSS will then use this information to minimize machine downtime by assigning the highest skilled mechanics to machines with higher complexity and product value. A modified version of the simplex method, the transportation problem, was used to perform the optimization. The DSS was built using the Visual Basic for Applications (VBA) language contained in the Microsoft Excel environment. A case study was developed from current existing data. The analysis consisted of forty-two machine groups and six mechanic categories with ten skill levels. Results showed that only 56% of the mechanic workforce was utilized. Thus, the company had available resources for meeting future maintenance requirements.

Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches.

PubMed

Yugandhar, K; Gromiha, M Michael

2014-09-01

Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions. © 2014 Wiley Periodicals, Inc.

Design of an autonomous Lunar construction utility vehicle

NASA Technical Reports Server (NTRS)

Ash, Robert L.; Chew, Mason; Dixon, Iain (Editor)

1990-01-01

In order to prepare a site for a manned lunar base, an autonomously operated construction vehicle is necessary. A Lunar Construction Utility Vehicle (LCUV), which utilizes interchangeable construction implements, was designed conceptually. Some elements of the machine were studied in greater detail. Design of an elastic loop track system has advanced to the testing stage. A standard coupling device was designed to insure a proper connection between the different construction tools and the LCUV. Autonomous control of the track drive motors was simulated successfully through the use of a joystick and computer interface. A study of hydrogen-oxygen fuel cells has produced estimates of reactant and product size requirements and identified multi-layer insulation techniques. Research on a 100 kW heat rejection system has determined that it is necessary to house a radiator panel on a utility trailer. The impact of a 720 hr use cycle has produced a very large logistical support lien which requires further study.

The development of damage identification methods for buildings with image recognition and machine learning techniques utilizing aerial photographs of the 2016 Kumamoto earthquake

NASA Astrophysics Data System (ADS)

Shohei, N.; Nakamura, H.; Fujiwara, H.; Naoichi, M.; Hiromitsu, T.

2017-12-01

It is important to get schematic information of the damage situation immediately after the earthquake utilizing photographs shot from an airplane in terms of the investigation and the decision-making for authorities. In case of the 2016 Kumamoto earthquake, we have acquired more than 1,800 orthographic projection photographs adjacent to damaged areas. These photos have taken between April 16th and 19th by airplanes, then we have distinguished damages of all buildings with 4 levels, and organized as approximately 296,000 GIS data corresponding to the fundamental Geospatial data published by Geospatial Information Authority of Japan. These data have organized by effort of hundreds of engineers. However, it is not considered practical for more extensive disasters like the Nankai Trough earthquake by only human powers. So, we have been developing the automatic damage identification method utilizing image recognition and machine learning techniques. First, we have extracted training data of more than 10,000 buildings which have equally damage levels divided in 4 grades. With these training data, we have been raster scanning in each scanning ranges of entire images, then clipping patch images which represents damage levels each. By utilizing these patch images, we have been developing discriminant models by two ways. One is a model using the Support Vector Machine (SVM). First, extract a feature quantity of each patch images. Then, with these vector values, calculate the histogram density as a method of Bag of Visual Words (BoVW), then classify borders with each damage grades by SVM. The other one is a model using the multi-layered Neural Network. First, design a multi-layered Neural Network. Second, input patch images and damage levels based on a visual judgement, and then, optimize learning parameters with error backpropagation method. By use of both discriminant models, we are going to discriminate damage levels in each patches, then create the image that shows building damage situations. It would be helpful for more prompt and widespread damage detection than visual judgement. Acknowledgment: This work was supported by CSTI through the Cross-ministerial Strategic Innovation Promotion Program (SIP), titled "Enhancement of societal resiliency against natural disasters"(Funding agency: JST).

Machine Learning Based Classifier for Falsehood Detection

NASA Astrophysics Data System (ADS)

Mallikarjun, H. M.; Manimegalai, P., Dr.; Suresh, H. N., Dr.

2017-08-01

The investigation of physiological techniques for Falsehood identification tests utilizing the enthusiastic aggravations started as a part of mid 1900s. The need of Falsehood recognition has been a piece of our general public from hundreds of years back. Different requirements drifted over the general public raising the need to create trick evidence philosophies for Falsehood identification. The established similar addressing tests have been having a tendency to gather uncertain results against which new hearty strategies are being explored upon for acquiring more productive Falsehood discovery set up. Electroencephalography (EEG) is a non-obtrusive strategy to quantify the action of mind through the anodes appended to the scalp of a subject. Electroencephalogram is a record of the electric signs produced by the synchronous activity of mind cells over a timeframe. The fundamental goal is to accumulate and distinguish the important information through this action which can be acclimatized for giving surmising to Falsehood discovery in future analysis. This work proposes a strategy for Falsehood discovery utilizing EEG database recorded on irregular people of various age gatherings and social organizations. The factual investigation is directed utilizing MATLAB v-14. It is a superior dialect for specialized registering which spares a considerable measure of time with streamlined investigation systems. In this work center is made on Falsehood Classification by Support Vector Machine (SVM). 72 Samples are set up by making inquiries from standard poll with a Wright and wrong replies in a diverse era from the individual in wearable head unit. 52 samples are trained and 20 are tested. By utilizing Bluetooth based Neurosky’s Mindwave kit, brain waves are recorded and qualities are arranged appropriately. In this work confusion matrix is derived by matlab programs and accuracy of 56.25 % is achieved.

