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

Yang, K; Yu, Z; Chen, H

Purpose: To implement VMAT in RayStation with the Elekta Synergy linac with the new Agility MLC, and to utilize the same vendor softwares to determine the optimum Elekta VMAT machine parameters in RayStation for accurate modeling and robust delivery. Methods: iCOMCat is utilized to create various beam patterns with user defined dose rate, gantry, MLC and jaw speed for each control point. The accuracy and stability of the output and beam profile are qualified for each isolated functional component of VMAT delivery using ion chamber and Profiler2 with isocentric mounting fixture. Service graphing on linac console is used to verifymore » the mechanical motion accuracy. The determined optimum Elekta VMAT machine parameters were configured in RayStation v4.5.1. To evaluate the system overall performance, TG-119 test cases and nine retrospective VMAT patients were planned on RayStation, and validated using both ArcCHECK (with plug and ion chamber) and MapCHECK2. Results: Machine output and profile varies <0.3% when only variable is dose rate (35MU/min-600MU/min). <0.9% output and <0.3% profile variation are observed with additional gantry motion (0.53deg/s–5.8deg/s both directions). The output and profile variation are still <1% with additional slow leaf motion (<1.5cm/s both direction). However, the profile becomes less symmetric, and >1.5% output and 7% profile deviation is seen with >2.5cm/s leaf motion. All clinical cases achieved comparable plan quality as treated IMRT plans. The gamma passing rate is 99.5±0.5% on ArcCheck (<3% iso center dose deviation) and 99.1±0.8% on MapCheck2 using 3%/3mm gamma (10% lower threshold). Mechanical motion accuracy in all VMAT deliveries is <1°/1mm. Conclusion: Accurate RayStation modeling and robust VMAT delivery is achievable on Elekta Agility for <2.5cm/s leaf motion and full range of dose rate and gantry speed determined by the same vendor softwares. Our TG-119 and patient results have provided us with the confidence to use VMAT clinically.« less

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

PubMed

Krepkovich, Eileen T; Perreault, Eric J

2008-01-01

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.

The human role in space (THURIS) applications study. Final briefing

NASA Technical Reports Server (NTRS)

Maybee, George W.

1987-01-01

The THURIS (The Human Role in Space) application is an iterative process involving successive assessments of man/machine mixes in terms of performance, cost and technology to arrive at an optimum man/machine mode for the mission application. The process begins with user inputs which define the mission in terms of an event sequence and performance time requirements. The desired initial operational capability date is also an input requirement. THURIS terms and definitions (e.g., generic activities) are applied to the input data converting it into a form which can be analyzed using the THURIS cost model outputs. The cost model produces tabular and graphical outputs for determining the relative cost-effectiveness of a given man/machine mode and generic activity. A technology database is provided to enable assessment of support equipment availability for selected man/machine modes. If technology gaps exist for an application, the database contains information supportive of further investigation into the relevant technologies. The present study concentrated on testing and enhancing the THURIS cost model and subordinate data files and developing a technology database which interfaces directly with the user via technology readiness displays. This effort has resulted in a more powerful, easy-to-use applications system for optimization of man/machine roles. Volume 1 is an executive summary.

Linear-hall sensor based force detecting unit for lower limb exoskeleton

NASA Astrophysics Data System (ADS)

Li, Hongwu; Zhu, Yanhe; Zhao, Jie; Wang, Tianshuo; Zhang, Zongwei

2018-04-01

This paper describes a knee-joint human-machine interaction force sensor for lower-limb force-assistance exoskeleton. The structure is designed based on hall sensor and series elastic actuator (SEA) structure. The work we have done includes the structure design, the parameter determination and dynamic simulation. By converting the force signal into macro displacement and output voltage, we completed the measurement of man-machine interaction force. And it is proved by experiments that the design is simple, stable and low-cost.

SU-E-T-257: Output Constancy: Reducing Measurement Variations in a Large Practice Group

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

Hedrick, K; Fitzgerald, T; Miller, R

2014-06-01

Purpose: To standardize output constancy check procedures in a large medical physics practice group covering multiple sites, in order to identify and reduce small systematic errors caused by differences in equipment and the procedures of multiple physicists. Methods: A standardized machine output constancy check for both photons and electrons was instituted within the practice group in 2010. After conducting annual TG-51 measurements in water and adjusting the linac to deliver 1.00 cGy/MU at Dmax, an acrylic phantom (comparable at all sites) and PTW farmer ion chamber are used to obtain monthly output constancy reference readings. From the collected charge reading,more » measurements of air pressure and temperature, and chamber Ndw and Pelec, a value we call the Kacrylic factor is determined, relating the chamber reading in acrylic to the dose in water with standard set-up conditions. This procedure easily allows for multiple equipment combinations to be used at any site. The Kacrylic factors and output results from all sites and machines are logged monthly in a central database and used to monitor trends in calibration and output. Results: The practice group consists of 19 sites, currently with 34 Varian and 8 Elekta linacs (24 Varian and 5 Elekta linacs in 2010). Over the past three years, the standard deviation of Kacrylic factors measured on all machines decreased by 20% for photons and high energy electrons as systematic errors were found and reduced. Low energy electrons showed very little change in the distribution of Kacrylic values. Small errors in linac beam data were found by investigating outlier Kacrylic values. Conclusion: While the use of acrylic phantoms introduces an additional source of error through small differences in depth and effective depth, the new standardized procedure eliminates potential sources of error from using many different phantoms and results in more consistent output constancy measurements.« less

Radiation tolerant combinational logic cell

NASA Technical Reports Server (NTRS)

Maki, Gary R. (Inventor); Whitaker, Sterling (Inventor); Gambles, Jody W. (Inventor)

2009-01-01

A system has a reduced sensitivity to Single Event Upset and/or Single Event Transient(s) compared to traditional logic devices. In a particular embodiment, the system includes an input, a logic block, a bias stage, a state machine, and an output. The logic block is coupled to the input. The logic block is for implementing a logic function, receiving a data set via the input, and generating a result f by applying the data set to the logic function. The bias stage is coupled to the logic block. The bias stage is for receiving the result from the logic block and presenting it to the state machine. The state machine is coupled to the bias stage. The state machine is for receiving, via the bias stage, the result generated by the logic block. The state machine is configured to retain a state value for the system. The state value is typically based on the result generated by the logic block. The output is coupled to the state machine. The output is for providing the value stored by the state machine. Some embodiments of the invention produce dual rail outputs Q and Q'. The logic block typically contains combinational logic and is similar, in size and transistor configuration, to a conventional CMOS combinational logic design. However, only a very small portion of the circuits of these embodiments, is sensitive to Single Event Upset and/or Single Event Transients.

Machine learning of frustrated classical spin models. I. Principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

User's manual for tooth contact analysis of face-milled spiral bevel gears with given machine-tool settings

NASA Technical Reports Server (NTRS)

Litvin, Faydor L.; Zhang, YI; Chen, Jui-Sheng

1991-01-01

Research was performed to develop a computer program that will: (1) simulate the meshing and bearing contact for face milled spiral beval gears with given machine tool settings; and (2) to obtain the output, some of the data is required for hydrodynamic analysis. It is assumed that the machine tool settings and the blank data will be taken from the Gleason summaries. The theoretical aspects of the program are based on 'Local Synthesis and Tooth Contact Analysis of Face Mill Milled Spiral Bevel Gears'. The difference between the computer programs developed herein and the other one is as follows: (1) the mean contact point of tooth surfaces for gears with given machine tool settings must be determined iteratively, while parameters (H and V) are changed (H represents displacement along the pinion axis, V represents the gear displacement that is perpendicular to the plane drawn through the axes of the pinion and the gear of their initial positions), this means that when V differs from zero, the axis of the pionion and the gear are crossed but not intersected; (2) in addition to the regular output data (transmission errors and bearing contact), the new computer program provides information about the contacting force for each contact point and the sliding and the so-called rolling velocity. The following topics are covered: (1) instructions for the users as to how to insert the input data; (2) explanations regarding the output data; (3) numerical example; and (4) listing of the program.

Augmentation of machine structure to improve its diagnosability

NASA Technical Reports Server (NTRS)

Hsieh, L.

1973-01-01

Two methods of augmenting the structure of a sequential machine so that it is diagnosable are presented. The checkable (checking sequences) and repeated symbol distinguishing sequences (RDS) are discussed. It was found that as few as twice the number of outputs of the given machine is sufficient for constructing a state-output augmentation with RDS. Techniques for minimizing the number of states in resolving convergences and in resolving equivalent and nonreduced cycles are developed.

Application of support vector machines for copper potential mapping in Kerman region, Iran

NASA Astrophysics Data System (ADS)

Shabankareh, Mahdi; Hezarkhani, Ardeshir

2017-04-01

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.

Heat engine generator control system

DOEpatents

Rajashekara, K.; Gorti, B.V.; McMullen, S.R.; Raibert, R.J.

1998-05-12

An electrical power generation system includes a heat engine having an output member operatively coupled to the rotor of a dynamoelectric machine. System output power is controlled by varying an electrical parameter of the dynamoelectric machine. A power request signal is related to an engine speed and the electrical parameter is varied in accordance with a speed control loop. Initially, the sense of change in the electrical parameter in response to a change in the power request signal is opposite that required to effectuate a steady state output power consistent with the power request signal. Thereafter, the electrical parameter is varied to converge the output member speed to the speed known to be associated with the desired electrical output power. 8 figs.

Heat engine generator control system

DOEpatents

Rajashekara, Kaushik; Gorti, Bhanuprasad Venkata; McMullen, Steven Robert; Raibert, Robert Joseph

1998-01-01

An electrical power generation system includes a heat engine having an output member operatively coupled to the rotor of a dynamoelectric machine. System output power is controlled by varying an electrical parameter of the dynamoelectric machine. A power request signal is related to an engine speed and the electrical parameter is varied in accordance with a speed control loop. Initially, the sense of change in the electrical parameter in response to a change in the power request signal is opposite that required to effectuate a steady state output power consistent with the power request signal. Thereafter, the electrical parameter is varied to converge the output member speed to the speed known to be associated with the desired electrical output power.

Human factors model concerning the man-machine interface of mining crewstations

NASA Technical Reports Server (NTRS)

Rider, James P.; Unger, Richard L.

1989-01-01

The U.S. Bureau of Mines is developing a computer model to analyze the human factors aspect of mining machine operator compartments. The model will be used as a research tool and as a design aid. It will have the capability to perform the following: simulated anthropometric or reach assessment, visibility analysis, illumination analysis, structural analysis of the protective canopy, operator fatigue analysis, and computation of an ingress-egress rating. The model will make extensive use of graphics to simplify data input and output. Two dimensional orthographic projections of the machine and its operator compartment are digitized and the data rebuilt into a three dimensional representation of the mining machine. Anthropometric data from either an individual or any size population may be used. The model is intended for use by equipment manufacturers and mining companies during initial design work on new machines. In addition to its use in machine design, the model should prove helpful as an accident investigation tool and for determining the effects of machine modifications made in the field on the critical areas of visibility and control reach ability.

Method for determining molten metal pool level in twin-belt continuous casting machines

DOEpatents

Kaiser, Timothy D.; Daniel, Sabah S.; Dykes, Charles D.

1989-03-21

A method for determining level of molten metal in the input of a continuous metal casting machine having at least one endless, flexible, revolving casting belt with a surface which engages the molten metal to be cast and a reverse, cooled surface along which is directed high velocity liquid coolant includes the steps of predetermining the desired range of positions of the molten metal pool and positioning at least seven heat-sensing transducers in bearing contact with the moving reverse belt surface and spaced in upstream-downstream relationship relative to belt travel spanning the desired pool levels. A predetermined temperature threshold is set, somewhat above coolant temperature and the output signals of the transducer sensors are scanned regarding their output signals indicative of temperatures of the moving reverse belt surface. Position of the molten pool is determined using temperature interpolation between any successive pair of upstream-downstream spaced sensors, which follows confirmation that two succeeding downstream sensors are at temperature levels exceeding threshold temperature. The method accordingly provides high resolution for determining pool position, and verifies the determined position by utilizing full-strength signals from two succeeding downstream sensors. In addition, dual sensors are used at each position spanning the desired range of molten metal pool levels to provide redundancy, wherein only the higher temperature of each pair of sensors at a station is utilized.

Agreement Between Institutional Measurements and Treatment Planning System Calculations for Basic Dosimetric Parameters as Measured by the Imaging and Radiation Oncology Core-Houston

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

Kerns, James R.; Followill, David S.; Imaging and Radiation Oncology Core-Houston, The University of Texas Health Science Center-Houston, Houston, Texas

Purpose: To compare radiation machine measurement data collected by the Imaging and Radiation Oncology Core at Houston (IROC-H) with institutional treatment planning system (TPS) values, to identify parameters with large differences in agreement; the findings will help institutions focus their efforts to improve the accuracy of their TPS models. Methods and Materials: Between 2000 and 2014, IROC-H visited more than 250 institutions and conducted independent measurements of machine dosimetric data points, including percentage depth dose, output factors, off-axis factors, multileaf collimator small fields, and wedge data. We compared these data with the institutional TPS values for the same points bymore » energy, class, and parameter to identify differences and similarities using criteria involving both the medians and standard deviations for Varian linear accelerators. Distributions of differences between machine measurements and institutional TPS values were generated for basic dosimetric parameters. Results: On average, intensity modulated radiation therapy–style and stereotactic body radiation therapy–style output factors and upper physical wedge output factors were the most problematic. Percentage depth dose, jaw output factors, and enhanced dynamic wedge output factors agreed best between the IROC-H measurements and the TPS values. Although small differences were shown between 2 common TPS systems, neither was superior to the other. Parameter agreement was constant over time from 2000 to 2014. Conclusions: Differences in basic dosimetric parameters between machine measurements and TPS values vary widely depending on the parameter, although agreement does not seem to vary by TPS and has not changed over time. Intensity modulated radiation therapy–style output factors, stereotactic body radiation therapy–style output factors, and upper physical wedge output factors had the largest disagreement and should be carefully modeled to ensure accuracy.« less

Methods, systems and apparatus for controlling third harmonic voltage when operating a multi-space machine in an overmodulation region

DOEpatents

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

2014-06-03

Methods, system and apparatus are provided for controlling third harmonic voltages when operating a multi-phase machine in an overmodulation region. The multi-phase machine can be, for example, a five-phase machine in a vector controlled motor drive system that includes a five-phase PWM controlled inverter module that drives the five-phase machine. Techniques for overmodulating a reference voltage vector are provided. For example, when the reference voltage vector is determined to be within the overmodulation region, an angle of the reference voltage vector can be modified to generate a reference voltage overmodulation control angle, and a magnitude of the reference voltage vector can be modified, based on the reference voltage overmodulation control angle, to generate a modified magnitude of the reference voltage vector. By modifying the reference voltage vector, voltage command signals that control a five-phase inverter module can be optimized to increase output voltages generated by the five-phase inverter module.

Scalable Low-Power Deep Machine Learning with Analog Computation

DTIC Science & Technology

2013-07-19

transimpedance amplifier (TIA) that measures the output current 7 V Cf Vbias MP1 MN1 Vdd = 3 V 2.5 V 2.6 V + − Vox = 4.4 V 0.1 V + − 7 V Cf Vbias MP1 MN1 Vddt... amplifier . The amplifier has Cf as its feedback capacitor and the FG voltage Vfg as its input. The two MUXs at the sources of MP1 and MP2 control the...as a simple operational transconductor amplifier (OTA), converts voltage Vout to output current Iout. Vref determines the nominal voltage of Vout

SU-F-T-485: Independent Remote Audits for TG51 NonCompliant Photon Beams Performed by the IROC Houston QA Center

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

Alvarez, P; Molineu, A; Lowenstein, J

Purpose: IROC-H conducts external audits for output check verification of photon and electron beams. Many of these beams can meet the geometric requirements of the TG 51 calibration protocol. For those photon beams that are non TG 51 compliant like Elekta GammaKnife, Accuray CyberKnife and TomoTherapy, IROC-H has specific audit tools to monitor the reference calibration. Methods: IROC-H used its TLD and OSLD remote monitoring systems to verify the output of machines with TG 51 non compliant beams. Acrylic OSLD miniphantoms are used for the CyberKnife. Special TLD phantoms are used for TomoTherapy and GammaKnife machines to accommodate the specificmore » geometry of each machine. These remote audit tools are sent to institutions to be irradiated and returned to IROC-H for analysis. Results: The average IROC-H/institution ratios for 480 GammaKnife, 660 CyberKnife and 907 rotational TomoTherapy beams are 1.000±0.021, 1.008±0.019, 0.974±0.023, respectively. In the particular case of TomoTherapy, the overall ratio is 0.977±0.022 for HD units. The standard deviations of all results are consistent with values determined for TG 51 compliant photon beams. These ratios have shown some changes compared to values presented in 2008. The GammaKnife results were corrected by an experimentally determined scatter factor of 1.025 in 2013. The TomoTherapy helical beam results are now from a rotational beam whereas in 2008 the results were from a static beam. The decision to change modality was based on recommendations from the users. Conclusion: External audits of beam outputs is a valuable tool to confirm the calibrations of photon beams regardless of whether the machine is TG 51 or TG 51 non compliant. The difference found for TomoTherapy units is under investigation. This investigation was supported by IROC grant CA180803 awarded by the NCI.« less

Oil Formation Volume Factor Determination Through a Fused Intelligence

NASA Astrophysics Data System (ADS)

Gholami, Amin

2016-12-01

Volume change of oil between reservoir condition and standard surface condition is called oil formation volume factor (FVF), which is very time, cost and labor intensive to determine. This study proposes an accurate, rapid and cost-effective approach for determining FVF from reservoir temperature, dissolved gas oil ratio, and specific gravity of both oil and dissolved gas. Firstly, structural risk minimization (SRM) principle of support vector regression (SVR) was employed to construct a robust model for estimating FVF from the aforementioned inputs. Subsequently, an alternating conditional expectation (ACE) was used for approximating optimal transformations of input/output data to a higher correlated data and consequently developing a sophisticated model between transformed data. Eventually, a committee machine with SVR and ACE was constructed through the use of hybrid genetic algorithm-pattern search (GA-PS). Committee machine integrates ACE and SVR models in an optimal linear combination such that makes benefit of both methods. A group of 342 data points was used for model development and a group of 219 data points was used for blind testing the constructed model. Results indicated that the committee machine performed better than individual models.

Data analysis on physical and mechanical properties of cassava pellets.

PubMed

Oguntunde, Pelumi E; Adejumo, Oluyemisi A; Odetunmibi, Oluwole A; Okagbue, Hilary I; Adejumo, Adebowale O

2018-02-01

In this data article, laboratory experimental investigation results carried out at National Centre for Agricultural Mechanization (NCAM) on moisture content, machine speed, die diameter of the rig, and the outputs (hardness, durability, bulk density, and unit density of the pellets) at different levels of cassava pellets were observed. Analysis of variance using randomized complete block design with factorial was used to perform analysis for each of the outputs: hardness, durability, bulk density, and unit density of the pellets. A clear description on each of these outputs was considered separately using tables and figures. It was observed that for all the output with the exception of unit density, their main factor effects as well as two and three ways interactions is significant at 5% level. This means that the hardness, bulk density and durability of cassava pellets respectively depend on the moisture content of the cassava dough, the machine speed, the die diameter of the extrusion rig and the combinations of these factors in pairs as well as the three altogether. Higher machine speeds produced more quality pellets at lower die diameters while lower machine speed is recommended for higher die diameter. Also the unit density depends on die diameter and the three-way interaction only. Unit density of cassava pellets is neither affected by machine parameters nor moisture content of the cassava dough. Moisture content of cassava dough, speed of the machine and die diameter of the extrusion rig are significant factors to be considered in pelletizing cassava to produce pellets. Increase in moisture content of cassava dough increase the quality of cassava pellets.

Complex pendulum biomass sensor

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

Hoskinson, Reed L.; Kenney, Kevin L.; Perrenoud, Ben C.

