Science.gov

Sample records for hybrid pm machines

  1. Superconducting PM undiffused machines with stationary superconducting coils

    DOEpatents

    Hsu, John S.; Schwenterly, S. William

    2004-03-02

    A superconducting PM machine has a stator, a rotor and a stationary excitation source without the need of a ferromagnetic frame which is cryogenically cooled for operation in the superconducting state. PM material is placed between poles on the rotor to prevent leakage or diffusion of secondary flux before reaching the main air gap, or to divert PM flux where it is desired to weaken flux in the main air gap. The PM material provides hop-along capability for the machine in the event of a fault condition.

  2. PM Motor Parametric Design Analyses for Hybrid Electric Vehicle Traction Drive Application: Interim Report

    SciTech Connect

    Staunton, R.H.

    2004-08-11

    The Department of Energy's (DOE) Office of FreedomCAR (Cooperative Automotive Research) and Vehicle Technologies has a strong interest in making rapid progress in permanent magnet (PM) machine development. The program is directing various technology development projects that will advance the technology and lead to request for proposals (RFP) for manufacturer prototypes. This aggressive approach is possible because the technology is clearly within reach and the approach is deemed essential, based on strong market demand, escalating fuel prices, and competitive considerations. In response, this study began parallel development paths that included a literature search/review, development and utilization of multiple parametric models to determine the effects of design parameters, verification of the modeling methodology, development of an interior PM (IPM) machine baseline design, development of alternative machine baseline designs, and cost analyses for several candidate machines. This interim progress report summarizes the results of these activities as of June 2004. This report provides background and summary information for recent machine parametric studies and testing programs that demonstrate both the potential capabilities and technical limitations of brushless PM machines (axial gap and radial gap), the IPM machine, the surface-mount PM machines (interior or exterior rotor), induction machines, and switched reluctance machines. The FreedomCAR program, while acknowledging the progress made by Oak Ridge National Laboratory, Delphi, Delco-Remy International, and others in these programs, has redirected efforts toward a ''short path'' to a marketable and competitive PM motor for hybrid electric vehicle traction applications. The program has developed a set of performance targets for the type of traction machine desired. The short-path approach entails a comprehensive design effort focusing on the IPM machine and meeting the performance targets. The selection of the

  3. PM Motor Parametric Design Analyses for a Hybrid Electric Vehicle Traction Drive Application

    SciTech Connect

    Staunton, R.H.

    2004-10-11

    The Department of Energy's (DOE) Office of FreedomCAR (Cooperative Automotive Research) and Vehicle Technologies office has a strong interest in making rapid progress in permanent magnet (PM) machine development. The DOE FreedomCAR program is directing various technology development projects that will advance the technology and hopefully lead to a near-term request for proposals (RFP) for a to-be-determined level of initial production. This aggressive approach is possible because the technology is clearly within reach and the approach is deemed essential, based on strong market demand, escalating fuel prices, and competitive considerations. In response, this study began parallel development paths that included a literature search/review, development and utilization of multiple parametric models, verification of the modeling methodology, development of an interior PM (IPM) machine baseline design, development of alternative machine baseline designs, and cost analyses for several candidate machines. This report summarizes the results of these activities as of September 2004. This report provides background and summary information for recent machine parametric studies and testing programs that demonstrate both the potential capabilities and technical limitations of brushless PM machines (axial gap and radial gap), the IPM machine, the surface-mount PM machines (interior or exterior rotor), induction machines, and switched-reluctance machines. The FreedomCAR program, while acknowledging the progress made by Oak Ridge National Laboratory (ORNL), Delphi, Delco-Remy International, and others in these programs, has redirected efforts toward a ''short path'' to a marketable and competitive PM motor for hybrid electric vehicle (HEV) traction applications. The program has developed a set of performance targets for the type of traction machine desired. The short-path approach entails a comprehensive design effort focusing on the IPM machine and meeting the performance targets

  4. A Parallel Vector Machine for the PM Programming Language

    NASA Astrophysics Data System (ADS)

    Bellerby, Tim

    2016-04-01

    PM is a new programming language which aims to make the writing of computational geoscience models on parallel hardware accessible to scientists who are not themselves expert parallel programmers. It is based around the concept of communicating operators: language constructs that enable variables local to a single invocation of a parallelised loop to be viewed as if they were arrays spanning the entire loop domain. This mechanism enables different loop invocations (which may or may not be executing on different processors) to exchange information in a manner that extends the successful Communicating Sequential Processes idiom from single messages to collective communication. Communicating operators avoid the additional synchronisation mechanisms, such as atomic variables, required when programming using the Partitioned Global Address Space (PGAS) paradigm. Using a single loop invocation as the fundamental unit of concurrency enables PM to uniformly represent different levels of parallelism from vector operations through shared memory systems to distributed grids. This paper describes an implementation of PM based on a vectorised virtual machine. On a single processor node, concurrent operations are implemented using masked vector operations. Virtual machine instructions operate on vectors of values and may be unmasked, masked using a Boolean field, or masked using an array of active vector cell locations. Conditional structures (such as if-then-else or while statement implementations) calculate and apply masks to the operations they control. A shift in mask representation from Boolean to location-list occurs when active locations become sufficiently sparse. Parallel loops unfold data structures (or vectors of data structures for nested loops) into vectors of values that may additionally be distributed over multiple computational nodes and then split into micro-threads compatible with the size of the local cache. Inter-node communication is accomplished using

  5. Development of Novel Pre-alloyed PM Steels for Optimization of Machinability and Fatigue Resistance of PM Components

    NASA Astrophysics Data System (ADS)

    Mardan, Milad; Blais, Carl

    2016-03-01

    It is well known that a large proportion of ferrous PM components require secondary machining operations for dimensional conformance or for producing geometrical features that cannot be generated during die compaction. Nevertheless, the machining behavior of PM parts is generally characterized as being "difficult" due to the presence of residual porosity that lowers thermal conductivity and induces interrupted cutting. Several admixed additives such as MnS and BN-h can be used to improve the machining behavior of PM steels. Nevertheless, their negative effect on mechanical properties, especially fatigue resistance, makes their utilization uninteresting for the fabrication of high-performance PM steel components. This article summarizes the work carried out to develop a novel PM steel that was especially engineered to form machinability enhancing precipitates. This new material is pre-alloyed with tin (Sn) in order to form Cu-Sn (Cu(α)) precipitates during transient liquid phase sintering. The newly developed material presents machinability improvement of 165% compared to reference material used in the PM industry as well as increases in toughness and fatigue resistance of 100% and 13%, respectively.

  6. Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

    PubMed Central

    Lary, D. J.; Lary, T.; Sattler, B.

    2015-01-01

    With the increasing awareness of health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground-level airborne particulate matter (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground based observations of PM2.5 from 8,329 measurement sites in 55 countries taken between 1997 and 2014 to train a machine learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present. We demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies. An analysis of Baltimore schizophrenia emergency room admissions is presented in terms of the levels of ambient pollution. PM2.5 appears to have an impact on some aspects of mental health. PMID:26005352

  7. Electric machine for hybrid motor vehicle

    DOEpatents

    Hsu, John Sheungchun

    2007-09-18

    A power system for a motor vehicle having an internal combustion engine and an electric machine is disclosed. The electric machine has a stator, a permanent magnet rotor, an uncluttered rotor spaced from the permanent magnet rotor, and at least one secondary core assembly. The power system also has a gearing arrangement for coupling the internal combustion engine to wheels on the vehicle thereby providing a means for the electric machine to both power assist and brake in relation to the output of the internal combustion engine.

  8. Hybrid-secondary uncluttered induction machine

    DOEpatents

    Hsu, John S.

    2001-01-01

    An uncluttered secondary induction machine (100) includes an uncluttered rotating transformer (66) which is mounted on the same shaft as the rotor (73) of the induction machine. Current in the rotor (73) is electrically connected to current in the rotor winding (67) of the transformer, which is not electrically connected to, but is magnetically coupled to, a stator secondary winding (40). The stator secondary winding (40) is alternately connected to an effective resistance (41), an AC source inverter (42) or a magnetic switch (43) to provide a cost effective slip-energy-controlled, adjustable speed, induction motor that operates over a wide speed range from below synchronous speed to above synchronous speed based on the AC line frequency fed to the stator.

  9. Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology

    PubMed Central

    Ju, Ying

    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. PMID:27478823

  10. Bidirectional hybrid PM-based RoF and VCSEL-based VLLC system.

    PubMed

    Li, Chung-Yi; Lu, Hai-Han; Chang, Ching-Hung; Lin, Chun-Yu; Wu, Po-Yi; Zheng, Jun-Ren; Lin, Chia-Rung

    2014-06-30

    A bidirectional hybrid phase modulation (PM)-based radio-over-fiber (RoF) and vertical cavity surface emitting laser (VCSEL)-based visible laser light communication (VLLC) systems employing injection-locked VCSEL-based PM-to-intensity modulation (IM) converters and optical interleavers (ILs) is proposed and demonstrated. To be the first one of using injection-locked VCSEL-based PM-to-IM converters and optical ILs in such bidirectional hybrid RoF and VLLC systems, the downstream light is successfully phase-remodulated with RoF signal for up-link transmission. Through a serious investigation in systems, bit error rate (BER) and eye diagram perform brilliantly over a 40-km single-mode fiber (SMF) transport and a 12-m free-space transmission. Such a bidirectional hybrid RoF and VLLC system would be very attractive for the integration of fiber backbone and in-door networks to provide broadband integrated services, including Internet and telecommunication services. PMID:24977870

  11. Machine vision system for inspecting characteristics of hybrid rice seed

    NASA Astrophysics Data System (ADS)

    Cheng, Fang; Ying, Yibin

    2004-03-01

    Obtaining clear images advantaged of improving the classification accuracy involves many factors, light source, lens extender and background were discussed in this paper. The analysis of rice seed reflectance curves showed that the wavelength of light source for discrimination of the diseased seeds from normal rice seeds in the monochromic image recognition mode was about 815nm for jinyou402 and shanyou10. To determine optimizing conditions for acquiring digital images of rice seed using a computer vision system, an adjustable color machine vision system was developed. The machine vision system with 20mm to 25mm lens extender produce close-up images which made it easy to object recognition of characteristics in hybrid rice seeds. White background was proved to be better than black background for inspecting rice seeds infected by disease and using the algorithms based on shape. Experimental results indicated good classification for most of the characteristics with the machine vision system. The same algorithm yielded better results in optimizing condition for quality inspection of rice seed. Specifically, the image processing can correct for details such as fine fissure with the machine vision system.

  12. Hybrid-secondary uncluttered permanent magnet machine and method

    DOEpatents

    Hsu, John S.

    2005-12-20

    An electric machine (40) has a stator (43), a permanent magnet rotor (38) with permanent magnets (39) and a magnetic coupling uncluttered rotor (46) for inducing a slip energy current in secondary coils (47). A dc flux can be produced in the uncluttered rotor when the secondary coils are fed with dc currents. The magnetic coupling uncluttered rotor (46) has magnetic brushes (A, B, C, D) which couple flux in through the rotor (46) to the secondary coils (47c, 47d) without inducing a current in the rotor (46) and without coupling a stator rotational energy component to the secondary coils (47c, 47d). The machine can be operated as a motor or a generator in multi-phase or single-phase embodiments and is applicable to the hybrid electric vehicle. A method of providing a slip energy controller is also disclosed.

  13. Thermal-mechanical modeling of laser ablation hybrid machining

    NASA Astrophysics Data System (ADS)

    Matin, Mohammad Kaiser

    2001-08-01

    Hard, brittle and wear-resistant materials like ceramics pose a problem when being machined using conventional machining processes. Machining ceramics even with a diamond cutting tool is very difficult and costly. Near net-shape processes, like laser evaporation, produce micro-cracks that require extra finishing. Thus it is anticipated that ceramic machining will have to continue to be explored with new-sprung techniques before ceramic materials become commonplace. This numerical investigation results from the numerical simulations of the thermal and mechanical modeling of simultaneous material removal from hard-to-machine materials using both laser ablation and conventional tool cutting utilizing the finite element method. The model is formulated using a two dimensional, planar, computational domain. The process simulation acronymed, LAHM (Laser Ablation Hybrid Machining), uses laser energy for two purposes. The first purpose is to remove the material by ablation. The second purpose is to heat the unremoved material that lies below the ablated material in order to ``soften'' it. The softened material is then simultaneously removed by conventional machining processes. The complete solution determines the temperature distribution and stress contours within the material and tracks the moving boundary that occurs due to material ablation. The temperature distribution is used to determine the distance below the phase change surface where sufficient ``softening'' has occurred, so that a cutting tool may be used to remove additional material. The model incorporated for tracking the ablative surface does not assume an isothermal melt phase (e.g. Stefan problem) for laser ablation. Both surface absorption and volume absorption of laser energy as function of depth have been considered in the models. LAHM, from the thermal and mechanical point of view is a complex machining process involving large deformations at high strain rates, thermal effects of the laser, removal of

  14. Permanent magnet machine and method with reluctance poles and non-identical PM poles for high density operation

    DOEpatents

    Hsu, John S.

    2010-05-18

    A method and apparatus in which a stator (11) and a rotor (12) define a primary air gap (20) for receiving AC flux and at least one source (23, 40), and preferably two sources (23, 24, 40) of DC excitation are positioned for inducing DC flux at opposite ends of the rotor (12). Portions of PM material (17, 17a) are provided as boundaries separating PM rotor pole portions from each other and from reluctance poles. The PM poles (18) and the reluctance poles (19) can be formed with poles of one polarity having enlarged flux paths in relation to flux paths for pole portions of an opposite polarity, the enlarged flux paths communicating with a core of the rotor (12) so as to increase reluctance torque produced by the electric machine. Reluctance torque is increased by providing asymmetrical pole faces. The DC excitation can also use asymmetric poles and asymmetric excitation sources. Several embodiments are disclosed with additional variations.

  15. HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan (Inventor)

    2005-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  16. Hybrid Neural Network and Support Vector Machine Method for Optimization

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan (Inventor)

    2007-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  17. HYTREM; A hybrid text-retrieval machine for large databases

    SciTech Connect

    Lee, D.L. ); Lochovsky, F.H. )

    1990-01-01

    This paper describes the design of a text-retrieval machine, called HYTREM (hybrid text-retrieval machine), for the support of large unformatted text databases. A signature file is used as an access method to reduce the amount of data that need to be searched directly. HYTREM consists of two major subsystems: a signature processor and a text processor. The signature processor is based on a word-parallel, bit-serial (WPBS) organization which is faster, more efficient, and more flexible than a word-serial, bit-parallel (WSBP) organization proposed in the literature. The text processor, called ALTEP (associative linear text processor), is a linear array of logic cells capable of matching regular expressions at a much higher speed than that of previous designs. Since both the signature processor and ALTEP are highly parallel processors, a high-speed multiple-response resolver (MRR) is provided to facilitate data transfer between the processors and the controllers over a single common bus. Issues about the design of a cost-effective mass-storage system (MSS) are discussed. The performance and implementation issues of HYTREM are discussed.

  18. Design of high-performing hybrid meta-heuristics for unrelated parallel machine scheduling with machine eligibility and precedence constraints

    NASA Astrophysics Data System (ADS)

    Afzalirad, Mojtaba; Rezaeian, Javad

    2016-04-01

    This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.

  19. Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model

    NASA Astrophysics Data System (ADS)

    Ivey, C. E.; Holmes, H. A.; Hu, Y. T.; Mulholland, J. A.; Russell, A. G.

    2015-01-01

    An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limitations are inherent in most source apportionment methods, which has led to the development of a novel hybrid approach that is used to estimate source impacts by combining the capabilities of receptor modeling (RM) and chemical transport modeling (CTM). The hybrid CTM-RM method calculates adjustment factors to refine the CTM-estimated impact of sources at monitoring sites using pollutant species observations and the results of CTM sensitivity analyses, though it does not directly generate spatial source impact fields. The CTM used here is the Community Multi-Scale Air Quality (CMAQ) model, and the RM approach is based on the Chemical Mass Balance model. This work presents a method that utilizes kriging to spatially interpolate source-specific impact adjustment factors to generate revised CTM source impact fields from the CTM-RM method results, and is applied to January 2004 over the continental United States. The kriging step is evaluated using data withholding and by comparing results to data from alternative networks. Directly applied and spatially interpolated hybrid adjustment factors at withheld monitors had a correlation coefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and an intercept of 0.14 ± 0.02. Refined source contributions reflect current knowledge of PM emissions (e.g., significant differences in biomass burning impact fields). Concentrations of 19 species and total PM2.5 mass were reconstructed for withheld monitors using directly applied and spatially interpolated hybrid adjustment factors. The mean concentrations of total PM2.5 for withheld monitors were 11.7 (± 8.3), 16.3 (± 11), 8.59 (± 4.7), and 9.20 (± 5.7) μg m-3 for the

  20. Marketing image categorization using hybrid human-machine combinations

    NASA Astrophysics Data System (ADS)

    Gnanasambandam, Nathan; Madhu, Himanshu

    2012-03-01

    Marketing instruments with nested, short-form, symbol loaded content need to be studied differently. Image classification in the Web2.0 world can dynamically use a configurable amount of internal and external data as well as varying levels of crowd-sourcing. Our work is one such examination of how to construct a hybrid technique involving learning and crowd-sourcing. Through a parameter called turkmix and a multitude of crowd-sourcing techniques available we show that we can control the trend of metrics such as precision and recall on the hybrid categorizer.

  1. Simplified Hybrid-Secondary Uncluttered Machine And Method

    DOEpatents

    Hsu, John S [Oak Ridge, TN

    2005-05-10

    An electric machine (40, 40') has a stator (43) and a rotor (46) and a primary air gap (48) has secondary coils (47c, 47d) separated from the rotor (46) by a secondary air gap (49) so as to induce a slip current in the secondary coils (47c, 47d). The rotor (46, 76) has magnetic brushes (A, B, C, D) or wires (80) which couple flux in through the rotor (46) to the secondary coils (47c, 47d) without inducing a current in the rotor (46) and without coupling a stator rotational energy component to the secondary coils (47c, 47d). The machine can be operated as a motor or a generator in multi-phase or single-phase embodiments. A method of providing a slip energy controller is also disclosed.

  2. A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5

    NASA Astrophysics Data System (ADS)

    Michanowicz, Drew R.; Shmool, Jessie L. C.; Tunno, Brett J.; Tripathy, Sheila; Gillooly, Sara; Kinnee, Ellen; Clougherty, Jane E.

    2016-04-01

    Characterizing near-source spatio-temporal variation is a long -standing challenge in air pollution epidemiology, and common intra-urban modeling approaches [e.g., land use regression (LUR)], do not account for short-term meteorological variation. Atmospheric dispersion modeling approaches, such as AERMOD, can account for near-source pollutant behavior by capturing source-meteorological interactions, but requires external validation and resolved background concentrations. In this study, we integrate AERMOD-based predictions for source-specific fine particle (PM2.5) concentrations into LUR models derived from total ambient PM2.5 measured at 36 unique sites selected to represent different source and elevation profiles, during summer and winter, 2012-2013 in Pittsburgh, Pennsylvania (PA). We modeled PM2.5 emissions from 207 local stationary sources in AERMOD, utilizing the monitoring locations as receptors, and hourly meteorological information matching each sampling period. Finally, we compare results of the integrated LUR/AERMOD hybrid model to those of the AERMOD + background and standard LUR models, at the full domain scale and within a 5 km2 sub-domain surrounding a large industrial facility. The hybrid model improved out-of-sample prediction accuracy by 2-10% over LUR alone, though performance differed by season, in part due to within-season temporal variability. We found differences up to 10 μg/m3 in predicted concentrations, and observed the largest differences within the industrial sub-domain. LUR underestimated concentrations from 500 to 2500 m downwind of major sources. The hybrid modeling approach we developed may help to improve intra-urban exposure estimates, particularly in regions of large industrial sources, sharp elevation gradients, or complex meteorology (e.g., frequent inversion events), such as Pittsburgh, PA. More broadly, the approach may inform the development of spatio-temporal modeling frameworks for air pollution exposure assessment for

  3. Design of drying chamber and biomass furnace for sun-biomass hybrid rice-drying machine

    NASA Astrophysics Data System (ADS)

    Satria, Dhimas; Haryadi, Austin, Ruben; Kurniawan, Bobby

    2016-03-01

    In most Asian countries, rice drying is carried out manually by exposing rice to sunlight. However, problem occurs when rain season comes. Lack of sunlight deters the drying process. This paper proposes a design of mechanical rice drying machine with hybrid sun-biomass energy source. Pahl & Beitz method, which consists of four steps process: function planning and clarification, design concept, design prototype, and design details; are used as design methodology. Based on design result and calculation, in this paper propose specifications for drying machine and biomass furnace. Drying chamber is a continuous flow system with pneumatic-conveyor as blower. This hybrid utilizes two types of energy sources, sun and biomass. The proposed machine has capacity of 500 kilograms per cycle using 455 Watt of energy, which is more efficient than ordinary heater. Biomass furnace utilizes heat transfer by means of arranging 64 pieces of stainless steel pipes of 0.65 diameters in parallel.

  4. Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model

    NASA Astrophysics Data System (ADS)

    Ivey, C. E.; Holmes, H. A.; Hu, Y. T.; Mulholland, J. A.; Russell, A. G.