Evaluating the Potential of Commercial GIS for Accelerator Configuration Management

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

T.L. Larrieu; Y.R. Roblin; K. White

2005-10-10

The Geographic Information System (GIS) is a tool used by industries needing to track information about spatially distributed assets. A water utility, for example, must know not only the precise location of each pipe and pump, but also the respective pressure rating and flow rate of each. In many ways, an accelerator such as CEBAF (Continuous Electron Beam Accelerator Facility) can be viewed as an ''electron utility''. Whereas the water utility uses pipes and pumps, the ''electron utility'' uses magnets and RF cavities. At Jefferson lab we are exploring the possibility of implementing ESRI's ArcGIS as the framework for buildingmore » an all-encompassing accelerator configuration database that integrates location, configuration, maintenance, and connectivity details of all hardware and software. The possibilities of doing so are intriguing. From the GIS, software such as the model server could always extract the most-up-to-date layout information maintained by the Survey & Alignment for lattice modeling. The Mechanical Engineering department could use ArcGIS tools to generate CAD drawings of machine segments from the same database. Ultimately, the greatest benefit of the GIS implementation could be to liberate operators and engineers from the limitations of the current system-by-system view of machine configuration and allow a more integrated regional approach. The commercial GIS package provides a rich set of tools for database-connectivity, versioning, distributed editing, importing and exporting, and graphical analysis and querying, and therefore obviates the need for much custom development. However, formidable challenges to implementation exist and these challenges are not only technical and manpower issues, but also organizational ones. The GIS approach would crosscut organizational boundaries and require departments, which heretofore have had free reign to manage their own data, to cede some control and agree to a centralized framework.« less

Measures and Metrics of Information Processing in Complex Systems: A Rope of Sand

ERIC Educational Resources Information Center

James, Ryan Gregory

2013-01-01

How much information do natural systems store and process? In this work we attempt to answer this question in multiple ways. We first establish a mathematical framework where natural systems are represented by a canonical form of edge-labeled hidden fc models called e-machines. Then, utilizing this framework, a variety of measures are defined and…

Paterson, New Jersey: America's Silk City. Teaching with Historic Places.

ERIC Educational Resources Information Center

Koman, Rita G.

Paterson, New Jersey, was established in the 1790s to utilize the power of the water that cascades through the Passaic River Gorge. Massive brick mill buildings lined the canals that transformed the power of the falls into energy to drive machines. These mills manufactured many things during the history of this industrial city. In the late 19th…

Why Not Take All of Me?

Treesearch

P. Koch

1974-01-01

Did you know that about one fifth of every pine tree harvested remains underground and not visible to the naked eye? Why then couldn't land managers utilize this wood, thus getting the most out of any tree cut and keeping the number harvested to a minimum? Good idea, says Forest Service researcher Dr. Peter Koch, who has designed a machine to pluck the taproot...

Permanent-Magnet Motors and Generators for Aircraft

NASA Technical Reports Server (NTRS)

Echolds, E. F.

1983-01-01

Electric motors and generators that use permarotating machinery, but aspects of control and power conditioning are also considered. The discussion is structured around three basic areas: rotating machine design considerations presents various configuration and material options, generator applications provides insight into utilization areas and shows actual hardware and test results, and motor applications provides the same type of information for drive systems.

Systems Analysis, Machineable Circulation Data and Library Users and Non-Users.

ERIC Educational Resources Information Center

Lubans, John, Jr.

A study to be made with computer-based circulation data of the non-use and use of a large academic library is discussed. A search of the literature reveals that computer-based circulation systems can be, but have not been, utilized to provide data bases for systematic analyses of library users and resources. The data gathered in the circulation…

Wind Power Utilization Guide.

DTIC Science & Technology

1981-09-01

The expres- sions for the rotor torque for a Darrieus machine can be found in Reference 4.16. The Darrieus wind turbine offers the following... turbine generators, wind -driven turbines , power conditioning, wind power, energy conservation, windmills, economic ana \\sis. 20 ABS 1"ACT (Conti,on... turbines , power conditioning requirements, siting requirements, and the economics of wind power under different conditions. Three examples are given to

Nondestructive evaluation of hardwood logs:CT scanning, machine vision and data utilization

Treesearch

Daniel L. Schmoldt; Luis G. Occena; A. Lynn Abbott; Nand K. Gupta

1999-01-01

Sawing of hardwood logs still relies on relatively simple technologies that, in spite of their lack of sophistication, have been successful for many years due to wood?s traditional low cost and ready availability. These characteristics of the hardwood resource have changed dramatically over the past 20 years, however, forcing wood processors to become more efficient in...

Utilizing residues from in-woods flail processing

Treesearch

Ronald K. Baughman; Bryce J. Stokes; William F. Watson

1990-01-01

A Barkbuster 1100 tub grinder has been employed to process debris discharged by a Manitowoc VFDD-1642. The machine successfully passed the material through a 7.62 cm screen and discharged the reduced debris into a chip van for transport. Fuel production is directly dependent upon the production of clean chips by the flail/chipper portion of the system and the available...

Designing Microstructures/Structures for Desired Functional Material and Local Fields

DTIC Science & Technology

2015-12-02

utilized to engineer multifunctional soft materials for multi-sensing, multi- actuating , human-machine interfaces. [3] Establish a theoretical framework...model for surface elasticity, (ii) derived a new type of Maxwell stress in soft materials due to quantum mechanical-elasticity coupling and...elucidated its ramification in engineering multifunctional soft materials, and (iii) demonstrated the possibility of concurrent magnetoelectricity and

New technology for low-grade hardwood utilization: System 6

Treesearch

Hugh W. Reynolds; Charles J. Gatchell

1982-01-01

System 6 is a technology for converting low-grade hardwood to high-valued end products such as furniture and kitchen cabinets. Among its concepts are: (1) a new, nonlumber product called standard-size blanks; (2) highly automated methods of converting the logs to blanks; (3) total processing of every board that contains a minimum-size cutting; and (4) minimized machine...

Evaluating Data Clustering Approach for Life-Cycle Facility Control

DTIC Science & Technology

2013-04-01

produce 90% matching accuracy with noise/variations up to 55%. KEYWORDS: Building Information Modelling ( BIM ), machine learning, pattern detection...reconciled to building information model elements and ultimately to an expected resource utilization schedule. The motivation for this integration is to...by interoperable data sources and building information models . Building performance modelling and simulation efforts such as those by Maile et al

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces.