A complex pendulum system biomass sensor having a plurality of pendulums. The plurality of pendulums allow the system to detect a biomass height and density. Each pendulum has an angular deflection sensor and a deflector at a unique height. The pendulums are passed through the biomass and readings from the angular deflection sensors are fed into a control system. The control system determines whether adjustment of machine settings is appropriate and either displays an output to the operator, or adjusts automatically adjusts the machine settings, such as the speed, at which the pendulums are passed through the biomass. In anmore » alternate embodiment, an entanglement sensor is also passed through the biomass to determine the amount of biomass entanglement. This measure of entanglement is also fed into the control system.« less

Technological and economical analysis of salient pole and permanent magnet synchronous machines designed for wind turbines

NASA Astrophysics Data System (ADS)

Gündoğdu, Tayfun; Kömürgöz, Güven

2012-08-01

Chinese export restrictions already reduced the planning reliability for investments in permanent magnet wind turbines. Today the production of permanent magnets consumes the largest proportion of rare earth elements, with 40% of the rare earth-based magnets used for generators and other electrical machines. The cost and availability of NdFeB magnets will likely determine the production rate of permanent magnet generators. The high volatility of rare earth metals makes it very difficult to quote a price. Prices may also vary from supplier to supplier to an extent of up to 50% for the same size, shape and quantity with a minor difference in quality. The paper presents the analysis and the comparison of salient pole with field winding and of peripheral winding synchronous electrical machines, presenting important advantages. A neodymium alloy magnet rotor structure has been considered and compared to the salient rotor case. The Salient Pole Synchronous Machine and the Permanent Magnet Synchronous Machine were designed so that the plate values remain constant. The Eddy current effect on the windings is taken into account during the design, and the efficiency, output power and the air-gap flux density obtained after the simulation were compared. The analysis results clearly indicate that Salient Pole Synchronous Machine designs would be attractive to wind power companies. Furthermore, the importance of the design of electrical machines and the determination of criteria are emphasized. This paper will be a helpful resource in terms of examination and comparison of the basic structure and magnetic features of the Salient Pole Synchronous Machine and Permanent Magnet Synchronous Machine. Furthermore, an economic analysis of the designed machines was conducted.

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2014 CFR

2014-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2012 CFR

2012-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2013 CFR

2013-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

On the need for quality assurance in superficial kilovoltage radiotherapy.

PubMed

Austerlitz, C; Mota, H; Gay, H; Campos, D; Allison, R; Sibata, C

2008-01-01

External auditing of beam output and energy qualities of four therapeutic X-ray machines were performed in three radiation oncology centres in northeastern Brazil. The output and half-value layers (HVLs) were determined using a parallel-plate ionisation chamber and high-purity aluminium foils, respectively. The obtained values of absorbed dose to water and energy qualities were compared with those obtained by the respective institutions. The impact on the prescribed dose was analysed by determining the half-value depth (D(1/2)). The beam outputs presented percent differences ranging from -13 to +25%. The ratio between the HVL in use by the institution and the measurements obtained in this study ranged from 0.75 to 2.33. Such deviations in HVL result in percent differences in dose at D(1/2) ranging from -52 to +8%. It was concluded that dosimetric quality audit programmes in radiation therapy should be expanded to include dermatological radiation therapy and such audits should include HVL verification.

STABCAR: A program for finding characteristic root systems having transcendental stability matrices

NASA Technical Reports Server (NTRS)

Adams, W. M., Jr.; Tiffany, S. H.; Newsom, J. R.; Peele, E. L.

1984-01-01

STABCAR can be used to determine the characteristic roots of flexible, actively controlled aircraft, including the effects of unsteady aerodynamics. A modal formulation and a transfer-matrix representation of the control system are employed. Operable in either a batch or an interactive mode, STABCAR can provide graphical or tabular output of the variation of the roots with velocity, density, altitude, dynamic pressure or feedback gains. Herein the mathematical model, program structure, input requirements, output capabilities, and a series of sample cases are detailed. STABCAR was written for use on CDC CYBER 175 equipment; modification would be required for operation on other machines.

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

Vanchurin, Vitaly, E-mail: vvanchur@d.umn.edu

We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or Gödel number) as input and output the corresponding probability. Similarly,more » CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps.« less

Cosmic logic: a computational model

NASA Astrophysics Data System (ADS)

Vanchurin, Vitaly

2016-02-01

We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or Gödel number) as input and output the corresponding probability. Similarly, CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Lu, Zhixin; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2017-12-01

We use recent advances in the machine learning area known as "reservoir computing" to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measurements as input to a high-dimensional dynamical system called a "reservoir." After the reservoir's response to the data is recorded, linear regression is used to learn a large set of parameters, called the "output weights." The learned output weights are then used to form a modified autonomous reservoir designed to be capable of producing an arbitrarily long time series whose ergodic properties approximate those of the input signal. When successful, we say that the autonomous reservoir reproduces the attractor's "climate." Since the reservoir equations and output weights are known, we can compute the derivatives needed to determine the Lyapunov exponents of the autonomous reservoir, which we then use as estimates of the Lyapunov exponents for the original input generating system. We illustrate the effectiveness of our technique with two examples, the Lorenz system and the Kuramoto-Sivashinsky (KS) equation. In the case of the KS equation, we note that the high dimensional nature of the system and the large number of Lyapunov exponents yield a challenging test of our method, which we find the method successfully passes.

Determining A Purely Symbolic Transfer Function from Symbol Streams: Theory and Algorithms

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

Griffin, Christopher H

Transfer function modeling is a \\emph{standard technique} in classical Linear Time Invariant and Statistical Process Control. The work of Box and Jenkins was seminal in developing methods for identifying parameters associated with classicalmore » $(r,s,k)$$ transfer functions. Discrete event systems are often \\emph{used} for modeling hybrid control structures and high-level decision problems. \\emph{Examples include} discrete time, discrete strategy repeated games. For these games, a \\emph{discrete transfer function in the form of} an accurate hidden Markov model of input-output relations \\emph{could be used to derive optimal response strategies.} In this paper, we develop an algorithm \\emph{for} creating probabilistic \\textit{Mealy machines} that act as transfer function models for discrete event dynamic systems (DEDS). Our models are defined by three parameters, $$(l_1, l_2, k)$ just as the Box-Jenkins transfer function models. Here $$l_1$$ is the maximal input history lengths to consider, $$l_2$$ is the maximal output history lengths to consider and $k$ is the response lag. Using related results, We show that our Mealy machine transfer functions are optimal in the sense that they maximize the mutual information between the current known state of the DEDS and the next observed input/output pair.« less

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

What makes an automated teller machine usable by blind users?

PubMed

Manzke, J M; Egan, D H; Felix, D; Krueger, H

1998-07-01

Fifteen blind and sighted subjects, who featured as a control group for acceptance, were asked for their requirements for automated teller machines (ATMs). Both groups also tested the usability of a partially operational ATM mock-up. This machine was based on an existing cash dispenser, providing natural speech output, different function menus and different key arrangements. Performance and subjective evaluation data of blind and sighted subjects were collected. All blind subjects were able to operate the ATM successfully. The implemented speech output was the main usability factor for them. The different interface designs did not significantly affect performance and subjective evaluation. Nevertheless, design recommendations can be derived from the requirement assessment. The sighted subjects were rather open for design modifications, especially the implementation of speech output. However, there was also a mismatch of the requirements of the two subject groups, mainly concerning the key arrangement.

Statistical machine translation for biomedical text: are we there yet?

PubMed

Wu, Cuijun; Xia, Fei; Deleger, Louise; Solti, Imre

2011-01-01

In our paper we addressed the research question: "Has machine translation achieved sufficiently high quality to translate PubMed titles for patients?". We analyzed statistical machine translation output for six foreign language - English translation pairs (bi-directionally). We built a high performing in-house system and evaluated its output for each translation pair on large scale both with automated BLEU scores and human judgment. In addition to the in-house system, we also evaluated Google Translate's performance specifically within the biomedical domain. We report high performance for German, French and Spanish -- English bi-directional translation pairs for both Google Translate and our system.

A study on stimulation of DC high voltage power of LCC series parallel resonant in projectile velocity measurement system

NASA Astrophysics Data System (ADS)

Lu, Dong-dong; Gu, Jin-liang; Luo, Hong-e.; Xia, Yan

2017-10-01

According to specific requirements of the X-ray machine system for measuring velocity of outfield projectile, a DC high voltage power supply system is designed for the high voltage or the smaller current. The system comprises: a series resonant circuit is selected as a full-bridge inverter circuit; a high-frequency zero-current soft switching of a high-voltage power supply is realized by PWM output by STM32; a nanocrystalline alloy transformer is chosen as a high-frequency booster transformer; and the related parameters of an LCC series-parallel resonant are determined according to the preset parameters of the transformer. The concrete method includes: a LCC series parallel resonant circuit and a voltage doubling circuit are stimulated by using MULTISM and MATLAB; selecting an optimal solution and an optimal parameter of all parts after stimulation analysis; and finally verifying the correctness of the parameter by stimulation of the whole system. Through stimulation analysis, the output voltage of the series-parallel resonant circuit gets to 10KV in 28s: then passing through the voltage doubling circuit, the output voltage gets to 120KV in one hour. According to the system, the wave range of the output voltage is so small as to provide the stable X-ray supply for the X-ray machine for measuring velocity of outfield projectile. It is fast in charging and high in efficiency.

Investigating output and energy variations and their relationship to delivery QA results using Statistical Process Control for helical tomotherapy.

PubMed

Binny, Diana; Mezzenga, Emilio; Lancaster, Craig M; Trapp, Jamie V; Kairn, Tanya; Crowe, Scott B

2017-06-01

The aims of this study were to investigate machine beam parameters using the TomoTherapy quality assurance (TQA) tool, establish a correlation to patient delivery quality assurance results and to evaluate the relationship between energy variations detected using different TQA modules. TQA daily measurement results from two treatment machines for periods of up to 4years were acquired. Analyses of beam quality, helical and static output variations were made. Variations from planned dose were also analysed using Statistical Process Control (SPC) technique and their relationship to output trends were studied. Energy variations appeared to be one of the contributing factors to delivery output dose seen in the analysis. Ion chamber measurements were reliable indicators of energy and output variations and were linear with patient dose verifications. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Self-Calibrating Surface Measuring Machine

NASA Astrophysics Data System (ADS)

Greenleaf, Allen H.

1983-04-01

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

“Investigations on the machinability of Waspaloy under dry environment”

NASA Astrophysics Data System (ADS)

Deepu, J.; Kuppan, P.; SBalan, A. S.; Oyyaravelu, R.

2016-09-01

Nickel based superalloy, Waspaloy is extensively used in gas turbine, aerospace and automobile industries because of their unique combination of properties like high strength at elevated temperatures, resistance to chemical degradation and excellent wear resistance in many hostile environments. It is considered as one of the difficult to machine superalloy due to excessive tool wear and poor surface finish. The present paper is an attempt for removing cutting fluids from turning process of Waspaloy and to make the processes environmentally safe. For this purpose, the effect of machining parameters such as cutting speed and feed rate on the cutting force, cutting temperature, surface finish and tool wear were investigated barrier. Consequently, the strength and tool wear resistance and tool life increased significantly. Response Surface Methodology (RSM) has been used for developing and analyzing a mathematical model which describes the relationship between machining parameters and output variables. Subsequently ANOVA was used to check the adequacy of the regression model as well as each machining variables. The optimal cutting parameters were determined based on multi-response optimizations by composite desirability approach in order to minimize cutting force, average surface roughness and maximum flank wear. The results obtained from the experiments shown that machining of Waspaloy using coated carbide tool with special ranges of parameters, cutting fluid could be completely removed from machining process

Electric converters of electromagnetic strike machine with battery power

NASA Astrophysics Data System (ADS)

Usanov, K. M.; Volgin, A. V.; Kargin, V. A.; Moiseev, A. P.; Chetverikov, E. A.

2018-03-01

At present, the application of pulse linear electromagnetic engines to drive strike machines for immersion of rod elements into the soil, strike drilling of shallow wells, dynamic probing of soils is recognized as quite effective. The pulse linear electromagnetic engine performs discrete consumption and conversion of electrical energy into mechanical work. Pulse dosing of a stream transmitted by the battery source to the pulse linear electromagnetic engine of the energy is provided by the electrical converter. The electric converters with the control of an electromagnetic strike machine as functions of time and armature movement, which form the unipolar supply pulses of voltage and current necessary for the normal operation of a pulse linear electromagnetic engine, are proposed. Electric converters are stable in operation, implement the necessary range of output parameters control determined by the technological process conditions, have noise immunity and automatic disconnection of power supply in emergency modes.

Machine Learning Classification of Heterogeneous Fields to Estimate Physical Responses

NASA Astrophysics Data System (ADS)

McKenna, S. A.; Akhriev, A.; Alzate, C.; Zhuk, S.

2017-12-01

The promise of machine learning to enhance physics-based simulation is examined here using the transient pressure response to a pumping well in a heterogeneous aquifer. 10,000 random fields of log10 hydraulic conductivity (K) are created and conditioned on a single K measurement at the pumping well. Each K-field is used as input to a forward simulation of drawdown (pressure decline). The differential equations governing groundwater flow to the well serve as a non-linear transform of the input K-field to an output drawdown field. The results are stored and the data set is split into training and testing sets for classification. A Euclidean distance measure between any two fields is calculated and the resulting distances between all pairs of fields define a similarity matrix. Similarity matrices are calculated for both input K-fields and the resulting drawdown fields at the end of the simulation. The similarity matrices are then used as input to spectral clustering to determine groupings of similar input and output fields. Additionally, the similarity matrix is used as input to multi-dimensional scaling to visualize the clustering of fields in lower dimensional spaces. We examine the ability to cluster both input K-fields and output drawdown fields separately with the goal of identifying K-fields that create similar drawdowns and, conversely, given a set of simulated drawdown fields, identify meaningful clusters of input K-fields. Feature extraction based on statistical parametric mapping provides insight into what features of the fields drive the classification results. The final goal is to successfully classify input K-fields into the correct output class, and also, given an output drawdown field, be able to infer the correct class of input field that created it.

Combining machine learning and ontological data handling for multi-source classification of nature conservation areas

NASA Astrophysics Data System (ADS)

Moran, Niklas; Nieland, Simon; Tintrup gen. Suntrup, Gregor; Kleinschmit, Birgit

2017-02-01

Manual field surveys for nature conservation management are expensive and time-consuming and could be supplemented and streamlined by using Remote Sensing (RS). RS is critical to meet requirements of existing laws such as the EU Habitats Directive (HabDir) and more importantly to meet future challenges. The full potential of RS has yet to be harnessed as different nomenclatures and procedures hinder interoperability, comparison and provenance. Therefore, automated tools are needed to use RS data to produce comparable, empirical data outputs that lend themselves to data discovery and provenance. These issues are addressed by a novel, semi-automatic ontology-based classification method that uses machine learning algorithms and Web Ontology Language (OWL) ontologies that yields traceable, interoperable and observation-based classification outputs. The method was tested on European Union Nature Information System (EUNIS) grasslands in Rheinland-Palatinate, Germany. The developed methodology is a first step in developing observation-based ontologies in the field of nature conservation. The tests show promising results for the determination of the grassland indicators wetness and alkalinity with an overall accuracy of 85% for alkalinity and 76% for wetness.

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

Bartolac, S; Letourneau, D; University of Toronto, Toronto, Ontario

Purpose: Application of process control theory in quality assurance programs promises to allow earlier identification of problems and potentially better quality in delivery than traditional paradigms based primarily on tolerances and action levels. The purpose of this project was to characterize underlying seasonal variations in linear accelerator output that can be used to improve performance or trigger preemptive maintenance. Methods: Review of runtime plots of daily (6 MV) output data acquired using in house ion chamber based devices over three years and for fifteen linear accelerators of varying make and model were evaluated. Shifts in output due to known interventionsmore » with the machines were subtracted from the data to model an uncorrected scenario for each linear accelerator. Observable linear trends were also removed from the data prior to evaluation of periodic variations. Results: Runtime plots of output revealed sinusoidal, seasonal variations that were consistent across all units, irrespective of manufacturer, model or age of machine. The average amplitude of the variation was on the order of 1%. Peak and minimum variations were found to correspond to early April and September, respectively. Approximately 48% of output adjustments made over the period examined were potentially avoidable if baseline levels had corresponded to the mean output, rather than to points near a peak or valley. Linear trends were observed for three of the fifteen units, with annual increases in output ranging from 2–3%. Conclusion: Characterization of cyclical seasonal trends allows for better separation of potentially innate accelerator behaviour from other behaviours (e.g. linear trends) that may be better described as true out of control states (i.e. non-stochastic deviations from otherwise expected behavior) and could indicate service requirements. Results also pointed to an optimal setpoint for accelerators such that output of machines is maintained within set tolerances and interventions are required less frequently.« less

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms.

PubMed

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.

Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

NASA Astrophysics Data System (ADS)

Fachrurrozi, Muhammad; Saparudin; Erwin

2017-04-01

Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.

An interval programming model for continuous improvement in micro-manufacturing

NASA Astrophysics Data System (ADS)

Ouyang, Linhan; Ma, Yizhong; Wang, Jianjun; Tu, Yiliu; Byun, Jai-Hyun

2018-03-01

Continuous quality improvement in micro-manufacturing processes relies on optimization strategies that relate an output performance to a set of machining parameters. However, when determining the optimal machining parameters in a micro-manufacturing process, the economics of continuous quality improvement and decision makers' preference information are typically neglected. This article proposes an economic continuous improvement strategy based on an interval programming model. The proposed strategy differs from previous studies in two ways. First, an interval programming model is proposed to measure the quality level, where decision makers' preference information is considered in order to determine the weight of location and dispersion effects. Second, the proposed strategy is a more flexible approach since it considers the trade-off between the quality level and the associated costs, and leaves engineers a larger decision space through adjusting the quality level. The proposed strategy is compared with its conventional counterparts using an Nd:YLF laser beam micro-drilling process.

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

DOEpatents

Gallegos-Lopez, Gabriel

2012-10-02

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

Exploring the potential of machine learning to break deadlock in convection parameterization

NASA Astrophysics Data System (ADS)

Pritchard, M. S.; Gentine, P.

2017-12-01

We explore the potential of modern machine learning tools (via TensorFlow) to replace parameterization of deep convection in climate models. Our strategy begins by generating a large ( 1 Tb) training dataset from time-step level (30-min) output harvested from a one-year integration of a zonally symmetric, uniform-SST aquaplanet integration of the SuperParameterized Community Atmosphere Model (SPCAM). We harvest the inputs and outputs connecting each of SPCAM's 8,192 embedded cloud-resolving model (CRM) arrays to its host climate model's arterial thermodynamic state variables to afford 143M independent training instances. We demonstrate that this dataset is sufficiently large to induce preliminary convergence for neural network prediction of desired outputs of SP, i.e. CRM-mean convective heating and moistening profiles. Sensitivity of the machine learning convergence to the nuances of the TensorFlow implementation are discussed, as well as results from pilot tests from the neural network operating inline within the SPCAM as a replacement to the (super)parameterization of convection.

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

NASA Astrophysics Data System (ADS)

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

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

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

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

Improved transistor-controlled and commutated brushless DC motors for electric vehicle propulsion

NASA Technical Reports Server (NTRS)

Demerdash, N. A.; Miller, R. H.; Nehl, T. W.; Nyamusa, T. A.

1983-01-01

The development, design, construction, and testing processes of two electronically (transistor) controlled and commutated permanent magnet brushless dc machine systems, for propulsion of electric vehicles are detailed. One machine system was designed and constructed using samarium cobalt for permanent magnets, which supply the rotor (field) excitation. Meanwhile, the other machine system was designed and constructed with strontium ferrite permanent magnets as the source of rotor (field) excitation. These machine systems were designed for continuous rated power output of 15 hp (11.2 kw), and a peak one minute rated power output of 35 hp (26.1 kw). Both power ratings are for a rated voltage of 115 volts dc, assuming a voltage drop in the source (battery) of about 5 volts. That is, an internal source voltage of 120 volts dc. Machine-power conditioner system computer-aided simulations were used extensively in the design process. These simulations relied heavily on the magnetic field analysis in these machines using the method of finite elements, as well as methods of modeling of the machine power conditioner system dynamic interaction. These simulation processes are detailed. Testing revealed that typical machine system efficiencies at 15 hp (11.2 kw) were about 88% and 84% for the samarium cobalt and strontium ferrite based machine systems, respectively. Both systems met the peak one minute rating of 35 hp.

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

X-ray power and yield measurements at the refurbished Z machine

DOE PAGES

Jones, M. C.; Ampleford, D. J.; Cuneo, M. E.; ...