    2015-07-01

    An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limitations are inherent in most source apportionment methods motivating the development of a novel hybrid approach that is used to estimate source impacts by combining the capabilities of receptor models (RMs) and chemical transport models (CTMs). The hybrid CTM-RM method calculates adjustment factors to refine the CTM-estimated impact of sources at monitoring sites using pollutant species observations and the results of CTM sensitivity analyses, though it does not directly generate spatial source impact fields. The CTM used here is the Community Multiscale Air Quality (CMAQ) model, and the RM approach is based on the chemical mass balance (CMB) model. This work presents a method that utilizes kriging to spatially interpolate source-specific impact adjustment factors to generate revised CTM source impact fields from the CTM-RM method results, and is applied for January 2004 over the continental United States. The kriging step is evaluated using data withholding and by comparing results to data from alternative networks. Data withholding also provides an estimate of method uncertainty. Directly applied (hybrid, HYB) and spatially interpolated (spatial hybrid, SH) hybrid adjustment factors at withheld observation sites had a correlation coefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and an intercept of 0.14 ± 0.02. Refined source contributions reflect current knowledge of PM emissions (e.g., significant differences in biomass burning impact fields). Concentrations of 19 species and total PM2.5 mass were reconstructed for withheld observation sites using HYB and SH adjustment factors. The mean concentrations of total PM2.5 at withheld observation sites were

  5. Microstructure of Laser-MAG Hybrid Welds of Sintered P/M Steel

    NASA Astrophysics Data System (ADS)

    Liu, Shuangyu; Zhang, Hong; Hu, Jiandong; Shi, Yan

    2013-01-01

    The microstructure and mechanical properties of iron-based powder metallurgical steels jointed by CO2 laser-metal active gas (MAG) hybrid welding were investigated. The cross-sectional morphology of hybrid weld bead consisted of arc zone and laser zone. The microstructure of arc zone consisted of columnar dendrite and fine acicular dendrite between the columnar dendrites, but that of laser zone was composed of fine equiaxed dendrite. The MAG weld had obvious heat-affected zone (HAZ) zone, while hybrid weld had very narrow HAZ zone because of the rapid cooling rate. The phase constitutions of the joint determined by x-ray diffraction were α-Fe (ferrite) and Cu. The 2θ value of α-Fe (200) peaks of hybrid weld was smaller than that of sintering compact. Compared to MAG weld, hybrid weld had finer grain size, higher micro-hardness, and higher micro-strain, which was caused by the difference of cooling rate and crystallizing.

  6. Position Error Compensation via a Variable Reluctance Sensor Applied to a Hybrid Vehicle Electric Machine

    PubMed Central

    Bucak, İhsan Ömür

    2010-01-01

    In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion. PMID:22294906

  7. Position error compensation via a variable reluctance sensor applied to a Hybrid Vehicle Electric machine.

    PubMed

    Bucak, Ihsan Ömür

    2010-01-01

    In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion. PMID:22294906

  8. Final Report on Control Algorithm to Improve the Partial-Load Efficiency of Surface PM Machines with Fractional-Slot Concentrated Windings

    SciTech Connect

    McKeever, John W; Reddy, Patel; Jahns, Thomas M

    2007-05-01

    Surface permanent magnet (SPM) synchronous machines using fractional-slot concentrated windings are being investigated as candidates for high-performance traction machines for automotive electric propulsion systems. It has been shown analytically and experimentally that such designs can achieve very wide constant-power speed ratios (CPSR) [1,2]. This work has shown that machines of this type are capable of achieving very low cogging torque amplitudes as well as significantly increasing the machine power density [3-5] compared to SPM machines using conventional distributed windings. High efficiency can be achieved in this class of SPM machine by making special efforts to suppress the eddy-current losses in the magnets [6-8], accompanied by efforts to minimize the iron losses in the rotor and stator cores. Considerable attention has traditionally been devoted to maximizing the full-load efficiency of traction machines at their rated operating points and along their maximum-power vs. speed envelopes for higher speeds [9,10]. For example, on-line control approaches have been presented for maximizing the full-load efficiency of PM synchronous machines, including the use of negative d-axis stator current to reduce the core losses [11,12]. However, another important performance specification for electric traction applications is the machine's efficiency at partial loads. Partial-load efficiency is particularly important if the target traction application requires long periods of cruising operation at light loads that are significantly lower than the maximum drive capabilities. While the design of the machine itself is clearly important, investigation has shown that this is a case where the choice of the control algorithm plays a critical role in determining the maximum partial-load efficiency that the machine actually achieves in the traction drive system. There is no evidence that this important topic has been addressed for this type of SPM machine by any other authors

  9. FINAL REPORT ON CONTROL ALGORITHM TO IMPROVE THE PARTIAL-LOAD EFFICIENCY OFSURFACE PM MACHINES WITH FRACTIONAL-SLOT CONCENTRATED WINDINGS

    SciTech Connect

    Reddy, P.B.; Jahns, T.M.

    2007-04-30

    Surface permanent magnet (SPM) synchronous machines using fractional-slot concentrated windings are being investigated as candidates for high-performance traction machines for automotive electric propulsion systems. It has been shown analytically and experimentally that such designs can achieve very wide constant-power speed ratios (CPSR) [1,2]. This work has shown that machines of this type are capable of achieving very low cogging torque amplitudes as well as significantly increasing the machine power density [3-5] compared to SPM machines using conventional distributed windings. High efficiency can be achieved in this class of SPM machine by making special efforts to suppress the eddy-current losses in the magnets [6-8], accompanied by efforts to minimize the iron losses in the rotor and stator cores. Considerable attention has traditionally been devoted to maximizing the full-load efficiency of traction machines at their rated operating points and along their maximum-power vs. speed envelopes for higher speeds [9,10]. For example, on-line control approaches have been presented for maximizing the full-load efficiency of PM synchronous machines, including the use of negative d-axis stator current to reduce the core losses [11,12]. However, another important performance specification for electric traction applications is the machine's efficiency at partial loads. Partial-load efficiency is particularly important if the target traction application requires long periods of cruising operation at light loads that are significantly lower than the maximum drive capabilities. While the design of the machine itself is clearly important, investigation has shown that this is a case where the choice of the control algorithm plays a critical role in determining the maximum partial-load efficiency that the machine actually achieves in the traction drive system. There is no evidence that this important topic has been addressed for this type of SPM machine by any other authors

  10. MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

    PubMed

    Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

    2014-01-01

    We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

  11. 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.

  12. A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

    NASA Astrophysics Data System (ADS)

    Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

    2015-08-01

    Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

  13. Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

    PubMed

    Liu, Y X; Zhang, N N; He, Y; Lun, L J

    2015-01-01

    Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning techniques to select a small set of informative genes, which will lead to improving classification accuracy. First feature filtering algorithms were applied to select a set of top-ranked genes, and then hierarchical clustering and collapsing dense clusters were used to select core cancer genes. Through empirical study, our approach is capable of selecting relatively few core cancer genes while making high-accuracy predictions. The biological significance of these genes was evaluated using systems biology analysis. Extensive functional pathway and network analyses have confirmed findings in previous studies and can bring new insights into common cancer mechanisms. PMID:26345818

  14. Accurate modeling of switched reluctance machine based on hybrid trained WNN

    SciTech Connect

    Song, Shoujun Ge, Lefei; Ma, Shaojie; Zhang, Man

    2014-04-15

    According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.

  15. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    PubMed

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange. PMID:24701205

  16. Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

    PubMed Central

    Banik, Shipra; Khodadad Khan, A. F. M.; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange. PMID:24701205

  17. Continuous decoding of intended movements with a hybrid kinetic and kinematic brain machine interface.

    PubMed

    Suminski, Aaron J; Willett, Francis R; Fagg, Andrew H; Bodenhamer, Matthew; Hatsopoulos, Nicholas G

    2011-01-01

    Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion, it is known that motor cortical activity also correlates with kinetic signals, including hand force and joint torque. In this experiment, a monkey used a cortically-controlled BMI to move a visual cursor and hit a sequence of randomly placed targets. By varying the contributions of separate kinetic and kinematic decoders to the movement of a virtual arm, we evaluated the hypothesis that a BMI incorporating both signals (Hybrid BMI) would outperform a BMI decoding kinematic information alone (Position BMI). We show that the trajectories generated by the Hybrid BMI during real-time decoding were straighter and smoother than those of the Position BMI. These results may have important implications for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment. PMID:22255659

  18. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

    NASA Astrophysics Data System (ADS)

    Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

    2012-08-01

    Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

  19. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    NASA Astrophysics Data System (ADS)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  20. Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method.

    PubMed

    Liu, Jin; Guo, Ting-ting; Li, Hao-chuan; Jia, Shi-qiang; Yan, Yan-lu; An, Dong; Zhang, Yao; Chen, Shao-jiang

    2015-11-01

    Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels. PMID:26978947

  1. Hybrid Processing: the Impact of Mechanical and Surface Thermal Treatment Integration onto the Machine Parts Quality

    NASA Astrophysics Data System (ADS)

    Skeeba, V. Yu; Ivancivsky, V. V.; Kutyshkin, A. V.; Parts, K. A.

    2016-04-01

    The comparative analysis of the two hybrid process technologies, which are based on the integration of mechanical treatment (abrasive grinding or turning) and a surface heat strengthening by high frequency current on the same processing equipment, is given in the paper. The acquired results demonstrate that the suggested integrating approach allows carrying out the processing on the one technological base, which leads to the increase in the quality of the machine parts surface layer. The conducted experimental research proves that a minor stock allowance value for the final mechanical processing (sparking out or diamond smoothing) ensures the absence of defects such as local abatement zones and provides strain hardening of the work piece surface. This leads to the formation of the work-hardened layer of 0.01 - 0.03 mm, increase in microhardness value by 12 - 17% and the level of residual compressive stress in the surface layer by 10 - 21 % respectively.

  2. Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

    PubMed Central

    Molla-Alizadeh-Zavardehi, S.; Tavakkoli-Moghaddam, R.; Lotfi, F. Hosseinzadeh

    2014-01-01

    This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms. PMID:24883359

  3. A hybrid dynamic harmony search algorithm for identical parallel machines scheduling

    NASA Astrophysics Data System (ADS)

    Chen, Jing; Pan, Quan-Ke; Wang, Ling; Li, Jun-Qing

    2012-02-01

    In this article, a dynamic harmony search (DHS) algorithm is proposed for the identical parallel machines scheduling problem with the objective to minimize makespan. First, an encoding scheme based on a list scheduling rule is developed to convert the continuous harmony vectors to discrete job assignments. Second, the whole harmony memory (HM) is divided into multiple small-sized sub-HMs, and each sub-HM performs evolution independently and exchanges information with others periodically by using a regrouping schedule. Third, a novel improvisation process is applied to generate a new harmony by making use of the information of harmony vectors in each sub-HM. Moreover, a local search strategy is presented and incorporated into the DHS algorithm to find promising solutions. Simulation results show that the hybrid DHS (DHS_LS) is very competitive in comparison to its competitors in terms of mean performance and average computational time.

  4. Hybrid metaheuristics for solving a fuzzy single batch-processing machine scheduling problem.

    PubMed

    Molla-Alizadeh-Zavardehi, S; Tavakkoli-Moghaddam, R; Lotfi, F Hosseinzadeh

    2014-01-01

    This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms. PMID:24883359

  5. Integrated Inverter For Driving Multiple Electric Machines

    DOEpatents

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

    2006-04-04

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

  6. A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

    NASA Astrophysics Data System (ADS)

    Wang, Li-Chih; Chen, Yin-Yann; Chen, Tzu-Li; Cheng, Chen-Yang; Chang, Chin-Wei

    2014-10-01

    This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

  7. Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models

    NASA Astrophysics Data System (ADS)

    Qin, Shanshan; Liu, Feng; Wang, Jianzhou; Sun, Beibei

    2014-12-01

    The analysis and forecasting of PM concentrations play a significant role in regulatory planning on the reduction and control of PM emission and precautionary strategies. However, accurate PM forecasting, which is needed to establish an early warning system, is still a huge challenge and a critical issue. Determining how to address the accurate forecasting problem becomes an even more significant and urgent task. Based on gray correlation analysis (GCA), Ensemble Empirical Mode Decomposition (EEMD), Cuckoo search (CS) and Back-propagation artificial neutral networks (BPANN), this paper proposes the CS-EEMD-BPANN model for forecasting PM concentrations. Prior to establishing this model, gray correlation has been uniquely used to search for possible predictors of PM among other air pollutants (CO, NO2, O3 and SO2) and meteorological environments (wind speed, wind direction, temperature, humidity and pressure). The proposed method was investigated in four major cities of China (Beijing, Shanghai, Guangzhou and Lanzhou) with different characteristics of climatic, terrain and emission sources. The results of the gray correlation analysis indicate that CO, NO2 and SO2 are more related to PM and that the incorporation of these predictors can significantly improve the model performance predictability, suggesting the effectiveness of our developed method.

  8. Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation.

    PubMed

    Clark, Alex M; Bunin, Barry A; Litterman, Nadia K; Schürer, Stephan C; Visser, Ubbo

    2014-01-01

    Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers. PMID:25165633

  9. Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

    PubMed Central

    Bunin, Barry A.; Litterman, Nadia K.; Schürer, Stephan C.; Visser, Ubbo

    2014-01-01

    Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers. PMID:25165633

  10. PM RESEARCH

    EPA Science Inventory

    Activity Area (F03): PM Implementation NRMRL conducts research to improve the techniques used to quantify PM and PM precursor emissions from stationary, mobile, and fugitive sources and investigates the performance and cost of innovative control technology systems. The emission...

  11. Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

    PubMed

    Komasi, Mehdi; Sharghi, Soroush

    2016-01-01

    Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process. PMID:27120649

  12. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    PubMed Central

    Rajesh Sharma, R.; Marikkannu, P.

    2015-01-01

    A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods. PMID:26509188

  13. Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines.

    PubMed

    Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

    2013-04-01

    We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562

  14. Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

    PubMed Central

    Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

    2013-01-01

    We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562

  15. An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines

    NASA Astrophysics Data System (ADS)

    Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min

    2013-12-01

    In this article, an effective shuffled frog-leaping algorithm (SFLA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines (HFSP-IPM). First, some novel heuristic decoding rules for both job order decision and machine assignment are proposed. Then, three hybrid decoding schemes are designed to decode job order sequences to schedules. A special bi-level crossover and multiple local search operators are incorporated in the searching framework of the SFLA to enrich the memetic searching behaviour and to balance the exploration and exploitation capabilities. Meanwhile, some theoretical analysis for the local search operators is provided for guiding the local search. The parameter setting of the algorithm is also investigated based on the Taguchi method of design of experiments. Finally, numerical testing based on well-known benchmarks and comparisons with some existing algorithms are carried out to demonstrate the effectiveness of the proposed algorithm.

  16. Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

    NASA Astrophysics Data System (ADS)

    Guo, Peng; Cheng, Wenming; Wang, Yi

    2015-11-01

    This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup-Hermann-Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.

  17. Source Identification of PM2.5 in Steubenville, Ohio Using a Hybrid Method for Highly Time-resolved Data

    EPA Science Inventory

    A new source-type identification method, Reduction and Species Clustering Using Episodes (ReSCUE), was developed to exploit the temporal synchronicity between species to form clusters of species that vary together. High time-resolution (30 min) PM2.5 sampling was condu...

  18. Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

    NASA Astrophysics Data System (ADS)

    Datta, Jinia; Chowdhuri, Sumana; Bera, Jitendranath

    2015-07-01

    This paper presents a novel scheme of remote condition monitoring of multi machine system where a secured and coded data of induction machine with different parameters is communicated between a state-of-the-art dedicated hardware Units (DHU) installed at the machine terminal and a centralized PC based machine data management (MDM) software. The DHUs are built for acquisition of different parameters from the respective machines, and hence are placed at their nearby panels in order to acquire different parameters cost effectively during their running condition. The MDM software collects these data through a communication channel where all the DHUs are networked using RS485 protocol. Before transmitting, the parameter's related data is modified with the adoption of differential pulse coded modulation (DPCM) and Huffman coding technique. It is further encrypted with a private key where different keys are used for different DHUs. In this way a data security scheme is adopted during its passage through the communication channel in order to avoid any third party attack into the channel. The hybrid mode of DPCM and Huffman coding is chosen to reduce the data packet length. A MATLAB based simulation and its practical implementation using DHUs at three machine terminals (one healthy three phase, one healthy single phase and one faulty three phase machine) proves its efficacy and usefulness for condition based maintenance of multi machine system. The data at the central control room are decrypted and decoded using MDM software. In this work it is observed that Chanel efficiency with respect to different parameter measurements has been increased very much.

  19. Source identification of PM2.5 in Steubenville, Ohio using a hybrid method for highly time-resolved data.

    PubMed

    Vedantham, Ram; Landis, Matthew S; Olson, David; Pancras, Joseph Patrick

    2014-01-01

    A new source-type identification method, Reduction and Species Clustering Using Episodes (ReSCUE), was developed to exploit the temporal synchronicity typically observed between ambient species in high time resolution fine particulate matter (PM2.5) data to form clusters that vary together. High time-resolution (30 min) PM2.5 sampling was conducted for a month during the summer of 2006 in Steubenville, OH, an EPA designated nonattainment area for the U.S. National Ambient Air Quality Standards (NAAQS). When the data were evaluated, the species clusters from ReSCUE matched extremely well with the source types identified by EPA Unmix demonstrating that ReSCUE is a valuable tool in identifying source types. Results from EPA Unmix show that contributions to PM2.5 are mostly from iron/steel manufacturing (36% ± 9%), crustal matter (33% ± 11%), and coal combustion (11% ± 19%). More importantly, ReSCUE was useful in (i) providing objective data driven guidance for the number of source factors and key fitting species for EPA Unmix, and (ii) detecting tenuous associations between some species and source types in the results derived by EPA Unmix. PMID:24387270

  20. Hybrid polylingual object model: an efficient and seamless integration of Java and native components on the Dalvik virtual machine.

    PubMed

    Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

    2014-01-01

    JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. PMID:25110745

  1. A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem

    NASA Astrophysics Data System (ADS)

    Liou, Cheng-Dar; Hsieh, Yi-Chih; Chen, Yin-Yann

    2013-01-01

    This article investigates the two-machine flow-shop group scheduling problem (GSP) with sequence-dependent setup and removal times, and job transportation times between machines. The objective is to minimise the total completion time. As known, this problem is an NP-hard problem and generalises the typical two-machine GSPs. In this article, a new encoding scheme based on permutation representation is proposed to transform a random job permutation to a feasible permutation for GSPs. The proposed encoding scheme simultaneously determines both the sequence of jobs in each group and the sequence of groups. By reasonably combining particle swarm optimisation (PSO) and genetic algorithm (GA), we develop a fast and easily implemented hybrid algorithm (HA) for solving the considered problems. The effectiveness and efficiency of the proposed HA are demonstrated and compared with those of standard PSO and GA by numerical results of various tested instances with group numbers up to 20. In addition, three different lower bounds are developed to evaluate the solution quality of the HA. Limited numerical results indicate that the proposed HA is a viable and effective approach for the studied two-machine flow-shop group scheduling problem.

  2. An Hybrid Approach Based on Machining and Dynamic Tests Data for the Identification of Material Constitutive Equations

    NASA Astrophysics Data System (ADS)

    Jomaa, Walid; Songmene, Victor; Bocher, Philippe

    2016-03-01

    In recent years, there has been growing interest for the identification of material constitutive equations using machining tests (inverse method). However, the inverse method has shown some drawbacks that could affect the accuracy of the identified material constants. On one hand, this approach requires the use of analytical model to estimate the cutting temperature. Nevertheless, the used temperature models lead to large discrepancies for the calculated temperatures even for the same work material and cutting conditions. On the other hand, some computation issues were observed when all material constants were determined, in the same time, using machining tests data. Therefore, this study attempts to provide a methodology for identifying the coefficients of the Marusich's constitutive equation (MCE) which demonstrated a good capability for the simulation of the material behavior in high speed machining. The proposed approach, which is based on an analytical inverse method together with dynamic tests, was applied to aluminum alloys AA6061-T6 and AA7075-T651, and induction hardened AISI 4340 steel (60 HRC). The response surface methodology was used in this approach. Two sets of material coefficients, for each tested work material, were determined using two different temperature models (Oxley and Loewen-Shaw). The obtained constitutive equations were validated using dynamic tests and finite element simulation of high speed machining. The predictions obtained are also compared to those performed with the corresponding Johnson and Cook constitutive equations (JCE) from the literature. The sensitivity analysis revealed that the selected temperature models used in the analytical inverse method can affect significantly the identified material constants and thereafter predicted dynamic response and machining data. Moreover, the MCE obtained using the hybrid method performed better than the JCE obtained by only dynamic tests.