PubMed

Liu, Yuhao; Norton, James J S; Qazi, Raza; Zou, Zhanan; Ammann, Kaitlyn R; Liu, Hank; Yan, Lingqing; Tran, Phat L; Jang, Kyung-In; Lee, Jung Woo; Zhang, Douglas; Kilian, Kristopher A; Jung, Sung Hee; Bretl, Timothy; Xiao, Jianliang; Slepian, Marvin J; Huang, Yonggang; Jeong, Jae-Woong; Rogers, John A

2016-11-01

Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics.

Normothermic ex-situ liver preservation: the new gold standard.

PubMed

Laing, Richard W; Mergental, Hynek; Mirza, Darius F

2017-06-01

Normothermic machine perfusion of the liver (NMP-L) is a novel technology recently introduced into the practice of liver transplantation. This review recapitulates benefits of normothermic perfusion over conventional static cold storage and summarizes recent publications in this area. The first clinical trials have demonstrated both safety and feasibility of NMP-L. They have shown that machine perfusion can entirely replace cold storage or be commenced following a period of cold ischaemia. The technology currently allows transplant teams to extend the period of organ preservation for up to 24 h. Results from the first randomized control trial comparing NMP-L with static cold storage will be available soon. One major advantage of NMP-L technology over other parallel technologies is the potential to assess liver function during NMP-L. Several case series have suggested parameters usable for liver viability testing during NMP-L including bile production and clearance of lactic acidosis. NMP-L allows viability testing of high-risk livers. It has shown the potential to increase utilization of donor organs and improve transplant procedure logistics. NMP-L is likely to become an important technology that will improve organ preservation as well as have the potential to improve utilization of extended criteria donor livers.

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces

PubMed Central

Liu, Yuhao; Norton, James J. S.; Qazi, Raza; Zou, Zhanan; Ammann, Kaitlyn R.; Liu, Hank; Yan, Lingqing; Tran, Phat L.; Jang, Kyung-In; Lee, Jung Woo; Zhang, Douglas; Kilian, Kristopher A.; Jung, Sung Hee; Bretl, Timothy; Xiao, Jianliang; Slepian, Marvin J.; Huang, Yonggang; Jeong, Jae-Woong; Rogers, John A.

2016-01-01

Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics. PMID:28138529

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

PubMed

Araki, Tadashi; Ikeda, Nobutaka; Shukla, Devarshi; Jain, Pankaj K; Londhe, Narendra D; Shrivastava, Vimal K; Banchhor, Sumit K; Saba, Luca; Nicolaides, Andrew; Shafique, Shoaib; Laird, John R; Suri, Jasjit S

2016-05-01

Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Integrated two-cylinder liquid piston Stirling engine

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

Yang, Ning; Rickard, Robert; Pluckter, Kevin

2014-10-06

Heat engines utilizing the Stirling cycle may run on low temperature differentials with the capacity to function at high efficiency due to their near-reversible operation. However, current approaches to building Stirling engines are laborious and costly. Typically the components are assembled by hand and additional components require a corresponding increase in manufacturing complexity, akin to electronics before the integrated circuit. We present a simple and integrated approach to fabricating Stirling engines with precisely designed cylinders. We utilize computer aided design and one-step, planar machining to form all components of the engine. The engine utilizes liquid pistons and displacers to harnessmore » useful work from heat absorption and rejection. As a proof of principle of the integrated design, a two-cylinder engine is produced and characterized and liquid pumping is demonstrated.« less

Evolution of Cardiac Biomodels from Computational to Therapeutics.

PubMed

Rathinam, Alwin Kumar; Mokhtar, Raja Amin Raja

2016-08-23

Biomodeling the human anatomy in exact structure and size is an exciting field of medical science. Utilizing medical data from various medical imaging topography, the data of an anatomical structure can be extracted and converted into a three-dimensional virtual biomodel; thereafter a physical biomodel can be generated utilizing rapid prototyping machines. Here, we have reviewed the utilization of this technology and have provided some guidelines to develop biomodels of cardiac structures. Cardiac biomodels provide insights for cardiothoracic surgeons, cardiologists, and patients alike. Additionally, the technology may have future usability for tissue engineering, robotic surgery, or routine hospital usage as a diagnostic and therapeutic tool for cardiovascular diseases (CVD). Given the broad areas of application of cardiac biomodels, attention should be given to further research and development of their potential.

Robot transparency, trust and utility

NASA Astrophysics Data System (ADS)

Wortham, Robert H.; Theodorou, Andreas

2017-07-01

As robot reasoning becomes more complex, debugging becomes increasingly hard based solely on observable behaviour, even for robot designers and technical specialists. Similarly, non-specialist users have difficulty creating useful mental models of robot reasoning from observations of robot behaviour. The EPSRC Principles of Robotics mandate that our artefacts should be transparent, but what does this mean in practice, and how does transparency affect both trust and utility? We investigate this relationship in the literature and find it to be complex, particularly in nonindustrial environments where, depending on the application and purpose of the robot, transparency may have a wider range of effects on trust and utility. We outline our programme of research to support our assertion that it is nevertheless possible to create transparent agents that are emotionally engaging despite having a transparent machine nature.

Integrated two-cylinder liquid piston Stirling engine

NASA Astrophysics Data System (ADS)

Yang, Ning; Rickard, Robert; Pluckter, Kevin; Sulchek, Todd

2014-10-01

Heat engines utilizing the Stirling cycle may run on low temperature differentials with the capacity to function at high efficiency due to their near-reversible operation. However, current approaches to building Stirling engines are laborious and costly. Typically the components are assembled by hand and additional components require a corresponding increase in manufacturing complexity, akin to electronics before the integrated circuit. We present a simple and integrated approach to fabricating Stirling engines with precisely designed cylinders. We utilize computer aided design and one-step, planar machining to form all components of the engine. The engine utilizes liquid pistons and displacers to harness useful work from heat absorption and rejection. As a proof of principle of the integrated design, a two-cylinder engine is produced and characterized and liquid pumping is demonstrated.

Cluster-based query expansion using external collections in medical information retrieval.