2014-08-04

Advancements have been made in the diagnostic techniques to measure accurately the total radiated x-ray yield and power from z-pinch loads at the Z Machine with high accuracy. The Z-accelerator is capable of outputting 2MJ and 330 TW of x-ray yield and power, and accurately measuring these quantities is imperative. We will describe work over the past several years which include the development of new diagnostics, improvements to existing diagnostics, and implementation of automated data analysis routines. A set of experiments were conducted on the Z machine where the load and machine configuration were held constant. During this shot series,more » it was observed that total z-pinch x-ray emission power determined from the two common techniques for inferring the x-ray power, Kimfol filtered x-ray diode diagnostic and the Total Power and Energy diagnostic gave 450 TW and 327 TW respectively. Our analysis shows the latter to be the more accurate interpretation. More broadly, the comparison demonstrates the necessity to consider spectral response and field of view when inferring xray powers from z-pinch sources.« less

SABRE, a 10-MV linear induction accelerator

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

Corely, J.P.; Alexander, J.A.; Pankuch, P.J.

SABRE (Sandia Accelerator and Beam Research Experiment) is a 10-MV, 250-kA, 40-ns linear induction accelerator. It was designed to be used in positive polarity output. Positive polarity accelerators are important for application to Sandia's ICF (Inertial Confinement Fusion) and LMF (Laboratory Microfusion Facility) program efforts. SABRE was built to allow a more detailed study of pulsed power issues associated with positive polarity output machines. MITL (Magnetically Insulated Transmission Line) voltage adder efficiency, extraction ion diode development, and ion beam transport and focusing. The SABRE design allows the system to operate in either positive polarity output for ion extraction applications ormore » negative polarity output for more conventional electron beam loads. Details of the design of SABRE and the results of initial machine performance in negative polarity operation are presented in this paper. 13 refs., 12 figs., 1 tab.« less

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

NASA Astrophysics Data System (ADS)

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

2010-08-01

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

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

PubMed Central

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel. PMID:24795875

TU-G-BRD-08: In-Vivo EPID Dosimetry: Quantifying the Detectability of Four Classes of Errors

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

Ford, E; Phillips, M; Bojechko, C

Purpose: EPID dosimetry is an emerging method for treatment verification and QA. Given that the in-vivo EPID technique is in clinical use at some centers, we investigate the sensitivity and specificity for detecting different classes of errors. We assess the impact of these errors using dose volume histogram endpoints. Though data exist for EPID dosimetry performed pre-treatment, this is the first study quantifying its effectiveness when used during patient treatment (in-vivo). Methods: We analyzed 17 patients; EPID images of the exit dose were acquired and used to reconstruct the planar dose at isocenter. This dose was compared to the TPSmore » dose using a 3%/3mm gamma criteria. To simulate errors, modifications were made to treatment plans using four possible classes of error: 1) patient misalignment, 2) changes in patient body habitus, 3) machine output changes and 4) MLC misalignments. Each error was applied with varying magnitudes. To assess the detectability of the error, the area under a ROC curve (AUC) was analyzed. The AUC was compared to changes in D99 of the PTV introduced by the simulated error. Results: For systematic changes in the MLC leaves, changes in the machine output and patient habitus, the AUC varied from 0.78–0.97 scaling with the magnitude of the error. The optimal gamma threshold as determined by the ROC curve varied between 84–92%. There was little diagnostic power in detecting random MLC leaf errors and patient shifts (AUC 0.52–0.74). Some errors with weak detectability had large changes in D99. Conclusion: These data demonstrate the ability of EPID-based in-vivo dosimetry in detecting variations in patient habitus and errors related to machine parameters such as systematic MLC misalignments and machine output changes. There was no correlation found between the detectability of the error using the gamma pass rate, ROC analysis and the impact on the dose volume histogram. Funded by grant R18HS022244 from AHRQ.« less

TH-B-BRC-00: How to Identify and Resolve Potential Clinical Errors Before They Impact Patients Treatment: Lessons Learned

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

NONE

2016-06-15

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews ofmore » some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.« less

TH-B-BRC-01: How to Identify and Resolve Potential Clinical Errors

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

Das, I.

2016-06-15

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews ofmore » some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.« less

A finite state machine read-out chip for integrated surface acoustic wave sensors

NASA Astrophysics Data System (ADS)

Rakshit, Sambarta; Iliadis, Agis A.

2015-01-01

A finite state machine based integrated sensor circuit suitable for the read-out module of a monolithically integrated SAW sensor on Si is reported. The primary sensor closed loop consists of a voltage controlled oscillator (VCO), a peak detecting comparator, a finite state machine (FSM), and a monolithically integrated SAW sensor device. The output of the system oscillates within a narrow voltage range that correlates with the SAW pass-band response. The period of oscillation is of the order of the SAW phase delay. We use timing information from the FSM to convert SAW phase delay to an on-chip 10 bit digital output operating on the principle of time to digital conversion (TDC). The control inputs of this digital conversion block are generated by a second finite state machine operating under a divided system clock. The average output varies with changes in SAW center frequency, thus tracking mass sensing events in real time. Based on measured VCO gain of 16 MHz/V our system will convert a 10 kHz SAW frequency shift to a corresponding mean voltage shift of 0.7 mV. A corresponding shift in phase delay is converted to a one or two bit shift in the TDC output code. The system can handle alternate SAW center frequencies and group delays simply by adjusting the VCO control and TDC delay control inputs. Because of frequency to voltage and phase to digital conversion, this topology does not require external frequency counter setups and is uniquely suitable for full monolithic integration of autonomous sensor systems and tags.

Versatile and Programmable DNA Logic Gates on Universal and Label-Free Homogeneous Electrochemical Platform.

PubMed

Ge, Lei; Wang, Wenxiao; Sun, Ximei; Hou, Ting; Li, Feng

2016-10-04

Herein, a novel universal and label-free homogeneous electrochemical platform is demonstrated, on which a complete set of DNA-based two-input Boolean logic gates (OR, NAND, AND, NOR, INHIBIT, IMPLICATION, XOR, and XNOR) is constructed by simply and rationally deploying the designed DNA polymerization/nicking machines without complicated sequence modulation. Single-stranded DNA is employed as the proof-of-concept target/input to initiate or prevent the DNA polymerization/nicking cyclic reactions on these DNA machines to synthesize numerous intact G-quadruplex sequences or binary G-quadruplex subunits as the output. The generated output strands then self-assemble into G-quadruplexes that render remarkable decrease to the diffusion current response of methylene blue and, thus, provide the amplified homogeneous electrochemical readout signal not only for the logic gate operations but also for the ultrasensitive detection of the target/input. This system represents the first example of homogeneous electrochemical logic operation. Importantly, the proposed homogeneous electrochemical logic gates possess the input/output homogeneity and share a constant output threshold value. Moreover, the modular design of DNA polymerization/nicking machines enables the adaptation of these homogeneous electrochemical logic gates to various input and output sequences. The results of this study demonstrate the versatility and universality of the label-free homogeneous electrochemical platform in the design of biomolecular logic gates and provide a potential platform for the further development of large-scale DNA-based biocomputing circuits and advanced biosensors for multiple molecular targets.

Robust Library Building for Autonomous Classification of Downhole Geophysical Logs Using Gaussian Processes

NASA Astrophysics Data System (ADS)

Silversides, Katherine L.; Melkumyan, Arman

2017-03-01

Machine learning techniques such as Gaussian Processes can be used to identify stratigraphically important features in geophysical logs. The marker shales in the banded iron formation hosted iron ore deposits of the Hamersley Ranges, Western Australia, form distinctive signatures in the natural gamma logs. The identification of these marker shales is important for stratigraphic identification of unit boundaries for the geological modelling of the deposit. Machine learning techniques each have different unique properties that will impact the results. For Gaussian Processes (GPs), the output values are inclined towards the mean value, particularly when there is not sufficient information in the library. The impact that these inclinations have on the classification can vary depending on the parameter values selected by the user. Therefore, when applying machine learning techniques, care must be taken to fit the technique to the problem correctly. This study focuses on optimising the settings and choices for training a GPs system to identify a specific marker shale. We show that the final results converge even when different, but equally valid starting libraries are used for the training. To analyse the impact on feature identification, GP models were trained so that the output was inclined towards a positive, neutral or negative output. For this type of classification, the best results were when the pull was towards a negative output. We also show that the GP output can be adjusted by using a standard deviation coefficient that changes the balance between certainty and accuracy in the results.

Adaptive vibration control using synchronous demodulation with machine tool controller motor commutation

DOEpatents

Hopkins, David James [Livermore, CA

2008-05-13

A control system and method for actively reducing vibration in a spindle housing caused by unbalance forces on a rotating spindle, by measuring the force-induced spindle-housing motion, determining control signals based on synchronous demodulation, and provide compensation for the measured displacement to cancel or otherwise reduce or attenuate the vibration. In particular, the synchronous demodulation technique is performed to recover a measured spindle housing displacement signal related only to the rotation of a machine tool spindle, and consequently rejects measured displacement not related to spindle motion or synchronous to a cycle of revolution. Furthermore, the controller actuates at least one voice-coil (VC) motor, to cancel the original force-induced motion, and adapts the magnitude of voice coil signal until this measured displacement signal is brought to a null. In order to adjust the signal to a null, it must have the correct phase relative to the spindle angle. The feedback phase signal is used to adjust a common (to both outputs) commutation offset register (offset relative to spindle encoder angle) to force the feedback phase signal output to a null. Once both of these feedback signals are null, the system is compensating properly for the spindle-induced motion.

Defense Logistics Standard Systems Functional Requirements.

DTIC Science & Technology

1987-03-01

Artificial Intelligence - the development of a machine capability to perform functions normally concerned with human intelligence, such as learning , adapting...Basic Data Base Machine Configurations .... ......... D- 18 xx ~ ?f~~~vX PART I: MODELS - DEFENSE LOGISTICS STANDARD SYSTEMS FUNCTIONAL REQUIREMENTS...On-line, Interactive Access. Integrating user input and machine output in a dynamic, real-time, give-and- take process is considered the optimum mode

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

PubMed

Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

2012-09-01

It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

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.

Self-assembled software and method of overriding software execution

DOEpatents

Bouchard, Ann M.; Osbourn, Gordon C.

2013-01-08

A computer-implemented software self-assembled system and method for providing an external override and monitoring capability to dynamically self-assembling software containing machines that self-assemble execution sequences and data structures. The method provides an external override machine that can be introduced into a system of self-assembling machines while the machines are executing such that the functionality of the executing software can be changed or paused without stopping the code execution and modifying the existing code. Additionally, a monitoring machine can be introduced without stopping code execution that can monitor specified code execution functions by designated machines and communicate the status to an output device.

Quantum cloning disturbed by thermal Davies environment

NASA Astrophysics Data System (ADS)

Dajka, Jerzy; Łuczka, Jerzy

2016-06-01

A network of quantum gates designed to implement universal quantum cloning machine is studied. We analyze how thermal environment coupled to auxiliary qubits, `blank paper' and `toner' required at the preparation stage of copying, modifies an output fidelity of the cloner. Thermal environment is described in terms of the Markovian Davies theory. We show that such a cloning machine is not universal any more but its output is independent of at least a part of parameters of the environment. As a case study, we consider cloning of states in a six-state cryptography's protocol. We also briefly discuss cloning of arbitrary input states.

SU-E-T-88: Comprehensive Automated Daily QA for Hypo- Fractionated Treatments

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

McGuinness, C; Morin, O

2014-06-01

Purpose: The trend towards more SBRT treatments with fewer high dose fractions places increased importance on daily QA. Patient plan specific QA with 3%/3mm gamma analysis and daily output constancy checks may not be enough to guarantee the level of accuracy required for SBRT treatments. But increasing the already extensive amount of QA procedures that are required is a daunting proposition. We performed a feasibility study for more comprehensive automated daily QA that could improve the diagnostic capabilities of QA without increasing workload. Methods: We performed the study on a Siemens Artiste linear accelerator using the integrated flat panel EPID.more » We included square fields, a picket fence, overlap and representative IMRT fields to measure output, flatness, symmetry, beam center, and percent difference from the standard. We also imposed a set of machine errors: MLC leaf position, machine output, and beam steering to compare with the standard. Results: Daily output was consistent within +/− 1%. Change in steering current by 1.4% and 2.4% resulted in a 3.2% and 6.3% change in flatness. 1 and 2mm MLC leaf offset errors were visibly obvious in difference plots, but passed a 3%/3mm gamma analysis. A simple test of transmission in a picket fence can catch a leaf offset error of a single leaf by 1mm. The entire morning QA sequence is performed in less than 30 minutes and images are automatically analyzed. Conclusion: Automated QA procedures could be used to provide more comprehensive information about the machine with less time and human involvement. We have also shown that other simple tests are better able to catch MLC leaf position errors than a 3%/3mm gamma analysis commonly used for IMRT and modulated arc treatments. Finally, this information could be used to watch trends of the machine and predict problems before they lead to costly machine downtime.« less

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

PubMed

Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

2015-01-01

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values.

PubMed

Franco-Villoria, Maria; Wright, Charlotte M; McColl, John H; Sherriff, Andrea; Pearce, Mark S

2016-01-07

To explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI). Cross-sectional observational study within population representative cohort study. Urban community, North East England Sample of 506 mothers of children aged 7-8 years, mean age 36.3 years. Participants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA). Height, weight, bioelectrical impedance (BIA). Lean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data. Estimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height(2). Of 112 women with BMI >30 kg/m(2), 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m(2)) only 5 (31%) also had low fat. Lean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values

PubMed Central

Franco-Villoria, Maria; Wright, Charlotte M; McColl, John H; Sherriff, Andrea; Pearce, Mark S

2016-01-01

Objectives To explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI). Design Cross-sectional observational study within population representative cohort study. Setting Urban community, North East England Participants Sample of 506 mothers of children aged 7–8 years, mean age 36.3 years. Methods Participants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA). Measures Height, weight, bioelectrical impedance (BIA). Outcome measures Lean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data. Results Estimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height2. Of 112 women with BMI >30 kg/m2, 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m2) only 5 (31%) also had low fat. Conclusions Lean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used. PMID:26743700

Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

NASA Astrophysics Data System (ADS)

Uezu, Tatsuya; Kiyokawa, Shuji

2016-06-01

We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

Creation of a Machine File and Subsequent Computer-Assisted Production of Publishing Outputs, Including a Translation Journal and an Index.

ERIC Educational Resources Information Center

Buckland, Lawrence F.; Weaver, Vance

Reported are the findings of the Uspekhi experiment in creating a labeled machine file, as well as sample products of this system - an article from a scientific journal and an index page. Production cost tables are presented for the machine file, primary journals, and journal indexes. Comparisons were made between the 1965 predicted costs and the…

Transient Stability Output Margin Estimation Based on Energy Function Method

NASA Astrophysics Data System (ADS)

Miwa, Natsuki; Tanaka, Kazuyuki

In this paper, a new method of estimating critical generation margin (CGM) in power systems is proposed from the viewpoint of transient stability diagnostic. The proposed method has the capability to directly compute the stability limit output for a given contingency based on transient energy function method (TEF). Since CGM can be directly obtained by the limit output using estimated P-θ curves and is easy to understand, it is more useful rather than conventional critical clearing time (CCT) of energy function method. The proposed method can also estimate CGM as its negative value that means unstable in present load profile, then negative CGM can be directly utilized as generator output restriction. The proposed method is verified its accuracy and fast solution ability by applying to simple 3-machine model and IEEJ EAST10-machine standard model. Furthermore the useful application to severity ranking of transient stability for a lot of contingency cases is discussed by using CGM.

The Radiological Physics Center's standard dataset for small field size output factors.

PubMed

Followill, David S; Kry, Stephen F; Qin, Lihong; Lowenstein, Jessica; Molineu, Andrea; Alvarez, Paola; Aguirre, Jose Francisco; Ibbott, Geoffrey S

2012-08-08

Delivery of accurate intensity-modulated radiation therapy (IMRT) or stereotactic radiotherapy depends on a multitude of steps in the treatment delivery process. These steps range from imaging of the patient to dose calculation to machine delivery of the treatment plan. Within the treatment planning system's (TPS) dose calculation algorithm, various unique small field dosimetry parameters are essential, such as multileaf collimator modeling and field size dependence of the output. One of the largest challenges in this process is determining accurate small field size output factors. The Radiological Physics Center (RPC), as part of its mission to ensure that institutions deliver comparable and consistent radiation doses to their patients, conducts on-site dosimetry review visits to institutions. As a part of the on-site audit, the RPC measures the small field size output factors as might be used in IMRT treatments, and compares the resulting field size dependent output factors to values calculated by the institution's treatment planning system (TPS). The RPC has gathered multiple small field size output factor datasets for X-ray energies ranging from 6 to 18 MV from Varian, Siemens and Elekta linear accelerators. These datasets were measured at 10 cm depth and ranged from 10 × 10 cm(2) to 2 × 2 cm(2). The field sizes were defined by the MLC and for the Varian machines the secondary jaws were maintained at a 10 × 10 cm(2). The RPC measurements were made with a micro-ion chamber whose volume was small enough to gather a full ionization reading even for the 2 × 2 cm(2) field size. The RPC-measured output factors are tabulated and are reproducible with standard deviations (SD) ranging from 0.1% to 1.5%, while the institutions' calculated values had a much larger SD range, ranging up to 7.9% [corrected].The absolute average percent differences were greater for the 2 × 2 cm(2) than for the other field sizes. The RPC's measured small field output factors provide institutions with a standard dataset against which to compare their TPS calculated values. Any discrepancies noted between the standard dataset and calculated values should be investigated with careful measurements and with attention to the specific beam model.

Asynchronous machine rotor speed estimation using a tabulated numerical approach

NASA Astrophysics Data System (ADS)

Nguyen, Huu Phuc; De Miras, Jérôme; Charara, Ali; Eltabach, Mario; Bonnet, Stéphane

2017-12-01

This paper proposes a new method to estimate the rotor speed of the asynchronous machine by looking at the estimation problem as a nonlinear optimal control problem. The behavior of the nonlinear plant model is approximated off-line as a prediction map using a numerical one-step time discretization obtained from simulations. At each time-step, the speed of the induction machine is selected satisfying the dynamic fitting problem between the plant output and the predicted output, leading the system to adopt its dynamical behavior. Thanks to the limitation of the prediction horizon to a single time-step, the execution time of the algorithm can be completely bounded. It can thus easily be implemented and embedded into a real-time system to observe the speed of the real induction motor. Simulation results show the performance and robustness of the proposed estimator.

Electromagnetic inhibition of high frequency thermal bonding machine

NASA Astrophysics Data System (ADS)

He, Hong; Zhang, Qing-qing; Li, Hang; Zhang, Da-jian; Hou, Ming-feng; Zhu, Xian-wei

2011-12-01

The traditional high frequency thermal bonding machine had serious radiation problems at dominant frequency, two times frequency and three times frequency. Combining with its working principle, the problems of electromagnetic compatibility were studied, three following measures were adopted: 1.At the head part of the high frequency thermal bonding machine, resonant circuit attenuator was designed. The notch groove and reaction field can make the radiation being undermined or absorbed; 2.The electromagnetic radiation shielding was made for the high frequency copper power feeder; 3.Redesigned the high-frequency oscillator circuit to reduce the output of harmonic oscillator. The test results showed that these measures can make the output according with the national standard of electromagnetic compatibility (GB4824-2004-2A), the problems of electromagnetic radiation leakage can be solved, and good social, environmental and economic benefits would be brought.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring.

PubMed

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-02-23

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

PubMed Central

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-01-01

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies. PMID:26907297

Multi-canister overpack project -- verification and validation, MCNP 4A

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

Goldmann, L.H.

This supporting document contains the software verification and validation (V and V) package used for Phase 2 design of the Spent Nuclear Fuel Multi-Canister Overpack. V and V packages for both ANSYS and MCNP are included. Description of Verification Run(s): This software requires that it be compiled specifically for the machine it is to be used on. Therefore to facilitate ease in the verification process the software automatically runs 25 sample problems to ensure proper installation and compilation. Once the runs are completed the software checks for verification by performing a file comparison on the new output file and themore » old output file. Any differences between any of the files will cause a verification error. Due to the manner in which the verification is completed a verification error does not necessarily indicate a problem. This indicates that a closer look at the output files is needed to determine the cause of the error.« less

The ergonomics of vertical turret lathe operation.

PubMed

Pratt, F M; Corlett, E N

1970-12-01

A study of the work load of 14 vertical turret lathe operators engaged on different work tasks in two factories is reported. For eight of these workers continuous heart rate recordings were made throughout the day. It was shown that in four cases improved technology was unlikely to lead to higher output and certain aspects of posture and equipment manipulation were major contributors to the limitations on increased output. The role of the work-rest schedule in increasing work loads was also demonstrated. Improvements in technology and methods to reduce the extent of certain work loads to enable heavy work to be done in shorter periods followed by light work or rest periods are given as means to modify and improve the output of these machines. Finally, the direction for the development of a predictive model for man-machine matching is introduced.