  3. Insect-machine hybrid system for understanding and evaluating sensory-motor control by sex pheromone in Bombyx mori.

    PubMed

    Kanzaki, Ryohei; Minegishi, Ryo; Namiki, Shigehiro; Ando, Noriyasu

    2013-11-01

    To elucidate the dynamic information processing in a brain underlying adaptive behavior, it is necessary to understand the behavior and corresponding neural activities. This requires animals which have clear relationships between behavior and corresponding neural activities. Insects are precisely such animals and one of the adaptive behaviors of insects is high-accuracy odor source orientation. The most direct way to know the relationships between neural activity and behavior is by recording neural activities in a brain from freely behaving insects. There is also a method to give stimuli mimicking the natural environment to tethered insects allowing insects to walk or fly at the same position. In addition to these methods an 'insect-machine hybrid system' is proposed, which is another experimental system meeting the conditions necessary for approaching the dynamic processing in the brain of insects for generating adaptive behavior. This insect-machine hybrid system is an experimental system which has a mobile robot as its body. The robot is controlled by the insect through its behavior or the neural activities recorded from the brain. As we can arbitrarily control the motor output of the robot, we can intervene at the relationship between the insect and the environmental conditions. PMID:23749329

  4. Design comparison of single phase outer and inner-rotor hybrid excitation flux switching motor for hybrid electric vehicles

    NASA Astrophysics Data System (ADS)

    Mazlan, Mohamed Mubin Aizat; Sulaiman, Erwan; Husin, Zhafir Aizat; Othman, Syed Muhammad Naufal Syed; Khan, Faisal

    2015-05-01

    In hybrid excitation machines (HEMs), there are two main flux sources which are permanent magnet (PM) and field excitation coil (FEC). These HEMs have better features when compared with the interior permanent magnet synchronous machines (IPMSM) used in conventional hybrid electric vehicles (HEVs). Since all flux sources including PM, FEC and armature coils are located on the stator core, the rotor becomes a single piece structure similar with switch reluctance machine (SRM). The combined flux generated by PM and FEC established more excitation fluxes that are required to produce much higher torque of the motor. In addition, variable DC FEC can control the flux capabilities of the motor, thus the machine can be applied for high-speed motor drive system. In this paper, the comparisons of single-phase 8S-4P outer and inner rotor hybrid excitation flux switching machine (HEFSM) are presented. Initially, design procedures of the HEFSM including parts drawing, materials and conditions setting, and properties setting are explained. Flux comparisons analysis is performed to investigate the flux capabilities at various current densities. Then the flux linkages of PM with DC FEC of various DC FEC current densities are examined. Finally torque performances are analyzed at various armature and FEC current densities for both designs. As a result, the outer-rotor HEFSM has higher flux linkage of PM with DC FEC and higher average torque of approximately 10% when compared with inner-rotor HEFSM.

  5. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

    PubMed Central

    Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

    2016-01-01

    Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust. PMID:27551829

  6. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    PubMed

    Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

    2016-01-01

    Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust. PMID:27551829

  7. Design of a Permanent Magnet Synchronous Machine for a Flywheel Energy Storage System within a Hybrid Electric Vehicle

    NASA Astrophysics Data System (ADS)

    Jiang, Ming

    As an energy storage device, the flywheel has significant advantages over conventional chemical batteries, including higher energy density, higher efficiency, longer life time, and less pollution to the environment. An effective flywheel system can be attributed to its good motor/generator (M/G) design. This thesis describes the research work on the design of a permanent magnet synchronous machine (PMSM) as an M/G suitable for integration in a flywheel energy storage system within a large hybrid electric vehicle (HEV). The operating requirements of the application include wide power and speed ranges combined with high total system efficiency. Along with presenting the design, essential issues related to PMSM design including cogging torque, iron losses and total harmonic distortion (THD) are investigated. An iterative approach combining lumped parameter analysis with 2D Finite Element Analysis (FEA) was used, and the final design is presented showing excellent performance.

  8. Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

    PubMed Central

    Salehi, Mojtaba

    2010-01-01

    Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

  9. Classification of urban vegetation patterns from hyperspectral imagery: hybrid algorithm based on genetic algorithm tuned fuzzy support vector machine

    NASA Astrophysics Data System (ADS)

    Zhou, Mandi; Shu, Jiong; Chen, Zhigang; Ji, Minhe

    2012-11-01

    Hyperspectral imagery has been widely used in terrain classification for its high resolution. Urban vegetation, known as an essential part of the urban ecosystem, can be difficult to discern due to high similarity of spectral signatures among some land-cover classes. In this paper, we investigate a hybrid approach of the genetic-algorithm tuned fuzzy support vector machine (GA-FSVM) technique and apply it to urban vegetation classification from aerial hyperspectral urban imagery. The approach adopts the genetic algorithm to optimize parameters of support vector machine, and employs the K-nearest neighbor algorithm to calculate the membership function for each fuzzy parameter, aiming to reduce the effects of the isolated and noisy samples. Test data come from push-broom hyperspectral imager (PHI) hyperspectral remote sensing image which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. Experimental results show the GA-FSVM model generates overall accuracy of 71.2%, outperforming the maximum likelihood classifier with 49.4% accuracy and the artificial neural network method with 60.8% accuracy. It indicates GA-FSVM is a promising model for vegetation classification from hyperspectral urban data, and has good advantage in the application of classification involving abundant mixed pixels and small samples problem.

  10. A Hybrid Wavelet-Machine Learning Approach for Short- and Long-Term Streamflow Forecasting in Western U.S. by Using Local and Global Climate Patterns

    NASA Astrophysics Data System (ADS)

    Ticlavilca, A. M.; Maslova, I.; McKee, M.

    2012-12-01

    This research is focused on a hybrid computational intelligence modeling approach based on wavelet techniques and multivariate machine learning regression to produce short- and long-term streamflow forecasts. The streamflow data are obtained from selected USGS gauge stations located in rivers across the Western U.S. The machine learning model is based on a multivariate Bayesian approach for regression. The inputs of the model utilize past information of streamflow, precipitation, temperature, snow water equivalent and Pacific sea surface temperature. These inputs are decomposed into meaningful components formulated in terms of wavelet multiresolution analysis and used to improve the forecasting potential of the machine learning model. The proposed hybrid modeling approach can incorporate important information from trends of the local and global climate time series into models that learn these patterns to produce improved streamflow predictions at different time scales. A bootstrap analysis is used to explore the robustness of the proposed modeling approach.

  11. A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control.

    PubMed

    Ma, Jiaxin; Zhang, Yu; Cichocki, Andrzej; Matsuno, Fumitoshi

    2015-03-01

    This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interface works in two modes: an EOG mode recognizes eye movements such as blinks, and an EEG mode detects event related potentials (ERPs) like P300. While both eye movements and ERPs have been separately used for implementing assistive interfaces, which help patients with motor disabilities in performing daily tasks, the proposed hybrid interface integrates them together. In this way, both the eye movements and ERPs complement each other. Therefore, it can provide a better efficiency and a wider scope of application. In this study, we design a threshold algorithm that can recognize four kinds of eye movements including blink, wink, gaze, and frown. In addition, an oddball paradigm with stimuli of inverted faces is used to evoke multiple ERP components including P300, N170, and VPP. To verify the effectiveness of the proposed system, two different online experiments are carried out. One is to control a multifunctional humanoid robot, and the other is to control four mobile robots. In both experiments, the subjects can complete tasks effectively by using the proposed interface, whereas the best completion time is relatively short and very close to the one operated by hand. PMID:25398172

  12. A hybrid prognostic model for multistep ahead prediction of machine condition

    NASA Astrophysics Data System (ADS)

    Roulias, D.; Loutas, T. H.; Kostopoulos, V.

    2012-05-01

    Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

  13. Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space.

    PubMed

    Ali, Farman; Hayat, Maqsood

    2016-08-21

    Extracellular Matrix (ECM) proteins are the vital type of proteins that are secreted by resident cells. ECM proteins perform several significant functions including adhesion, differentiation, cell migration and proliferation. In addition, ECM proteins regulate angiogenesis process, embryonic development, tumor growth and gene expression. Due to tremendous biological significance of the ECM proteins and rapidly increases of protein sequences in databases, it is indispensable to introduce a new high throughput computation model that can accurately identify ECM proteins. Various traditional models have been developed, but they are laborious and tedious. In this work, an effective and high throughput computational classification model is proposed for discrimination of ECM proteins. In this model, protein sequences are formulated using amino acid composition, pseudo amino acid composition (PseAAC) and di-peptide composition (DPC) techniques. Further, various combination of feature extraction techniques are fused to form hybrid feature spaces. Several classifiers were employed. Among these classifiers, K-Nearest Neighbor obtained outstanding performance in combination with the hybrid feature space of PseAAC and DPC. The obtained accuracy of our proposed model is 96.76%, which the highest success rate has been reported in the literature so far. PMID:27179459

  14. Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

    NASA Astrophysics Data System (ADS)

    Purnami, S. W.; Khasanah, P. M.; Sumartini, S. H.; Chosuvivatwong, V.; Sriplung, H.

    2016-04-01

    According to the WHO, every two minutes there is one patient who died from cervical cancer. The high mortality rate is due to the lack of awareness of women for early detection. There are several factors that supposedly influence the survival of cervical cancer patients, including age, anemia status, stage, type of treatment, complications and secondary disease. This study wants to classify/predict cervical cancer survival based on those factors. Various classifications methods: classification and regression tree (CART), smooth support vector machine (SSVM), three order spline SSVM (TSSVM) were used. Since the data of cervical cancer are imbalanced, synthetic minority oversampling technique (SMOTE) is used for handling imbalanced dataset. Performances of these methods are evaluated using accuracy, sensitivity and specificity. Results of this study show that balancing data using SMOTE as preprocessing can improve performance of classification. The SMOTE-SSVM method provided better result than SMOTE-TSSVM and SMOTE-CART.

  15. Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine.

    PubMed

    Wang, Shuihua; Chen, Mengmeng; Li, Yang; Shao, Ying; Zhang, Yudong; Du, Sidan; Wu, Jane

    2016-01-01

    Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm-RTSVM- which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research. PMID:27547530

  16. Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

    PubMed Central

    Wang, Shuihua; Chen, Mengmeng; Li, Yang; Shao, Ying; Zhang, Yudong

    2016-01-01

    Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm–RTSVM— which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research. PMID:27547530

  17. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    PubMed

    Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  18. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    PubMed Central

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  19. PM CHEMISTRY

    EPA Science Inventory

    Although PM2.5 can be directly introduced into the atmosphere through primary emissions, its mass concentration is also strongly affected by secondary processes such as nucleation or condensation of nonvolatile and semivolatile compounds on pre-existing aerosols. Chemical modules...

  20. Towards a Naturalistic Brain-Machine Interface: Hybrid Torque and Position Control Allows Generalization to Novel Dynamics

    PubMed Central

    Chhatbar, Pratik Y.; Francis, Joseph T.

    2013-01-01

    Realization of reaching and grasping movements by a paralytic person or an amputee would greatly facilitate her/his activities of daily living. Towards this goal, control of a computer cursor or robotic arm using neural signals has been demonstrated in rodents, non-human primates and humans. This technology is commonly referred to as a Brain-Machine Interface (BMI) and is achieved by predictions of kinematic parameters, e.g. position or velocity. However, execution of natural movements, such as swinging baseball bats of different weights at the same speed, requires advanced planning for necessary context-specific forces in addition to kinematic control. Here we show, for the first time, the control of a virtual arm with representative inertial parameters using real-time neural control of torques in non-human primates (M. radiata). We found that neural control of torques leads to ballistic, possibly more naturalistic movements than position control alone, and that adding the influence of position in a hybrid torque-position control changes the feedforward behavior of these BMI movements. In addition, this level of control was achievable utilizing the neural recordings from either contralateral or ipsilateral M1. We also observed changed behavior of hybrid torque-position control under novel external dynamic environments that was comparable to natural movements. Our results demonstrate that inclusion of torque control to drive a neuroprosthetic device gives the user a more direct handle on the movement execution, especially when dealing with novel or changing dynamic environments. We anticipate our results to be a starting point of more sophisticated algorithms for sensorimotor neuroprostheses, eliminating the need of fully automatic kinematic-to-dynamic transformations as currently used by traditional kinematic-based decoders. Thus, we propose that direct control of torques, or other force related variables, should allow for more natural neuroprosthetic movements by

  1. Towards a naturalistic brain-machine interface: hybrid torque and position control allows generalization to novel dynamics.

    PubMed

    Chhatbar, Pratik Y; Francis, Joseph T

    2013-01-01

    Realization of reaching and grasping movements by a paralytic person or an amputee would greatly facilitate her/his activities of daily living. Towards this goal, control of a computer cursor or robotic arm using neural signals has been demonstrated in rodents, non-human primates and humans. This technology is commonly referred to as a Brain-Machine Interface (BMI) and is achieved by predictions of kinematic parameters, e.g. position or velocity. However, execution of natural movements, such as swinging baseball bats of different weights at the same speed, requires advanced planning for necessary context-specific forces in addition to kinematic control. Here we show, for the first time, the control of a virtual arm with representative inertial parameters using real-time neural control of torques in non-human primates (M. radiata). We found that neural control of torques leads to ballistic, possibly more naturalistic movements than position control alone, and that adding the influence of position in a hybrid torque-position control changes the feedforward behavior of these BMI movements. In addition, this level of control was achievable utilizing the neural recordings from either contralateral or ipsilateral M1. We also observed changed behavior of hybrid torque-position control under novel external dynamic environments that was comparable to natural movements. Our results demonstrate that inclusion of torque control to drive a neuroprosthetic device gives the user a more direct handle on the movement execution, especially when dealing with novel or changing dynamic environments. We anticipate our results to be a starting point of more sophisticated algorithms for sensorimotor neuroprostheses, eliminating the need of fully automatic kinematic-to-dynamic transformations as currently used by traditional kinematic-based decoders. Thus, we propose that direct control of torques, or other force related variables, should allow for more natural neuroprosthetic movements by

  2. Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

    NASA Astrophysics Data System (ADS)

    Kachach, Redouane; Cañas, José María

    2016-05-01

    Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

  3. Evaluation of performance and magnetic characteristics of a radial-radial flux compound-structure permanent-magnet synchronous machine used for hybrid electric vehicle

    NASA Astrophysics Data System (ADS)

    Zheng, Ping; Liu, Ranran; Shen, Lin; Li, Lina; Fan, Weiguang; Wu, Qian; Zhao, Jing

    2008-04-01

    A breed of compound-structure permanent-magnet synchronous machine (CS-PMSM) is used for power-split hybrid electric vehicles (HEVs). It can help to fulfill both the speed and torque control of the internal combustion engine and, thus, realize the optimum operation of the HEV. In this paper, a radial-radial flux CS-PMSM, which is integrated by two machines radially [one stator machine (SM) and one double-rotor machine (DRM)], is designed and investigated. The machine performance is evaluated with finite-element method (FEM) and satisfactory results are obtained. The back electromotive force curves of the two machines are somewhat similar to sinusoidal ones; the average torques both meet the requirements; and due to the adoption of skewed slots, the cogging torques and torque ripples are quite small. The inductance parameter is calculated with a phasor diagram based two-dimensional FEM and the magnetic saturation and cross-magnetization effect are discussed. It is concluded that the SM is slightly saturated with no or little cross-magnetization phenomenon, whereas the DRM has deep-degree magnetic saturation and the cross-magnetization effect is notable.

  4. A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine.

    PubMed

    Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

    2013-10-01

    In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

  5. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

    NASA Astrophysics Data System (ADS)

    Vasu, Nikhil N.; Lee, Seung-Rae

    2016-06-01

    An ever-increasing trend of extreme rainfall events in South Korea owing to climate change is causing shallow landslides and debris flows in mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion KRW (South Korean Won) in losses in addition to fatalities every year. The most common type of landslide observed is the shallow landslide, which occurs at 1-3 m depth, and may mobilize into more catastrophic flow-type landslides. Hence, to predict potential landslide areas, susceptibility maps are developed in a geographical information system (GIS) environment utilizing available morphological, hydrological, geotechnical, and geological data. Landslide susceptibility models were developed using 163 landslide points and an equal number of nonlandslide points in Mt. Woomyeon, Seoul, and 23 landslide conditioning factors. However, because not all of the factors contribute to the determination of the spatial probability for landslide initiation, and a simple filter or wrapper-based approach is not efficient in identifying all of the relevant features, a feedback-loop-based hybrid algorithm was implemented in conjunction with a learning scheme called an extreme learning machine, which is based on a single-layer, feed-forward network. Validation of the constructed susceptibility model was conducted using a testing set of landslide inventory data through a prediction rate curve. The model selected 13 relevant conditioning factors out of the initial 23; and the resulting susceptibility map shows a success rate of 85% and a prediction rate of 89.45%, indicating a good performance, in contrast to the low success and prediction rate of 69.19% and 56.19%, respectively, as obtained using a wrapper technique.

  6. Dynamism in a Semiconductor Industrial Machine Allocation Problem using a Hybrid of the Bio-inspired and Musical-Harmony Approach

    NASA Astrophysics Data System (ADS)

    Kalsom Yusof, Umi; Nor Akmal Khalid, Mohd

    2015-05-01

    Semiconductor industries need to constantly adjust to the rapid pace of change in the market. Most manufactured products usually have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of the machine allocation plan known for its complexity. Various studies have been performed to balance productivity and flexibility in the flexible manufacturing system (FMS). Many approaches have been developed by the researchers to determine the suitable balance between exploration (global improvement) and exploitation (local improvement). However, not much work has been focused on the domain of machine allocation problem that considers the effects of machine breakdowns. This paper develops a model to minimize the effect of machine breakdowns, thus increasing the productivity. The objectives are to minimize system unbalance and makespan as well as increase throughput while satisfying the technological constraints such as machine time availability. To examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on real industrial datasets were performed with applications of intelligence techniques, that is, a hybrid of genetic algorithm and harmony search. The result aims to obtain a feasible solution to the domain problem.

  7. Evaluation of the magnetic coupling degree and performance of an axial-axial flux compound-structure permanent-magnet synchronous machine used for hybrid electric vehicles

    NASA Astrophysics Data System (ADS)

    Zheng, Ping; Zhao, Jing; Wu, Qian; Fan, Weiguang; Shen, Lin; Li, Lina; Liu, Ranran

    2008-04-01

    A novel axial-axial flux compound-structure permanent-magnet synchronous machine (CS-PMSM), which is a hybrid electric vehicle (HEV) power train concept, is integrated by two axial flux disk machines. As the two machines share a common rotor [a magnet rotor with permanent magnets (PMs) on both sides], there may be magnetic coupling between them. Three-dimensional (3D) finite-element method (FEM) calculation shows that the two machines have little magnetic coupling if they have the same pole number and consistent magnetization direction of the two layers of PMs on the common rotor. The performance of the CS-PMSM is evaluated on criteria such as power, power per unit volume and mass, torque, and torque ripple. The power and torque equations of this type of machine are deduced and verified with 3D FEM. After the optimization of diameter ratio and pole number, the power and power per unit active volume and mass are high. The torque ripple is much reduced due to the optimization of the pole arc embrace and magnet skewing angle.

  8. Using Phun to Study "Perpetual Motion" Machines

    ERIC Educational Resources Information Center

    Kores, Jaroslav

    2012-01-01

    The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th-century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over…

  9. A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression

    NASA Astrophysics Data System (ADS)

    Di, Qian; Koutrakis, Petros; Schwartz, Joel

    2016-04-01

    GEOS-Chem, a chemical transport model, provides time-space continuous estimates of atmospheric pollutants including PM2.5 and its major components, but model predictions are not highly correlated with ground monitoring data. In addition, its spatial resolution is usually too coarse to characterize the spatial pattern in pollutant concentrations in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorological variables, land-use terms and spatial-temporal lagged terms. Major PM2.5 components of our interest include sulfate, nitrate, organic carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resolution of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We determined the accuracy of the spatial-temporal predictions using ten-fold cross-validation and "leave-one-day-out" cross-validation techniques. We found a high total R2 for PM2.5 mass (all data R2 0.85, yearly values: 0.80-0.88) and PM2.5 components (R2 for individual components were around 0.70-0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM2.5 mass and components for epidemiological studies.

  10. Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine

    PubMed Central

    Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

    2014-01-01

    JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. PMID:25110745

  11. Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

    PubMed Central

    McMullen, David P.; Hotson, Guy; Katyal, Kapil D.; Wester, Brock A.; Fifer, Matthew S.; McGee, Timothy G.; Harris, Andrew; Johannes, Matthew S.; Vogelstein, R. Jacob; Ravitz, Alan D.; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

    2014-01-01

    To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

  12. Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

    PubMed

    McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

    2014-07-01

    To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

  13. Real Time Flux Control in PM Motors

    SciTech Connect

    Otaduy, P.J.

    2005-09-27

    Significant research at the Oak Ridge National Laboratory (ORNL) Power Electronics and Electric Machinery Research Center (PEEMRC) is being conducted to develop ways to increase (1) torque, (2) speed range, and (3) efficiency of traction electric motors for hybrid electric vehicles (HEV) within existing current and voltage bounds. Current is limited by the inverter semiconductor devices' capability and voltage is limited by the stator wire insulation's ability to withstand the maximum back-electromotive force (emf), which occurs at the upper end of the speed range. One research track has been to explore ways to control the path and magnitude of magnetic flux while the motor is operating. The phrase, real time flux control (RTFC), refers to this mode of operation in which system parameters are changed while the motor is operating to improve its performance and speed range. RTFC has potential to meet an increased torque demand by introducing additional flux through the main air gap from an external source. It can augment the speed range by diverting flux away from the main air gap to reduce back-emf at high speeds. Conventional RTFC technology is known as vector control [1]. Vector control decomposes the stator current into two components; one that produces torque and a second that opposes (weakens) the magnetic field generated by the rotor, thereby requiring more overall stator current and reducing the efficiency. Efficiency can be improved by selecting a RTFC method that reduces the back-emf without increasing the average current. This favors methods that use pulse currents or very low currents to achieve field weakening. Foremost in ORNL's effort to develop flux control is the work of J. S. Hsu. Early research [2,3] introduced direct control of air-gap flux in permanent magnet (PM) machines and demonstrated it with a flux-controlled generator. The configuration eliminates the problem of demagnetization because it diverts all the flux from the magnets instead of

  14. 973 nm wavelength stabilized hybrid ns-MOPA diode laser system with 15.5 W peak power and a spectral line width below 10 pm

    NASA Astrophysics Data System (ADS)

    Vu, Thi N.; Klehr, Andreas; Sumpf, Bernd; Wenzel, Hans; Erbert, Götz; Tränkle, Günther

    2014-05-01

    A master oscillator power amplifier (MOPA) system for the generation of ns-pulses with high peak power, narrow spectral line width, and stabilized emission wavelength will be presented. The master oscillator is a distributed feedback (DFB) ridge waveguide (RW) laser. The tapered amplifier consists of one RW section and one flared gain-guided section. The DFB laser is operated in continuous wave mode and emits at 973.5 nm with a spectral line width below 10 pm. The RW section of the amplifier acts as an optical gate. The tapered section amplifies the generated optical pulse. An optical peak power of 15.5 W for a pulse width of 8 ns is obtained. The emission wavelength remains constant at all output power levels of the MOPA system for a fixed current into the DFB laser. The spectral power density of the ASE is 37 dB smaller than the lasing spectral power density. The spectral line width is smaller than 10 pm, limited by the resolution of the optical spectrum analyzer.