PubMed

Oh, Heung-Seon; Jung, Yuchul

2015-12-01

Utilizing external collections to improve retrieval performance is challenging research because various test collections are created for different purposes. Improving medical information retrieval has also gained much attention as various types of medical documents have become available to researchers ever since they started storing them in machine processable formats. In this paper, we propose an effective method of utilizing external collections based on the pseudo relevance feedback approach. Our method incorporates the structure of external collections in estimating individual components in the final feedback model. Extensive experiments on three medical collections (TREC CDS, CLEF eHealth, and OHSUMED) were performed, and the results were compared with a representative expansion approach utilizing the external collections to show the superiority of our method. Copyright © 2015 Elsevier Inc. All rights reserved.

Mining key elements for severe convection prediction based on CNN

NASA Astrophysics Data System (ADS)

Liu, Ming; Pan, Ning; Zhang, Changan; Sha, Hongzhou; Zhang, Bolei; Liu, Liang; Zhang, Meng

2017-04-01

Severe convective weather is a kind of weather disasters accompanied by heavy rainfall, gust wind, hail, etc. Along with recent developments on remote sensing and numerical modeling, there are high-volume and long-term observational and modeling data accumulated to capture massive severe convective events over particular areas and time periods. With those high-volume and high-variety weather data, most of the existing studies and methods carry out the dynamical laws, cause analysis, potential rule study, and prediction enhancement by utilizing the governing equations from fluid dynamics and thermodynamics. In this study, a key-element mining method is proposed for severe convection prediction based on convolution neural network (CNN). It aims to identify the key areas and key elements from huge amounts of historical weather data including conventional measurements, weather radar, satellite, so as numerical modeling and/or reanalysis data. Under this manner, the machine-learning based method could help the human forecasters on their decision-making on operational weather forecasts on severe convective weathers by extracting key information from the real-time and historical weather big data. In this paper, it first utilizes computer vision technology to complete the data preprocessing work of the meteorological variables. Then, it utilizes the information such as radar map and expert knowledge to annotate all images automatically. And finally, by using CNN model, it cloud analyze and evaluate each weather elements (e.g., particular variables, patterns, features, etc.), and identify key areas of those critical weather elements, then help forecasters quickly screen out the key elements from huge amounts of observation data by current weather conditions. Based on the rich weather measurement and model data (up to 10 years) over Fujian province in China, where the severe convective weathers are very active during the summer months, experimental tests are conducted with the new machine-learning method via CNN models. Based on the analysis of those experimental results and case studies, the proposed new method have below benefits for the severe convection prediction: (1) helping forecasters to narrow down the scope of analysis and saves lead-time for those high-impact severe convection; (2) performing huge amount of weather big data by machine learning methods rather relying on traditional theory and knowledge, which provide new method to explore and quantify the severe convective weathers; (3) providing machine learning based end-to-end analysis and processing ability with considerable scalability on data volumes, and accomplishing the analysis work without human intervention.

Adjustable mounting device for high-volume production of beam-shaping systems for high-power diode lasers

NASA Astrophysics Data System (ADS)

Haag, Sebastian; Bernhardt, Henning; Rübenach, Olaf; Haverkamp, Tobias; Müller, Tobias; Zontar, Daniel; Brecher, Christian

2015-02-01

In many applications for high-power diode lasers, the production of beam-shaping and homogenizing optical systems experience rising volumes and dynamical market demands. The automation of assembly processes on flexible and reconfigurable machines can contribute to a more responsive and scalable production. The paper presents a flexible mounting device designed for the challenging assembly of side-tab based optical systems. It provides design elements for precisely referencing and fixating two optical elements in a well-defined geometric relation. Side tabs are presented to the machine allowing the application of glue and a rotating mechanism allows the attachment to the optical elements. The device can be adjusted to fit different form factors and it can be used in high-volume assembly machines. The paper shows the utilization of the device for a collimation module consisting of a fast-axis and a slow-axis collimation lens. Results regarding the repeatability and process capability of bonding side tab assemblies as well as estimates from 3D simulation for overall performance indicators achieved such as cycle time and throughput will be discussed.

Machine learning techniques applied to the determination of road suitability for the transportation of dangerous substances.

PubMed

Matías, J M; Taboada, J; Ordóñez, C; Nieto, P G

2007-08-17

This article describes a methodology to model the degree of remedial action required to make short stretches of a roadway suitable for dangerous goods transport (DGT), particularly pollutant substances, using different variables associated with the characteristics of each segment. Thirty-one factors determining the impact of an accident on a particular stretch of road were identified and subdivided into two major groups: accident probability factors and accident severity factors. Given the number of factors determining the state of a particular road segment, the only viable statistical methods for implementing the model were machine learning techniques, such as multilayer perceptron networks (MLPs), classification trees (CARTs) and support vector machines (SVMs). The results produced by these techniques on a test sample were more favourable than those produced by traditional discriminant analysis, irrespective of whether dimensionality reduction techniques were applied. The best results were obtained using SVMs specifically adapted to ordinal data. This technique takes advantage of the ordinal information contained in the data without penalising the computational load. Furthermore, the technique permits the estimation of the utility function that is latent in expert knowledge.

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.

Concurrent Image Processing Executive (CIPE)

NASA Technical Reports Server (NTRS)

Lee, Meemong; Cooper, Gregory T.; Groom, Steven L.; Mazer, Alan S.; Williams, Winifred I.

1988-01-01

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are discussed. The target machine for this software is a JPL/Caltech Mark IIIfp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules; (1) user interface, (2) host-resident executive, (3) hypercube-resident executive, and (4) application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube a data management method which distributes, redistributes, and tracks data set information was implemented.

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

PubMed

Ogunyemi, Omolola; Kermah, Dulcie

2015-01-01

Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing retinopathy. Using clinical data from urban safety net clinics as well as public health data from the CDC's National Health and Nutrition Examination Survey, we examined different machine learning approaches for predicting retinopathy from clinical or public health data. All datasets utilized exhibited a class imbalance. Classifiers learned on the clinical data were modestly predictive of retinopathy with the best model having an AUC of 0.72, sensitivity of 69.2% and specificity of 55.9%. Classifiers learned on public health data were not predictive of retinopathy. Successful approaches to detecting latent retinopathy using machine learning could help safety net and other clinics identify unscreened patients who are most at risk of developing retinopathy and the use of ensemble classifiers on clinical data shows promise for this purpose.