Prediction of Radial Vibration in Switched Reluctance Machines

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

Lin, CJ; Fahimi, B

2013-12-01

Origins of vibration in switched reluctance machines (SRMs) are investigated. Accordingly, an input-output model based on the mechanical impulse response of the SRMis developed. The proposed model is derived using an experimental approach. Using the proposed approach, vibration of the stator frame is captured and experimentally verified.

Development of techniques to enhance man/machine communication

NASA Technical Reports Server (NTRS)

Targ, R.; Cole, P.; Puthoff, H.

1974-01-01

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

USSR Report, Machine Tools and Metalworking Equipment, No. 6

DTIC Science & Technology

1983-05-18

production output per machine tool at a tool plant average 2-3 times the figures for tool shops. This is explained by the well-known advantages of...specialized production. Specifically, the advantages of standardization and unification of machine- attachment design can be fully exploited in...lemiiiiä IS MVCti\\e UtiUzation °f appropriate special equipmeT ters)! million thread-cutting dies, and 2.3 million milling cut- The advantages of

Alternator control for battery charging

DOEpatents

Brunstetter, Craig A.; Jaye, John R.; Tallarek, Glen E.; Adams, Joseph B.

2015-07-14

In accordance with an aspect of the present disclosure, an electrical system for an automotive vehicle has an electrical generating machine and a battery. A set point voltage, which sets an output voltage of the electrical generating machine, is set by an electronic control unit (ECU). The ECU selects one of a plurality of control modes for controlling the alternator based on an operating state of the vehicle as determined from vehicle operating parameters. The ECU selects a range for the set point voltage based on the selected control mode and then sets the set point voltage within the range based on feedback parameters for that control mode. In an aspect, the control modes include a trickle charge mode and battery charge current is the feedback parameter and the ECU controls the set point voltage within the range to maintain a predetermined battery charge current.

Application of a support vector machine algorithm to the safety precaution technique of medium-low pressure gas regulators

NASA Astrophysics Data System (ADS)

Hao, Xuejun; An, Xaioran; Wu, Bo; He, Shaoping

2018-02-01

In the gas pipeline system, safe operation of a gas regulator determines the stability of the fuel gas supply, and the medium-low pressure gas regulator of the safety precaution system is not perfect at the present stage in the Beijing Gas Group; therefore, safety precaution technique optimization has important social and economic significance. In this paper, according to the running status of the medium-low pressure gas regulator in the SCADA system, a new method for gas regulator safety precaution based on the support vector machine (SVM) is presented. This method takes the gas regulator outlet pressure data as input variables of the SVM model, the fault categories and degree as output variables, which will effectively enhance the precaution accuracy as well as save significant manpower and material resources.

Fast Query-Optimized Kernel-Machine Classification

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; DeCoste, Dennis

2004-01-01

A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.

Scientific bases of human-machine communication by voice.

PubMed Central

Schafer, R W

1995-01-01

The scientific bases for human-machine communication by voice are in the fields of psychology, linguistics, acoustics, signal processing, computer science, and integrated circuit technology. The purpose of this paper is to highlight the basic scientific and technological issues in human-machine communication by voice and to point out areas of future research opportunity. The discussion is organized around the following major issues in implementing human-machine voice communication systems: (i) hardware/software implementation of the system, (ii) speech synthesis for voice output, (iii) speech recognition and understanding for voice input, and (iv) usability factors related to how humans interact with machines. PMID:7479802

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

PubMed

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

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution outperformed all other models (P < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OFs. The OFs showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%-4% as the field radius was reduced to 2.5 cm. Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements. © 2018 American Association of Physicists in Medicine.

Modelling innovation performance of European regions using multi-output neural networks

PubMed Central

Henriques, Roberto

2017-01-01

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes. PMID:28968449

Modelling innovation performance of European regions using multi-output neural networks.

PubMed

Hajek, Petr; Henriques, Roberto

2017-01-01

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Modeling and Analysis of CNC Milling Process Parameters on Al3030 based Composite

NASA Astrophysics Data System (ADS)

Gupta, Anand; Soni, P. K.; Krishna, C. M.

2018-04-01

The machining of Al3030 based composites on Computer Numerical Control (CNC) high speed milling machine have assumed importance because of their wide application in aerospace industries, marine industries and automotive industries etc. Industries mainly focus on surface irregularities; material removal rate (MRR) and tool wear rate (TWR) which usually depends on input process parameters namely cutting speed, feed in mm/min, depth of cut and step over ratio. Many researchers have carried out researches in this area but very few have taken step over ratio or radial depth of cut also as one of the input variables. In this research work, the study of characteristics of Al3030 is carried out at high speed CNC milling machine over the speed range of 3000 to 5000 r.p.m. Step over ratio, depth of cut and feed rate are other input variables taken into consideration in this research work. A total nine experiments are conducted according to Taguchi L9 orthogonal array. The machining is carried out on high speed CNC milling machine using flat end mill of diameter 10mm. Flatness, MRR and TWR are taken as output parameters. Flatness has been measured using portable Coordinate Measuring Machine (CMM). Linear regression models have been developed using Minitab 18 software and result are validated by conducting selected additional set of experiments. Selection of input process parameters in order to get best machining outputs is the key contributions of this research work.

Chinese-English Machine Translation System.

ERIC Educational Resources Information Center

Wang, William S-Y; And Others

The report documents results of a two-year R&D effort directed at the completion of a prototype system for Chinese-English machine translation of S&T literature. The system, designated QUINCE, accepts Chinese input exactly as printed, with no pre-editing of any kind, and produces English output on experimental basis. Coding of Chinese text via…

COM: Decisions and Applications in a Small University Library.

ERIC Educational Resources Information Center

Schwarz, Philip J.

Computer-output microfilm (COM) is used at the University of Wisconsin-Stout Library to generate reports from its major machine readable data bases. Conditions indicating the need to convert to COM include existence of a machine readable data base and high cost of report production. Advantages and disadvantages must also be considered before…

Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; García-Gonzalo, Esperanza; Vilán, José Antonio Vilán; Robleda, Abraham Segade

2015-12-01

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-SVM-based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, this hybrid PSO-SVM-based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

Deviation Value for Conventional X-ray in Hospitals in South Sulawesi Province from 2014 to 2016

NASA Astrophysics Data System (ADS)

Bachtiar, Ilham; Abdullah, Bualkar; Tahir, Dahlan

2018-03-01

This paper describes the conventional X-ray machine parameters tested in the region of South Sulawesi from 2014 to 2016. The objective of this research is to know deviation of every parameter of conventional X-ray machine. The testing parameters were analyzed by using quantitative methods with participatory observational approach. Data collection was performed by testing the output of conventional X-ray plane using non-invasive x-ray multimeter. The test parameters include tube voltage (kV) accuracy, radiation output linearity, reproducibility and radiation beam value (HVL) quality. The results of the analysis show four conventional X-ray test parameters have varying deviation spans, where the tube voltage (kV) accuracy has an average value of 4.12%, the average radiation output linearity is 4.47% of the average reproducibility of 0.62% and the averaged of the radiation beam (HVL) is 3.00 mm.

[Research on infrared safety protection system for machine tool].

PubMed

Zhang, Shuan-Ji; Zhang, Zhi-Ling; Yan, Hui-Ying; Wang, Song-De

2008-04-01

In order to ensure personal safety and prevent injury accident in machine tool operation, an infrared machine tool safety system was designed with infrared transmitting-receiving module, memory self-locked relay and voice recording-playing module. When the operator does not enter the danger area, the system has no response. Once the operator's whole or part of body enters the danger area and shades the infrared beam, the system will alarm and output an control signal to the machine tool executive element, and at the same time, the system makes the machine tool emergency stop to prevent equipment damaged and person injured. The system has a module framework, and has many advantages including safety, reliability, common use, circuit simplicity, maintenance convenience, low power consumption, low costs, working stability, easy debugging, vibration resistance and interference resistance. It is suitable for being installed and used in different machine tools such as punch machine, pour plastic machine, digital control machine, armor plate cutting machine, pipe bending machine, oil pressure machine etc.

Improvement of Computer Software Quality through Software Automated Tools.

DTIC Science & Technology

1986-08-30

information that are returned from the tools to the human user, and the forms in which these outputs are presented. Page 2 of 4 STAGE OF DEVELOPMENT: What... AUTOMIATED SOFTWARE TOOL MONITORING SYSTEM APPENDIX 2 2-1 INTRODUCTION This document and Automated Software Tool Monitoring Program (Appendix 1) are...t Output Output features provide links from the tool to both the human user and the target machine (where applicable). They describe the types

An investigation of chatter and tool wear when machining titanium

NASA Technical Reports Server (NTRS)

Sutherland, I. A.

1974-01-01

The low thermal conductivity of titanium, together with the low contact area between chip and tool and the unusually high chip velocities, gives rise to high tool tip temperatures and accelerated tool wear. Machining speeds have to be considerably reduced to avoid these high temperatures with a consequential loss of productivity. Restoring this lost productivity involves increasing other machining variables, such as feed and depth-of-cut, and can lead to another machining problem commonly known as chatter. This work is to acquaint users with these problems, to examine the variables that may be encountered when machining a material like titanium, and to advise the machine tool user on how to maximize the output from the machines and tooling available to him. Recommendations are made on ways of improving tolerances, reducing machine tool instability or chatter, and improving productivity. New tool materials, tool coatings, and coolants are reviewed and their relevance examined when machining titanium.

Electrical leakage detection circuit

DOEpatents

Wild, Arthur

2006-09-05

A method is provided for detecting electrical leakage between a power supply and a frame of a vehicle or machine. The disclosed method includes coupling a first capacitor between a frame and a first terminal of a power supply for a predetermined period of time. The current flowing between the frame and the first capacitor is limited to a predetermined current limit. It is determined whether the voltage across the first capacitor exceeds a threshold voltage. A first output signal is provided when the voltage across the capacitor exceeds the threshold voltage.

Design and analysis of an unconventional permanent magnet linear machine for energy harvesting

NASA Astrophysics Data System (ADS)

Zeng, Peng

This Ph.D. dissertation proposes an unconventional high power density linear electromagnetic kinetic energy harvester, and a high-performance two-stage interface power electronics to maintain maximum power abstraction from the energy source and charge the Li-ion battery load with constant current. The proposed machine architecture is composed of a double-sided flat type silicon steel stator with winding slots, a permanent magnet mover, coil windings, a linear motion guide and an adjustable spring bearing. The unconventional design of the machine is that NdFeB magnet bars in the mover are placed with magnetic fields in horizontal direction instead of vertical direction and the same magnetic poles are facing each other. The derived magnetic equivalent circuit model proves the average air-gap flux density of the novel topology is as high as 0.73 T with 17.7% improvement over that of the conventional topology at the given geometric dimensions of the proof-of-concept machine. Subsequently, the improved output voltage and power are achieved. The dynamic model of the linear generator is also developed, and the analytical equations of output maximum power are derived for the case of driving vibration with amplitude that is equal, smaller and larger than the relative displacement between the mover and the stator of the machine respectively. Furthermore, the finite element analysis (FEA) model has been simulated to prove the derived analytical results and the improved power generation capability. Also, an optimization framework is explored to extend to the multi-Degree-of-Freedom (n-DOF) vibration based linear energy harvesting devices. Moreover, a boost-buck cascaded switch mode converter with current controller is designed to extract the maximum power from the harvester and charge the Li-ion battery with trickle current. Meanwhile, a maximum power point tracking (MPPT) algorithm is proposed and optimized for low frequency driving vibrations. Finally, a proof-of-concept unconventional permanent magnet (PM) linear generator is prototyped and tested to verify the simulation results of the FEA model. For the coil windings of 33, 66 and 165 turns, the output power of the machine is tested to have the output power of 65.6 mW, 189.1 mW, and 497.7 mW respectively with the maximum power density of 2.486 mW/cm3.

A technology mapping based on graph of excitations and outputs for finite state machines

NASA Astrophysics Data System (ADS)

Kania, Dariusz; Kulisz, Józef

2017-11-01

A new, efficient technology mapping method of FSMs, dedicated for PAL-based PLDs is proposed. The essence of the method consists in searching for the minimal set of PAL-based logic blocks that cover a set of multiple-output implicants describing the transition and output functions of an FSM. The method is based on a new concept of graph: the Graph of Excitations and Outputs. The proposed algorithm was tested using the FSM benchmarks. The obtained results were compared with the classical technology mapping of FSM.

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

PubMed Central

Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

2015-01-01

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment. PMID:26368541

Accelerometer method and apparatus for integral display and control functions

NASA Astrophysics Data System (ADS)

Bozeman, Richard J., Jr.

1992-06-01

Vibration analysis has been used for years to provide a determination of the proper functioning of different types of machinery, including rotating machinery and rocket engines. A determination of a malfunction, if detected at a relatively early stage in its development, will allow changes in operating mode or a sequenced shutdown of the machinery prior to a total failure. Such preventative measures result in less extensive and/or less expensive repairs, and can also prevent a sometimes catastrophic failure of equipment. Standard vibration analyzers are generally rather complex, expensive, and of limited portability. They also usually result in displays and controls being located remotely from the machinery being monitored. Consequently, a need exists for improvements in accelerometer electronic display and control functions which are more suitable for operation directly on machines and which are not so expensive and complex. The invention includes methods and apparatus for detecting mechanical vibrations and outputting a signal in response thereto. The apparatus includes an accelerometer package having integral display and control functions. The accelerometer package is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine condition over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase over the selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated. The benefits of a vibration recording and monitoring system with controls and displays readily mountable on the machinery being monitored and having capabilities described will be appreciated by those working in the art.

Accelerometer method and apparatus for integral display and control functions

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1992-01-01

Vibration analysis has been used for years to provide a determination of the proper functioning of different types of machinery, including rotating machinery and rocket engines. A determination of a malfunction, if detected at a relatively early stage in its development, will allow changes in operating mode or a sequenced shutdown of the machinery prior to a total failure. Such preventative measures result in less extensive and/or less expensive repairs, and can also prevent a sometimes catastrophic failure of equipment. Standard vibration analyzers are generally rather complex, expensive, and of limited portability. They also usually result in displays and controls being located remotely from the machinery being monitored. Consequently, a need exists for improvements in accelerometer electronic display and control functions which are more suitable for operation directly on machines and which are not so expensive and complex. The invention includes methods and apparatus for detecting mechanical vibrations and outputting a signal in response thereto. The apparatus includes an accelerometer package having integral display and control functions. The accelerometer package is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine condition over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase over the selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated. The benefits of a vibration recording and monitoring system with controls and displays readily mountable on the machinery being monitored and having capabilities described will be appreciated by those working in the art.

GLISP User's Manual. Revised.

ERIC Educational Resources Information Center

Novak, Gordon S., Jr.

GLISP is a LISP-based language which provides high-level language features not found in ordinary LISP. The GLISP language is implemented by means of a compiler which accepts GLISP as input and produces ordinary LISP as output. This output can be further compiled to machine code by the LISP compiler. GLISP is available for several LISP dialects,…

Highly fault-tolerant parallel computation

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

Spielman, D.A.

We re-introduce the coded model of fault-tolerant computation in which the input and output of a computational device are treated as words in an error-correcting code. A computational device correctly computes a function in the coded model if its input and output, once decoded, are a valid input and output of the function. In the coded model, it is reasonable to hope to simulate all computational devices by devices whose size is greater by a constant factor but which are exponentially reliable even if each of their components can fail with some constant probability. We consider fine-grained parallel computations inmore » which each processor has a constant probability of producing the wrong output at each time step. We show that any parallel computation that runs for time t on w processors can be performed reliably on a faulty machine in the coded model using w log{sup O(l)} w processors and time t log{sup O(l)} w. The failure probability of the computation will be at most t {center_dot} exp(-w{sup 1/4}). The codes used to communicate with our fault-tolerant machines are generalized Reed-Solomon codes and can thus be encoded and decoded in O(n log{sup O(1)} n) sequential time and are independent of the machine they are used to communicate with. We also show how coded computation can be used to self-correct many linear functions in parallel with arbitrarily small overhead.« less

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

NASA Astrophysics Data System (ADS)

Ageta, Takasuke

2000-05-01

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

Predicting Power Output of Upper Body using the OMNI-RES Scale.

PubMed

Bautista, Iker J; Chirosa, Ignacio J; Tamayo, Ignacio Martín; González, Andrés; Robinson, Joseph E; Chirosa, Luis J; Robertson, Robert J

2014-12-09

The main aim of this study was to determine the optimal training zone for maximum power output. This was to be achieved through estimating mean bar velocity of the concentric phase of a bench press using a prediction equation. The values for the prediction equation would be obtained using OMNI-RES scale values of different loads of the bench press exercise. Sixty males (age 23.61 2.81 year; body height 176.29 6.73 cm; body mass 73.28 4.75 kg) voluntarily participated in the study and were tested using an incremental protocol on a Smith machine to determine one repetition maximum (1RM) in the bench press exercise. A linear regression analysis produced a strong correlation (r = -0.94) between rating of perceived exertion (RPE) and mean bar velocity (Velmean). The Pearson correlation analysis between real power output (PotReal) and estimated power (PotEst) showed a strong correlation coefficient of r = 0.77, significant at a level of p = 0.01. Therefore, the OMNI-RES scale can be used to predict Velmean in the bench press exercise to control the intensity of the exercise. The positive relationship between PotReal and PotEst allowed for the identification of a maximum power-training zone.

Predicting Power Output of Upper Body using the OMNI-RES Scale

PubMed Central

Bautista, Iker J.; Chirosa, Ignacio J.; Tamayo, Ignacio Martín; González, Andrés; Robinson, Joseph E.; Chirosa, Luis J.; Robertson, Robert J.

2014-01-01

The main aim of this study was to determine the optimal training zone for maximum power output. This was to be achieved through estimating mean bar velocity of the concentric phase of a bench press using a prediction equation. The values for the prediction equation would be obtained using OMNI–RES scale values of different loads of the bench press exercise. Sixty males (age 23.61 2.81 year; body height 176.29 6.73 cm; body mass 73.28 4.75 kg) voluntarily participated in the study and were tested using an incremental protocol on a Smith machine to determine one repetition maximum (1RM) in the bench press exercise. A linear regression analysis produced a strong correlation (r = −0.94) between rating of perceived exertion (RPE) and mean bar velocity (Velmean). The Pearson correlation analysis between real power output (PotReal) and estimated power (PotEst) showed a strong correlation coefficient of r = 0.77, significant at a level of p = 0.01. Therefore, the OMNI–RES scale can be used to predict Velmean in the bench press exercise to control the intensity of the exercise. The positive relationship between PotReal and PotEst allowed for the identification of a maximum power-training zone. PMID:25713677

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

Wang Yinan; Shi Handuo; Xiong Zhaoxi

We present a unified universal quantum cloning machine, which combines several different existing universal cloning machines together, including the asymmetric case. In this unified framework, the identical pure states are projected equally into each copy initially constituted by input and one half of the maximally entangled states. We show explicitly that the output states of those universal cloning machines are the same. One importance of this unified cloning machine is that the cloning procession is always the symmetric projection, which reduces dramatically the difficulties for implementation. Also, it is found that this unified cloning machine can be directly modified tomore » the general asymmetric case. Besides the global fidelity and the single-copy fidelity, we also present all possible arbitrary-copy fidelities.« less

An Evaluation of Output Quality of Machine Translation (Padideh Software vs. Google Translate)

ERIC Educational Resources Information Center

Azer, Haniyeh Sadeghi; Aghayi, Mohammad Bagher

2015-01-01

This study aims to evaluate the translation quality of two machine translation systems in translating six different text-types, from English to Persian. The evaluation was based on criteria proposed by Van Slype (1979). The proposed model for evaluation is a black-box type, comparative and adequacy-oriented evaluation. To conduct the evaluation, a…

20131201-1231_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-01-08

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Dec to 31 Dec 2013.

20131101-1130_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2013-12-02

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Nov to 30 Nov 2013.

20130416_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-04-24

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 4/16/13.

20131001-1031_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2013-11-05

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 1 Oct 2013 to 31 Oct 2013.

20140201-0228_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-03-03

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Feb to 28 Feb 2014.

20130801-0831_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-09-10

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 8/1/13 to 8/31/13.

20140101-0131_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-02-03

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Jan to 31 Jan 2014.