  15. A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

    NASA Technical Reports Server (NTRS)

    Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2014-01-01

    The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions

  16. A novel design of DC-AC electrical machine rotary converter for hybrid solar and wind energy applications

    NASA Astrophysics Data System (ADS)

    Mohammed, K. G.; Ramli, A. Q.; Amirulddin, U. A. U.

    2013-06-01

    This paper proposes the design of a new bi-directional DC-AC rotary converter machine to convert a d.c. voltage to three-phase voltage and vice-versa using a two-stage energy conversion machine. The rotary converter consists of two main stages which are combined into single frame. These two stages are constructed from three main electromagnetic components. The first inner electromagnetic component represents the input stage that enables the DC power generated by solar energy from photo-voltaic cells to be transformed by the second and third components electro-magnetically to produce multi-phase voltages at the output stage. At the same time, extra kinetic energy from wind, which is sufficiently available, can be added to existing torque on the second electromagnetic component. Both of these input energies will add up to the final energy generated at the output terminals. Therefore, the machine will be able to convert solar and wind energies to the output terminals simultaneously. If the solar energy is low, the available wind energy will be able to provide energy to the output terminals and at the same time charges the batteries which are connected as backup system. At this moment, the machine behaves as wind turbine. The energy output from the machine benefits from two energy sources which are solar and wind. At night when the solar energy is not available and also the load is low, the wind energy is able to charge the batteries and at the same time provides output electrical power to the remaining the load. Therefore, the proposed system will have high usage of available renewable energy as compared to separated wind or solar systems. MATLAB codes are used to calculate the required dimensions, the magnetic and electrical circuits parameters to design of the new bi-directional rotary converter machine.

  17. Developing a Hybrid Model to Predict Student First Year Retention in STEM Disciplines Using Machine Learning Techniques

    ERIC Educational Resources Information Center

    Alkhasawneh, Ruba; Hargraves, Rosalyn Hobson

    2014-01-01

    The purpose of this research was to develop a hybrid framework to model first year student retention for underrepresented minority (URM) students comprising African Americans, Hispanic Americans, and Native Americans. Identifying inputs that best contribute to student retention provides significant information for institutions to learn about…

  18. Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers

    PubMed Central

    Mishra, Gunjan; Ananth, Vivek; Shelke, Kalpesh; Sehgal, Deepak; Valadi, Jayaraman

    2016-01-01

    Hepatitis is an emerging global threat to public health due to associated mortality, morbidity, cancer and HIV co-infection. Available diagnostics and therapeutics are inadequate to intercept the course and transmission of the disease. Antimicrobial peptides (AMP) are widely studied and broad-spectrum host defense peptides are investigated as a targeted anti-viral. Therefore, it is of interest to describe the supervised identification of anti-hepatitis peptides. We used a hybrid Support Vector Machine (SVM) with Ant Colony Optimization (ACO) algorithm for simultaneous classification and domain feature selection. The described model shows a 10 fold cross-validation accuracy of 94 percent. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity PMID:27212838

  19. Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers.

    PubMed

    Mishra, Gunjan; Ananth, Vivek; Shelke, Kalpesh; Sehgal, Deepak; Deepak, Jayaraman

    2016-01-01

    Hepatitis is an emerging global threat to public health due to associated mortality, morbidity, cancer and HIV co-infection. Available diagnostics and therapeutics are inadequate to intercept the course and transmission of the disease. Antimicrobial peptides (AMP) are widely studied and broad-spectrum host defense peptides are investigated as a targeted anti-viral. Therefore, it is of interest to describe the supervised identification of anti-hepatitis peptides. We used a hybrid Support Vector Machine (SVM) with Ant Colony Optimization (ACO) algorithm for simultaneous classification and domain feature selection. The described model shows a 10 fold cross-validation accuracy of 94 percent. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity. PMID:27212838

  20. A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement.

    PubMed

    He, Yan-Lin; Geng, Zhi-Qiang; Xu, Yuan; Zhu, Qun-Xiong

    2015-09-01

    In this paper, a robust hybrid model integrating an enhanced inputs based extreme learning machine with the partial least square regression (PLSR-EIELM) was proposed. The proposed PLSR-EIELM model can overcome two main flaws in the extreme learning machine (ELM), i.e. the intractable problem in determining the optimal number of the hidden layer neurons and the over-fitting phenomenon. First, a traditional extreme learning machine (ELM) is selected. Second, a method of randomly assigning is applied to the weights between the input layer and the hidden layer, and then the nonlinear transformation for independent variables can be obtained from the output of the hidden layer neurons. Especially, the original input variables are regarded as enhanced inputs; then the enhanced inputs and the nonlinear transformed variables are tied together as the whole independent variables. In this way, the PLSR can be carried out to identify the PLS components not only from the nonlinear transformed variables but also from the original input variables, which can remove the correlation among the whole independent variables and the expected outputs. Finally, the optimal relationship model of the whole independent variables with the expected outputs can be achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the PLSR-EIELM model served as an intelligent measurement tool for the key variables of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. The experimental results show that the predictive accuracy of PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, PLSR-EIELM can achieve much smaller predicted relative errors in these two applications. PMID:26112928

  1. Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

    NASA Astrophysics Data System (ADS)

    Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

    2016-03-01

    The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

  2. Case for a field-programmable gate array multicore hybrid machine for an image-processing application

    NASA Astrophysics Data System (ADS)

    Rakvic, Ryan N.; Ives, Robert W.; Lira, Javier; Molina, Carlos

    2011-01-01

    General purpose computer designers have recently begun adding cores to their processors in order to increase performance. For example, Intel has adopted a homogeneous quad-core processor as a base for general purpose computing. PlayStation3 (PS3) game consoles contain a multicore heterogeneous processor known as the Cell, which is designed to perform complex image processing algorithms at a high level. Can modern image-processing algorithms utilize these additional cores? On the other hand, modern advancements in configurable hardware, most notably field-programmable gate arrays (FPGAs) have created an interesting question for general purpose computer designers. Is there a reason to combine FPGAs with multicore processors to create an FPGA multicore hybrid general purpose computer? Iris matching, a repeatedly executed portion of a modern iris-recognition algorithm, is parallelized on an Intel-based homogeneous multicore Xeon system, a heterogeneous multicore Cell system, and an FPGA multicore hybrid system. Surprisingly, the cheaper PS3 slightly outperforms the Intel-based multicore on a core-for-core basis. However, both multicore systems are beaten by the FPGA multicore hybrid system by >50%.

  3. PM SUPERSITES PROGRAM

    EPA Science Inventory

    In 1997, the EPA administrator published National Ambient Air Quality Standards (NAAQS) for Particulate Matter (PM) that included new standards for PM2.5 (PM with diameters less than 2.5 um). These revised standards stimulated national concern over uncertainties regarding the ex...

  4. A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

    PubMed

    Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

    2014-01-01

    As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

  5. A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

    PubMed Central

    Goo, Yeong-Jia James; Shen, Zone-De

    2014-01-01

    As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

  6. TEMPTING system: a hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries.

    PubMed

    Chang, Yung-Chun; Dai, Hong-Jie; Wu, Johnny Chi-Yang; Chen, Jian-Ming; Tsai, Richard Tzong-Han; Hsu, Wen-Lian

    2013-12-01

    Patient discharge summaries provide detailed medical information about individuals who have been hospitalized. To make a precise and legitimate assessment of the abundant data, a proper time layout of the sequence of relevant events should be compiled and used to drive a patient-specific timeline, which could further assist medical personnel in making clinical decisions. The process of identifying the chronological order of entities is called temporal relation extraction. In this paper, we propose a hybrid method to identify appropriate temporal links between a pair of entities. The method combines two approaches: one is rule-based and the other is based on the maximum entropy model. We develop an integration algorithm to fuse the results of the two approaches. All rules and the integration algorithm are formally stated so that one can easily reproduce the system and results. To optimize the system's configuration, we used the 2012 i2b2 challenge TLINK track dataset and applied threefold cross validation to the training set. Then, we evaluated its performance on the training and test datasets. The experiment results show that the proposed TEMPTING (TEMPoral relaTion extractING) system (ranked seventh) achieved an F-score of 0.563, which was at least 30% better than that of the baseline system, which randomly selects TLINK candidates from all pairs and assigns the TLINK types. The TEMPTING system using the hybrid method also outperformed the stage-based TEMPTING system. Its F-scores were 3.51% and 0.97% better than those of the stage-based system on the training set and test set, respectively. PMID:24060600

  7. Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Nguyen, Thien; Ahn, Sangtae; Jang, Hyojung; Jun, Sung C.; Kim, Jae G.

    2016-03-01

    Driver's condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver's status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher's linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.

  8. Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm.

    PubMed

    Saberkari, Hamidreza; Shamsi, Mousa; Joroughi, Mahsa; Golabi, Faegheh; Sedaaghi, Mohammad Hossein

    2014-10-01

    Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. For this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microarray data. An appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, we use selective independent component analysis (SICA) for decreasing the dimension of microarray data. Using this selective algorithm, we can solve the instability problem occurred in the case of employing conventional independent component analysis (ICA) methods. First, the reconstruction error and selective set are analyzed as independent components of each gene, which have a small part in making error in order to reconstruct new sample. Then, some of the modified support vector machine (υ-SVM) algorithm sub-classifiers are trained, simultaneously. Eventually, the best sub-classifier with the highest recognition rate is selected. The proposed algorithm is applied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existing methods. The results illustrate that the proposed algorithm (SICA + υ-SVM) has higher accuracy and validity in order to increase the classification accuracy. Such that, our proposed algorithm exhibits relative improvements of 3.3% in correctness rate over ICA + SVM and SVM algorithms in lung cancer dataset. PMID:25426433

  9. Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

    PubMed

    Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

    2014-03-01

    Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. PMID:24444751

  10. Machinability of hypereutectic silicon-aluminum alloys

    NASA Astrophysics Data System (ADS)

    Tanaka, T.; Akasawa, T.

    1999-08-01

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

  11. Using support vector regression to predict PM10 and PM2.5

    NASA Astrophysics Data System (ADS)

    Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

    2014-03-01

    Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM10 and PM2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM10 and PM2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010-2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM10 and PM2.5. In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying.

  12. [Characteristics of PM10 and PM2.5 concentrations in mountain background region of East China].

    PubMed

    Su, Bin-Bin; Liu, Xin-Dong; Tao, Jun

    2013-02-01

    The online PM10 and PM2.5 concentrations were measured from March 2011 'to February 2012 at the national atmospheric background monitoring station in Wuyishan of Fujian Province to discuss the characteristic of PM10 and PM2.5 concentrations and the impact factors in forest and mountain background region of East China. HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) Model was used to investigate the potential sources of particulates during the pollution episodes. The results showed that the background concentrations of PM10 and PM2.5 were (23 +/- 16) microg.m-3 and (18 +/- 12) microg.m-3, respectively. Seasonal variations of PMl0 and PM2.5 loadings were observed, and loadings decreased in the same order: spring > autumn > winter > summer. PM10 and PM2.5 concentrations were obviously higher in spring than in other seasons because of the transportation of dust storm. The fine particles were the dominant pollutant which accounted for 76% of PM10. The good correlation between PM10/PM2.5 and gas pollutants suggested that regional transportation and secondary aerosol were the major sources in the background station. One episode occurring in April 2011 was related with the transportation of dust storm. However, another episode occurring in September 2011 had close relationship with the transportation of higher pollutant loadings in East China. PMID:23668109

  13. Tribological and microstructural comparison of HIPped PM212 and PM212/Au self-lubricating composites

    NASA Technical Reports Server (NTRS)

    Bogdanski, Michael S.; Sliney, Harold E.; Dellacorte, Christopher

    1992-01-01

    The feasibility of replacing the silver with the volumetric equivalent of gold in the chromium carbide-based self-lubricating composite PM212 (70 wt. percent NiCo-Cr3C2, 15 percent BaF2/CaF2 eutectic) was studied. The new composite, PM212/Au has the following composition: 62 wt. percent NiCo-Cr3C2, 25 percent Au, 13 percent BaF2/CaF2 eutectic. The silver was replaced with gold to minimize the potential reactivity of the composite with possible environmental contaminants such as sulfur. The composites were fabricated by hot isostatic pressing (HIPping) and machined into pin specimens. The pins were slid against nickel-based superalloy disks. Sliding velocities ranged from 0.27 to 10.0 m/s and temperatures from 25 to 900 C. Friction coefficients ranged from 0.25 to 0.40 and wear factors for the pin and disk were typically low 10(exp -5) cu mm/N-m. HIPped PM212 measured fully dense, whereas PM212/Au had 15 percent residual porosity. Examination of the microstructures with optical and scanning electron microscopy revealed the presence of pores in PM212/Au that were not present in PM212. Though the exact reason for the residual porosity in PM212/Au was not determined, it may be due to particle morphology differences between the gold and silver and their effect on powder metallurgy processing.

  14. Tribological and microstructural comparison of HIPped PM212 and PM212/Au self-lubricating composites

    NASA Technical Reports Server (NTRS)

    Bogdanski, Michael S.; Sliney, Harold E.; Dellacorte, Christopher

    1992-01-01

    The feasibility of replacing the silver with the volumetric equivalent of gold in the chromium carbide-based self-lubricating composite PM212 (70 wt percent NiCo-Cr3C2, 15 percent BaF2/CaF2 eutectic) was studied. The new composite, PM212/Au has the following composition: 62 wt percent NiCo-Cr3C2, 25 percent Au, 13 percent BaF2/CaF2 eutectic. The silver was replaced with gold to minimize the potential reactivity of the composite with possible environmental contaminants such as sulfur. The composites were fabricated by hot isostatic pressing (HIPping) and machined into pin specimens. The pins were slid against nickel-based superalloy disks. Sliding velocities ranged from 0.27 to 10.0 m/s and temperatures from 25 to 900 C. Frictions coefficients ranged from 0.25 to 0.40 and wear factors for the pin and disk were typically low 10(exp -5) cu mm/N-m. HIPped PM212 measured fully dense, whereas PM212/Au had 15 percent residual porosity. Examination of the microstructures with optical and scanning electron microscopy revealed the presence of pores in PM212/Au that were not present in PM212. Though the exact reason for the residual porosity PM212/Au was not determined, it may be due to practice morphology differences between the gold and silver and their effect on powder metallurgy processing.

  15. Business Machines

    ERIC Educational Resources Information Center

    Pactor, Paul

    1970-01-01

    The U.S. Department of Labor has projected a 106 percent increase in the demand for office machine operators over the next 10 years. Machines with a high frequency of use include printing calculators, 10-key adding machines, and key punch machines. The 12th grade is the logical time for teaching business machines. (CH)

  16. Machine Shop Grinding Machines.

    ERIC Educational Resources Information Center

    Dunn, James

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

  17. Laser machining of ceramic

    SciTech Connect

    Laudel, A.

    1980-01-01

    The Kansas City Division of The Bendix Corporation manufactures hybrid microcircuits (HMCs) using both thin film and thick film technologies. Laser machining is used to contour the ceramic substrates and to drill holes in the ceramic for frontside-backside interconnections (vias) and holes for mounting components. A 1000 W CO/sub 2/ type laser is used. The laser machining process, and methods used for removing protruding debris and debris from holes, for cleaning the machined surfaces, and for refiring are described. The laser machining process described consistently produces vias, component holes and contours with acceptable surface quality, hole locations, diameter, flatness and metallization adhesion. There are no cracks indicated by dipping in fluorescent dye penetrant and the substances are resistant to repeated thermal shock.

  18. Georgia After 3PM

    ERIC Educational Resources Information Center

    Afterschool Alliance, 2009

    2009-01-01

    Each afternoon across the U.S., 15 million children are alone and unsupervised after school. The parents of 18 million would enroll their children in an afterschool program, if one were available. These are some of the key findings from the nation's most in-depth study of how America's children spend their afternoons. "America After 3PM" was…

  19. Florida After 3PM

    ERIC Educational Resources Information Center

    Afterschool Alliance, 2009

    2009-01-01

    Each afternoon across the U.S., 15 million children are alone and unsupervised after school. The parents of 18 million would enroll their children in an afterschool program, if one were available. These are some of the key findings from the nation's most in-depth study of how America's children spend their afternoons. "America After 3PM" was…

  20. A DNA machine for sensitive and homogeneous DNA detection via lambda exonuclease assisted amplification.

    PubMed

    Liu, Lin; Lei, Jianping; Gao, Fenglei; Ju, Huangxian

    2013-10-15

    This work designs a DNA machine with three assistant DNAs and lambda exonuclease (Exo-λ) for sensitive and homogeneous fluorescent detection of DNA. The selective digestion of Exo-λ to blunt or recessed 5'-phosphorylated strand of probe 1-probe 2 duplex results in the release of target DNA and probe 2 to produce the fluorescence restoring of fluorophore labeled to probe 1. The released target DNA could hybridize with another probe 1-probe 2 duplex to trigger the target recycling for signal amplification, while the released probe 2 hybridized with molecular beacon to restore its fluorescence for signal enhancement. This DNA machine showed a fast response to target DNA with a linear concentration range from 0.4 pM to 4 nM. The limit of detection was 68 fM at a signal-to-noise ratio of 3. The high selectivity of the method may result from the Exo-λ's specific recognition-site of double-stranded DNA and the specific hybridization of target DNA with probe 1-probe 2 duplex. This DNA machine with the homogenous detection, rapid response as well as simplicity provides a new approach for sensitive detection of DNA. PMID:24054668

  1. Simultaneous monitoring and compositions analysis of PM1 and PM2.5 in Shanghai: Implications for characterization of haze pollution and source apportionment.

    PubMed

    Qiao, Ting; Zhao, Mengfei; Xiu, Guangli; Yu, Jianzhen

    2016-07-01

    A year-long simultaneous observation of PM1 and PM2.5 were conducted at ECUST campus in Shanghai, the compositions were analyzed and compared. Results showed that PM2.5 was dominated by PM1 on clear days while the contribution of PM1-2.5 to PM2.5 increased on haze days, indicating that PM2.5 should be given priority to characterize or predict haze pollution. On haze days, accumulation of organic carbon (OC), elemental carbon (EC) and primary organic carbon (POC) in PM1-2.5 was faster than that in PM1. Humic-like substances carbon (Hulis-C) in both PM2.5 and PM1 formed faster than water soluble organic carbon (WSOC) on haze days, hence Hulis-C/WSOC increased with the intensification of haze pollution. In terms of water soluble ions, NO3(-)/SO4(2-) in PM1 increased with the aggravation of haze pollution, implying that mobile sources dominated on haze days, so is nitrogen oxidation ratio (NOR). Liquid water content (LWC) in both PM1 and PM2.5 had positive correlations with relative humidity (RH) but negative correlations with visibility, implying that hygroscopic growth might be a factor for visibility impairment, especially LWC in PM1. By comparison with multi-linear equations of LWC in PM1 and PM2.5, NO3(-) exerted a higher influence on hygroscopicity of PM1 than PM2.5, while RH, WSOC, SO4(2-) and NH4(+) had higher effects on PM2.5, especially WSOC. Source apportionment of PM2.5 was also investigated to provide reference for policy making. Cluster analysis by HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory) model showed that PM2.5 originated from marine aerosols, middle-scale transportation and large-scale transportation. Furthermore, PM2.5 on haze days was dominated by middle-scale transportation. In line with source apportionment by positive matrix factorization (PMF) model, PM2.5 was attributed to secondary inorganics, aged sea salt, combustion emissions, hygroscopic growth and secondary organics. Secondary formation was the principle source of

  2. COMPARISON OF PM 2.5 AND PM 10 MONITORS

    EPA Science Inventory

    An extensive PM monitoring study was conducted during the 1998 Baltimore PM Epidemiology-Exposure Study of the Elderly. One goal was to investigate the mass concentration comparability between various monitoring instrumentation located across residential indoor, residential out...

  3. Database machines

    NASA Technical Reports Server (NTRS)

    Stiefel, M. L.

    1983-01-01

    The functions and performance characteristics of data base machines (DBM), including machines currently being studied in research laboratories and those currently offered on a commerical basis are discussed. The cost/benefit considerations that must be recognized in selecting a DBM are discussed, as well as the future outlook for such machines.

  4. Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

    PubMed

    Malvoni, M; De Giorgi, M G; Congedo, P M

    2016-12-01

    The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material. PMID:27622206

  5. Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

    NASA Astrophysics Data System (ADS)

    Fahimi, F.; El-Shafie, A. H.