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

PubMed

Kebschull, Moritz; Papapanou, Panos N

2017-01-01

Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of -omics data.

Chapter 9: The FTU Machine - Design Construction and Assembly

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

Pizzuto, A.; Annino, C.; Baldarelli, M.

2004-05-15

The main design features and guidelines for the construction of the 8-T cryogenically cooled Frascati Tokamak Upgrade (FTU) are presented. The main features include the very compact toroidal magnets based on the concept of the 'Bitter' type of coil with wedge-shaped turns, utilized for the first time for the Alcator A and C magnets, and the original configuration of the vacuum vessel (VV) structure, which is fully welded in order to achieve the required high strength and electric resistivity. The present toroidal limiter has been installed following several years of operation, and this installation has required the development of specificmore » remote-handling tools. The toroidal limiter consists of 12 independent sectors made of stainless steel carriers and molybdenum alloy (TZM) tiles. The main fabrication processes developed for the toroidal and poloidal coils as well as for the VV are described. It is to be noted that the assembly procedure has required very accurate machining of all the structures requiring several trials and steps. The machine has shown no problem in operating routinely at its maximum design values (8 T, 1.6 MA)« less

Intelligent excavator control system for lunar mining system

NASA Astrophysics Data System (ADS)

Lever, Paul J. A.; Wang, Fei-Yue

1995-01-01

A major benefit of utilizing local planetary resources is that it reduces the need and cost of lifting materials from the Earth's surface into Earth orbit. The location of the moon makes it an ideal site for harvesting the materials needed to assist space activities. Here, lunar excavation will take place in the dynamic unstructured lunar environment, in which conditions are highly variable and unpredictable. Autonomous mining (excavation) machines are necessary to remove human operators from this hazardous environment. This machine must use a control system structure that can identify, plan, sense, and control real-time dynamic machine movements in the lunar environment. The solution is a vision-based hierarchical control structure. However, excavation tasks require force/torque sensor feedback to control the excavation tool after it has penetrated the surface. A fuzzy logic controller (FLC) is used to interpret the forces and torques gathered from a bucket mounted force/torque sensor during excavation. Experimental results from several excavation tests using the FLC are presented here. These results represent the first step toward an integrated sensing and control system for a lunar mining system.

An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.

PubMed

Wei, Ning; You, Jia; Friehs, Karl; Flaschel, Erwin; Nattkemper, Tim Wilhelm

2007-08-15

Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and viability during fermentation processes. For this purpose, an in situ probe has been developed, which utilizes a dark field illumination unit to obtain high contrast images with an integrated CCD camera. To test the probe, brewer's yeast Saccharomyces cerevisiae is chosen as the target microorganism. Images of the yeast cells in the bioreactors are captured, processed, and analyzed automatically by means of mechatronics, image processing, and machine learning. Two support vector machine based classifiers are used for separating cells from background, and for distinguishing live from dead cells afterwards. The evaluation of the in situ experiments showed strong correlation between results obtained by the probe and those by widely accepted standard methods. Thus, the in situ probe has been proved to be a feasible device for on-line monitoring of both cell density and viability with high accuracy and stability. (c) 2007 Wiley Periodicals, Inc.

Analytical Modeling of a Double-Sided Flux Concentrating E-Core Transverse Flux Machine with Pole Windings

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

Muljadi, Eduard; Hasan, Iftekhar; Husain, Tausif

In this paper, a nonlinear analytical model based on the Magnetic Equivalent Circuit (MEC) method is developed for a double-sided E-Core Transverse Flux Machine (TFM). The proposed TFM has a cylindrical rotor, sandwiched between E-core stators on both sides. Ferrite magnets are used in the rotor with flux concentrating design to attain high airgap flux density, better magnet utilization, and higher torque density. The MEC model was developed using a series-parallel combination of flux tubes to estimate the reluctance network for different parts of the machine including air gaps, permanent magnets, and the stator and rotor ferromagnetic materials, in amore » two-dimensional (2-D) frame. An iterative Gauss-Siedel method is integrated with the MEC model to capture the effects of magnetic saturation. A single phase, 1 kW, 400 rpm E-Core TFM is analytically modeled and its results for flux linkage, no-load EMF, and generated torque, are verified with Finite Element Analysis (FEA). The analytical model significantly reduces the computation time while estimating results with less than 10 percent error.« less

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

PubMed

Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332

Design and analysis of a 3D-flux flux-switching permanent magnet machine with SMC cores and ferrite magnets

NASA Astrophysics Data System (ADS)

Liu, Chengcheng; Wang, Youhua; Lei, Gang; Guo, Youguang; Zhu, Jianguo

2017-05-01

Since permanent magnets (PM) are stacked between the adjacent stator teeth and there are no windings or PMs on the rotor, flux-switching permanent magnet machine (FSPMM) owns the merits of good flux concentrating and robust rotor structure. Compared with the traditional PM machines, FSPMM can provide higher torque density and better thermal dissipation ability. Combined with the soft magnetic composite (SMC) material and ferrite magnets, this paper proposes a new 3D-flux FSPMM (3DFFSPMM). The topology and operation principle are introduced. It can be found that the designed new 3DFFSPMM has many merits over than the traditional FSPMM for it can utilize the advantages of SMC material. Moreover, the PM flux of this new motor can be regulated by using the mechanical method. 3D finite element method (FEM) is used to calculate the magnetic field and parameters of the motor, such as flux density, inductance, PM flux linkage and efficiency map. The demagnetization analysis of the ferrite magnet is also addressed to ensure the safety operation of the proposed motor.