20140430_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-05-05

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 April to 30 April 2014.

20140301-0331_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-04-07

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Mar to 31 Mar 2014.

20140501-0531_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-06-02

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 May to 31 May 2014.

20140601-0630_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-06-30

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 June to 30 June 2014.

20140701-0731_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2014-07-31

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 July to 31 July 2014.

20130901-0930_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Thibedeau, Joe

2013-10-25

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 1 September 2013 to 30 September 2013.

Green Machine Florida Canyon Hourly Data 20130731

DOE Data Explorer

Vanderhoff, Alex

2013-08-30

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 7/1/13 to 7/31/13.

20130501-20130531_Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-06-18

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from May 2013

Green Machine Florida Canyon Hourly Data

DOE Data Explorer

Vanderhoff, Alex

2013-07-15

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 6/1/13 to 6/30/13

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

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

A study pertaining to inertial energy storage machine designs for space applications

NASA Technical Reports Server (NTRS)

Zowarka, R. C.

1981-01-01

The preliminary design of a counterrotating fast discharge homopolar generator (HPG) and a counterrotating active rotary flux compressor (CARFC) for space application is reported. The HPG is a counterrotating spool-type homopolar with superconducting field coil excitation. It delivers a 20-ms, 145-kJ pulse to a magnetoplasmahydrodynamic thruster. The peak output current is 42.7 kA at 240 V. After 20 ms the current is 29.7 kA at 167 V. The CARFC delivers ten 50-kJ, 250 microsecond pulses at 50-ms interval to six Xenon flash lamps pumping an Nd glass laser. The flux compressor is counterrotating for torque compensation. Current is started in the machine with a 5-kV, 5-kJ pulse-charged capacitor. Both designs were based upon demonstrated technology. The sensitivity of the designs to technology that may be available in five to ten years was determined.

Quantifying Pollutant Emissions from Office Equipment Phase IReport

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

Maddalena, R.L.; Destaillats, H.; Hodgson, A.T.

2006-12-01

Although office equipment has been a focal point for governmental efforts to promote energy efficiency through programs such as Energy Star, little is known about the relationship between office equipment use and indoor air quality. This report provides results of the first phase (Phase I) of a study in which the primary objective is to measure emissions of organic pollutants and particulate matter from a selected set of office equipment typically used in residential and office environments. The specific aims of the overall research effort are: (1) use screening-level measurements to identify and quantify the concentrations of air pollutants ofmore » interest emitted by major categories of distributed office equipment in a controlled environment; (2) quantify the emissions of air pollutants from generally representative, individual machines within each of the major categories in a controlled chamber environment using well defined protocols; (3) characterize the effects of ageing and use on emissions for individual machines spanning several categories; (4) evaluate the importance of operational factors that can be manipulated to reduce pollutant emissions from office machines; and (5) explore the potential relationship between energy consumption and pollutant emissions for machines performing equivalent tasks. The study includes desktop computers (CPU units), computer monitors, and three categories of desktop printing devices. The printer categories are: (1) printers and multipurpose devices using color inkjet technology; (2) low- to medium output printers and multipurpose devices employing monochrome or color laser technology; and (3) high-output monochrome and color laser printers. The literature review and screening level experiments in Phase 1 were designed to identify substances of toxicological significance for more detailed study. In addition, these screening level measurements indicate the potential relative importance of different categories of office equipment with respect to human exposures. The more detailed studies of the next phase of research (Phase II) are meant to characterize changes in emissions with time and may identify factors that can be modified to reduce emissions. These measurements may identify 'win-win' situations in which low energy consumption machines have lower pollutant emissions. This information will be used to compare machines to determine if some are substantially better than their peers with respect to their emissions of pollutants.« less

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

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

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.

SU-E-T-379: Evaluation of An EPID-Based System for Daily Dosimetry Check by Comparison with a Widely-Used Ionization Chamber-Based Device

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

McDonald, D; Koch, N; Peng, J

2015-06-15

Purpose: To examine the feasibility of using Varian’s EPID-based Machine Performance Check (MPC) system to track daily machine output through comparison with Sun Nuclear’s DailyQA3 (DQA) device. Methods: Daily machine outputs for two photon energies (6 and 16MV) and five electron energies (6, 9, 12, 16, 20MeV) were measured for one month using both MPC and DQA. Baselines measurements for MPC were taken at the start of the measurement series, while DQA baselines were set at an earlier date. In order to make absolute comparisons with MPC, all DQA readings were referenced to the average of the first three DQAmore » readings in that series, minimizing systematic differences between the measurement techniques due to baseline differences. In addition to daily output measurements, weekly averages were also calculated and compared. Finally, the electron energy dependence of each measurement technique was examined by comparing energy-specific measurements to the average electron output of all energies each day. Results: For 6 and 16MV photons, the largest absolute percent differences between MPC and DQA were 0.60% and 0.73%, respectively. Weekly averages were within 0.17% and 0.23%, respectively. For all five electron energies, the greatest absolute percent differences between MPC and DQA for each energy ranged from 0.49%–0.83%. Weekly averages ranged from 0.07%–0.28%. DQA energy-specific electron readings matched the average electron output within 0.29% for all days and all energies. MPC energy-specific readings matched the average within 0.21% for 9–20MeV. However, 6MeV showed a larger distribution about the average with four days showing a difference greater than 0.30% and a maximum difference of 0.51%. Conclusion: MPC output measurements correlated well with the widely-used DQA3 for most beam energies, making it a reliable back up technique for daily output monitoring. However, MPC may display an energy dependence for lower electrons energies, requiring additional investigation.« less

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

The Circle of Meaning: From Translation to Paraphrasing and Back

ERIC Educational Resources Information Center

Madnani, Nitin

2010-01-01

The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is…

New Tools for Hybrid Systems

DTIC Science & Technology

2007-05-02

stability of a class of discrete event systems ", IEEE Transactions on Automatic Control , vol. 39, no. 2... stability , input/output stability , external stability and incremental input/output stability , as they apply to deterministic finite state machine systems ... class of systems , incremental 1/0 stability and external stability are equivalent notions, stronger than the notion of I/O stability . 15. SUBJECT

Directed Design of Experiments for Validating Probability of Detection Capability of a Testing System

NASA Technical Reports Server (NTRS)

Generazio, Edward R. (Inventor)

2012-01-01

A method of validating a probability of detection (POD) testing system using directed design of experiments (DOE) includes recording an input data set of observed hit and miss or analog data for sample components as a function of size of a flaw in the components. The method also includes processing the input data set to generate an output data set having an optimal class width, assigning a case number to the output data set, and generating validation instructions based on the assigned case number. An apparatus includes a host machine for receiving the input data set from the testing system and an algorithm for executing DOE to validate the test system. The algorithm applies DOE to the input data set to determine a data set having an optimal class width, assigns a case number to that data set, and generates validation instructions based on the case number.

Self-Centering Reciprocating-Permanent-Magnet Machine

NASA Technical Reports Server (NTRS)

Bhate, Suresh; Vitale, Nick

1988-01-01

New design for monocoil reciprocating-permanent-magnet electric machine provides self-centering force. Linear permanent-magnet electrical motor includes outer stator, inner stator, and permanent-magnet plunger oscillateing axially between extreme left and right positions. Magnets arranged to produce centering force and allows use of only one coil of arbitrary axial length. Axial length of coil chosen to provide required efficiency and power output.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.

PubMed

Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

2013-11-02

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

PubMed Central

2013-01-01

Background An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. Results The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Conclusions Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory. PMID:24180526

Understanding the electrical characteristics of micromotors

NASA Astrophysics Data System (ADS)

Emadi, Ali; Irudayaraj, Sujay S.

2005-06-01

This paper presents a comprehensive list of issues related to the electrical characteristics of both electrostatic and electromagnetic micromotors and aims at understanding the behavior of the micromotor from the electrical standpoint. The paper takes the step-by-step approach by first presenting an overview of the laws of electrostatics and electromagnetism for micromachines, their applicability, features and limitations, and then progresses to independently analyze some of the important machine related quantities like electromotive torque, force-output, angular frequencies, supply conditions and requirements, for different types of electrostatic and electromagnetic micromotor constructions. A thorough study on the electric machine parameters that affect the performance of the micromotor need to be performed, since it would serve as a useful link in integrating the micromachine output performance with the fabrication process and challenges associated with it. Achieving such integration would then determine the optimized working condition for the micromotor. The main reason for this study is that although significant advancements have fostered the growth of micromotors in the recent past which has led to the establishment of the micromotor as quite a remarkable machine for powering micromechanical devices, and also as an industrial requirement for various applications, there has always been a concern about the optimal performance of the micromotor, since there is more than just one technology that is being incorporated to realize the micromotor. With fields ranging from surface engineering and chemistry to material science engineering exerting influence on the micromotor design, it becomes very important to completely comprehend the electrophysics of the micromachine that would in turn interact with the science of fabrication to result in the development of better micromotors with considerably less functional complexity.

Energy Survey of Machine Tools: Separating Power Information of the Main Transmission System During Machining Process

NASA Astrophysics Data System (ADS)

Liu, Shuang; Liu, Fei; Hu, Shaohua; Yin, Zhenbiao

The major power information of the main transmission system in machine tools (MTSMT) during machining process includes effective output power (i.e. cutting power), input power and power loss from the mechanical transmission system, and the main motor power loss. These information are easy to obtain in the lab but difficult to evaluate in a manufacturing process. To solve this problem, a separation method is proposed here to extract the MTSMT power information during machining process. In this method, the energy flow and the mathematical models of major power information of MTSMT during the machining process are set up first. Based on the mathematical models and the basic data tables obtained from experiments, the above mentioned power information during machining process can be separated just by measuring the real time total input power of the spindle motor. The operation program of this method is also given.

Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel

NASA Astrophysics Data System (ADS)

Deris, A. M.; Zain, A. M.; Sallehuddin, R.; Sharif, S.

2017-09-01

Electric discharge machine (EDM) is one of the widely used nonconventional machining processes for hard and difficult to machine materials. Due to the large number of machining parameters in EDM and its complicated structural, the selection of the optimal solution of machining parameters for obtaining minimum machining performance is remain as a challenging task to the researchers. This paper proposed experimental investigation and optimization of machining parameters for EDM process on stainless steel 316L work piece using Harmony Search (HS) algorithm. The mathematical model was developed based on regression approach with four input parameters which are pulse on time, peak current, servo voltage and servo speed to the output response which is dimensional accuracy (DA). The optimal result of HS approach was compared with regression analysis and it was found HS gave better result y giving the most minimum DA value compared with regression approach.

An iterative learning control method with application for CNC machine tools

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

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Application of earth resources technology satellite data to urban and regional planning: Test site, County of Los Angeles

NASA Technical Reports Server (NTRS)

Raje, S.; Mcknight, J.; Willoughby, G.; Economy, R. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The County of Los Angeles photointerpreted ERTS film products to define problems of interest, coordinated ground truth over the complex test site including interfaces with secondary users as well as participated in on-line analyses of the GE multispectral information extraction systems. Interactive machine analyses were carried out, developing techniques and procedures as well as evaluating the outputs for community and regional planning. Extensive aircraft underflight coverage was provided that was valuable both in inputs preparation and outputs evaluation of the machine-aided analyses. One of the nonstandard ERTS images led to the discovery of a major new fault lineament on the northern slope of the Santa Monica Mountains.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

System software for the finite element machine

NASA Technical Reports Server (NTRS)

Crockett, T. W.; Knott, J. D.

1985-01-01

The Finite Element Machine is an experimental parallel computer developed at Langley Research Center to investigate the application of concurrent processing to structural engineering analysis. This report describes system-level software which has been developed to facilitate use of the machine by applications researchers. The overall software design is outlined, and several important parallel processing issues are discussed in detail, including processor management, communication, synchronization, and input/output. Based on experience using the system, the hardware architecture and software design are critiqued, and areas for further work are suggested.

SU-C-304-07: Are Small Field Detector Correction Factors Strongly Dependent On Machine-Specific Characteristics?

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

Mathew, D; Tanny, S; Parsai, E

2015-06-15

Purpose: The current small field dosimetry formalism utilizes quality correction factors to compensate for the difference in detector response relative to dose deposited in water. The correction factors are defined on a machine-specific basis for each beam quality and detector combination. Some research has suggested that the correction factors may only be weakly dependent on machine-to-machine variations, allowing for determinations of class-specific correction factors for various accelerator models. This research examines the differences in small field correction factors for three detectors across two Varian Truebeam accelerators to determine the correction factor dependence on machine-specific characteristics. Methods: Output factors were measuredmore » on two Varian Truebeam accelerators for equivalently tuned 6 MV and 6 FFF beams. Measurements were obtained using a commercial plastic scintillation detector (PSD), two ion chambers, and a diode detector. Measurements were made at a depth of 10 cm with an SSD of 100 cm for jaw-defined field sizes ranging from 3×3 cm{sup 2} to 0.6×0.6 cm{sup 2}, normalized to values at 5×5cm{sup 2}. Correction factors for each field on each machine were calculated as the ratio of the detector response to the PSD response. Percent change of correction factors for the chambers are presented relative to the primary machine. Results: The Exradin A26 demonstrates a difference of 9% for 6×6mm{sup 2} fields in both the 6FFF and 6MV beams. The A16 chamber demonstrates a 5%, and 3% difference in 6FFF and 6MV fields at the same field size respectively. The Edge diode exhibits less than 1.5% difference across both evaluated energies. Field sizes larger than 1.4×1.4cm2 demonstrated less than 1% difference for all detectors. Conclusion: Preliminary results suggest that class-specific correction may not be appropriate for micro-ionization chamber. For diode systems, the correction factor was substantially similar and may be useful for class-specific reference conditions.« less

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

Parsimonious extreme learning machine using recursive orthogonal least squares.

PubMed

Wang, Ning; Er, Meng Joo; Han, Min

2014-10-01

Novel constructive and destructive parsimonious extreme learning machines (CP- and DP-ELM) are proposed in this paper. By virtue of the proposed ELMs, parsimonious structure and excellent generalization of multiinput-multioutput single hidden-layer feedforward networks (SLFNs) are obtained. The proposed ELMs are developed by innovative decomposition of the recursive orthogonal least squares procedure into sequential partial orthogonalization (SPO). The salient features of the proposed approaches are as follows: 1) Initial hidden nodes are randomly generated by the ELM methodology and recursively orthogonalized into an upper triangular matrix with dramatic reduction in matrix size; 2) the constructive SPO in the CP-ELM focuses on the partial matrix with the subcolumn of the selected regressor including nonzeros as the first column while the destructive SPO in the DP-ELM operates on the partial matrix including elements determined by the removed regressor; 3) termination criteria for CP- and DP-ELM are simplified by the additional residual error reduction method; and 4) the output weights of the SLFN need not be solved in the model selection procedure and is derived from the final upper triangular equation by backward substitution. Both single- and multi-output real-world regression data sets are used to verify the effectiveness and superiority of the CP- and DP-ELM in terms of parsimonious architecture and generalization accuracy. Innovative applications to nonlinear time-series modeling demonstrate superior identification results.

A Systematic Approach to Human Factors Measurement

DTIC Science & Technology

1978-10-01

terms . of an actual or simulated mission goal. 4. Research studies must be validated (replicated) in or with operational systems. 5 . All system...Indeterminacy has significant impact on the measurement strategy adopted. SCha.pter Six - CRITERIA FOR SELECTING AND EVALUATING PSM RESEARCH - 1. The...machine outputs merely aid the presentation of stimuli and the researcher need not be overly concerned with 5 ~machine characteristics (except in

Evaluating the Use of Machine Translation Post-Editing in the Foreign Language Class

ERIC Educational Resources Information Center

Nino, Ana

2008-01-01

Generalised access to the Internet and globalisation has led to increased demand for translation services and a resurgence in the use of machine translation (MT) systems. MT post-editing or the correction of MT output to an acceptable standard is known to be one of the ways to face the huge demand on multilingual communication. Given that the use…

Guide for machine tool task force members

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

Sutton, G.P.

1978-09-01

The purpose of the guide is to assist members of the Machine Tool Task Force (MTTF) in doing the job, preparing technical summary papers, and helping to achieve a uniform, high-quality output from this comprehensive study effort. It supplements the MTTF Plan (UCRL-52552) which contains other important information on the method of operation of MTTF that is related to the preparation of MTTF reports.

Hardware accuracy counters for application precision and quality feedback

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

de Paula Rosa Piga, Leonardo; Majumdar, Abhinandan; Paul, Indrani

Methods, devices, and systems for capturing an accuracy of an instruction executing on a processor. An instruction may be executed on the processor, and the accuracy of the instruction may be captured using a hardware counter circuit. The accuracy of the instruction may be captured by analyzing bits of at least one value of the instruction to determine a minimum or maximum precision datatype for representing the field, and determining whether to adjust a value of the hardware counter circuit accordingly. The representation may be output to a debugger or logfile for use by a developer, or may be outputmore » to a runtime or virtual machine to automatically adjust instruction precision or gating of portions of the processor datapath.« less

Acceptance, commissioning and clinical use of the WOmed T-200 kilovoltage X-ray therapy unit

PubMed Central

Zucchetti, Paolo

2015-01-01

Objective: The objective of this work was to characterize the performance of the WOmed T-200-kilovoltage (kV) therapy machine. Methods: Mechanical functionality, radiation leakage, alignment and interlocks were investigated. Half-value layers (HVLs) (first and second HVLs) from X-ray beams generated from tube potentials between 30 and 200 kV were measured. Reference dose was determined in water. Beam start-up characteristics, dose linearity and reproducibility, beam flatness, and uniformity as well as deviations from inverse square law were assessed. Relative depth doses (RDDs) were determined in water and water-equivalent plastic. The quality assurance program included a dosimetry audit with thermoluminescent dosemeters. Results: All checks on machine performance were satisfactory. HVLs ranged between 0.45–4.52 mmAl and 0.69–1.78 mmCu. Dose rates varied between 0.2 and 3 Gy min−1 with negligible time-end errors. There were differences in measured RDDs from published data. Beam outputs were confirmed with the dosimetry audit. The use of published backscatter factors was implemented to account for changes in phantom scatter for treatments with irregularly shaped fields. Conclusion: Guidance on the determination of HVL and RDD in kV beams can be contradictory. RDDs were determined through measurement and curve fitting. These differed from published RDD data, and the differences observed were larger in the low-kV energy range. Advances in knowledge: This article reports on the comprehensive and novel approach to the acceptance, commissioning and clinical use of a modern kV therapy machine. The challenges in the dosimetry of kV beams faced by the medical physicist in the clinic are highlighted. PMID:26224430

Shape classification of wear particles by image boundary analysis using machine learning algorithms

NASA Astrophysics Data System (ADS)

Yuan, Wei; Chin, K. S.; Hua, Meng; Dong, Guangneng; Wang, Chunhui

2016-05-01

The shape features of wear particles generated from wear track usually contain plenty of information about the wear states of a machinery operational condition. Techniques to quickly identify types of wear particles quickly to respond to the machine operation and prolong the machine's life appear to be lacking and are yet to be established. To bridge rapid off-line feature recognition with on-line wear mode identification, this paper presents a new radial concave deviation (RCD) method that mainly involves the use of the particle boundary signal to analyze wear particle features. Signal output from the RCDs subsequently facilitates the determination of several other feature parameters, typically relevant to the shape and size of the wear particle. Debris feature and type are identified through the use of various classification methods, such as linear discriminant analysis, quadratic discriminant analysis, naïve Bayesian method, and classification and regression tree method (CART). The average errors of the training and test via ten-fold cross validation suggest CART is a highly suitable approach for classifying and analyzing particle features. Furthermore, the results of the wear debris analysis enable the maintenance team to diagnose faults appropriately.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

NASA Astrophysics Data System (ADS)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Integrated intelligent sensor for the textile industry

NASA Astrophysics Data System (ADS)

Peltie, Philippe; David, Dominique

1996-08-01

A new sensor has been developed for pantyhose inspection. Unlike a first complete inspection machine devoted to post- manufacturing control of the whole panty, this sensor will be directly integrated on currently existing manufacturing machines, and will combine advantages of miniaturization is to design an intelligent, compact and very cheap product, which should be integrated without requiring any modifications of host machines. The sensor part was designed to achieve closed acquisition, and various solutions have been explored to maintain adequate depth of field. The illumination source will be integrated in the device. The processing part will include correction facilities and electronic processing. Finally, high-level information will be output in order to interface directly with the manufacturing machine automate.