    2014-07-01

    Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques that have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012), which was published in the journal Hydrology and Earth System Sciences in 2012, was a valuable piece of research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of self organizing map and least square support vector machine (SOM-LSSVM), autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

  6. Design study and performance analysis of 12S-14P field excitation flux switching motor for hybrid electric vehicle

    NASA Astrophysics Data System (ADS)

    Husin, Zhafir Aizat; Sulaiman, Erwan; Khan, Faisal; Mazlan, Mohamed Mubin Aizat; Othman, Syed Muhammad Naufal Syed

    2015-05-01

    This paper presents a new structure of 12slot-14pole field excitation flux switching motor (FEFSM) as an alternative candidate of non-Permanent Magnet (PM) machine for HEV drives. Design study, performance analysis and optimization of field excitation flux switching machine with non-rare-earth magnet for hybrid electric vehicle drive applications is done. The stator of projected machine consists of iron core made of electromagnetic steels, armature coils and field excitation coils as the only field mmf source. The rotor is consisted of only stack of iron and hence, it is reliable and appropriate for high speed operation. The design target is a machine with the maximum torque, power and power density, more than 210Nm, 123kW and 3.5kW/kg, respectively, which competes with interior permanent magnet synchronous machine used in existing hybrid electric vehicle. Some design feasibility studies on FEFSM based on 2D-FEA and deterministic optimization method will be applied to design the proposed machine.

  7. Architecture of the type IVa pilus machine.

    PubMed

    Chang, Yi-Wei; Rettberg, Lee A; Treuner-Lange, Anke; Iwasa, Janet; Søgaard-Andersen, Lotte; Jensen, Grant J

    2016-03-11

    Type IVa pili are filamentous cell surface structures observed in many bacteria. They pull cells forward by extending, adhering to surfaces, and then retracting. We used cryo-electron tomography of intact Myxococcus xanthus cells to visualize type IVa pili and the protein machine that assembles and retracts them (the type IVa pilus machine, or T4PM) in situ, in both the piliated and nonpiliated states, at a resolution of 3 to 4 nanometers. We found that T4PM comprises an outer membrane pore, four interconnected ring structures in the periplasm and cytoplasm, a cytoplasmic disc and dome, and a periplasmic stem. By systematically imaging mutants lacking defined T4PM proteins or with individual proteins fused to tags, we mapped the locations of all 10 T4PM core components and the minor pilins, thereby providing insights into pilus assembly, structure, and function. PMID:26965631

  8. EPA's new PM standards

    SciTech Connect

    Cavallaro, A.

    2006-11-15

    The US Environmental Protection Agency (EPA) announced its adjustments to the national air quality standards in late September after a mandatory five-year review process. The National Ambient Air Quality Standards (NAAQS) address fine and coarse particle pollution, also known as particulate matter (PM). The final action changes the 24-hour allowance for fine particulates, such as those emitted from coal-fired generation stacks, from 65 micrograms of particles per cubic meter of air to 35 {mu}g/m{sup 3}. EPA said this measure protects people from short-term exposure to fine particles. The annual standard will remain the same at 15 {mu}g/m{sup 3}. Carl Weilert of Burns and McDonnell gave some comments on implications of the standards in an interview with Power Engineering. 1 ref.

  9. Nonplanar machines

    SciTech Connect

    Ritson, D. )

    1989-05-01

    This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs.

  10. Electric machine

    DOEpatents

    El-Refaie, Ayman Mohamed Fawzi; Reddy, Patel Bhageerath

    2012-07-17

    An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

  11. Permutation Machines.

    PubMed

    Bhatia, Swapnil; LaBoda, Craig; Yanez, Vanessa; Haddock-Angelli, Traci; Densmore, Douglas

    2016-08-19

    We define a new inversion-based machine called a permuton of n genetic elements, which allows the n elements to be rearranged in any of the n·(n - 1)·(n - 2)···2 = n! distinct orderings. We present two design algorithms for architecting such a machine. We define a notion of a feasible design and use the framework to discuss the feasibility of the permuton architectures. We have implemented our design algorithms in a freely usable web-accessible software for exploration of these machines. Permutation machines could be used as memory elements or state machines and explicitly illustrate a rational approach to designing biological systems. PMID:27383067

  12. Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

    NASA Astrophysics Data System (ADS)

    Fahimi, F.; El-Shafie, A. H.

    2013-11-01

    Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques, which have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012) which has been published in Journal of Hydrology and Earth System Sciences in 2012 was a valuable research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of SOM-LSSVM, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

  13. Mining machine

    SciTech Connect

    Parrott, G.A.

    1985-05-07

    A haulage system for a mining machine comprises a mining machine mounted on and/or guided by a conveyor and reciprocable with respect thereto, the conveyor being provided with a rack having plural rows of teeth of identical pitch, with the teeth of one row staggered with respect to an adjacent row(s), and the machine being provided with at least one power driven haulage sprocket comprising plural sets of peripherally arranged teeth of identical pitch, one set being angularly staggered with respect to an adjacent set(s), whereby one set is engageable with each row of teeth of the rack. The invention also includes a mining machine provided with such a power driven haulage sprocket, and a rack as above described and provided with end fittings for securing in articulated manner to an adjacent rack.

  14. Monel Machining

    NASA Technical Reports Server (NTRS)

    1983-01-01

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

  15. A restriction enzyme-powered autonomous DNA walking machine: its application for a highly sensitive electrochemiluminescence assay of DNA

    NASA Astrophysics Data System (ADS)

    Chen, Ying; Xiang, Yun; Yuan, Ruo; Chai, Yaqin

    2014-12-01

    The construction of a restriction enzyme (Nt.AlwI)-powered DNA walking machine and its application for highly sensitive detection of DNA are described. DNA nanostructure tracks containing four overhang sequences with electrochemiluminescence (ECL) labels and complementary to the walker (target DNA) are self-assembled on the sensing electrode. The walker hybridizes with the complementary sequences on the tracks and forms specific recognition sites for Nt.AlwI, which cleaves the overhang sequences, releases the ECL labels and enables directional movement of the walker along the tracks. The formation of the nanostructure tracks and the Nt.AlwI-assisted cleavage of the overhang sequences in the presence of the walker are verified by using polyacrylamide gel electrophoresis analysis and cyclic voltammetry. The successive movement of the walker on the nanostructure tracks leads to continuous removal of massive ECL labels from the sensing electrode, which results in a significantly amplified suppression of the ECL emission for highly sensitive detection of sequence-specific DNA down to 0.19 pM. Results show that this DNA walking machine can also offer single-base mismatch discrimination capability. The successful application of the DNA walking machine for sequence-specific DNA detection can thus offer new opportunities for molecular machines in biosensing applications.The construction of a restriction enzyme (Nt.AlwI)-powered DNA walking machine and its application for highly sensitive detection of DNA are described. DNA nanostructure tracks containing four overhang sequences with electrochemiluminescence (ECL) labels and complementary to the walker (target DNA) are self-assembled on the sensing electrode. The walker hybridizes with the complementary sequences on the tracks and forms specific recognition sites for Nt.AlwI, which cleaves the overhang sequences, releases the ECL labels and enables directional movement of the walker along the tracks. The formation of the

  16. PM science and regional haze

    SciTech Connect

    Casuccio, G.; Watson, J.

    1999-07-01

    Excessive levels of suspended particle are measured in many urban areas throughout the world. The U.S. EPA has promulgated new ambient air quality standards for PM2.5 and PM10 (particles with aerodynamic diameters less than 10 and 2.5 microns, respectively). The new PM10 standards are less stringent than the prior standards, setting targets of 3-year average 98th percentiles for 24-hour PM2.5 averages, 99th percentiles for 24-hour PM10 averages, and three-year averages in place of annual averages. This means that infrequent events, such as fires or industrial upsets, will not greatly influence compliance status. The acceptable PM2.5 levels are strict for the annual average at 15 {micro}g/m{sup 3}, but compliance will be determined by a spatial average from several monitors rather than for a single monitor. Carbon, ammonium, sulfate, and nitrate are the major PM2.5 components in most areas, with geological material constituting only 5% to 15% of the mass. Chemical concentrations in the PM2.5 size fraction are also the major cause of urban and regional haze. This haze results from both the scattering and absorption of light by small particles. PM2.5 will use only population-oriented monitors to determine attainment, and ``fence line'' sites located to determine maximum impact from a facility will not be used to determine compliance as they have been in the past. Primary particles and precursor gases from fuel combustion in vehicles, homes, and industries will become the pollutants under greatest scrutiny in non-attainment areas.

  17. The Role of Reluctance in PM Motors

    SciTech Connect

    Otaduy, P.J.

    2005-06-16

    The international research community has lately focused efforts on interior permanent magnet (IPM) motors to produce a traction motor for hybrid electric vehicles (HEV). One of the beneficial features of this technology is the additional torque produced by reluctance. The objective of this report is to analytically describe the role that reluctance plays in permanent magnet (PM) motors, to explore ways to increase reluctance torque without sacrificing the torque produced by the PMs, and to compare three IPM configurations with respect to torque, power, amount of magnet material required (cost), and percentage of reluctance torque. Results of this study will be used to determine future research directions in utilizing reluctance to obtain maximum torque and power while using a minimum amount of magnet material.

  18. An Approach to Improve the Performance of PM Forecasters

    PubMed Central

    de Mattos Neto, Paulo S. G.; Cavalcanti, George D. C.; Madeiro, Francisco; Ferreira, Tiago A. E.

    2015-01-01

    The particulate matter (PM) concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth’s atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governments to alert the population. Forecasting systems based on Artificial Neural Networks (ANNs) have been highlighted in the literature due to their performances. In general, three ANN-based approaches have been found for this task: ANN trained via learning algorithms, hybrid systems that combine search algorithms with ANNs, and hybrid systems that combine ANN with other forecasters. Independent of the approach, it is common to suppose that the residuals (error series), obtained from the difference between actual series and forecasting, have a white noise behavior. However, it is possible that this assumption is infringed due to: misspecification of the forecasting model, complexity of the time series or temporal patterns of the phenomenon not captured by the forecaster. This paper proposes an approach to improve the performance of PM forecasters from residuals modeling. The approach analyzes the remaining residuals recursively in search of temporal patterns. At each iteration, if there are temporal patterns in the residuals, the approach generates the forecasting of the residuals in order to improve the forecasting of the PM time series. The proposed approach can be used with either only one forecaster or by combining two or more forecasting models. In this study, the approach is used to improve the performance of a hybrid system (HS) composed by genetic algorithm (GA) and ANN from residuals modeling performed by two methods, namely, ANN and own hybrid system. Experiments were performed for PM2.5 and PM10 concentration series in Kallio and Vallila stations in Helsinki and

  19. PM MASS METHODS RESEARCH AND DEVELOPMENT

    EPA Science Inventory

    This task supports research into methodologies for determining particulate matter (PM) mass concentrations. Due to the complexity of PM (composition, size distribution, and concentration), developing PM methods that perform acceptably under most weather conditions at most U.S. l...

  20. Workout Machine

    NASA Technical Reports Server (NTRS)

    1995-01-01

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

  1. Machine generation of machine-executable state-change instructions for magnetic resonance imaging

    SciTech Connect

    Hoenninger, J.C. III; Crooks, L.E.

    1987-11-17

    A process of machine generating machine executable state-change instructions for a sequence controller of a multi-slice magnetic resonance imaging (MRI) system is described comprising the steps of: defining predetermined program-change tables T1, T2,...Tn of MRI parameter values in a machine accessible memory; defining predetermined slice-specific program segments P1, P2,...Pm of machine executable MRI sequencer instructions in a machine accessible memory, which segments are referenced by predetermined respective symbolic addresses and which segments include pointer-references to the tables; and machine replicating a predetermined set of the slice-specific program segments in a predetermined order while indexing the corresponding symbolic addresses and referenced table entries in a predetermined sequence so as to maintain proper correspondence between slice-specific main programs and subroutines in each replicated segment.

  2. Wacky Machines

    ERIC Educational Resources Information Center

    Fendrich, Jean

    2002-01-01

    Collectors everywhere know that local antique shops and flea markets are treasure troves just waiting to be plundered. Science teachers might take a hint from these hobbyists, for the next community yard sale might be a repository of old, quirky items that are just the things to get students thinking about simple machines. By introducing some…

  3. Modular PM Motor Drives for Automotive Traction Applications

    SciTech Connect

    Su, G.J.

    2001-10-29

    This paper presents modular permanent magnet (PM) motor drives for automotive traction applications. A partially modularized drive system consisting of a single PM motor and multiple inverters is described. The motor has multiple three-phase stator winding sets and each winding set is driven with a separate three-phase inverter module. A truly modularized inverter and motor configuration based on an axial-gap PM motor is then introduced, in which identical PM motor modules are mounted on a common shaft and each motor module is powered by a separate inverter module. The advantages of the modular approach for both inverter and motor include: (1) power rating scalability--one design meets different power requirements by simply stacking an adequate number of modules, thus avoiding redesigning and reducing the development cost, (2) increased fault tolerance, and (3) easy repairing. A prototype was constructed by using two inverters and an axial-gap PM motor with two sets of three-phase stat or windings, and it is used to assist the diesel engine in a hybrid electric vehicle converted from a Chevrolet Suburban. The effect of different pulse-width-modulation strategies for both motoring and regenerative modes on current control is analyzed. Torque and regenerative control algorithms are implemented with a digital signal processor. Analytical and initial testing results are included in the paper.

  4. Advanced Propulsion Power Distribution System for Next Generation Electric/Hybrid Vehicle. Phase 1; Preliminary System Studies

    NASA Technical Reports Server (NTRS)

    Bose, Bimal K.; Kim, Min-Huei

    1995-01-01

    The report essentially summarizes the work performed in order to satisfy the above project objective. In the beginning, different energy storage devices, such as battery, flywheel and ultra capacitor are reviewed and compared, establishing the superiority of the battery. Then, the possible power sources, such as IC engine, diesel engine, gas turbine and fuel cell are reviewed and compared, and the superiority of IC engine has been established. Different types of machines for drive motor/engine generator, such as induction machine, PM synchronous machine and switched reluctance machine are compared, and the induction machine is established as the superior candidate. Similar discussion was made for power converters and devices. The Insulated Gate Bipolar Transistor (IGBT) appears to be the most superior device although Mercury Cadmium Telluride (MCT) shows future promise. Different types of candidate distribution systems with the possible combinations of power and energy sources have been discussed and the most viable system consisting of battery, IC engine and induction machine has been identified. Then, HFAC system has been compared with the DC system establishing the superiority of the former. The detailed component sizing calculations of HFAC and DC systems reinforce the superiority of the former. A preliminary control strategy has been developed for the candidate HFAC system. Finally, modeling and simulation study have been made to validate the system performance. The study in the report demonstrates the superiority of HFAC distribution system for next generation electric/hybrid vehicle.

  5. Characterization of PM 2.5, PM 2.5-10 and PM > 10 in ambient air, Yokohama, Japan

    NASA Astrophysics Data System (ADS)

    Khan, Md. Firoz; Shirasuna, Yuichiro; Hirano, Koichiro; Masunaga, Shigeki

    2010-04-01

    This study elucidated the characteristics of ambient PM 2.5, PM 2.5-10 and PM > 10 with water soluble ions, i.e., Cl -, NO 3-, SO 42-, Na +, NH 4+, K +, Mg 2+ and Ca 2+ and carbonaceous aerosol, i.e., EC and OC in above size fractions from the samples collected for the period of 2007-2008. The total numbers of PM 2.5, PM 2.5-10 and PM > 10 samples collected with MCI sampler were 91, 87 and 79, respectively. The ambient particulate samples were collected twice in a week for a period of 24 h at the roof of a three-storied building in Yokohama National University. The annual arithmetic mean concentrations of PM 2.5, PM 2.5-10 and PM > 10 were 20.6, 9.6 and 5.1 µg m - 3 , respectively. The results of the daily PM 2.5 concentrations indicated that 67% of the daily PM 2.5 exceeded USEPA National Ambient Air Quality Standards (NAAQS) (15 µg m - 3 ) while 95% in respect of WHO ambient air quality guidelines (10 µg m - 3 ). The concentrations of water soluble ions in PM 2.5, PM 2.5-10 and PM > 10 accounted for 40%, 31% and 19%, respectively. The estimation of non-sea-salt particles implies that the major sources of water soluble ions in PM 2.5 are anthropogenic. On the other hand, a large proportion of sea salt particles contributes to PM 2.5-10 and PM > 10 . Spearman correlation indicated that the concentrations of OC and EC in PM 2.5 can originate from similar type of sources. However, the concentration of OC and EC in PM 2.5-10 and PM > 10 can have multiple sources. In addition, some atmospheric reactions were also characterized in this study.

  6. Drilling Machines: Vocational Machine Shop.

    ERIC Educational Resources Information Center

    Thomas, John C.

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

  7. BIOLOGICAL ASSESSMENT OF THE TOXICITY OF PM AND PM COMPONENTS

    EPA Science Inventory

    (August 1, 2009 – July 31, 2010):

    1. In vitro Toxicity Assessment of Baltimore PM.

      Overview and summary: Previously, we have utilized human bron...

    2. Fullerene Machines

      NASA Technical Reports Server (NTRS)

      Globus, Al; Saini, Subhash (Technical Monitor)

      1998-01-01

      Fullerenes possess remarkable properties and many investigators have examined the mechanical, electronic and other characteristics of carbon SP2 systems in some detail. In addition, C-60 can be functionalized with many classes of molecular fragments and we may expect the caps of carbon nanotubes to have a similar chemistry. Finally, carbon nanotubes have been attached to t he end of scanning probe microscope (Spill) tips. Spills can be manipulated with sub-angstrom accuracy. Together, these investigations suggest that complex molecular machines made of fullerenes may someday be created and manipulated with very high accuracy. We have studied some such systems computationally (primarily functionalized carbon nanotube gears and computer components). If such machines can be combined appropriately, a class of materials may be created that can sense their environment, calculate a response, and act. The implications of such hypothetical materials are substantial.

    3. Fullerene Machines

      NASA Technical Reports Server (NTRS)

      Globus, Al; Saini, Subhash

      1998-01-01

      Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically accessible and of great interest. We have computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Preliminary results suggest that these gears can be cooled by a helium atmosphere and a laser motor can power fullerene gears if a positive and negative charge have been added to form a dipole. In addition, we have unproven concepts based on experimental and computational evidence for support structures, computer control, a system architecture, a variety of components, and manufacture. Combining fullerene machines with the remarkable mechanical properties of carbon nanotubes, there is some reason to believe that a focused effort to develop fullerene nanotechnology could yield materials with tremendous properties.

    4. Charging machine

      DOEpatents

      Medlin, John B.

      1976-05-25

      A charging machine for loading fuel slugs into the process tubes of a nuclear reactor includes a tubular housing connected to the process tube, a charging trough connected to the other end of the tubular housing, a device for loading the charging trough with a group of fuel slugs, means for equalizing the coolant pressure in the charging trough with the pressure in the process tubes, means for pushing the group of fuel slugs into the process tube and a latch and a seal engaging the last object in the group of fuel slugs to prevent the fuel slugs from being ejected from the process tube when the pusher is removed and to prevent pressure liquid from entering the charging machine.

    5. 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.

    6. Case study of PM pollution in playgrounds in Istanbul

      NASA Astrophysics Data System (ADS)

      Ozdemir, Huseyin; Mertoglu, Bulent; Demir, Goksel; Deniz, Ali; Toros, Hüseyin

      2012-05-01

      In a world where at least 50% of the population is living in urban environments, air pollution and specifically particulate matter (PM) have become one of the most critical issues for human health. Children are more susceptible than adults to air pollution and its adverse effects because they inhale and retain larger amounts of air pollutants per unit of body weight. In this study, PM pollution, particularly PM10 and PM2.5, at selected playgrounds were investigated in Istanbul city. Istanbul is a megacity of over 15 million inhabitants, and on-road traffic is increasing rapidly (over 3 million vehicles on the road). To estimate the effect of traffic emissions on children, the location of the playgrounds were selected according to traffic density. Measurements were carried out at five different playgrounds throughout the city in 2009. Field results show that the values of PM10 and PM2.5 have reached critical limits at the playgrounds close to the main roads, especially at P-1. Thus, we focused on this location and investigated a source other than traffic emissions. One of the episode days has been observed on 5-7 March 2009. Evaluations of meteorological events are very important to determine air pollution sources and their long-range transport. Therefore, the Weather Research and Forecasting model (WRF) was used to simulate and forecast meteorological parameters and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) applied to investigate long-range transport. According to the WRF model outputs, there was a low-pressure system over Geneva gulf on the 500-hPa level, and its core had been located over Britain on 5 March 2009 00UTC. The system had been sweeping dust from the Sahara Desert and carrying the air particles over Istanbul. Similarly, backward HYSPLIT analysis showed that air particles had moved through Istanbul from Northern Africa.

    7. Electrical machine

      DOEpatents

      De Bock, Hendrik Pieter Jacobus; Alexander, James Pellegrino; El-Refaie, Ayman Mohamed Fawzi; Gerstler, William Dwight; Shah, Manoj Ramprasad; Shen, Xiaochun

      2016-06-21

      An apparatus, such as an electrical machine, is provided. The apparatus can include a rotor defining a rotor bore and a conduit disposed in and extending axially along the rotor bore. The conduit can have an annular conduit body defining a plurality of orifices disposed axially along the conduit and extending through the conduit body. The rotor can have an inner wall that at least partially defines the rotor bore. The orifices can extend through the conduit body along respective orifice directions, and the rotor and conduit can be configured to provide a line of sight along the orifice direction from the respective orifices to the inner wall.

    8. TEMPO machine

      SciTech Connect

      Rohwein, G.J.; Lancaster, K.T.; Lawson, R.N.

      1986-06-01

      TEMPO is a transformer powered megavolt pulse generator with an output pulse of 100 ns duration. The machine was designed for burst mode operation at pulse repetition rates up to 10 Hz with minimum pulse-to-pulse voltage variations. To meet the requirement for pulse duration a nd a 20-..omega.. output impedance within reasonable size constraints, the pulse forming transmission line was designed as two parallel water-insulated, strip-type Blumleins. Stray capacitance and electric fields along the edges of the line elements were controlled by lining the tank with plastic sheet.