The Application of LT-Table in TRIZ Contradiction Resolving Process

NASA Astrophysics Data System (ADS)

Wei, Zihui; Li, Qinghai; Wang, Donglin; Tian, Yumei

TRIZ is used to resolve invention problems. ARIZ is the most powerful systematic method which integrates all of TRIZ heuristics. Definition of ideal final result (IFR), identification of contradictions and resource utilization are main lines of ARIZ. But resource searching of ARIZ has fault of blindness. Alexandr sets up mathematical model of transformation of the hereditary information in an invention problem using the theory of catastrophes, and provides method of resource searching using LT-table. The application of LT-table on contradiction resolving is introduced. Resource utilization using LT-table is joined into ARIZ step as an addition of TRIZ, apply this method in separator paper punching machine design.

Induction motor control

NASA Technical Reports Server (NTRS)

Hansen, Irving G.

1990-01-01

Electromechanical actuators developed to date have commonly ultilized permanent magnet (PM) synchronous motors. More recently switched reluctance (SR) motors have been advocated due to their robust characteristics. Implications of work which utilized induction motors and advanced control techniques are discussed. When induction motors are operated from an energy source capable of controlling voltages and frequencies independently, drive characteristics are obtained which are superior to either PM or SR motors. By synthesizing the machine frequency from a high-frequency carrier (nominally 20 kHz), high efficiencies, low distortion, and rapid torque response are available. At this time multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of aerospace actuators. This effort is based upon high-frequency power distribution and management techniques developed by NASA for Space Station Freedom.

Gestural cue analysis in automated semantic miscommunication annotation

PubMed Central

Inoue, Masashi; Ogihara, Mitsunori; Hanada, Ryoko; Furuyama, Nobuhiro

2011-01-01

The automated annotation of conversational video by semantic miscommunication labels is a challenging topic. Although miscommunications are often obvious to the speakers as well as the observers, it is difficult for machines to detect them from the low-level features. We investigate the utility of gestural cues in this paper among various non-verbal features. Compared with gesture recognition tasks in human-computer interaction, this process is difficult due to the lack of understanding on which cues contribute to miscommunications and the implicitness of gestures. Nine simple gestural features are taken from gesture data, and both simple and complex classifiers are constructed using machine learning. The experimental results suggest that there is no single gestural feature that can predict or explain the occurrence of semantic miscommunication in our setting. PMID:23585724

Design of control system for optical fiber drawing machine driven by double motor

NASA Astrophysics Data System (ADS)

Yu, Yue Chen; Bo, Yu Ming; Wang, Jun

2018-01-01

Micro channel Plate (MCP) is a kind of large-area array electron multiplier with high two-dimensional spatial resolution, used as high-performance night vision intensifier. The high precision control of the fiber is the key technology of the micro channel plate manufacturing process, and it was achieved by the control of optical fiber drawing machine driven by dual-motor in this paper. First of all, utilizing STM32 chip, the servo motor drive and control circuit was designed to realize the dual motor synchronization. Secondly, neural network PID control algorithm was designed for controlling the fiber diameter fabricated in high precision; Finally, the hexagonal fiber was manufactured by this system and it shows that multifilament diameter accuracy of the fiber is +/- 1.5μm.

Rapid fabrication of microfluidic chips based on the simplest LED lithography

NASA Astrophysics Data System (ADS)

Li, Yue; Wu, Ping; Luo, Zhaofeng; Ren, Yuxuan; Liao, Meixiang; Feng, Lili; Li, Yuting; He, Liqun

2015-05-01

Microfluidic chips are generally fabricated by a soft lithography method employing commercial lithography equipment. These heavy machines require a critical room environment and high lamp power, and the cost remains too high for most normal laboratories. Here we present a novel microfluidics fabrication method utilizing a portable ultraviolet (UV) LED as an alternative UV source for photolithography. With this approach, we can repeat several common microchannels as do these conventional commercial exposure machines, and both the verticality of the channel sidewall and lithography resolution are proved to be acceptable. Further microfluidics applications such as mixing, blood typing and microdroplet generation are implemented to validate the practicability of the chips. This simple but innovative method decreases the cost and requirement of chip fabrication dramatically and may be more popular with ordinary laboratories.

Modeling and prediction of human word search behavior in interactive machine translation

NASA Astrophysics Data System (ADS)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

Discriminative Cooperative Networks for Detecting Phase Transitions

NASA Astrophysics Data System (ADS)

Liu, Ye-Hua; van Nieuwenburg, Evert P. L.

2018-04-01

The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCNs). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data. The new scheme is efficient enough for dealing with phase diagrams in two-dimensional parameter spaces, where we can utilize an active contour model—the snake—from computer vision to host the two networks. The snake, with a DCN "brain," moves and learns actively in the parameter space, and locates phase boundaries automatically.

Structural design of the Sandia 34-M Vertical Axis Wind Turbine

NASA Astrophysics Data System (ADS)

Berg, D. E.

Sandia National Laboratories, as the lead DOE laboratory for Vertical Axis Wind Turbine (VAWT) development, is currently designing a 34-meter diameter Darrieus-type VAWT. This turbine will be a research test bed which provides a focus for advancing technology and validating design and fabrication techniques in a size range suitable for utility use. Structural data from this machine will allow structural modeling to be refined and verified for a turbine on which the gravity effects and stochastic wind loading are significant. Performance data from it will allow aerodynamic modeling to be refined and verified. The design effort incorporates Sandia's state-of-the-art analysis tools in the design of a complete machine. The analytic tools used in this design are discussed and the conceptual design procedure is described.

Design and operating experience on the U.S. Department of Energy Experimental Mod-O 100 kW Wind Turbine

NASA Technical Reports Server (NTRS)

Glasgow, J. C.; Birchenough, A. G.

1978-01-01

The Mod-O 100 kW Experimental Wind Turbine was designed and fabricated by NASA, as part of the Federal Wind Energy Program, to assess technology requirements and engineering problems of large wind turbines. The machine became operational in October 1975 and has demonstrated successful operation in all of its design modes. During the course of its operations the machine has generated a wealth of experimental data and has served as a prototype developmental test bed for the Mod-OA operational wind turbines which are currently used on utility networks. This paper describes the mechanical and control systems as they evolved in operational tests and describes some of the experience with various systems in the downwind rotor configuration.