Review of the energy check of an electron-only linear accelerator over a 6 year period: sensitivity of the technique to energy shift.

PubMed

Biggs, Peter J

2003-04-01

The calibration and monthly QA of an electron-only linear accelerator dedicated to intra-operative radiation therapy has been reviewed. Since this machine is calibrated prior to every procedure, there was no necessity to adjust the output calibration at any time except, perhaps, when the magnetron is changed, provided the machine output is reasonably stable. This gives a unique opportunity to study the dose output of the machine per monitor unit, variation in the timer error, flatness and symmetry of the beam and the energy check as a function of time. The results show that, although the dose per monitor unit varied within +/- 2%, the timer error within +/- 0.005 MU and the asymmetry within 1-2%, none of these parameters showed any systematic change with time. On the other hand, the energy check showed a linear drift with time for 6, 9, and 12 MeV (2.1, 3.5, and 2.5%, respectively, over 5 years), while at 15 and 18 MeV, the energy check was relatively constant. It is further shown that based on annual calibrations and RPC TLD checks, the energy of each beam is constant and that therefore the energy check is an exquisitely sensitive one. The consistency of the independent checks is demonstrated.

China Report, Economic Affairs.

DTIC Science & Technology

1986-02-14

consumer goods developed fairly rapidly. The output of sugar, machine-made paper, dairy products and beer doubled and redoubled. The output of sugar...same technology. The existing taxation is also un- favorable for those who introduce foreign technology. This is shown in the fact that except for...processing areas deter- mines that they must only adopt the externally oritned or export-oriented strategy, and regard earning foreign exchange through

High-efficiency machining methods for aviation materials

NASA Astrophysics Data System (ADS)

Kononov, V. K.

1991-07-01

The papers contained in this volume present results of theoretical and experimental studies aimed at increasing the efficiency of cutting tools during the machining of high-temperature materials and titanium alloys. Specific topics discussed include a study of the performance of disk cutters during the machining of flexible parts of a high-temperature alloy, VZhL14N; a study of the wear resistance of cutters of hard alloys of various types; effect of a deformed electric field on the precision of the electrochemical machining of gas turbine engine components; and efficient machining of parts of composite materials. The discussion also covers the effect of the technological process structure on the residual stress distribution in the blades of gas turbine engines; modeling of the multiparameter assembly of engineering products for a specified priority of geometrical output parameters; and a study of the quality of the surface and surface layer of specimens machined by a high-temperature pulsed plasma.

175Hp contrarotating homopolar motor design report

NASA Astrophysics Data System (ADS)

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

1994-06-01

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

Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Design of robotic cells based on relative handling modules with use of SolidWorks system

NASA Astrophysics Data System (ADS)

Gaponenko, E. V.; Anciferov, S. I.

2018-05-01

The article presents a diagramed engineering solution for a robotic cell with six degrees of freedom for machining of complex details, consisting of the base with a tool installation module and a detail machining module made as parallel structure mechanisms. The output links of the detail machining module and the tool installation module can move along X-Y-Z coordinate axes each. A 3D-model of the complex is designed in the SolidWorks system. It will be used further for carrying out engineering calculations and mathematical analysis and obtaining all required documentation.

Nanowire nanocomputer as a finite-state machine.

PubMed

Yao, Jun; Yan, Hao; Das, Shamik; Klemic, James F; Ellenbogen, James C; Lieber, Charles M

2014-02-18

Implementation of complex computer circuits assembled from the bottom up and integrated on the nanometer scale has long been a goal of electronics research. It requires a design and fabrication strategy that can address individual nanometer-scale electronic devices, while enabling large-scale assembly of those devices into highly organized, integrated computational circuits. We describe how such a strategy has led to the design, construction, and demonstration of a nanoelectronic finite-state machine. The system was fabricated using a design-oriented approach enabled by a deterministic, bottom-up assembly process that does not require individual nanowire registration. This methodology allowed construction of the nanoelectronic finite-state machine through modular design using a multitile architecture. Each tile/module consists of two interconnected crossbar nanowire arrays, with each cross-point consisting of a programmable nanowire transistor node. The nanoelectronic finite-state machine integrates 180 programmable nanowire transistor nodes in three tiles or six total crossbar arrays, and incorporates both sequential and arithmetic logic, with extensive intertile and intratile communication that exhibits rigorous input/output matching. Our system realizes the complete 2-bit logic flow and clocked control over state registration that are required for a finite-state machine or computer. The programmable multitile circuit was also reprogrammed to a functionally distinct 2-bit full adder with 32-set matched and complete logic output. These steps forward and the ability of our unique design-oriented deterministic methodology to yield more extensive multitile systems suggest that proposed general-purpose nanocomputers can be realized in the near future.

Nanowire nanocomputer as a finite-state machine

PubMed Central

Yao, Jun; Yan, Hao; Das, Shamik; Klemic, James F.; Ellenbogen, James C.; Lieber, Charles M.

2014-01-01

Implementation of complex computer circuits assembled from the bottom up and integrated on the nanometer scale has long been a goal of electronics research. It requires a design and fabrication strategy that can address individual nanometer-scale electronic devices, while enabling large-scale assembly of those devices into highly organized, integrated computational circuits. We describe how such a strategy has led to the design, construction, and demonstration of a nanoelectronic finite-state machine. The system was fabricated using a design-oriented approach enabled by a deterministic, bottom–up assembly process that does not require individual nanowire registration. This methodology allowed construction of the nanoelectronic finite-state machine through modular design using a multitile architecture. Each tile/module consists of two interconnected crossbar nanowire arrays, with each cross-point consisting of a programmable nanowire transistor node. The nanoelectronic finite-state machine integrates 180 programmable nanowire transistor nodes in three tiles or six total crossbar arrays, and incorporates both sequential and arithmetic logic, with extensive intertile and intratile communication that exhibits rigorous input/output matching. Our system realizes the complete 2-bit logic flow and clocked control over state registration that are required for a finite-state machine or computer. The programmable multitile circuit was also reprogrammed to a functionally distinct 2-bit full adder with 32-set matched and complete logic output. These steps forward and the ability of our unique design-oriented deterministic methodology to yield more extensive multitile systems suggest that proposed general-purpose nanocomputers can be realized in the near future. PMID:24469812

LANDMARK-BASED SPEECH RECOGNITION: REPORT OF THE 2004 JOHNS HOPKINS SUMMER WORKSHOP.

PubMed

Hasegawa-Johnson, Mark; Baker, James; Borys, Sarah; Chen, Ken; Coogan, Emily; Greenberg, Steven; Juneja, Amit; Kirchhoff, Katrin; Livescu, Karen; Mohan, Srividya; Muller, Jennifer; Sonmez, Kemal; Wang, Tianyu

2005-01-01

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a first-pass recognizer, and the resulting combination score is used to compute a second-pass speech recognition output.

Flow widening through a Darrieus wind turbine - Theory and experiment

NASA Astrophysics Data System (ADS)

Comolet, R.; Harajli, I.; Mercier Des Rochettes, P.; Yeznasni, A.

1982-11-01

A two-dimensional multiple stream tube model is developed for the air flow through a Darrieus wind turbine. The model is configured to account for the widening of the flux tubes as they cross the interior of the actuator disk. Note is made of the lateral broadening of the flow as it moves through the area, leaving a turbulent wake. A relation is defined between the variation in the kinetic energy of the flow and the aerodynamic forces acting on the blades. The global efficiency and the power output of the machine are calculated. Experimental results are reported for a machine equipped with two NACA 0015 blades, each 110 cm long and with a 10 cm chord. The Darrieus had a 1 m diam and was tested in a wind tunnel at wind speeds of 0-18 m/sec. Soap bubbles inflated with He were used for visualization. Power output was found to match prediction. The model is recommended for use in calculating the forces acting on the machine and studying vibration and fatigue causative mechanisms.

Quaternion Based Thermal Condition Monitoring System

NASA Astrophysics Data System (ADS)

Wong, Wai Kit; Loo, Chu Kiong; Lim, Way Soong; Tan, Poi Ngee

In this paper, we will propose a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. In simulation, we also discover that log-polar mapping actually help solving rotation and scaling invariant problems in quaternion based thermal image correlation. Beside that, log-polar mapping can have a two fold of data compression capability. Log-polar mapping can help smoother up the output correlation plane too, hence makes a better measurement way for PSR and p-values. Simulation results also show that the proposed system is an efficient machine condition monitoring system with accuracy more than 98%.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint

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

Florita, Anthony R; Elgindy, Tarek; Hodge, Brian S

A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmosphericmore » measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.« less

Prediction of properties of wheat dough using intelligent deep belief networks

NASA Astrophysics Data System (ADS)

Guha, Paramita; Bhatnagar, Taru; Pal, Ishan; Kamboj, Uma; Mishra, Sunita

2017-11-01

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

Inverse analysis of turbidites by machine learning

NASA Astrophysics Data System (ADS)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small deviation from the true parameters. Comparing to previous inverse modeling of turbidity currents, our methodology is superior especially in the efficiency of computation. Also, our methodology has advantage in extensibility and applicability to various sediment transport processes such as pyroclastic flows or debris flows.

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

2015-06-01

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

CMM Data Analysis Tool

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

Due to the increase in the use of Coordinate Measuring Machines (CMMs) to measure fine details and complex geometries in manufacturing, many programs have been made to compile and analyze the data. These programs typically require extensive setup to determine the expected results in order to not only track the pass/fail of a dimension, but also to use statistical process control (SPC). These extra steps and setup times have been addressed through the CMM Data Analysis Tool, which only requires the output of the CMM to provide both pass/fail analysis on all parts run to the same inspection program asmore » well as provide graphs which help visualize where the part measures within the allowed tolerances. This provides feedback not only to the customer for approval of a part during development, but also to machining process engineers to identify when any dimension is drifting towards an out of tolerance condition during production. This program can handle hundreds of parts with complex dimensions and will provide an analysis within minutes.« less

Triboelectrification based motion sensor for human-machine interfacing.

PubMed

Yang, Weiqing; Chen, Jun; Wen, Xiaonan; Jing, Qingshen; Yang, Jin; Su, Yuanjie; Zhu, Guang; Wu, Wenzuo; Wang, Zhong Lin

2014-05-28

We present triboelectrification based, flexible, reusable, and skin-friendly dry biopotential electrode arrays as motion sensors for tracking muscle motion and human-machine interfacing (HMI). The independently addressable, self-powered sensor arrays have been utilized to record the electric output signals as a mapping figure to accurately identify the degrees of freedom as well as directions and magnitude of muscle motions. A fast Fourier transform (FFT) technique was employed to analyse the frequency spectra of the obtained electric signals and thus to determine the motion angular velocities. Moreover, the motion sensor arrays produced a short-circuit current density up to 10.71 mA/m(2), and an open-circuit voltage as high as 42.6 V with a remarkable signal-to-noise ratio up to 1000, which enables the devices as sensors to accurately record and transform the motions of the human joints, such as elbow, knee, heel, and even fingers, and thus renders it a superior and unique invention in the field of HMI.

Video time encoding machines.

PubMed

Lazar, Aurel A; Pnevmatikakis, Eftychios A

2011-03-01

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire spiking mechanism with feedback. We show that analog information is represented by the neural circuits as projections on a set of band-limited functions determined by the spike sequence. Under Nyquist-type and frame conditions, the encoded signal can be recovered from these projections with arbitrary precision. For the video time encoding machine architecture, we demonstrate that band-limited video streams of finite energy can be faithfully recovered from the spike trains and provide a stable algorithm for perfect recovery. The key condition for recovery calls for the number of neurons in the population to be above a threshold value.

Video Time Encoding Machines

PubMed Central

Lazar, Aurel A.; Pnevmatikakis, Eftychios A.

2013-01-01

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire spiking mechanism with feedback. We show that analog information is represented by the neural circuits as projections on a set of band-limited functions determined by the spike sequence. Under Nyquist-type and frame conditions, the encoded signal can be recovered from these projections with arbitrary precision. For the video time encoding machine architecture, we demonstrate that band-limited video streams of finite energy can be faithfully recovered from the spike trains and provide a stable algorithm for perfect recovery. The key condition for recovery calls for the number of neurons in the population to be above a threshold value. PMID:21296708

Research on electrodischarge drilling of polycrystalline diamond with increased gap voltage

NASA Astrophysics Data System (ADS)

Skoczypiec, Sebastian; Bizoń, Wojciech; Żyra, Agnieszka

2018-05-01

This paper presents an experimental investigation of the machining characteristics of polycrystalline diamond (PCD). Machining of PCD by conventional technologies is not an effective solution. Due to presence of cobalt this material can be machined by application of electrical discharges. On the other side, electrical conductivity of PCD is on the limit of electrodischarge machining (EDM) possibilities. Proposed paper reports experimental investigation on electrodischarge drilling of PCD samples. The test were carried out with application on of high-voltage (up to 550 V) pulse power unit for two kinds of dielectrics: carbon based (Exxsol D80) and de-ionized water. As output parameters machining accuracy (side gap), material removal rate were selected. Also, based on SEM photographs and energy dispersive X-ray spectroscopy (EDS) analysis, a qualitative evaluation of the obtained results was presented.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

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

An introduction to quantum machine learning

NASA Astrophysics Data System (ADS)

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2015-04-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Beam Output Audit results within the EORTC Radiation Oncology Group network.

PubMed

Hurkmans, Coen W; Christiaens, Melissa; Collette, Sandra; Weber, Damien Charles

2016-12-15

Beam Output Auditing (BOA) is one key process of the EORTC radiation therapy quality assurance program. Here the results obtained between 2005 and 2014 are presented and compared to previous results.For all BOA reports the following parameters were scored: centre, country, date of audit, beam energies and treatment machines audited, auditing organisation, percentage of agreement between stated and measured dose.Four-hundred and sixty-one BOA reports were analyzed containing the results of 1790 photon and 1366 electron beams, delivered by 755 different treatment machines. The majority of beams (91.1%) were within the optimal limit of ≤ 3%. Only 13 beams (0.4%; n = 9 electrons; n = 4 photons), were out of the range of acceptance of ≤ 5%. Previous reviews reported a much higher percentage of 2.5% or more of the BOAs with >5% deviation.The majority of EORTC centres present beam output variations within the 3% tolerance cutoff value and only 0.4% of audited beams presented with variations of more than 5%. This is an important improvement compared to previous BOA results.

Productive High Performance Parallel Programming with Auto-tuned Domain-Specific Embedded Languages

DTIC Science & Technology

2013-01-02

Compilation JVM Java Virtual Machine KB Kilobyte KDT Knowledge Discovery Toolbox LAPACK Linear Algebra Package LLVM Low-Level Virtual Machine LOC Lines...different starting points. Leo Meyerovich also helped solidify some of the ideas here in discussions during Par Lab retreats. I would also like to thank...multi-timestep computations by blocking in both time and space. 88 Implementation Output Approx DSL Type Language Language Parallelism LoC Graphite

Robotics control using isolated word recognition of voice input

NASA Technical Reports Server (NTRS)

Weiner, J. M.

1977-01-01

A speech input/output system is presented that can be used to communicate with a task oriented system. Human speech commands and synthesized voice output extend conventional information exchange capabilities between man and machine by utilizing audio input and output channels. The speech input facility is comprised of a hardware feature extractor and a microprocessor implemented isolated word or phrase recognition system. The recognizer offers a medium sized (100 commands), syntactically constrained vocabulary, and exhibits close to real time performance. The major portion of the recognition processing required is accomplished through software, minimizing the complexity of the hardware feature extractor.

Inference of Surface Parameters from Near-Infrared Spectra of Crystalline H2O Ice with Neural Learning

NASA Astrophysics Data System (ADS)

Zhang, Lili; Merényi, Erzsébet; Grundy, William M.; Young, Eliot F.

2010-07-01

The near-infrared spectra of icy volatiles collected from planetary surfaces can be used to infer surface parameters, which in turn may depend on the recent geologic history. The high dimensionality and complexity of the spectral data, the subtle differences between the spectra, and the highly nonlinear interplay between surface parameters make it often difficult to accurately derive these surface parameters. We use a neural machine, with a Self-Organizing Map (SOM) as its hidden layer, to infer the latent physical parameters, temperature and grain size from near-infrared spectra of crystalline H2O ice. The output layer of the SOM-hybrid machine is customarily trained with only the output from the SOM winner. We show that this scheme prevents simultaneous achievement of high prediction accuracies for both parameters. We propose an innovative neural architecture we call Conjoined Twins that allows multiple (k) SOM winners to participate in the training of the output layer and in which the customization of k can be limited automatically to a small range. With this novel machine we achieve scientifically useful accuracies, 83.0 ± 2.7% and 100.0 ± 0.0%, for temperature and grain size, respectively, from simulated noiseless spectra. We also show that the performance of the neural model is robust under various noisy conditions. A primary application of this prediction capability is planned for spectra returned from the Pluto-Charon system by New Horizons.

(Machine) learning to do more with less

NASA Astrophysics Data System (ADS)

Cohen, Timothy; Freytsis, Marat; Ostdiek, Bryan

2018-02-01

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (which relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail — both analytically and numerically — with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to a class of systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC. Example code is provided on GitHub.

Detonation wave compression in gas turbines

NASA Technical Reports Server (NTRS)

Wortman, A.

1986-01-01

A study was made of the concept of augmenting the performance of low pressure ratio gas turbines by detonation wave compression of part of the flow. The concept exploits the constant volume heat release of detonation waves to increase the efficiency of the Brayton cycle. In the models studied, a fraction of the compressor output was channeled into detonation ducts where it was processed by transient transverse detonation waves. Gas dynamic studies determined the maximum cycling frequency of detonation ducts, proved that upstream propagation of pressure pulses represented no problems and determined the variations of detonation duct output with time. Mixing and wave compression were used to recombine the combustor and detonation duct flows and a concept for a spiral collector to further smooth the pressure and temperature pulses was presented as an optional component. The best performance was obtained with a single firing of the ducts so that the flow could be re-established before the next detonation was initiated. At the optimum conditions of maximum frequency of the detonation ducts, the gas turbine efficiency was found to be 45 percent while that of a corresponding pressure ratio 5 conventional gas turbine was only 26%. Comparable improvements in specific fuel consumption data were found for gas turbines operating as jet engines, turbofans, and shaft output machines. Direct use of the detonation duct output for jet propulsion proved unsatisfactory. Careful analysis of the models of the fluid flow phenomena led to the conclusion that even more elaborate calculations would not diminish the uncertainties in the analysis of the system. Feasibility of the concept to work as an engine now requires validation in an engineering laboratory experiment.

Advanced Military Pay System Concepts. Evaluation of Opportunities through Information Technology.

DTIC Science & Technology

1980-07-01

trans- mdtter (UART) to interface with a modem . The main processor was then responsible for input and output between main memory and the UART...digital, "run-length" encoding scheme which is very effective in reducing the amount of data to be transmitted. Machines of this type include a modem ...Output control as well as data compression will be combined with appropriate modems or interfaces to digital transmission channels and microprocessor

On Intelligent Design and Planning Method of Process Route Based on Gun Breech Machining Process

NASA Astrophysics Data System (ADS)

Hongzhi, Zhao; Jian, Zhang

2018-03-01

The paper states an approach of intelligent design and planning of process route based on gun breech machining process, against several problems, such as complex machining process of gun breech, tedious route design and long period of its traditional unmanageable process route. Based on gun breech machining process, intelligent design and planning system of process route are developed by virtue of DEST and VC++. The system includes two functional modules--process route intelligent design and its planning. The process route intelligent design module, through the analysis of gun breech machining process, summarizes breech process knowledge so as to complete the design of knowledge base and inference engine. And then gun breech process route intelligently output. On the basis of intelligent route design module, the final process route is made, edited and managed in the process route planning module.

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

PubMed

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

2017-12-12

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

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

PubMed Central

Segreto, Tiziana; Karam, Sara; Teti, Roberto

2017-01-01

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

Tempest gas turbine extends EGT product line

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

Chellini, R.

With the introduction of the 7.8 MW (mechanical output) Tempest gas turbine, ECT has extended the company`s line of its small industrial turbines. The new Tempest machine, featuring a 7.5 MW electric output and a 33% thermal efficiency, ranks above the company`s single-shaft Typhoon gas turbine, rated 3.2 and 4.9 MW, and the 6.3 MW Tornado gas turbine. All three machines are well-suited for use in combined heat and power (CHP) plants, as demonstrated by the fact that close to 50% of the 150 Typhoon units sold are for CHP applications. This experience has induced EGT, of Lincoln, England, tomore » announce the introduction of the new gas turbine prior to completion of the testing program. The present single-shaft machine is expected to be used mainly for industrial trial cogeneration. This market segment, covering the needs of paper mills, hospitals, chemical plants, ceramic industry, etc., is a typical local market. Cogeneration plants are engineered according to local needs and have to be assisted by local organizations. For this reason, to efficiently cover the world market, EGT has selected a number of associates that will receive from Lincoln completely engineered machine packages and will engineer the cogeneration system according to custom requirements. These partners will also assist the customer and dispose locally of the spares required for maintenance operations.« less

It's More than an Art Project. Activity.