    9. A Double-Sided Linear Primary Permanent Magnet Vernier Machine

      PubMed Central

      2015-01-01

      The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed. PMID:25874250

    10. Tunneling machine

      SciTech Connect

      Snyder, L.L.

      1980-02-19

      A diametrically compact tunneling machine for boring tunnels is disclosed. The machine includes a tubular support frame having a hollow piston mounted therein which is movable from a retracted position in the support frame to an extended position. A drive shaft is rotatably mounted in the hollow piston and carries a cutter head at one end. The hollow piston is restrained against rotational movement relative to the support frame and the drive shaft is constrained against longitudinal movement relative to the hollow piston. A plurality of radially extendible feet project from the support frame to the tunnel wall to grip the tunnel wall during a tunneling operation wherein the hollow piston is driven forwardly so that the cutter head works on the tunnel face. When the hollow piston is fully extended, a plurality of extendible support feet, which are fixed to the rearward and forward ends of the hollow piston, are extended, the radially extendible feet are retracted and the support frame is shifted forwardly by the piston so that a further tunneling operation may be initiated.

    11. Implementing the PM Programming Language using MPI and OpenMP - a New Tool for Programming Geophysical Models on Parallel Systems

      NASA Astrophysics Data System (ADS)

      Bellerby, Tim

      2015-04-01

      ) or tasks are divided out among the available processors (number of tasks > number of processors). Nested parallel statements may further subdivide the processor set owned by a given task. Tasks or processors are distributed evenly by default, but uneven distributions are possible under programmer control. It is also possible to explicitly enable child tasks to migrate within the processor set owned by their parent task, reducing load unbalancing at the potential cost of increased inter-processor message traffic. PM incorporates some programming structures from the earlier MIST language presented at a previous EGU General Assembly, while adopting a significantly different underlying parallelisation model and type system. PM code is available at www.pm-lang.org under an unrestrictive MIT license. Reference Ruymán Reyes, Antonio J. Dorta, Francisco Almeida, Francisco de Sande, 2009. Automatic Hybrid MPI+OpenMP Code Generation with llc, Recent Advances in Parallel Virtual Machine and Message Passing Interface, Lecture Notes in Computer Science Volume 5759, 185-195

    12. Interaction with Machine Improvisation

      NASA Astrophysics Data System (ADS)

      Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

      We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

    13. Parallel machines: Parallel machine languages

      SciTech Connect

      Iannucci, R.A. )

      1990-01-01

      This book presents a framework for understanding the tradeoffs between the conventional view and the dataflow view with the objective of discovering the critical hardware structures which must be present in any scalable, general-purpose parallel computer to effectively tolerate latency and synchronization costs. The author presents an approach to scalable general purpose parallel computation. Linguistic Concerns, Compiling Issues, Intermediate Language Issues, and hardware/technological constraints are presented as a combined approach to architectural Develoement. This book presents the notion of a parallel machine language.

    14. Open burning and open detonation PM10 mass emission factor measurements with optical remote sensing.

      PubMed

      Yuen, Wangki; Johnsen, David L; Koloutsou-Vakakis, Sotiria; Rood, Mark J; Kim, Byung J; Kemme, Michael R

      2014-02-01

      Emission factors (EFs) of particulate matter with aerodynamic diameter <10 microm (PM10) from the open burning/open detonation (OB/OD) of energetic materials were measured using a hybrid-optical remote sensing (hybrid-ORS) method. This method is based on the measurement of range-resolved PM backscattering values with a micropulse light detection and ranging (LIDAR; MPL) device. Field measurements were completed during March 2010 at Tooele Army Depot, Utah, which is an arid continental site. PM10 EFs were quantified for OB of M1 propellant and OD of 2,4,6-trinitrotoluene (TNT). EFs from this study are compared with previous OB/OD measurements reported in the literature that have been determined with point measurements either in enclosed or ambient environments, and with concurrent airborne point measurements. PM10 mass EFs, determined with the hybrid-ORS method, were 7.8 x 10(-3) kg PM10/kg M1 from OB of M1 propellant, and 0.20 kg PM10/kg TNT from OD of TNT. Compared with previous results reported in the literature, the hybrid-ORS method EFs were 13% larger for OB and 174% larger for OD. Compared with the concurrent airborne measurements, EF values from the hybrid-ORS method were 37% larger for OB and 54% larger for OD. For TNT, no statistically significant differences were observed for the EFs measured during the detonation of 22.7 and 45.4 kg of TNT, supporting that the total amount of detonated mass in this mass range does not have an effect on the EFs for OD of TNT. PMID:24654390

  1. HEAVY-DUTY DIESEL FINE PM EMISSIONS

    EPA Science Inventory

    Fine PM emissions from diesel powered vehicles continues to be a concern for those responsible for implementating the PM-2.5 National Ambient Air Quality Standards (NAAQS). Diesel generated PM is nanometer in size, incorporates a number of toxic air pollutants (including carcinog...

  2. SCIENCE VERSION OF PM CHEMISTRY MODEL

    EPA Science Inventory

    PM chemistry models containing detailed treatments of key chemical processes controlling ambient concentrations of inorganic and organic compounds in PM2.5 are needed to develop strategies for reducing PM2.5 concentrations. This task, that builds on previous research conducted i...

  3. CMAQ VERSION OF PM CHEMISTRY MODEL

    EPA Science Inventory

    PM chemistry models containing detailed treatments of key chemical processes controlling ambient concentrations of compounds in PM2.5 are needed to develop strategies for reducing PM2.5 concentrations. Specific activities to be carried out under this task include (1) in 2005 re...

  4. PM POPULATION EXPOSURE AND DOSE MODELS

    EPA Science Inventory

    The overall objective of this study is the development of a refined probabilistic exposure and dose model for particulate matter (PM) suitable for predicting PM10 and PM2.5 population exposures. This modeling research will be conducted both in-house by EPA scientists and through...

  5. A MOOC on Approaches to Machine Translation

    ERIC Educational Resources Information Center

    Costa-jussà, Mart R.; Formiga, Lluís; Torrillas, Oriol; Petit, Jordi; Fonollosa, José A. R.

    2015-01-01

    This paper describes the design, development, and analysis of a MOOC entitled "Approaches to Machine Translation: Rule-based, statistical and hybrid", and provides lessons learned and conclusions to be taken into account in the future. The course was developed within the Canvas platform, used by recognized European universities. It…

  6. NodePM: a remote monitoring alert system for energy consumption using probabilistic techniques.

    PubMed

    Filho, Geraldo P R; Ueyama, Jó; Villas, Leandro A; Pinto, Alex R; Gonçalves, Vinícius P; Pessin, Gustavo; Pazzi, Richard W; Braun, Torsten

    2014-01-01

    In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out. PMID:24399157

  7. NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

    PubMed Central

    Filho, Geraldo P. R.; Ueyama, Jó; Villas, Leandro A.; Pinto, Alex R.; Gonçalves, Vinícius P.; Pessin, Gustavo; Pazzi, Richard W.; Braun, Torsten

    2014-01-01

    In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out. PMID:24399157

  8. Direct control of air gap flux in permanent magnet machines

    DOEpatents

    Hsu, John S.

    2000-01-01

    A method and apparatus for field weakening in PM machines uses field weakening coils (35, 44, 45, 71, 72) to produce flux in one or more stators (34, 49, 63, 64), including a flux which counters flux normally produced in air gaps between the stator(s) (34, 49, 63, 64) and the rotor (20, 21, 41, 61) which carries the PM poles. Several modes of operation are introduced depending on the magnitude and polarity of current in the field weakening coils (35, 44, 45, 71, 72). The invention is particularly useful for, but not limited to, the electric vehicle drives and PM generators.

  9. CARDIOVASCULAR MORTALITY IN PHOENIX: PM1 IS A BETTER INDICATOR THAN PM2.5.

    EPA Science Inventory

    EPA has obtained a 3-year database of particulate matter (PM) in Phoenix, AZ from 1995 - 1997 that includes elemental analysis by XRF of daily PM2.5. During this time period PM1 and PM2.5 TEOMs were run simultaneously for about 7 months during two periods of the year. Regressio...

  10. Diagnostics for hybrid reactors

    NASA Astrophysics Data System (ADS)

    Orsitto, Francesco Paolo

    2012-06-01

    The Hybrid Reactor(HR) can be considered an attractive actinide-burner or a fusion assisted transmutation for destruction of transuranic(TRU) nuclear waste. The hybrid reactor has two important subsystems: the tokamak neutron source and the blanket which includes a fuel zone where the TRU are placed and a tritium breeding zone. The diagnostic system for a HR must be as simple and robust as possible to monitor and control the plasma scenario, guarantee the protection of the machine and monitor the transmutation.

  11. Hybrid network intrusion detection

    NASA Astrophysics Data System (ADS)

    Tahmoush, David

    2014-05-01

    We report on a machine learning classifier that can be used to discover the patterns hidden within large networking data flows. It utilizes an existing intrusion detection system (IDS) as an oracle to learn a faster, less resource intensive normalcy classifier as a front-end to a hybrid network IDS. This system has the capability to recognize new attacks that are similar to known attack signatures. It is also more highly scalable and distributable than the signature-based IDS. The new hybrid design also allows distributed updates and retraining of the normalcy classifier to stay up-to-date with current threats.

  12. Diagnostics for hybrid reactors

    SciTech Connect

    Orsitto, Francesco Paolo

    2012-06-19

    The Hybrid Reactor(HR) can be considered an attractive actinide-burner or a fusion assisted transmutation for destruction of transuranic(TRU) nuclear waste. The hybrid reactor has two important subsystems: the tokamak neutron source and the blanket which includes a fuel zone where the TRU are placed and a tritium breeding zone. The diagnostic system for a HR must be as simple and robust as possible to monitor and control the plasma scenario, guarantee the protection of the machine and monitor the transmutation.

  13. Machine wanting.

    PubMed

    McShea, Daniel W

    2013-12-01

    Wants, preferences, and cares are physical things or events, not ideas or propositions, and therefore no chain of pure logic can conclude with a want, preference, or care. It follows that no pure-logic machine will ever want, prefer, or care. And its behavior will never be driven in the way that deliberate human behavior is driven, in other words, it will not be motivated or goal directed. Therefore, if we want to simulate human-style interactions with the world, we will need to first understand the physical structure of goal-directed systems. I argue that all such systems share a common nested structure, consisting of a smaller entity that moves within and is driven by a larger field that contains it. In such systems, the smaller contained entity is directed by the field, but also moves to some degree independently of it, allowing the entity to deviate and return, to show the plasticity and persistence that is characteristic of goal direction. If all this is right, then human want-driven behavior probably involves a behavior-generating mechanism that is contained within a neural field of some kind. In principle, for goal directedness generally, the containment can be virtual, raising the possibility that want-driven behavior could be simulated in standard computational systems. But there are also reasons to believe that goal-direction works better when containment is also physical, suggesting that a new kind of hardware may be necessary. PMID:23792091

  14. Machine musicianship

    NASA Astrophysics Data System (ADS)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  15. Machine intelligence for robotics applications

    SciTech Connect

    Weisbin, C.R.; Barhen, J.; de Saussure, G.; Hamel, W.R.; Jorgensen, C.; Oblow, E.M.; Ricks, R.E.

    1985-01-01

    The purpose of this paper is to review research in machine intelligence ongoing at the Center for Engineering Systems Advanced Research (CESAR). As a result of initial experimentation with our HERMIES-I mobile robot, hardware and software upgrades were implemented which enable fully asynchronous sonar operation, improved stepper motor control for the sensory platform, and more reliable wheel drive control. The current system, designated as HERMIES-II, is discussed. Successful demonstration of dead-reckoning navigation and the development of a sensor-based exploration and discovery algorithm which can now handle typical maze problems are reported. The development of HERMIES ''brain'' as a hypercube ensemble machine with concurrent computation and associated message passing is described. Algorithms for mapping precedence-constrained task graphs onto a hypercube yield results with high efficiency and proper load balance. A framework for a hybrid uncertainty analysis theory for decision making is described.

  16. A dual-channel flux-switching permanent magnet motor for hybrid electric vehicles

    NASA Astrophysics Data System (ADS)

    Hua, Wei; Wu, Zhongze; Cheng, Ming; Wang, Baoan; Zhang, Jianzhong; Zhou, Shigui

    2012-04-01

    The flux-switching permanent magnet (FSPM) motor is a relatively novel brushless machine having both magnets and concentrated windings in the stator, which exhibits inherently sinusoidal PM flux-linkage, back-EMF waveforms, and high torque capability. However, in the application of hybrid electric vehicles, it is essential to prevent magnets and armature windings moving in radial direction due to the possible vibration during operation, and to ensure fault-tolerant capability. Hence, in this paper based on an original FSPM motor, a dual-channel FSPM (DC-FSPM) motor with modified structure to fix both armature windings and magnets and improved reliability is proposed for a practical 10 kW integral starter/generator (ISG) in hybrid electric vehicles. The influences of different solutions and the end-effect on the static characteristics, are evaluated based on the 2D and 3D finite element analysis, respectively. Finally, both the predicted and experimental results, compared with a prototype DC-FSPM motor and an interior PM motor used in Honda Civic, confirm that the more sinusoidal back-EMF waveform and lower torque ripple can be achieved in the DC-FSPM motor, whereas the torque is smaller under the same coil current.

  17. 40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 40 Protection of Environment 6 2012-07-01 2012-07-01 false Test Specifications for PM10, PM2.5 and... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods Specification PM10 PM2.5 Class I Class II Class III...

  18. Applied machine vision

    SciTech Connect

    Not Available

    1984-01-01

    This book presents the papers given at a conference on robot vision. Topics considered at the conference included the link between fixed and flexible automation, general applications of machine vision, the development of a specification for a machine vision system, machine vision technology, machine vision non-contact gaging, and vision in electronics manufacturing.

  19. Machine Shop Lathes.

    ERIC Educational Resources Information Center

    Dunn, James

    This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

  20. A Machine Approach for Field Weakening of Permanent-Magnet Motors

    SciTech Connect

    Hsu, J.S.

    2000-04-02

    The commonly known technology of field weakening for permanent-magnet (PM) motors is achieved by controlling the direct-axis current component through an inverter, without using mechanical variation of the air gap, a new machine approach for field weakening of PM machines by direct control of air-gap fluxes is introduced. The demagnetization situation due to field weakening is not an issue with this new method. In fact, the PMs are strengthened at field weakening. The field-weakening ratio can reach 1O:1 or higher. This technology is particularly useful for the PM generators and electric vehicle drives.

  1. 40 CFR 93.117 - Criteria and procedures: Compliance with PM10 and PM2.5 control measures.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... with PM10 and PM2.5 control measures. 93.117 Section 93.117 Protection of Environment ENVIRONMENTAL....117 Criteria and procedures: Compliance with PM10 and PM2.5 control measures. The FHWA/FTA project must comply with any PM10 and PM2.5 control measures in the applicable implementation plan....

  2. 40 CFR 93.117 - Criteria and procedures: Compliance with PM10 and PM2.5 control measures.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... with PM10 and PM2.5 control measures. 93.117 Section 93.117 Protection of Environment ENVIRONMENTAL....117 Criteria and procedures: Compliance with PM10 and PM2.5 control measures. The FHWA/FTA project must comply with any PM10 and PM2.5 control measures in the applicable implementation plan....

  3. 40 CFR 93.117 - Criteria and procedures: Compliance with PM10 and PM2.5 control measures.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... with PM10 and PM2.5 control measures. 93.117 Section 93.117 Protection of Environment ENVIRONMENTAL....117 Criteria and procedures: Compliance with PM10 and PM2.5 control measures. The FHWA/FTA project must comply with any PM10 and PM2.5 control measures in the applicable implementation plan....

  4. 40 CFR 93.117 - Criteria and procedures: Compliance with PM10 and PM2.5 control measures.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... with PM10 and PM2.5 control measures. 93.117 Section 93.117 Protection of Environment ENVIRONMENTAL....117 Criteria and procedures: Compliance with PM10 and PM2.5 control measures. The FHWA/FTA project must comply with any PM10 and PM2.5 control measures in the applicable implementation plan....

  5. 40 CFR 93.117 - Criteria and procedures: Compliance with PM10 and PM2.5 control measures.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... with PM10 and PM2.5 control measures. 93.117 Section 93.117 Protection of Environment ENVIRONMENTAL....117 Criteria and procedures: Compliance with PM10 and PM2.5 control measures. The FHWA/FTA project must comply with any PM10 and PM2.5 control measures in the applicable implementation plan....

  6. Elemental characterization of PM10, PM2.5 and PM1 in the town of Genoa (Italy).

    PubMed

    Ariola, V; D'Alessandro, A; Lucarelli, F; Marcazzan, G; Mazzei, F; Nava, S; Garcia-Orellana, I; Prati, P; Valli, G; Vecchi, R; Zucchiatti, A

    2006-01-01

    The particulate matter (PM) concentration and composition, the PM10, PM2.5, PM1 fractions, were studied in the urban area of Genoa, a coastal town in the northwest of Italy. Two instruments, the continuous monitor TEOM and the sequential sampler PARTISOL, were operated almost continuously on the same site from July 2001 to September 2004. Samples collected by PARTISOL were weighted to obtain PM concentration and then analysed by PIXE (particle induced X-ray emission) and by ED-XRF (energy dispersion X-ray fluorescence), obtaining concentrations for elements from Na to Pb. Some of the filters used in the TEOM microbalance were analysed by ED-XRF to calculate Pb concentration values averaged over 7-30 d periods. PMID:15982708

  7. Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels

    PubMed Central

    Feng, Qi; Wu, Shengjun; Du, Yun; Xue, Huaiping; Xiao, Fei; Ban, Xuan; Li, Xiaodong

    2013-01-01

    Abstract Fugitive dust deriving from construction sites is a serious local source of particulate matter (PM) that leads to air pollution in cities undergoing rapid urbanization in China. In spite of this fact, no study has yet been published relating to prediction of high levels of PM with diameters <10 μm (PM10) as adjudicated by the Individual Air Quality Index (IAQI) on fugitive dust from nearby construction sites. To combat this problem, the Construction Influence Index (Ci) is introduced in this article to improve forecasting models based on three neural network models (multilayer perceptron, Elman, and support vector machine) in predicting daily PM10 IAQI one day in advance. To obtain acceptable forecasting accuracy, measured time series data were decomposed into wavelet representations and wavelet coefficients were predicted. Effectiveness of these forecasters were tested using a time series recorded between January 1, 2005, and December 31, 2011, at six monitoring stations situated within the urban area of the city of Wuhan, China. Experimental trials showed that the improved models provided low root mean square error values and mean absolute error values in comparison to the original models. In addition, these improved models resulted in higher values of coefficients of determination and AHPC (the accuracy rate of high PM10 IAQI caused by nearby construction activity) compared to the original models when predicting high PM10 IAQI levels attributable to fugitive dust from nearby construction sites. PMID:24381481

  8. The PM-200 lubrication system

    NASA Technical Reports Server (NTRS)

    Sliney, Harold E.

    1991-01-01

    Plasma sprayed composite coating of metal-bonded chromium carbide with additions of silver and thermochemically stable fluorides were previously reported to be lubricative in pin on desk bench tests from room temperature to 900 C. An early coating formulation of this type, designated as PS-200, was successfully tested as a cylinder coating in a Stirling engine at a TRRT of 760 C in a hydrogen atmosphere, and as a backup lubricant for gas bearings to 650 C. A subsequent optimization program has shown that tribological properties are further improved by increasing the solid lubricant content. The improved coating is designated as PS-212. The same powder formulation was used to make free-standing powder metallurgy (PM-212) parts by sintering or hot isostatic pressing. The process is very attractive for making parts that cannot be readily plasma sprayed such as bushings and cylinders that have small bore diameters and/or high length to diameter ratios. The properties of coatings and free-standing parts fabricated from these powders are reviewed.

  9. A PROBABILISTIC POPULATION EXPOSURE MODEL FOR PM10 AND PM 2.5

    EPA Science Inventory

    A first generation probabilistic population exposure model for Particulate Matter (PM), specifically for predicting PM10, and PM2.5, exposures of an urban, population has been developed. This model is intended to be used to predict exposure (magnitude, frequency, and duration) ...

  10. Hard Machinable Machining of Cobalt Super Alloys

    NASA Astrophysics Data System (ADS)

    Čep, Robert; Janásek, Adam; Petrů, Jana; Čepová, Lenka; Sadílek, Marek; Kratochvíl, Jiří

    2012-12-01

    The article deals with difficult-to-machine cobalt super alloys. The main aim is to test the basic properties of cobalt super alloys and propose suitable cutting materials and machining parameters under the designation 188 when machining. Although the development of technology in chipless machining such as moulding, precision casting and other manufacturing methods continues to advance, machining is still the leading choice for piece production, typical for energy and chemical engineering. Nowadays, super alloys are commonly used in turbine engines in regions that are subject to high temperatures, which require high strength, high temperature resistance, phase stability, as well as corrosion or oxidation resistance.

  11. Women, Men, and Machines.

    ERIC Educational Resources Information Center

    Form, William; McMillen, David Byron

    1983-01-01

    Data from the first national study of technological change show that proportionately more women than men operate machines, are more exposed to machines that have alienating effects, and suffer more from the negative effects of technological change. (Author/SSH)

  12. Tube Alinement for Machining

    NASA Technical Reports Server (NTRS)

    Garcia, J.