Installation and checkout of the DOE/NASA Mod-1 2000-kW wind turbine generator

NASA Technical Reports Server (NTRS)

Puthoff, R. L.; Collins, J. L.; Wolf, R. A.

1980-01-01

The paper describes the DOE/NASA Mod-1 wind turbine generator, its assembly and testing, and its installation at Boone, North Carolina. The paper concludes with performance data taken during the initial tests conducted on the machine. The successful installation and initial operation of the Mod-1 wind turbine generator has had the following results: (1) megawatt-size wind turbines can be operated satisfactorily on utility grids; (2) the structural loads can be predicted by existing codes; (3) assembly of the machine on top of the tower presents no major problem; (4) large blades 100 ft long can be transported long distances and over mountain roads; and (5) operating experience and performance data will contribute substantially to the design of future low-cost wind turbines.

AQUAPLEX An Environmentally Aware Model Lunar Settlement

NASA Astrophysics Data System (ADS)

Preble, Darel

2003-01-01

The construction and operation of a replica Lunar settlement (CELSS), can provide many lessons in in-situ resource utilization, telerobotic operation and reducing the hygiene water demanded by existing models of Lunar operation - a larger settlement may be operated with the same amount of precious water. Hypes and Hall and all other CELSS models found in the literature propose quantities of hygiene water far in excess of what would be needed in actual operation using simple, environmentally aware technologies. By using modern zero water toilets, low water showers, CO2 dry cleaning machines, energy efficient washing machines and other hardware, water use can be slashed. The Space Solar Power Workshop sees great opportunity to advance the prospects for Lunar settlement through involving the environmental community in this fun design exercise.

The engine design engine. A clustered computer platform for the aerodynamic inverse design and analysis of a full engine

NASA Technical Reports Server (NTRS)

Sanz, J.; Pischel, K.; Hubler, D.

1992-01-01

An application for parallel computation on a combined cluster of powerful workstations and supercomputers was developed. A Parallel Virtual Machine (PVM) is used as message passage language on a macro-tasking parallelization of the Aerodynamic Inverse Design and Analysis for a Full Engine computer code. The heterogeneous nature of the cluster is perfectly handled by the controlling host machine. Communication is established via Ethernet with the TCP/IP protocol over an open network. A reasonable overhead is imposed for internode communication, rendering an efficient utilization of the engaged processors. Perhaps one of the most interesting features of the system is its versatile nature, that permits the usage of the computational resources available that are experiencing less use at a given point in time.

A study of the utilization of EREP data from the Wabash River basin

NASA Technical Reports Server (NTRS)

Silva, L. F. (Principal Investigator)

1976-01-01

The author has identified the following significant results. The study of the multispectral data sets indicate that better land use delineation using machine processing techniques can be obtained with data from multispectral scanners than digitized S190A photographic sensor data. Results of the multiemulsion photographic data set were a little better than the multiband photographic data set. Comparison results of the interim and filtered S191 data indicate that the data were improved some for machine processing techniques. Results of the S191 X-5 detector array studied over a wintertime scene indicate that a good quality far infrared channel can be useful. The S191 spectroradiometer study results indicate that the data from the S191 was usable, and it was possible to estimate the path radiance.

Evaluation of surface integrity of WEDM processed inconel 718 for jet engine application

NASA Astrophysics Data System (ADS)

Sharma, Priyaranjan; Tripathy, Ashis; Sahoo, Narayan

2018-03-01

A unique superalloy, Inconel 718 has been serving for aerospace industries since last two decades. Due to its attractive properties such as high strength at elevated temperature, improved corrosion and oxidation resistance, it is widely employed in the manufacturing of jet engine components. These components require complex shape without affecting the parent material properties. Traditional machining methods seem to be ineffective to fulfil the demand of aircraft industries. Therefore, an advanced feature of wire electrical discharge machining (WEDM) has been utilized to improve the surface features of the jet engine components. With the help of trim-offset technology, it became possible to achieve considerable amount of residual stresses, lower peak to valley height, reduced density of craters and micro globules, minimum hardness alteration and negligible recast layer formation.

A high performance parallel algorithm for 1-D FFT

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

Agarwal, R.C.; Gustavson, F.G.; Zubair, M.

1994-12-31

In this paper the authors propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. They use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. They show that the multi-dimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. They implementedmore » this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.« less

Machine learning of big data in gaining insight into successful treatment of hypertension.

PubMed

Koren, Gideon; Nordon, Galia; Radinsky, Kira; Shalev, Varda

2018-06-01

Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta-analyses of these studies. The objective of this study was to test the utility of machine learning of big data in gaining insight into the treatment of hypertension. We applied machine learning techniques such as decision trees and neural networks, to identify determinants that contribute to the success of hypertension drug treatment on a large set of patients. We also identified concomitant drugs not considered to have antihypertensive activity, which may contribute to lowering blood pressure (BP) control. Higher initial BP predicts lower success rates. Among the medication options and their combinations, treatment with beta blockers appears to be more commonly effective, which is not reflected in contemporary guidelines. Among numerous concomitant drugs taken by hypertensive patients, proton pump inhibitors (PPIs), and HMG CO-A reductase inhibitors (statins) significantly improved the success rate of hypertension. In conclusions, machine learning of big data is a novel method to identify effective antihypertensive therapy and for repurposing medications already on the market for new indications. Our results related to beta blockers, stemming from machine learning of a large and diverse set of big data, in contrast to the much narrower criteria for randomized clinic trials (RCTs), should be corroborated and affirmed by other methods, as they hold potential promise for an old class of drugs which may be presently underutilized. These previously unrecognized effects of PPIs and statins have been very recently identified as effective in lowering BP in preliminary clinical observations, lending credibility to our big data results.

SSL - THE SIMPLE SOCKETS LIBRARY

NASA Technical Reports Server (NTRS)

Campbell, C. E.