ERIC Educational Resources Information Center

Wright, Phyllis; Wright, Thomas

1998-01-01

Shows how a first-grade class explored the activities that are completed by people who design products, develop production systems, use materials and machines to make goods, and market the output from manufacturing plants. (JOW)

Machining and characterization of self-reinforced polymers

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Development of Compact Ozonizer with High Ozone Output by Pulsed Power

NASA Astrophysics Data System (ADS)

Tanaka, Fumiaki; Ueda, Satoru; Kouno, Kanako; Sakugawa, Takashi; Akiyama, Hidenori; Kinoshita, Youhei

Conventional ozonizer with a high ozone output using silent or surface discharges needs a cooling system and a dielectric barrier, and therefore becomes a large machine. A compact ozonizer without the cooling system and the dielectric barrier has been developed by using a pulsed power generated discharge. The wire to plane electrodes made of metal have been used. However, the ozone output was low. Here, a compact and high repetition rate pulsed power generator is used as an electric source of a compact ozonizer. The ozone output of 6.1 g/h and the ozone yield of 86 g/kWh are achieved at 500 pulses per second, input average power of 280 W and an air flow rate of 20 L/min.

FEM analysis of an single stator dual PM rotors axial synchronous machine

NASA Astrophysics Data System (ADS)

Tutelea, L. N.; Deaconu, S. I.; Popa, G. N.

2017-01-01

The actual e - continuously variable transmission (e-CVT) solution for the parallel Hybrid Electric Vehicle (HEV) requires two electric machines, two inverters, and a planetary gear. A distinct electric generator and a propulsion electric motor, both with full power converters, are typical for a series HEV. In an effort to simplify the planetary-geared e-CVT for the parallel HEV or the series HEV we hereby propose to replace the basically two electric machines and their two power converters by a single, axial-air-gap, electric machine central stator, fed from a single PWM converter with dual frequency voltage output and two independent PM rotors. The proposed topologies, the magneto-motive force analysis and quasi 3D-FEM analysis are the core of the paper.

Addressing uncertainty in atomistic machine learning.

PubMed

Peterson, Andrew A; Christensen, Rune; Khorshidi, Alireza

2017-05-10

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate of the uncertainty when the width is comparable to that in the training data. Intriguingly, we also show that the uncertainty can be localized to specific atoms in the simulation, which may offer hints for the generation of training data to strategically improve the machine-learned representation.

Automated manual transmission clutch controller

DOEpatents

Lawrie, Robert E.; Reed, Jr., Richard G.; Rausen, David J.

1999-11-30

A powertrain system for a hybrid vehicle. The hybrid vehicle includes a heat engine, such as a diesel engine, and an electric machine, which operates as both an electric motor and an alternator, to power the vehicle. The hybrid vehicle also includes a manual-style transmission configured to operate as an automatic transmission from the perspective of the driver. The engine and the electric machine drive an input shaft which in turn drives an output shaft of the transmission. In addition to driving the transmission, the electric machine regulates the speed of the input shaft in order to synchronize the input shaft during either an upshift or downshift of the transmission by either decreasing or increasing the speed of the input shaft. When decreasing the speed of the input shaft, the electric motor functions as an alternator to produce electrical energy which may be stored by a storage device. Operation of the transmission is controlled by a transmission controller which receives input signals and generates output signals to control shift and clutch motors to effect smooth launch, upshift shifts, and downshifts of the transmission, so that the transmission functions substantially as an automatic transmission from the perspective of the driver, while internally substantially functioning as a manual transmission.

Automated manual transmission shift sequence controller

DOEpatents

Lawrie, Robert E.; Reed, Richard G.; Rausen, David J.

2000-02-01

A powertrain system for a hybrid vehicle. The hybrid vehicle includes a heat engine, such as a diesel engine, and an electric machine, which operates as both, an electric motor and an alternator, to power the vehicle. The hybrid vehicle also includes a manual-style transmission configured to operate as an automatic transmission from the perspective of the driver. The engine and the electric machine drive an input shaft which in turn drives an output shaft of the transmission. In addition to driving the transmission, the electric machine regulates the speed of the input shaft in order to synchronize the input shaft during either an upshift or downshift of the transmission by either decreasing or increasing the speed of the input shaft. When decreasing the speed of the input shaft, the electric motor functions as an alternator to produce electrical energy which may be stored by a storage device. Operation of the transmission is controlled by a transmission controller which receives input signals and generates output signals to control shift and clutch motors to effect smooth launch, upshift shifts, and downshifts of the transmission, so that the transmission functions substantially as an automatic transmission from the perspective of the driver, while internally substantially functioning as a manual transmission.

Automated manual transmission mode selection controller

DOEpatents

Lawrie, Robert E.

1999-11-09

A powertrain system for a hybrid vehicle. The hybrid vehicle includes a heat engine, such as a diesel engine, and an electric machine, which operates as both an electric motor and an alternator, to power the vehicle. The hybrid vehicle also includes a manual-style transmission configured to operate as an automatic transmission from the perspective of the driver. The engine and the electric machine drive an input shaft which in turn drives an output shaft of the transmission. In addition to driving the transmission, the electric machine regulates the speed of the input shaft in order to synchronize the input shaft during either an upshift or downshift of the transmission by either decreasing or increasing the speed of the input shaft. When decreasing the speed of the input shaft, the electric motor functions as an alternator to produce electrical energy which may be stored by a storage device. Operation of the transmission is controlled by a transmission controller which receives input signals and generates output signals to control shift and clutch motors to effect smooth launch, upshift shifts, and downshifts of the transmission, so that the transmission functions substantially as an automatic transmission from the perspective of the driver, while internally substantially functioning as a manual transmission.

Automated manual transmission controller

DOEpatents

Lawrie, Robert E.; Reed, Jr., Richard G.; Bernier, David R.

1999-12-28

A powertrain system for a hybrid vehicle. The hybrid vehicle includes a heat engine, such as a diesel engine, and an electric machine, which operates as both an electric motor and an alternator, to power the vehicle. The hybrid vehicle also includes a manual-style transmission configured to operate as an automatic transmission from the perspective of the driver. The engine and the electric machine drive an input shaft which in turn drives an output shaft of the transmission. In addition to driving the transmission, the electric machine regulates the speed of the input shaft in order to synchronize the input shaft during either an upshift or downshift of the transmission by either decreasing or increasing the speed of the input shaft. When decreasing the speed of the input shaft, the electric motor functions as an alternator to produce electrical energy which may be stored by a storage device. Operation of the transmission is controlled by a transmission controller which receives input signals and generates output signals to control shift and clutch motors to effect smooth launch, upshift shifts, and downshifts of the transmission, so that the transmission functions substantially as an automatic transmission from the perspective of the driver, while internally substantially functioning as a manual transmission.

On-line Gibbs learning. II. Application to perceptron and multilayer networks

NASA Astrophysics Data System (ADS)

Kim, J. W.; Sompolinsky, H.

1998-08-01

In the preceding paper (``On-line Gibbs Learning. I. General Theory'') we have presented the on-line Gibbs algorithm (OLGA) and studied analytically its asymptotic convergence. In this paper we apply OLGA to on-line supervised learning in several network architectures: a single-layer perceptron, two-layer committee machine, and a winner-takes-all (WTA) classifier. The behavior of OLGA for a single-layer perceptron is studied both analytically and numerically for a variety of rules: a realizable perceptron rule, a perceptron rule corrupted by output and input noise, and a rule generated by a committee machine. The two-layer committee machine is studied numerically for the cases of learning a realizable rule as well as a rule that is corrupted by output noise. The WTA network is studied numerically for the case of a realizable rule. The asymptotic results reported in this paper agree with the predictions of the general theory of OLGA presented in paper I. In all the studied cases, OLGA converges to a set of weights that minimizes the generalization error. When the learning rate is chosen as a power law with an optimal power, OLGA converges with a power law that is the same as that of batch learning.

Operating experience with four 200 kW Mod-0A wind turbine generators

NASA Technical Reports Server (NTRS)

Birchenough, A. G.; Saunders, A. L.; Nyland, T. W.; Shaltens, R. K.

1982-01-01

The windpowered generator, Mod-0A, and its advantages and disadvantages, particularly as it affects reliability, are discussed. The machine performance with regard to power availability and power output is discussed.

Induction generators for Wind Energy Conversion Systems. Part I: review of induction generator with squirrel cage rotor. Part II: the Double Output Induction Generator (DOIG). Progress report, July-December 1975

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

Jayadev, T.S.

1976-02-01

The application of induction generators in Wind Energy Conversion Systems (WECS) is described. The conventional induction generator, which is an induction machine with a squirrel cage rotor, had been used in large wind power plants in Europe, but has not caught much attention until now by designers of large systems in this country. The induction generator with a squirrel cage rotor is described and useful design techniques to build induction generators for wind energy application are outlined. The Double Output Induction Generator (DOIG) - so called because power is fed into the grid from the stator, as well as themore » rotor is described. It is a wound rotor induction machine with power electronics to convert rotor slip frequency power to that of line frequency.« less

Energy Harvesting with a Liquid-Metal Microfluidic Influence Machine

NASA Astrophysics Data System (ADS)

Conner, Christopher; de Visser, Tim; Loessberg, Joshua; Sherman, Sam; Smith, Andrew; Ma, Shuo; Napoli, Maria Teresa; Pennathur, Sumita; Weld, David

2018-04-01

We describe and demonstrate an alternative energy-harvesting technology based on a microfluidic realization of a Wimshurst influence machine. The prototype device converts the mechanical energy of a pressure-driven flow into electrical energy, using a multiphase system composed of droplets of liquid mercury surrounded by insulating oil. Electrostatic induction between adjacent metal droplets drives charge through external electrode paths, resulting in continuous charge amplification and collection. We demonstrate a power output of 4 nW from the initial prototype and present calculations suggesting that straightforward device optimization could increase the power output by more than 3 orders of magnitude. At that level, the power efficiency of this energy-harvesting mechanism, limited by viscous dissipation, could exceed 90%. The microfluidic context enables straightforward scaling and parallelization, as well as hydraulic matching to a variety of ambient mechanical energy sources, such as human locomotion.

Lithofacies classification of the Barnett Shale gas reservoir using neural network

NASA Astrophysics Data System (ADS)

Aliouane, Leila; Ouadfeul, Sid-Ali

2017-04-01

Here, we show the contribution of the artificial intelligence such as neural network to predict the lithofacies in the lower Barnett shale gas reservoir. The Multilayer Perceptron (MLP) neural network with Hidden Weight Optimization Algorithm is used. The input is raw well-logs data recorded in a horizontal well drilled in the Lower Barnett shale formation, however the output is the concentration of the Clay and the Quartz calculated using the ELAN model and confirmed with the core rock measurement. After training of the MLP machine weights of connection are calculated, the raw well-logs data of two other horizontal wells drilled in the same reservoir are propagated though the neural machine and an output is calculated. Comparison between the predicted and measured clay and Quartz concentrations in these two horizontal wells shows the ability of neural network to improve shale gas reservoirs characterization.

Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach

NASA Astrophysics Data System (ADS)

Diaz, Kristians; Castaneda, Benjamin

2008-03-01

This paper presents a semi-automated algorithm for prostate boundary segmentation from three-dimensional (3D) ultrasound (US) images. The US volume is sampled into 72 slices which go through the center of the prostate gland and are separated at a uniform angular spacing of 2.5 degrees. The approach requires the user to select four points from slices (at 0, 45, 90 and 135 degrees) which are used to initialize a discrete dynamic contour (DDC) algorithm. 4 Support Vector Machines (SVMs) are trained over the output of the DDC and classify the rest of the slices. The output of the SVMs is refined using binary morphological operations and DDC to produce the final result. The algorithm was tested on seven ex vivo 3D US images of prostate glands embedded in an agar mold. Results show good agreement with manual segmentation.

Deep neural mapping support vector machines.

PubMed

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Interpolator for numerically controlled machine tools

DOEpatents

Bowers, Gary L.; Davenport, Clyde M.; Stephens, Albert E.

1976-01-01

A digital differential analyzer circuit is provided that depending on the embodiment chosen can carry out linear, parabolic, circular or cubic interpolation. In the embodiment for parabolic interpolations, the circuit provides pulse trains for the X and Y slide motors of a two-axis machine to effect tool motion along a parabolic path. The pulse trains are generated by the circuit in such a way that parabolic tool motion is obtained from information contained in only one block of binary input data. A part contour may be approximated by one or more parabolic arcs. Acceleration and initial velocity values from a data block are set in fixed bit size registers for each axis separately but simultaneously and the values are integrated to obtain the movement along the respective axis as a function of time. Integration is performed by continual addition at a specified rate of an integrand value stored in one register to the remainder temporarily stored in another identical size register. Overflows from the addition process are indicative of the integral. The overflow output pulses from the second integration may be applied to motors which position the respective machine slides according to a parabolic motion in time to produce a parabolic machine tool motion in space. An additional register for each axis is provided in the circuit to allow "floating" of the radix points of the integrand registers and the velocity increment to improve position accuracy and to reduce errors encountered when the acceleration integrand magnitudes are small when compared to the velocity integrands. A divider circuit is provided in the output of the circuit to smooth the output pulse spacing and prevent motor stall, because the overflow pulses produced in the binary addition process are spaced unevenly in time. The divider has the effect of passing only every nth motor drive pulse, with n being specifiable. The circuit inputs (integrands, rates, etc.) are scaled to give exactly n times the desired number of pulses out, in order to compensate for the divider.

Applications of Machine Learning to Downscaling and Verification

NASA Astrophysics Data System (ADS)

Prudden, R.

2017-12-01

Downscaling, sometimes known as super-resolution, means converting model data into a more detailed local forecast. It is a problem which could be highly amenable to machine learning approaches, provided that sufficient historical forecast data and observations are available. It is also closely linked to the subject of verification, since improving a forecast requires a way to measure that improvement. This talk will describe some early work towards downscaling Met Office ensemble forecasts, and discuss how the output may be usefully evaluated.

Anytime query-tuned kernel machine classifiers via Cholesky factorization

NASA Technical Reports Server (NTRS)

DeCoste, D.

2002-01-01

We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

PubMed

Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

2014-01-01

Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions.

PubMed

Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

2017-07-05

Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions. Copyright © 2017 Elsevier Inc. All rights reserved.

Development of a Tri-Axial Cutting Force Sensor for the Milling Process

PubMed Central

Li, Yingxue; Zhao, Yulong; Fei, Jiyou; Zhao, You; Li, Xiuyuan; Gao, Yunxiang

2016-01-01

This paper presents a three-component fixed dynamometer based on a strain gauge, which reduces output errors produced by the cutting force imposed on different milling positions of the workpiece. A reformative structure of tri-layer cross beams is proposed, sensitive areas were selected, and corresponding measuring circuits were arranged to decrease the inaccuracy brought about by positional variation. To simulate the situation with a milling cutter moving on the workpiece and validate the function of reducing the output errors when the milling position changes, both static calibration and dynamic milling tests were implemented on different parts of the workpiece. Static experiment results indicate that with standard loads imposed, the maximal deviation between the measured forces and the standard inputs is 4.87%. The results of the dynamic milling test illustrate that with identical machining parameters, the differences in output variation between the developed sensor and standard dynamometer are no larger than 6.61%. Both static and dynamic experimental results demonstrate that the developed dynamometer is suitable for measuring milling force imposed on different positions of the workpiece, which shows potential applicability in machining a monitoring system. PMID:27007374

Control method and system for hydraulic machines employing a dynamic joint motion model

DOEpatents

Danko, George [Reno, NV

2011-11-22

A control method and system for controlling a hydraulically actuated mechanical arm to perform a task, the mechanical arm optionally being a hydraulically actuated excavator arm. The method can include determining a dynamic model of the motion of the hydraulic arm for each hydraulic arm link by relating the input signal vector for each respective link to the output signal vector for the same link. Also the method can include determining an error signal for each link as the weighted sum of the differences between a measured position and a reference position and between the time derivatives of the measured position and the time derivatives of the reference position for each respective link. The weights used in the determination of the error signal can be determined from the constant coefficients of the dynamic model. The error signal can be applied in a closed negative feedback control loop to diminish or eliminate the error signal for each respective link.

Disc-geometry homopolar synchronous machine

NASA Astrophysics Data System (ADS)

Evans, P. D.; Eastham, J. F.

1980-09-01

Results of an experimental and theoretical investigation of a disc-geometry homopolar synchronous machine with field excitation on the primary side are presented. The unlaminated mild-steel rotor contains no windings and is brushless. The prototype machine produces approximately 7.5 kW of mechanical output at 3000 rev/min, with a product of power factor and efficiency greater than 0.7. The construction of the stator core is unusual and incorporates both laminated and unlaminated portions. The magnetic circuit is also arranged to minimize the axial force between the stator and rotor. A novel rotor design which achieves a reduced quadrature-axis reactance is shown experimentally to be superior to the conventional homopolar rotor.

Application of exergetic sustainability index to a nano-scale irreversible Brayton cycle operating with ideal Bose and Fermi gasses

NASA Astrophysics Data System (ADS)

Açıkkalp, Emin; Caner, Necmettin

2015-09-01

In this study, a nano-scale irreversible Brayton cycle operating with quantum gasses including Bose and Fermi gasses is researched. Developments in the nano-technology cause searching the nano-scale machines including thermal systems to be unavoidable. Thermodynamic analysis of a nano-scale irreversible Brayton cycle operating with Bose and Fermi gasses was performed (especially using exergetic sustainability index). In addition, thermodynamic analysis involving classical evaluation parameters such as work output, exergy output, entropy generation, energy and exergy efficiencies were conducted. Results are submitted numerically and finally some useful recommendations were conducted. Some important results are: entropy generation and exergetic sustainability index are affected mostly for Bose gas and power output and exergy output are affected mostly for the Fermi gas by x. At the high temperature conditions, work output and entropy generation have high values comparing with other degeneracy conditions.

Attitude profile design program

NASA Technical Reports Server (NTRS)

1991-01-01

The Attitude Profile Design (APD) Program was designed to be used as a stand-alone addition to the Simplex Computation of Optimum Orbital Trajectories (SCOOT). The program uses information from a SCOOT output file and the user defined attitude profile to produce time histories of attitude, angular body rates, and accelerations. The APD program is written in standard FORTRAN77 and should be portable to any machine that has an appropriate compiler. The input and output are through formatted files. The program reads the basic flight data, such as the states of the vehicles, acceleration profiles, and burn information, from the SCOOT output file. The user inputs information about the desired attitude profile during coasts in a high level manner. The program then takes these high level commands and executes the maneuvers, outputting the desired information.

Carnot efficiency at divergent power output

NASA Astrophysics Data System (ADS)

Polettini, Matteo; Esposito, Massimiliano

2017-05-01

The widely debated feasibility of thermodynamic machines achieving Carnot efficiency at finite power has been convincingly dismissed. Yet, the common wisdom that efficiency can only be optimal in the limit of infinitely slow processes overlooks the dual scenario of infinitely fast processes. We corroborate that efficient engines at divergent power output are not theoretically impossible, framing our claims within the theory of Stochastic Thermodynamics. We inspect the case of an electronic quantum dot coupled to three particle reservoirs to illustrate the physical rationale.