    1984-01-01

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

  13. Stirling machine operating experience

    NASA Technical Reports Server (NTRS)

    Ross, Brad; Dudenhoefer, James E.

    1991-01-01

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

  14. Sources of PM(10) and PM (2.5) in Cairo's ambient air.

    PubMed

    Abu-Allaban, M; Lowenthal, D H; Gertler, A W; Labib, M

    2007-10-01

    A source attribution study was performed to assess the contributions of specific pollutant source types to the observed particulate matter (PM) levels in the greater Cairo Area using the chemical mass balance (CMB) receptor model. Three intensive ambient monitoring studies were carried out during the period of February 21-March 3, 1999, October 27-November 27, 1999, and June 8-June 26, 2002. PM(10), PM(2.5), and polycyclic aromatic hydrocarbons (PAHs) were measured on a 24-h basis at six sampling stations during each of the intensive periods. The six intensive measurement sites represented background levels, mobile source impacts, industrial impacts, and residential exposure. Major contributors to PM(10) included geological material, mobile source emissions, and open burning. PM(2.5) tended to be dominated by mobile source emissions, open burning, and secondary species. This paper presents the results of the PM(10) and PM(2.5), source contribution estimates. PMID:17268919

  15. Levels and major sources of PM2.5 and PM10 in Bangkok Metropolitan Region.

    PubMed

    Chuersuwan, Nares; Nimrat, Subuntith; Lekphet, Sukanda; Kerdkumrai, Tida

    2008-07-01

    This research was the first long-term attempt to concurrently measure and identify major sources of both PM(10) and PM(2.5) in Bangkok Metropolitan Region (BMR). Ambient PM(10) and PM(2.5) were evaluated at four monitoring stations and analyzed for elemental compositions, water-soluble ions, and total carbon during February 2002-January 2003. Fifteen chemical elements, four water-soluble ions, and total carbon were analyzed to assist major source identification by a receptor model approach, known as chemical mass balance. PM(10) and PM(2.5) were significantly different (p<0.05) at all sites and 24 h averages were high at traffic location while two separated residential sites were similar. Seasonal difference of PM(10) and PM(2.5) concentrations was distinct between dry and wet seasons. Major source of PM(10) at the traffic site indicated that automobile emissions and biomass burning-related sources contributed approximately 33% each. Automobiles contributed approximately 39 and 22% of PM(10) mass at two residential sites while biomass burning contributed about 36 and 28%. PM(10) from re-suspended soil and cooking sources accounted for 10 to 15% at a residential site. Major sources of PM(2.5) at traffic site were automobile and biomass burning, contributing approximately 32 and 26%, respectively. Biomass burning was the major source of PM(2.5) mass concentrations at residential sites. Meat cooking also accounted for 31% of PM(2.5) mass at a low impact site. Automobile, biomass burning, and road dust were less significant, contributed 10, 6, and 5%, respectively. Major sources identification at some location had difficulty to achieve performance criteria due to limited source profiles. Improved in characterize other sources profiles will help local authority to better air quality. PMID:18258301

  16. Automatic Inspection During Machining

    NASA Technical Reports Server (NTRS)

    Ransom, Clyde L.

    1988-01-01

    In experimental manufacturing process, numerically-controlled machine tool temporarily converts into inspection machine by installing electronic touch probes and specially-developed numerical-control software. Software drives probes in paths to and on newly machined parts and collects data on dimensions of parts.

  17. Machining lead wafers

    SciTech Connect

    Schamaun, R.T.

    1987-09-01

    Recently, MEC-6 machined some 4-inch-diameter lead wafers to precision tolerances. The tolerance on the wafer thickness was +-0.000080 inch. A diamond tool was used to machine the wafers on a Moore No. 3 lathe. This report discusses the methods used to machine the wafers, the fixtures used to hold the wafers, and the inspection methods and results.

  18. Apprentice Machine Theory Outline.

    ERIC Educational Resources Information Center

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

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

  19. Intelligent, Energy Saving Power Supply and Control System of Hoisting Mine Machine with Compact and Hybrid Drive System / Inteligentne, Energooszczędne Układy Zasilania I Sterowania Górniczych Maszyn Wyciągowych Z Napędem Zintegrowanym Lub Hybrydowym

    NASA Astrophysics Data System (ADS)

    Szymański, Zygmunt

    2015-03-01

    In the paper present's an analysis of suitableness an application of compact and hybrid drive system in hoisting machine. In the paper presented the review of constructional solutions of hoisting machines drive system, driving with AC and DC motor. In the paper presented conception of modern, energy sparing hoisting machine supply system, composed with compact motor, an supplied with transistor or thyristor converter supply system, and intelligent control system composed with multilevel microprocessor controller. In the paper present's also analysis of suitableness application an selected method of artificial intelligent in hoisting machine control system, automation system, and modern diagnostic system. In the paper one limited to analysis of: fuzzy logic method, genetic algorithms method, and modern neural net II and III generation. That method enables realization of complex control algorithms of hosting machine with insurance of energy sparing exploitation conditions, monitoring of exploitation parameters, and prediction diagnostic of hoisting machine technical state, minimization a number of failure states. In the paper present's a conception of control and diagnostic system of the hoisting machine based on fuzzy logic neural set control. In the chapter presented also a selected control algorithms and results of computer simulations realized for particular mathematical models of hoisting machine. Results of theoretical investigation were partly verified in laboratory and industrial experiments. Przedstawiono analizę celowości wprowadzania, napędów zintegrowanych oraz napędów hybrydowych, do układów napędowych maszyn wyciągowych. Zamieszczono przegląd rozwiązań konstrukcyjnych wybranych hybrydowych oraz zintegrowanych napędów maszyn wyciągowych z silnikami DC i AC. Opisano koncepcję nowoczesnego, energooszczędnego układu zasilania górniczych maszyny wyciągowej, złożonego z silnika zintegrowanego, (tranzystorowego

  20. YHV-responsive gene expression under the influence of PmRelish regulation.

    PubMed

    Visetnan, Suwattana; Supungul, Premruethai; Tang, Sureerat; Hirono, Ikuo; Tassanakajon, Anchalee; Rimphanitchayakit, Vichien

    2015-11-01

    In animals, infection by Gram-negative bacteria and certain viruses activates the Imd signaling pathway wherein the a NF-κB transcription factor, Relish, is a key regulatory protein for the synthesis of antimicrobial proteins. Infection by yellow head virus (YHV) activates the Imd pathway. To investigate the expression of genes involved in YHV infection and under the influence of PmRelish regulation, RNA interference and suppression subtractive hybridization (SSH) are employed. The genes in forward library expressed in shrimp after YHV infection and under the activity of PmRelish were obtained by subtracting the cDNAs from YHV-infected and PmRelish-knockdown shrimp with cDNAs from YHV-infected shrimp. Opposite subtraction gave a reverse library whereby an alternative set of genes under YHV infection and no PmRelish expression were obtained. Nucleotide sequences of 252 and 99 cDNA clones from the forward and reverse libraries, respectively, were obtained and annotated through blast search against the GenBank sequences. Genes involved in defense and homeostasis were abundant in both libraries, 31% and 23% in the forward and reverse libraries, respectively. They were predominantly antimicrobial proteins, proteinases and proteinase inhibitors. The expression of antimicrobial protein genes, ALFPm3, crustinPm1, penaeidin3 and penaeidin5 were tested under PmRelish silencing and Gram-negative bacterium Vibrio harveyi infection. Together with the results using YHV infection previously reported, the expression of penaeidin5 and also penaeidin3 but not ALFPm3 and crustinPm1 were under the regulation of PmRelish in the Imd pathway. PMID:26434714

  1. Shrimp Serine Proteinase Homologues PmMasSPH-1 and -2 Play a Role in the Activation of the Prophenoloxidase System

    PubMed Central

    Jearaphunt, Miti; Amparyup, Piti; Sangsuriya, Pakkakul; Charoensapsri, Walaiporn; Senapin, Saengchan; Tassanakajon, Anchalee

    2015-01-01

    Melanization mediated by the prophenoloxidase (proPO) activating system is a rapid immune response used by invertebrates against intruding pathogens. Several masquerade-like and serine proteinase homologues (SPHs) have been demonstrated to play an essential role in proPO activation in insects and crustaceans. In a previous study, we characterized the masquerade-like SPH, PmMasSPH1, in the black tiger shrimp Penaeus monodon as a multifunctional immune protein based on its recognition and antimicrobial activity against the Gram-negative bacteria Vibrio harveyi. In the present study, we identify a novel SPH, known as PmMasSPH2, composed of an N-terminal clip domain and a C-terminal SP-like domain that share high similarity to those of other insect and crustacean SPHs. We demonstrate that gene silencing of PmMasSPH1 and PmMasSPH2 significantly reduces PO activity, resulting in a high number of V. harveyi in the hemolymph. Interestingly, knockdown of PmMasSPH1 suppressed not only its gene transcript but also other immune-related genes in the proPO system (e.g., PmPPAE2) and antimicrobial peptides (e.g., PenmonPEN3, PenmonPEN5, crustinPm1 and Crus-likePm). The PmMasSPH1 and PmMasSPH2 also show binding activity to peptidoglycan (PGN) of Gram-positive bacteria. Using a yeast two-hybrid analysis and co-immunoprecipitation, we demonstrate that PmMasSPH1 specifically interacted with the final proteinase of the proPO cascade, PmPPAE2. Furthermore, the presence of both PmMasSPH1 and PmPPAE2 enhances PGN-induced PO activity in vitro. Taken together, these results suggest the importance of PmMasSPHs in the activation of the shrimp proPO system. PMID:25803442

  2. The distribution of PM10 and PM2.5 carbonaceous aerosol in Baotou, China

    NASA Astrophysics Data System (ADS)

    Zhou, Haijun; He, Jiang; Zhao, Boyi; Zhang, Lijun; Fan, Qingyun; Lü, Changwei; Dudagula; Liu, Tao; Yuan, Yinghui

    2016-09-01

    Particulate matter (PM), including PM10 and PM2.5, is one of the major impacts on air quality, visibility, climate change, earth radiation balance, and public health. Organic carbon (OC) and elemental carbon (EC) are the major components of PM. 804 samples (PM10 and PM2.5) were simultaneously collected from six urban sites covering 3 districts in Baotou, in January, April, September, and November 2014. As to a long-term study on the effects of carbonaceous aerosol, data were collected annually at Environmental Protection Agency of Baotou (EPB). The concentrations of PM10 and PM2.5, the spatial distribution and content of OC and EC, the relationship between OC and EC, and the formation of secondary organic carbon (SOC) have been investigated. The findings indicated that the concentrations of these particle matter are higher than that in US or European standards. The average concentrations of OC in PM10 and PM2.5 follow the order: January > November > April > September; and for EC in PM10 and PM2.5 follow the order: January > November > September > April. Affected by metrological factors, it was indicated that high wind speed and low relative humidity were beneficial for removal of OC and EC in January and November. Pearson correlations and cluster analysis on OC and EC concentrations in PM10 and PM2.5 with gaseous pollutants (SO2, NO2, and CO) suggested that OC shared the same emission sources with SO2 and CO from combustion, while EC's sources mainly came from vehicles exhaust and combustion which contributed to NO2 as well. The OC concentration is mainly primary in warm months, while it appears secondary in cold months in Baotou. There is a common characteristic among the cities with higher SOC in winter, wherever the coal combustion can lead to the severe pollution. This work is important for the construction of the database of OC and EC concentrations in PM10 and PM2.5 at spatial and time intervals, and it can provide scientific suggestion for similar PM

  3. Creep Behavior and Damage of Ni-Base Superalloys PM 1000 and PM 3030

    NASA Astrophysics Data System (ADS)

    Nganbe, M.; Heilmaier, M.

    2009-12-01

    Two oxide dispersion strengthening (ODS) nickel-base superalloys, a solely dispersion-strengthened alloy (PM 1000) and an additionally γ'-strengthened alloy (PM 3030) are investigated regarding creep resistance at temperatures between 600 °C and 1000 °C. The creep strength advantage of PM 3030 over PM 1000 decreases as the temperature increases due to the thermal instability of the γ' phase. The particle strengthening contribution in both alloys increases linearly with load. However, solid solution softening leads to an apparent drop in particle strengthening in PM 1000. Deformation concentration in slip bands is more accentuated in PM 3030-R34 due to additional γ' strengthening combined with strongly textured coarse and elongated grain structure. Finer, equiaxed grains reduce creep strength at higher temperatures due to grain boundary deformation processes and premature pore formation, but have only minor impact at low and intermediate temperatures.

  4. Perspex machine: VII. The universal perspex machine

    NASA Astrophysics Data System (ADS)

    Anderson, James A. D. W.

    2006-01-01

    The perspex machine arose from the unification of projective geometry with the Turing machine. It uses a total arithmetic, called transreal arithmetic, that contains real arithmetic and allows division by zero. Transreal arithmetic is redefined here. The new arithmetic has both a positive and a negative infinity which lie at the extremes of the number line, and a number nullity that lies off the number line. We prove that nullity, 0/0, is a number. Hence a number may have one of four signs: negative, zero, positive, or nullity. It is, therefore, impossible to encode the sign of a number in one bit, as floating-point arithmetic attempts to do, resulting in the difficulty of having both positive and negative zeros and NaNs. Transrational arithmetic is consistent with Cantor arithmetic. In an extension to real arithmetic, the product of zero, an infinity, or nullity with its reciprocal is nullity, not unity. This avoids the usual contradictions that follow from allowing division by zero. Transreal arithmetic has a fixed algebraic structure and does not admit options as IEEE, floating-point arithmetic does. Most significantly, nullity has a simple semantics that is related to zero. Zero means "no value" and nullity means "no information." We argue that nullity is as useful to a manufactured computer as zero is to a human computer. The perspex machine is intended to offer one solution to the mind-body problem by showing how the computable aspects of mind and, perhaps, the whole of mind relates to the geometrical aspects of body and, perhaps, the whole of body. We review some of Turing's writings and show that he held the view that his machine has spatial properties. In particular, that it has the property of being a 7D lattice of compact spaces. Thus, we read Turing as believing that his machine relates computation to geometrical bodies. We simplify the perspex machine by substituting an augmented Euclidean geometry for projective geometry. This leads to a general

  5. Efficiency of Big Spring Number Eight (BSNE) and Modified Wilson and Cook (MWAC) samplers to collect PM10, PM2.5 and PM1

    NASA Astrophysics Data System (ADS)

    Mendez, Mariano J.; Funk, Roger; Buschiazzo, Daniel E.

    2016-06-01

    The internal efficiency of Big Spring Number Eight (BSNE) and Modified Wilson and Cook (MWAC) samplers for trapping PM10, PM2.5 and PM1 were tested in a wind tunnel, at two wind speeds (3.0 and 6.8 m s-1) in the saltation zone (SAZ) and the suspension zone (SAZ). PM concentrations measured in the inlet and the outlet of both samplers were correlated and the slopes of fitting equations were used for calculating sampling efficiencies. Results showed that BSNE efficiencies ranged from 12% to 32% for PM10, from 0% to 19% for PM2.5 and from 0% to 12% for PM1. The BSNE's efficiency decreased with decreasing particle sizes in SAZ and SUZ at both wind speeds as a consequence of the very low deposition velocity of the finest size particles. The BSNE's efficiency increased with increasing wind speed in SAZ for PM10 and PM2.5 and in SUZ for PM2.5. The MWAC's efficiency ranged from 1% to 20% for PM10, from 0% to 15% for PM2.5 and from 0% to 16% for PM1. The MWAC efficiency was 0% for PM10, PM2.5 and PM1 in the SUZ at 3 m s-1 and for PM2.5 and PM1 in the SUZ at 6.8 m s-1. These results provide evidence that the efficiency of BSNE and MWAC for trapping PM10 change with wind speed and position of the sampler. Results also show that BSNEs and MWACs can potentially be used for PM10 emission studies but more research is needed in order to understand and improve their efficiency.

  6. 40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of Environment... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5...

  7. 40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of Environment... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5...

  8. PM2.5 and PM10 concentrations in four dairies on the Southern High Plains

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Air quality was determined in 4 dairies at the boundary, commodity barn, and compost field. Two laser DustTrak PM10 aerosol monitors and four RAAS -300 gravimetric monitors, 2 PM2.5 and 2 PM10 were employed. The DustTrak flow rate was set at 1.7 L/min and the RAAS were set at 16.6 L/min. Monitors we...

  9. 75 FR 26898 - Determination of Attainment for PM-10; Fort Hall PM-10 Nonattainment Area, Idaho

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-05-13

    ...EPA is proposing under the Clean Air Act (CAA) to determine that the Fort Hall PM-10 nonattainment area on the Fort Hall Indian Reservation in Idaho has attained the National Ambient Air Quality Standards (NAAQS) for particulate matter with an aerodynamic diameter of less than or equal to 10 microns (PM-10). EPA's proposed finding that the Fort Hall PM-10 nonattainment area has attained the......

  10. Intraurban variability of PM10 and PM2.5 in an Eastern Mediterranean city

    NASA Astrophysics Data System (ADS)

    Massoud, Rawad; Shihadeh, Alan. L.; Roumié, Mohamed; Youness, Myriam; Gerard, Jocelyne; Saliba, Nada; Zaarour, Rita; Abboud, Maher; Farah, Wehbeh; Saliba, Najat Aoun

    2011-09-01

    The results of the first large scale chemical characterization of PM10 and PM2.5 at three different sites in the urban city of Beirut, Lebanon, are presented. Between May 2009 and April 2010 a total of 304 PM10 and PM2.5 samples were collected by sampling every sixth day at three different sites in Beirut. Observed mass concentrations varied between 19.7 and 521.2 μg m - 3 for PM10 and between 8.4 and 72.2 μg m - 3 for PM2.5, respectively. Inorganic concentrations accounted for 29.7-35.6 μg m - 3 and 46.0-53.5 μg m - 3 of the total mass of PM10 and PM2.5, respectively. Intra-city temporal and spatial variations were assessed based on the study of three factors: correlation coefficients (R) for PM and chemical components, coefficient of divergence (CODs), and source apportionment using positive matrix factorization (PMF). Based on R and COD of PM concentrations, the three sites appear homogeneous. However, when individual elements were compared, heterogeneity among sites was found. This latter was attributed to the variability in the percent contribution of biogenic and local anthropogenic source factors such as traffic related sources and dust resuspension. Other factors included the proximity to the Mediterranean sea, the population density and the topographical structure of the city. Hence, despite its small size (20.8 km 2), one PM monitoring site does not reflect an accurate PM level in Beirut.

  11. 16,000-rpm Interior Permanent Magnet Reluctance Machine with Brushless Field Excitation

    SciTech Connect

    Hsu, J.S.; Burress, T.A.; Lee, S.T.; Wiles, R.H.; Coomer, C.L.; McKeever, J.W.; Adams, D.J.

    2007-10-31

    The reluctance interior permanent magnet (RIPM) motor is currently used by many leading auto manufacturers for hybrid vehicles. The power density for this type of motor is high compared with that of induction motors and switched reluctance motors. The primary drawback of the RIPM motor is the permanent magnet (PM) because during high-speed operation, the fixed PM produces a huge back electromotive force (emf) that must be reduced before the current will pass through the stator windings. This reduction in back-emf is accomplished with a significant direct-axis (d-axis) demagnetization current, which opposes the PM's flux to reduce the flux seen by the stator wires. This may lower the power factor and efficiency of the motor and raise the requirement on the alternate current (ac) power supply; consequently, bigger inverter switching components, thicker motor winding conductors, and heavier cables are required. The direct current (dc) link capacitor is also affected when it must accommodate heavier harmonic currents. It is commonly agreed that, for synchronous machines, the power factor can be optimized by varying the field excitation to minimize the current. The field produced by the PM is fixed and cannot be adjusted. What can be adjusted is reactive current to the d-axis of the stator winding, which consumes reactive power but does not always help to improve the power factor. The objective of this project is to avoid the primary drawbacks of the RIPM motor by introducing brushless field excitation (BFE). This offers both high torque per ampere (A) per core length at low speed by using flux, which is enhanced by increasing current to a fixed excitation coil, and flux, which is weakened at high speed by reducing current to the excitation coil. If field weakening is used, the dc/dc boost converter used in a conventional RIPM motor may be eliminated to reduce system costs. However, BFE supports a drive system with a dc/dc boost converter, because it can further extend

  12. Validation of PM6 & PM7 semiempirical methods on polarizability calculations

    SciTech Connect

    Praveen, P. A.; Babu, R. Ramesh; Ramamurthi, K.

    2015-06-24

    Modern semiempirical methods such as PM6 and PM7 are often used to explore the electronic structure dependent properties of molecules. In this work we report the evaluation of PM6 and PM7 methods towards linear and nonlinear optical polarizability calculations for different molecules and solid nanoclusters. The results are compared with reported experimental results as well as theoretical results from other high level theories for the same systems. It is found that both methods produce accurate results for small molecules and the accuracy increases with the increase in asymmetry of the medium sized organic molecules and accuracy reduces for solid nanoclusters.