1994-01-01

The Simple Sockets Library (SSL) allows C programmers to develop systems of cooperating programs using Berkeley streaming Sockets running under the TCP/IP protocol over Ethernet. The SSL provides a simple way to move information between programs running on the same or different machines and does so with little overhead. The SSL can create three types of Sockets: namely a server, a client, and an accept Socket. The SSL's Sockets are designed to be used in a fashion reminiscent of the use of FILE pointers so that a C programmer who is familiar with reading and writing files will immediately feel comfortable with reading and writing with Sockets. The SSL consists of three parts: the library, PortMaster, and utilities. The user of the SSL accesses it by linking programs to the SSL library. The PortMaster initializes connections between clients and servers. The PortMaster also supports a "firewall" facility to keep out socket requests from unapproved machines. The "firewall" is a file which contains Internet addresses for all approved machines. There are three utilities provided with the SSL. SKTDBG can be used to debug programs that make use of the SSL. SPMTABLE lists the servers and port numbers on requested machine(s). SRMSRVR tells the PortMaster to forcibly remove a server name from its list. The package also includes two example programs: multiskt.c, which makes multiple accepts on one server, and sktpoll.c, which repeatedly attempts to connect a client to some server at one second intervals. SSL is a machine independent library written in the C-language for computers connected via Ethernet using the TCP/IP protocol. It has been successfully compiled and implemented on a variety of platforms, including Sun series computers running SunOS, DEC VAX series computers running VMS, SGI computers running IRIX, DECstations running ULTRIX, DEC alpha AXPs running OSF/1, IBM RS/6000 computers running AIX, IBM PC and compatibles running BSD/386 UNIX and HP Apollo 3000/4000/9000/400T computers running HP-UX. SSL requires 45K of RAM to run under SunOS and 80K of RAM to run under VMS. For use on IBM PC series computers and compatibles running DOS, SSL requires Microsoft C 6.0 and the Wollongong TCP/IP package. Source code for sample programs and debugging tools are provided. The documentation is available on the distribution medium in TeX and PostScript formats. The standard distribution medium for SSL is a .25 inch streaming magnetic tape cartridge (QIC-24) in UNIX tar format. It is also available on a 3.5 inch diskette in UNIX tar format and a 5.25 inch 360K MS-DOS format diskette. The SSL was developed in 1992 and was updated in 1993.

Automatic Inference of Cryptographic Key Length Based on Analysis of Proof Tightness

DTIC Science & Technology

2016-06-01

within an attack tree structure, then expand attack tree methodology to include cryptographic reductions. We then provide the algorithms for...maintaining and automatically reasoning about these expanded attack trees . We provide a software tool that utilizes machine-readable proof and attack metadata...and the attack tree methodology to provide rapid and precise answers regarding security parameters and effective security. This eliminates the need

The Utilization of Navy People-Related RDT&E (Research, Development, Test, and Evaluation): Fiscal Year 1983.

DTIC Science & Technology

1984-06-01

emostraion. Tese eserch ool wee deignted and experimental demonstrations wre successfully con- for demonstrations. These research tools wre designated ...Topics 4.02 Instructional Systems Design Methodology Instructional Systems Development and Effectiveness Evaluation .................................... 1...6 53 0 0 67w Report Page 10.07 Human Performance Variables/Factors 10.08 Man-Machine Design Methodology Computer Assisted Methods for Human

Evaluation of the Effectiveness of Simulation for M4 Marksmanship Training

DTIC Science & Technology

2014-02-01

DEMOGRAPHIC QUESTIONNAIRE ................................................. 34 APPENDIX C: ANALYSIS OF MARKSMANSHIP PERFORMANCE DATA TO IDENTIFY POTENTIAL...machine guns and anti- armour weapons. In these simulators, firers aim a modified weapon at a target image on a screen. When the firer pulls the trigger...investigate predictors of live-fire LF6 qualification. Specifically, we examined the utility of LF6 simulator scores and trainee demographic data as

Real-Time Ada Problem Solution Study

DTIC Science & Technology

1989-03-24

been performed, there is a larger base of information concerning standards and guidelines for Ada usage, as well "lessons learned ". A number of...the target machine and operate in conjunction with the application programs, they also require system resources (CPU,memory). The utilization of...Transporter-Consumer 1694 154 6. Producer-Transpt-Buffer- Transp -Consumer 2248 204 7. Relay 906 82 8. Conditional Entry - no rendezvous 170 15

MEMS Cantilever Sensor for THz Photoacoustic Chemical Sensing and Spectroscopy

DTIC Science & Technology

2013-12-26

meaning the detector didn’t have to be cryogenically cooled. Piezoresistive cantilever style sensor designs have been fabricated for wind and...made a two cantilever pizeoresistive wind speed sensor that utilized a Wheatstone bridge configuration. The designed cantilevers, etched out of...Murakami et al. in Japan fabricated diaphragm and cantilever PZT microphone sensors for anomaly detection in machines such as turbines or engines

USSR Report, International Affairs

DTIC Science & Technology

1987-04-09

of the Council on Utilization of Foreign Experience attached to the USSR Gosplan and doctor of economic sciences, and Professor N. V . Bautina, a ... V . Bautina, a member of this council and head of the Department of Planning Activity Collaboration of the CEMA International Institute of Economic...each comprising a machine tool and a robot. Incidentally, these modules can be put together to make up flexible produc- tion systems. V . Tsarenko

Will Anything Useful Come Out of Virtual Reality? Examination of a Naval Application

DTIC Science & Technology

1993-05-01

The term virtual reality can encompass varying meanings, but some generally accepted attributes of a virtual environment are that it is immersive...technology, but at present there are few practical applications which are utilizing the broad range of virtual reality technology. This paper will discuss an...Operability, operator functions, Virtual reality , Man-machine interface, Decision aids/decision making, Decision support. ASW.

Real time computer controlled weld skate

NASA Technical Reports Server (NTRS)

Wall, W. A., Jr.

1977-01-01

A real time, adaptive control, automatic welding system was developed. This system utilizes the general case geometrical relationships between a weldment and a weld skate to precisely maintain constant weld speed and torch angle along a contoured workplace. The system is compatible with the gas tungsten arc weld process or can be adapted to other weld processes. Heli-arc cutting and machine tool routing operations are possible applications.