  13. Hybrid micromachining using a nanosecond pulsed laser and micro EDM

    NASA Astrophysics Data System (ADS)

    Kim, Sanha; Kim, Bo Hyun; Chung, Do Kwan; Shin, Hong Shik; Chu, Chong Nam

    2010-01-01

    Micro electrical discharge machining (micro EDM) is a well-known precise machining process that achieves micro structures of excellent quality for any conductive material. However, the slow machining speed and high tool wear are main drawbacks of this process. Though the use of deionized water instead of kerosene as a dielectric fluid can reduce the tool wear and increase the machine speed, the material removal rate (MRR) is still low. In contrast, laser ablation using a nanosecond pulsed laser is a fast and non-wear machining process but achieves micro figures of rather low quality. Therefore, the integration of these two processes can overcome the respective disadvantages. This paper reports a hybrid process of a nanosecond pulsed laser and micro EDM for micromachining. A novel hybrid micromachining system that combines the two discrete machining processes is introduced. Then, the feasibility and characteristics of the hybrid machining process are investigated compared to conventional EDM and laser ablation. It is verified experimentally that the machining time can be effectively reduced in both EDM drilling and milling by rapid laser pre-machining prior to micro EDM. Finally, some examples of complicated 3D micro structures fabricated by the hybrid process are shown.

  14. Apparatus for alternatives to PM-10 sampling

    SciTech Connect

    Wente, M.; Wente, W.E.; Moore, M.E.

    1995-12-31

    Because of questions of whether PM-10 adequately characterizes the size fraction that is a challenge to human health, we have developed devices and samplers that will provide PM-2.5 and PM-1 size fractions. Each of these systems utilizes a cyclone for the fractionation process. Models have been developed to predict both the cutpoint and the fractional efficiency curves for single inlet cyclones are geometrically similar to a form that was utilized by Lapple. Modeling has been done with two geometrical forms of multiple inlet cyclones. Each form has six inlets, and one form has the body shape of a Lapple cyclone, while the other has a shortened body. The models for cutpoints are based on log-linear correlations between D{sub 0.5}/d{sub o} and a flow Reynolds number; where D{sub 0.5} = cutpoint size, d{sub o} = cyclone body diameter, and the flow Reynolds number is based on the inlet velocity and the outlet tube diameter. PM-1 and PM-2.5 fractionators with flow rates of 16.7 L/min have been tested in both laboratory and field environments. One version of the fractionator is a stand-alone cylone that has been commercialized by URG, Inc., and a second version is made as an adaptor to the Graseby Andersen Inc. Model 246 inlet for the dichotomous sampler. A third version of the PM-2.5 inlet is one that includes its own wind speed decelerator, bug screen and cyclonic pre-fractionator, where the latter device is used for reducing the aerosol mass that will be deposited in the PM-2.5 cyclone. A prototype field sampler has been developed that incorporates flow control, and a easily changeable filter cartridge.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  16. Fault Tolerant State Machines

    NASA Technical Reports Server (NTRS)

    Burke, Gary R.; Taft, Stephanie

    2004-01-01

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

  17. Machine tool locator

    DOEpatents

    Hanlon, John A.; Gill, Timothy J.

    2001-01-01

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

  18. Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States.

    PubMed

    Di, Qian; Kloog, Itai; Koutrakis, Petros; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2016-05-01

    A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R(2) of 0.84 on the left out monitors. Regional R(2) could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily predictions of PM2.5 at 1 km × 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short-term and the long-term. PMID:27023334

  19. Chemical characterization and mass closure of PM10 and PM2.5 at an urban site in Karachi - Pakistan

    NASA Astrophysics Data System (ADS)

    Shahid, Imran; Kistler, Magdalena; Mukhtar, Azam; Ghauri, Badar M.; Ramirez-Santa Cruz, Carlos; Bauer, Heidi; Puxbaum, Hans

    2016-03-01

    A mass balance method is applied to assess main source contributions to PM2.5 and PM10 levels in Karachi. Carbonaceous species (elemental carbon, organic carbon, carbonate carbon), soluble ions (Ca++, Mg++, Na+, K+, NH4+, Cl‑, NO3‑, SO4‑), saccharides (levoglucosan, galactosan, mannosan, sucrose, fructose, glucose, arabitol and mannitol) were determined in atmospheric fine (PM2.5) and coarse (PM10) aerosol samples collected under pre-monsoon conditions (March-April 2009) at an urban site in Karachi (Pakistan). The concentrations of PM2.5 and PM10 were found to be 75 μg/m3 and 437 μg/m3 respectively. The large difference between PM10 and PM2.5 originated predominantly from mineral dust. "Calcareous dust" and "siliceous dust" were the over all dominating material in PM, with 46% contribution to PM2.5 and 78% to PM10-2.5. Combustion particles and secondary organics (EC + OM) comprised 23% of PM2.5 and 6% of PM10-2.5. EC, as well as OC ambient levels were higher (59% and 56%) in PM10-2.5 than in PM2.5. Biomass burning contributed about 3% to PM2.5, and had a share of about 13% of "EC + OM" in PM2.5. The impact of bioaerosol (fungal spores) was minor and had a share of 1 and 2% of the OC in the PM2.5 and PM10-2.5 size fractions. In case of secondary inorganic aerosols, ammonium sulphate (NH4)2SO4 contributes 4.4% to PM2.5 and no detectable quantity were found in fraction PM10-2.5. The sea salt contribution is about 2% both to PM2.5 and PM10-2.5.

  20. Chaotic Boltzmann machines

    PubMed Central

    Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki

    2013-01-01

    The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. PMID:23558425

  1. Two-machine flow shop scheduling integrated with preventive maintenance planning

    NASA Astrophysics Data System (ADS)

    Wang, Shijin; Liu, Ming

    2016-02-01

    This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.

  2. Breeding highbush blueberry cultivars adapted to machine harvest for the fresh market

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In recent years, world blueberry production has been split evenly between processing and fresh fruit markets. Machine harvest of highbush blueberry [northern highbush (NHB, Vaccinium corymbosum L.), southern highbush (SHB, Vaccinium corymbosum interspecific hybrids), and rabbiteye (RE, Vaccinium vi...

  3. Identification of Pm8 Suppressor at Pm3 Locus in Soft 1 Red Winter Wheat

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The 1BL.1RS wheat-rye translocation possesses the Pm8, Yr9, Lr26, and Sr31 genes for resistance to several major fungal pathogens of small grains. However, not all wheat cultivars with the 1RS translocation are resistant to Pm8-avirulent isolates of Blumeria graminis f. sp. tritici (Bgt), the causal...

  4. PM2.5 and PM10 Emission from Agricultural Soils by Wind Erosion

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil tillage and wind erosion are a major source of particulate matter less than 2.5 and 10 µm (PM2.5 and PM10) emission from cultivated soil. Fifteen cultivated soils collected from 5 states were tested as crushed (<2.0 mm) and uncrushed (natural aggregation) at 8, 10, and 13 m s-1 wind velocity in...

  5. Energy harvesting using AC machines with high effective pole count

    NASA Astrophysics Data System (ADS)

    Geiger, Richard Theodore

    In this thesis, ways to improve the power conversion of rotating generators at low rotor speeds in energy harvesting applications were investigated. One method is to increase the pole count, which increases the generator back-emf without also increasing the I2R losses, thereby increasing both torque density and conversion efficiency. One machine topology that has a high effective pole count is a hybrid "stepper" machine. However, the large self inductance of these machines decreases their power factor and hence the maximum power that can be delivered to a load. This effect can be cancelled by the addition of capacitors in series with the stepper windings. A circuit was designed and implemented to automatically vary the series capacitance over the entire speed range investigated. The addition of the series capacitors improved the power output of the stepper machine by up to 700%. At low rotor speeds, with the addition of series capacitance, the power output of the hybrid "stepper" was more than 200% that of a similarly sized PMDC brushed motor. Finally, in this thesis a hybrid lumped parameter / finite element model was used to investigate the impact of number, shape and size of the rotor and stator teeth on machine performance. A typical off-the-shelf hybrid stepper machine has significant cogging torque by design. This cogging torque is a major problem in most small energy harvesting applications. In this thesis it was shown that the cogging and ripple torque can be dramatically reduced. These findings confirm that high-pole-count topologies, and specifically the hybrid stepper configuration, are an attractive choice for energy harvesting applications.

  6. Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

    NASA Astrophysics Data System (ADS)

    Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

    2014-09-01

    In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

  7. Diamond machine tool face lapping machine

    DOEpatents

    Yetter, H.H.

    1985-05-06

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

  8. Long-range potential source contributions of episodic aerosol events to PM 10 profile of a megacity

    NASA Astrophysics Data System (ADS)

    Karaca, Ferhat; Anil, Ismail; Alagha, Omar

    2009-12-01

    This paper evaluates possible long-range source contributions to the PM 10 profile of Istanbul, Turkey. A novel method for classifying PM 10 episodic events resulting from long-range transport, as opposed to local ones, was implemented. Hourly PM 10 mass concentrations from ten stations distributed throughout Istanbul during the year 2008 were used for this purpose. Hourly backward trajectories for the arrival of air masses to the center of Istanbul for the year 2008 were calculated using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model. Significant episodes from these backward trajectories were selected and employed in Potential Source Contribution Function (PSCF) analysis to estimate the possible contribution of long-range PM 10 transport (LRPMT) to observed PM 10 concentrations. The PSCF results showed significant seasonal variations. Based on the results obtained, PM 10 concentrations observed in Istanbul during summer and autumn are not heavily affected by LRPMT. Mediterranean countries, especially those of the central part of northern Africa (northern Algeria and Libya) are the most significant potential PM 10 contributors to Istanbul's atmosphere during springtime. During winter, Balkan countries, including the Aegean part of Turkey, Greece, Bulgaria, Serbia, and Croatia, as well as northern Italy, eastern France, southern Germany, Austria and the eastern part of Russia, were the most important LRPMT source regions for high PSCF values.

  9. High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America.

    PubMed

    van Donkelaar, Aaron; Martin, Randall V; Spurr, Robert J D; Burnett, Richard T

    2015-09-01

    We used a geographically weighted regression (GWR) statistical model to represent bias of fine particulate matter concentrations (PM2.5) derived from a 1 km optimal estimate (OE) aerosol optical depth (AOD) satellite retrieval that used AOD-to-PM2.5 relationships from a chemical transport model (CTM) for 2004-2008 over North America. This hybrid approach combined the geophysical understanding and global applicability intrinsic to the CTM relationships with the knowledge provided by observational constraints. Adjusting the OE PM2.5 estimates according to the GWR-predicted bias yielded significant improvement compared with unadjusted long-term mean values (R(2) = 0.82 versus R(2) = 0.62), even when a large fraction (70%) of sites were withheld for cross-validation (R(2) = 0.78) and developed seasonal skill (R(2) = 0.62-0.89). The effect of individual GWR predictors on OE PM2.5 estimates additionally provided insight into the sources of uncertainty for global satellite-derived PM2.5 estimates. These predictor-driven effects imply that local variability in surface elevation and urban emissions are important sources of uncertainty in geophysical calculations of the AOD-to-PM2.5 relationship used in satellite-derived PM2.5 estimates over North America, and potentially worldwide. PMID:26261937

  10. Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China.

    PubMed

    Wang, Xinhua; Bi, Xinhui; Sheng, Guoying; Fu, Jiamo

    2006-07-31

    PM10 and PM2.5 samples were collected in the indoor environments of four hospitals and their adjacent outdoor environments in Guangzhou, China during the summertime. The concentrations of 18 target elements in particles were also quantified. The results showed that indoor PM2.5 levels with an average of 99 microg m(-3) were significantly higher than outdoor PM2.5 standard of 65 microg m(-3) recommended by USEPA [United States Environmental Protection Agency. Office of Air and Radiation, Office of Air Quality Planning and Standards, Fact Sheet. EPA's Revised Particulate Matter Standards, 17, July 1997] and PM2.5 constituted a large fraction of indoor respirable particles (PM10) by an average of 78% in four hospitals. High correlation between PM2.5 and PM10 (R(2) of 0.87 for indoors and 0.90 for outdoors) suggested that PM2.5 and PM10 came from similar particulate emission sources. The indoor particulate levels were correlated with the corresponding outdoors (R(2) of 0.78 for PM2.5 and 0.67 for PM10), demonstrating that outdoor infiltration could lead to direct transportation into indoors. In addition to outdoor infiltration, human activities and ventilation types could also influence indoor particulate levels in four hospitals. Total target elements accounted for 3.18-5.56% of PM2.5 and 4.38-9.20% of PM10 by mass, respectively. Na, Al, Ca, Fe, Mg, Mn and Ti were found in the coarse particles, while K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As and Se existed more in the fine particles. The average indoor concentrations of total elements were lower than those measured outdoors, suggesting that indoor elements originated mainly from outdoor emission sources. Enrichment factors (EF) for trace element were calculated to show that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) were highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Factor analysis was used to identify possible pollution source-types, namely street dust, road traffic

  11. Monitoring trace elements by nuclear techniques in PM10 and PM2.5

    NASA Astrophysics Data System (ADS)

    Freitas, M. Carmo; Almeida, S. Marta; Reis, Miguel A.; Oliveira, Orlando R.

    2003-06-01

    As part of a contract for air quality monitoring at an urban waste incinerator neighborhood, measurements of PM10 and PM2.5 are being routinely evaluated. Two samples are collected for 24 h at the weekend and a working day, using a Gent collector, which separates the particulate in two fractions: PM2.5 and PM2.5-10. Filters are polycarbonate Nuclepore, sized 47 mm, which, for analysis, are cut as: one half to be analyzed by instrumental neutron activation analysis (INAA) and one quarter for proton induced X-rays emission (PIXE). Both techniques are multielemental determining together around 25 chemical elements. Comparison of results is just possible for potassium, iron and zinc, which are compared in this work. A better agreement is obtained in PM2.5 suggesting a homogeneity trend. Fe and K compare quite well and Zn may show quite different results.

  12. 75 FR 64162 - Determination of Attainment for PM10

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-10-19

    ...-based standards. On July 1, 1987 (52 FR 24634), EPA promulgated two primary standards for PM 10 : A 24... December 18, 2006, EPA revoked the annual PM 10 standard but retained the 24-hour PM 10 standard. 71 FR... 50, appendix K, section 1.0. ] B. The Eagle River PM10 Nonattainment Area On August 7, 1987 (52...

  13. 75 FR 45485 - Determination of Attainment for PM10

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-08-03

    ... standards. On July 1, 1987 (52 FR 24634), EPA promulgated two primary standards for PM 10 : A 24-hour..., 2006, EPA revoked the annual PM 10 standard but retained the 24-hour PM 10 standard. 71 FR 61144...). These areas included all former Group I PM 10 planning areas identified in 52 FR 29383 (August 7,...

  14. Functional specification of the Performance Measurement (PM) module

    NASA Technical Reports Server (NTRS)

    Berliner, J. E.

    1980-01-01

    The design of the Performance Measurement Module is described with emphasis on what the PM Module would do, and what it would look like to the user. The PM Module as described could take several man-years to develop. An evolutionary approach to the implementation of the PM Module is presented which would provide an operational baseline PM Module within a few months.

  15. Flare Hybrids

    NASA Astrophysics Data System (ADS)

    Tomczak, M.; Dubieniecki, P.

    2015-12-01

    On the basis of the Solar Maximum Mission observations, Švestka ( Solar Phys. 121, 399, 1989) introduced a new class of flares, the so-called flare hybrids. When they start, they look like typical compact flares (phase 1), but later on, they look like flares with arcades of magnetic loops (phase 2). We summarize the characteristic features of flare hybrids in soft and hard X-rays as well as in the extreme ultraviolet; these features allow us to distinguish flare hybrids from other flares. In this article, additional energy release or long plasma cooling timescales are suggested as possible causes of phase 2. We estimate the frequency of flare hybrids, and study the magnetic configurations favorable for flare hybrid occurrence. Flare hybrids appear to be quite frequent, and the difference between the lengths of magnetic loops in the two interacting loop systems seem to be a crucial parameter for determining their characteristics.

  16. Machine Translation Project

    NASA Technical Reports Server (NTRS)

    Bajis, Katie

    1993-01-01

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

  17. Simple Machines Made Simple.

    ERIC Educational Resources Information Center

    St. Andre, Ralph E.

    Simple machines have become a lost point of study in elementary schools as teachers continue to have more material to cover. This manual provides hands-on, cooperative learning activities for grades three through eight concerning the six simple machines: wheel and axle, inclined plane, screw, pulley, wedge, and lever. Most activities can be…

  18. Simple Machine Junk Cars

    ERIC Educational Resources Information Center

    Herald, Christine

    2010-01-01

    During the month of May, the author's eighth-grade physical science students study the six simple machines through hands-on activities, reading assignments, videos, and notes. At the end of the month, they can easily identify the six types of simple machine: inclined plane, wheel and axle, pulley, screw, wedge, and lever. To conclude this unit,…

  19. Compound taper milling machine

    NASA Technical Reports Server (NTRS)

    Campbell, N. R.

    1969-01-01

    Simple, inexpensive milling machine tapers panels from a common apex to a uniform height at panel edge regardless of the panel perimeter configuration. The machine consists of an adjustable angled beam upon which the milling tool moves back and forth above a rotatable table upon which the workpiece is held.

  20. Technique for Machining Glass

    NASA Technical Reports Server (NTRS)

    Rice, S. H.

    1982-01-01

    Process for machining glass with conventional carbide tools requires a small quantity of a lubricant for aluminum applied to area of glass to be machined. A carbide tool is then placed against workpiece with light pressure. Tool is raised periodically to clear work of glass dust and particles. Additional lubricant is applied as it is displaced.

  1. THE TEACHING MACHINE.

    ERIC Educational Resources Information Center

    KLEIN, CHARLES; WAYNE, ELLIS

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

  2. Stirling machine operating experience

    SciTech Connect

    Ross, B.; Dudenhoefer, J.E.

    1994-09-01

    Numerous Stirling machines have been built and operated, but the operating experience of these machines is not well known. It is important to examine this operating experience in detail, because it largely substantiates the claim that stirling machines are capable of reliable and lengthy operating lives. The amount of data that exists is impressive, considering that many of the machines that have been built are developmental machines intended to show proof of concept, and are not expected to operate for lengthy periods of time. Some Stirling machines (typically free-piston machines) achieve long life through non-contact bearings, while other Stirling machines (typically kinematic) have achieved long operating lives through regular seal and bearing replacements. In addition to engine and system testing, life testing of critical components is also considered. The record in this paper is not complete, due to the reluctance of some organizations to release operational data and because several organizations were not contacted. The authors intend to repeat this assessment in three years, hoping for even greater participation.

  3. An asymptotical machine

    NASA Astrophysics Data System (ADS)

    Cristallini, Achille

    2016-07-01

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

  4. Machining heavy plastic sections

    NASA Technical Reports Server (NTRS)

    Stalkup, O. M.

    1967-01-01

    Machining technique produces consistently satisfactory plane-parallel optical surfaces for pressure windows, made of plexiglass, required to support a photographic study of liquid rocket combustion processes. The surfaces are machined and polished to the required tolerances and show no degradation from stress relaxation over periods as long as 6 months.

  5. Friction-Testing Machine

    NASA Technical Reports Server (NTRS)

    Benz, F. J.; Dixon, D. S.; Shaw, R. C.

    1986-01-01

    Testing machine evaluates wear and ignition characteristics of materials in rubbing contact. Offers advantages over other laboratory methods of measuring wear because it simulates operating conditions under which material will actually be used. Machine used to determine wear characteristics, rank and select materials for service with such active oxidizers as oxygen, halogens, and oxides of nitrogen, measure wear characteristics, and determine coefficients of friction.

  6. BRITISH MOLDING MACHINE, PBQ AUTOMATIC COPE AND DRAG MOLDING MACHINE ...

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

    BRITISH MOLDING MACHINE, PBQ AUTOMATIC COPE AND DRAG MOLDING MACHINE MAKES BOTH MOLD HALVES INDIVIDUALLY WHICH ARE LATER ROTATED, ASSEMBLED, AND LOWERED TO POURING CONVEYORS BY ASSISTING MACHINES. - Southern Ductile Casting Company, Casting, 2217 Carolina Avenue, Bessemer, Jefferson County, AL

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

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

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

  8. Machine tool evaluation and machining operation development

    SciTech Connect

    Morris, T.O.; Kegg, R.

    1997-03-15

    The purpose of this CRADA was to support Cincinnati Milacron`s needs in fabricating precision components, from difficult to machine materials, while maintaining and enhancing the precision manufacturing skills of the Oak Ridge Complex. Oak Ridge and Cincinnati Milacron personnel worked in a team relationship wherein each contributed equally to the success of the program. Process characterization, control technologies, machine tool capabilities, and environmental issues were the primary focus areas. In general, Oak Ridge contributed a wider range of expertise in machine tool testing and monitoring, and environmental testing on machining fluids to the defined tasks while Cincinnati Milacron personnel provided equipment, operations-specific knowledge and shop-floor services to each task. Cincinnati Milacron was very pleased with the results of all of the CRADA tasks. However, some of the environmental tasks were not carried through to a desired completion due to an expanding realization of need as the work progressed. This expansion of the desired goals then exceeded the time length of the CRADA. Discussions are underway on continuing these tasks under either a Work for Others agreement or some alternate funding.

  9. Micro-machining.

    PubMed

    Brinksmeier, Ekkard; Preuss, Werner

    2012-08-28

    Manipulating bulk material at the atomic level is considered to be the domain of physics, chemistry and nanotechnology. However, precision engineering, especially micro-machining, has become a powerful tool for controlling the surface properties and sub-surface integrity of the optical, electronic and mechanical functional parts in a regime where continuum mechanics is left behind and the quantum nature of matter comes into play. The surprising subtlety of micro-machining results from the extraordinary precision of tools, machines and controls expanding into the nanometre range-a hundred times more precise than the wavelength of light. In this paper, we will outline the development of precision engineering, highlight modern achievements of ultra-precision machining and discuss the necessity of a deeper physical understanding of micro-machining. PMID:22802498

  10. Introduction to machine learning.

    PubMed

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods. PMID:24272434