Design and analysis of a novel doubly salient permanent- magnet generator

NASA Astrophysics Data System (ADS)

Sarlioglu, Bulent

Improvements in permanent magnets and power electronics technologies have made it possible to devise different configurations of electrical machines which were not previously possible to implement. In this dissertation, a novel Doubly Salient Permanent Magnet (DSPM) generator has been designed, analyzed, and tested. The DSPM generator has four stator poles and six rotor poles. Two high density permanent magnets are located in the stator yoke. Since there are no windings or permanent magnets in the rotor, the DSPM generator has several advantages: the rotor has low inertia, no copper loss, no PM attachments, no brushes, and no slip rings. This type of rotor can be manufactured easily, and can be run at very high speeds as in the case of a switched reluctance machine. Compared to induction and switched reluctance machines, the DSPM generator can produce more power from the same geometry. Moreover, the efficiency of the DSPM generator is higher, since there is no copper loss associated with excitation of the machine. Another advantage of the DSPM generator is that the output AC voltage can easily be rectified by a diode bridge rectifier, while in the case of the switched reluctance machine one needs to use active semiconductor switches for power generation. If greater utilization and control of power production capability are desired, the AC output of the DSPM generator can be rectified using an active converter. In this dissertation, a novel doubly salient permanent magnet generator is introduced. First, the theory of the DSPM generator is given. Later, this novel generator is investigated using conventional magnetic circuits, nonlinear finite element analysis, and simulations with first order approximations and nonlinear modeling. It is compared with other generators. Static and no-load testing of the prototype DSPM generator are presented, and generator performance is evaluated with various power electronic circuits.

Modelling and Prediction of Spark-ignition Engine Power Performance Using Incremental Least Squares Support Vector Machines

NASA Astrophysics Data System (ADS)

Wong, Pak-kin; Vong, Chi-man; Wong, Hang-cheong; Li, Ke

2010-05-01

Modern automotive spark-ignition (SI) power performance usually refers to output power and torque, and they are significantly affected by the setup of control parameters in the engine management system (EMS). EMS calibration is done empirically through tests on the dynamometer (dyno) because no exact mathematical engine model is yet available. With an emerging nonlinear function estimation technique of Least squares support vector machines (LS-SVM), the approximate power performance model of a SI engine can be determined by training the sample data acquired from the dyno. A novel incremental algorithm based on typical LS-SVM is also proposed in this paper, so the power performance models built from the incremental LS-SVM can be updated whenever new training data arrives. With updating the models, the model accuracies can be continuously increased. The predicted results using the estimated models from the incremental LS-SVM are good agreement with the actual test results and with the almost same average accuracy of retraining the models from scratch, but the incremental algorithm can significantly shorten the model construction time when new training data arrives.

Applications of quantum cloning

NASA Astrophysics Data System (ADS)

Pomarico, E.; Sanguinetti, B.; Sekatski, P.; Zbinden, H.; Gisin, N.

2011-10-01

Quantum Cloning Machines (QCMs) allow for the copying of information, within the limits imposed by quantum mechanics. These devices are particularly interesting in the high-gain regime, i.e., when one input qubit generates a state of many output qubits. In this regime, they allow for the study of certain aspects of the quantum to classical transition. The understanding of these aspects is the root of the two recent applications that we will review in this paper: the first one is the Quantum Cloning Radiometer, a device which is able to produce an absolute measure of spectral radiance. This device exploits the fact that in the quantum regime information can be copied with only finite fidelity, whereas when a state becomes macroscopic, this fidelity gradually increases to 1. Measuring the fidelity of the cloning operation then allows to precisely determine the absolute spectral radiance of the input optical source. We will then discuss whether a Quantum Cloning Machine could be used to produce a state visible by the naked human eye, and the possibility of a Bell Experiment with humans playing the role of detectors.

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

PubMed

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-12-07

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

45 CFR 602.41 - Financial reporting.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION UNIFORM... under this part. (5) Federal agencies may provide computer outputs to grantees to expedite or contribute... machine usable format or computer printouts instead of prescribed forms. (6) Federal agencies may waive...

45 CFR 602.41 - Financial reporting.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION UNIFORM... under this part. (5) Federal agencies may provide computer outputs to grantees to expedite or contribute... machine usable format or computer printouts instead of prescribed forms. (6) Federal agencies may waive...

45 CFR 602.41 - Financial reporting.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION UNIFORM... under this part. (5) Federal agencies may provide computer outputs to grantees to expedite or contribute... machine usable format or computer printouts instead of prescribed forms. (6) Federal agencies may waive...

45 CFR 602.41 - Financial reporting.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION UNIFORM... under this part. (5) Federal agencies may provide computer outputs to grantees to expedite or contribute... machine usable format or computer printouts instead of prescribed forms. (6) Federal agencies may waive...

45 CFR 602.41 - Financial reporting.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION UNIFORM... under this part. (5) Federal agencies may provide computer outputs to grantees to expedite or contribute... machine usable format or computer printouts instead of prescribed forms. (6) Federal agencies may waive...

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

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

Mechanical design of walking machines.

PubMed

Arikawa, Keisuke; Hirose, Shigeo

2007-01-15

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

Reversibility in Quantum Models of Stochastic Processes

NASA Astrophysics Data System (ADS)

Gier, David; Crutchfield, James; Mahoney, John; James, Ryan

Natural phenomena such as time series of neural firing, orientation of layers in crystal stacking and successive measurements in spin-systems are inherently probabilistic. The provably minimal classical models of such stochastic processes are ɛ-machines, which consist of internal states, transition probabilities between states and output values. The topological properties of the ɛ-machine for a given process characterize the structure, memory and patterns of that process. However ɛ-machines are often not ideal because their statistical complexity (Cμ) is demonstrably greater than the excess entropy (E) of the processes they represent. Quantum models (q-machines) of the same processes can do better in that their statistical complexity (Cq) obeys the relation Cμ >= Cq >= E. q-machines can be constructed to consider longer lengths of strings, resulting in greater compression. With code-words of sufficiently long length, the statistical complexity becomes time-symmetric - a feature apparently novel to this quantum representation. This result has ramifications for compression of classical information in quantum computing and quantum communication technology.

Radar detection with the Neyman-Pearson criterion using supervised-learning-machines trained with the cross-entropy error

NASA Astrophysics Data System (ADS)

Jarabo-Amores, María-Pilar; la Mata-Moya, David de; Gil-Pita, Roberto; Rosa-Zurera, Manuel

2013-12-01

The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman-Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.

Extending Landauer's bound from bit erasure to arbitrary computation

NASA Astrophysics Data System (ADS)

Wolpert, David

The minimal thermodynamic work required to erase a bit, known as Landauer's bound, has been extensively investigated both theoretically and experimentally. However, when viewed as a computation that maps inputs to outputs, bit erasure has a very special property: the output does not depend on the input. Existing analyses of thermodynamics of bit erasure implicitly exploit this property, and thus cannot be directly extended to analyze the computation of arbitrary input-output maps. Here we show how to extend these earlier analyses of bit erasure to analyze the thermodynamics of arbitrary computations. Doing this establishes a formal connection between the thermodynamics of computers and much of theoretical computer science. We use this extension to analyze the thermodynamics of the canonical ``general purpose computer'' considered in computer science theory: a universal Turing machine (UTM). We consider a UTM which maps input programs to output strings, where inputs are drawn from an ensemble of random binary sequences, and prove: i) The minimal work needed by a UTM to run some particular input program X and produce output Y is the Kolmogorov complexity of Y minus the log of the ``algorithmic probability'' of Y. This minimal amount of thermodynamic work has a finite upper bound, which is independent of the output Y, depending only on the details of the UTM. ii) The expected work needed by a UTM to compute some given output Y is infinite. As a corollary, the overall expected work to run a UTM is infinite. iii) The expected work needed by an arbitrary Turing machine T (not necessarily universal) to compute some given output Y can either be infinite or finite, depending on Y and the details of T. To derive these results we must combine ideas from nonequilibrium statistical physics with fundamental results from computer science, such as Levin's coding theorem and other theorems about universal computation. I would like to ackowledge the Santa Fe Institute, Grant No. TWCF0079/AB47 from the Templeton World Charity Foundation, Grant No. FQXi-RHl3-1349 from the FQXi foundation, and Grant No. CHE-1648973 from the U.S. National Science Foundation.

Overview of nanofluid application through minimum quantity lubrication (MQL) in metal cutting process

NASA Astrophysics Data System (ADS)

Sharif, Safian; Sadiq, Ibrahim Ogu; Suhaimi, Mohd Azlan; Rahim, Shayfull Zamree Abd

2017-09-01

Pollution related activities in addition to handling cost of conventional cutting fluid application in metal cutting industry has generated a lot of concern over time. The desire for a green machining environment which will preserve the environment through reduction or elimination of machining related pollution, reduction in oil consumption and safety of the machine operators without compromising an efficient machining process led to search for alternatives to conventional cutting fluid. Amongst the alternatives of dry machining, cryogenic cooling, high pressure cooling, near dry or minimum quantity lubrication (MQL), MQL have shown remarkable performance in terms of cost, machining output, safety of environment and machine operators. However, the MQL under aggressive machining or very high speed machining pose certain restriction as the lubrication media cannot perform efficiently at elevated temperature. In compensating for the shortcomings of MQL technique, high thermal conductivity nanoparticles are introduced in cutting fluids for use in the MQL lubrication process. They have indicated enhanced performance of machining process and significant reduction of loads on the environment. The present work is aimed at evaluating the application and performance of nanofluid in metal cutting process through MQL lubrication technique highlighting their impacts and prospects as lubrication strategy in metal cutting process for sustainable green manufacturing. Enhanced performance of vegetable oil based nanofluids over mineral oil-based nanofluids have been reported and thus highlighted.

Spinning-disk generation and drying of monodisperse solid aerosols with output concentrations sufficient for single-breath inhalation studies.

PubMed

Byron, P R; Hickey, A J

1987-01-01

The air-driven spinning-disk aerosol generator was modified to allow the production of monodisperse dry spherical aerosols of disodium fluorescein (as model solute) in high output concentrations. Output concentrations were determined by filtration. Optical and aerodynamic size distributions were determined microscopically (after electrostatic precipitation) and by cascade impaction. The generator housing allowed the entrainment of 25-microns primary aqueous solution droplets in a 10-L X min-1 downward flow of dry, filtered air. Internal equipment surfaces were machined flush and polished to minimize aerosol losses. Primary droplets were dried within a stainless steel pipe encased in a tube furnace. Water vapor was removed by diffusion drying. Disk-driven air, satellite droplets, and additional dilution air were vented to waste without using a vacuum. Generator yields were increased by reducing the size of the satellite droplet extraction gap. Aerosols were generated reproducibly by delivering aqueous solutions at a rate of 0.2 mL X min-1 to the center of the disk and spinning at 1000 rps. Dry aerosols, with mass median aerodynamic diameters of 2, 4.9, and 9 microns, were produced in concentrations of 0.89, 5.48, and 54.6 micrograms X L-1 from aqueous solutions containing 0.0374, 0.584, and 3.4% solute by weight. Geometric standard deviations were less than 1.2 in all cases. Concentrations are several times higher than others in the literature and are suitable for single-breath inhalation studies of therapeutic aerosol deposition and effect.

Effect of magnetic polarity on surface roughness during magnetic field assisted EDM of tool steel

NASA Astrophysics Data System (ADS)

Efendee, A. M.; Saifuldin, M.; Gebremariam, MA; Azhari, A.

2018-04-01

Electrical discharge machining (EDM) is one of the non-traditional machining techniques where the process offers wide range of parameters manipulation and machining applications. However, surface roughness, material removal rate, electrode wear and operation costs were among the topmost issue within this technique. Alteration of magnetic device around machining area offers exciting output to be investigated and the effects of magnetic polarity on EDM remain unacquainted. The aim of this research is to investigate the effect of magnetic polarity on surface roughness during magnetic field assisted electrical discharge machining (MFAEDM) on tool steel material (AISI 420 mod.) using graphite electrode. A Magnet with a force of 18 Tesla was applied to the EDM process at selected parameters. The sparks under magnetic field assisted EDM produced better surface finish than the normal conventional EDM process. At the presence of high magnetic field, the spark produced was squeezed and discharge craters generated on the machined surface was tiny and shallow. Correct magnetic polarity combination of MFAEDM process is highly useful to attain a high efficiency machining and improved quality of surface finish to meet the demand of modern industrial applications.

Documentation of model input and output values for simulation of pumping effects in Paradise Valley, a basin tributary to the Humboldt River, Humboldt County, Nevada

USGS Publications Warehouse

Carey, A.E.; Prudic, David E.

1996-01-01

Documentation is provided of model input and sample output used in a previous report for analysis of ground-water flow and simulated pumping scenarios in Paradise Valley, Humboldt County, Nevada.Documentation includes files containing input values and listings of sample output. The files, in American International Standard Code for Information Interchange (ASCII) or binary format, are compressed and put on a 3-1/2-inch diskette. The decompressed files require approximately 8.4 megabytes of disk space on an International Business Machine (IBM)- compatible microcomputer using the MicroSoft Disk Operating System (MS-DOS) operating system version 5.0 or greater.

Technical Report: Reference photon dosimetry data for Varian accelerators based on IROC-Houston site visit data.

PubMed

Kerns, James R; Followill, David S; Lowenstein, Jessica; Molineu, Andrea; Alvarez, Paola; Taylor, Paige A; Stingo, Francesco C; Kry, Stephen F

2016-05-01

Accurate data regarding linear accelerator (Linac) radiation characteristics are important for treatment planning system modeling as well as regular quality assurance of the machine. The Imaging and Radiation Oncology Core-Houston (IROC-H) has measured the dosimetric characteristics of numerous machines through their on-site dosimetry review protocols. Photon data are presented and can be used as a secondary check of acquired values, as a means to verify commissioning a new machine, or in preparation for an IROC-H site visit. Photon data from IROC-H on-site reviews from 2000 to 2014 were compiled and analyzed. Specifically, data from approximately 500 Varian machines were analyzed. Each dataset consisted of point measurements of several dosimetric parameters at various locations in a water phantom to assess the percentage depth dose, jaw output factors, multileaf collimator small field output factors, off-axis factors, and wedge factors. The data were analyzed by energy and parameter, with similarly performing machine models being assimilated into classes. Common statistical metrics are presented for each machine class. Measurement data were compared against other reference data where applicable. Distributions of the parameter data were shown to be robust and derive from a student's t distribution. Based on statistical and clinical criteria, all machine models were able to be classified into two or three classes for each energy, except for 6 MV for which there were eight classes. Quantitative analysis of the measurements for 6, 10, 15, and 18 MV photon beams is presented for each parameter; supplementary material has also been made available which contains further statistical information. IROC-H has collected numerous data on Varian Linacs and the results of photon measurements from the past 15 years are presented. The data can be used as a comparison check of a physicist's acquired values. Acquired values that are well outside the expected distribution should be verified by the physicist to identify whether the measurements are valid. Comparison of values to this reference data provides a redundant check to help prevent gross dosimetric treatment errors.

Mechanical strength of laser-welded cobalt-chromium alloy.

PubMed

Baba, N; Watanabe, I; Liu, J; Atsuta, M

2004-05-15

The purpose of this study was to investigate the effect of the output energy of laser welding and welding methods on the joint strength of cobalt-chromium (Co-Cr) alloy. Two types of cast Co-Cr plates were prepared, and transverse sections were made at the center of the plate. The cut surfaces were butted against one another, and the joints welded with a laser-welding machine at several levels of output energy with the use of two methods. The fracture force required to break specimens was determined by means of tensile testing. For the 0.5-mm-thick specimens, the force required to break the 0.5-mm laser-welded specimens at currents of 270 and 300 A was not statistically different (p > 0.05) from the results for the nonwelded control specimens. The force required to break the 1.0-mm specimens double-welded at a current of 270 A was the highest value among the 1.0-mm laser-welded specimens. The results suggested that laser welding under the appropriate conditions improved the joint strength of cobalt- chromium alloy. Copyright 2004 Wiley Periodicals, Inc.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

PubMed

Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J; Ji, Guoli

2017-10-07

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

Implementing a quantum cloning machine in separate cavities via the optical coherent pulse as a quantum communication bus

NASA Astrophysics Data System (ADS)

Zhu, Meng-Zheng; Ye, Liu

2015-04-01

An efficient scheme is proposed to implement a quantum cloning machine in separate cavities based on a hybrid interaction between electron-spin systems placed in the cavities and an optical coherent pulse. The coefficient of the output state for the present cloning machine is just the direct product of two trigonometric functions, which ensures that different types of quantum cloning machine can be achieved readily in the same framework by appropriately adjusting the rotated angles. The present scheme can implement optimal one-to-two symmetric (asymmetric) universal quantum cloning, optimal symmetric (asymmetric) phase-covariant cloning, optimal symmetric (asymmetric) real-state cloning, optimal one-to-three symmetric economical real-state cloning, and optimal symmetric cloning of qubits given by an arbitrary axisymmetric distribution. In addition, photon loss of the qubus beams during the transmission and decoherence effects caused by such a photon loss are investigated.

Optical design of laser transmission system

NASA Astrophysics Data System (ADS)

Zhang, Yulan; Feng, Jinliang; Li, Yongliang; Yang, Jiandong

1998-08-01

This paper discusses a design of optical transfer system used in carbon-dioxide laser therapeutic machine. The design of this system is according to the requirement of the therapeutic machine. The therapeutic machine requires the movement of laser transfer system is similar to the movement of human beings arms, which possesses 7 rotating hinges. We use optical hinges, which is composed of 45 degree mirrors. Because the carbon-dioxide laser mode is not good, light beam diameter at focus and divergence angle dissemination are big, we use a collecting lens at the transfer system output part in order to make the light beam diameter at focus in 0.2 to approximately 0.3 mm. For whole system the focus off-axis error is less than 0.5 mm, the transfer power consumption is smaller than 10%. The system can move in three dimension space freely and satisfies the therapeutic machine requirement.

DOE/NASA Mod-0 100KW wind turbine test results

NASA Technical Reports Server (NTRS)

Glasgow, J. C.

1978-01-01

The Wind Turbine demonstrates the capability of automatic unattended operation, including startup, achieving synchronism, and shutdown as dictated by wind conditions. During the course of these operations, a wealth of engineering data was generated. Some of the data which is associated with rotor and machine dynamics problems encountered, and the machine modifications incorporated as a solution are presented. These include high blade loads due to tower shadow, excessive nacelle yawing motion, and power oscillations. The results of efforts to correlate measured wind velocity with power output and wind turbine loads are also discussed.

A new self-regulated self-excited single-phase induction generator using a squirrel cage three-phase induction machine

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

Fukami, Tadashi; Imamura, Michinori; Kaburaki, Yuichi

1995-12-31

A new single-phase capacitor self-excited induction generator with self-regulating feature is presented. The new generator consists of a squirrel cage three-phase induction machine and three capacitors connected in series and parallel with a single phase load. The voltage regulation of this generator is very small due to the effect of the three capacitors. Moreover, since a Y-connected stator winding is employed, the waveform of the output voltage becomes sinusoidal. In this paper the system configuration and the operating principle of the new generator are explained, and the basic characteristics are also investigated by means of a simple analysis and experimentsmore » with a laboratory machine.« less

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

Olivares, Stefano

We investigate the performance of a selective cloning machine based on linear optical elements and Gaussian measurements, which allows one to clone at will one of the two incoming input states. This machine is a complete generalization of a 1{yields}2 cloning scheme demonstrated by Andersen et al. [Phys. Rev. Lett. 94, 240503 (2005)]. The input-output fidelity is studied for a generic Gaussian input state, and the effect of nonunit quantum efficiency is also taken into account. We show that, if the states to be cloned are squeezed states with known squeezing parameter, then the fidelity can be enhanced using amore » third suitable squeezed state during the final stage of the cloning process. A binary communication protocol based on the selective cloning machine is also discussed.« less

Integrated human-machine intelligence in space systems

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1992-01-01

The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.

Rotational magnetization: Problems in experimental and theoretical studies of electrical steels and amorphous magnetic materials

NASA Astrophysics Data System (ADS)

Moses, A. J.

1994-03-01

Flux rotating in the plane of laminations of amorphous materials or electrical steels can cause additional losses in electrical machines. To make full use of laboratory rotational magnetization studies, a better understanding of the nature of rotational flux in machine cores is needed. This paper highlights the need for careful laboratory simulation of the conditions which occur in actual machines. Single specimen tests must produce uniform flux over a given measuring region and output from field and flux sensors need careful analysis. Differences between thermal and flux sensing methods are shown as well as anomalies caused when the magnetisation direction is reversed in an anistropic specimen. Methods of overcoming these problems are proposed.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

MetaJC++: A flexible and automatic program transformation technique using meta framework

NASA Astrophysics Data System (ADS)

Beevi, Nadera S.; Reghu, M.; Chitraprasad, D.; Vinodchandra, S. S.

2014-09-01

Compiler is a tool to translate abstract code containing natural language terms to machine code. Meta compilers are available to compile more than one languages. We have developed a meta framework intends to combine two dissimilar programming languages, namely C++ and Java to provide a flexible object oriented programming platform for the user. Suitable constructs from both the languages have been combined, thereby forming a new and stronger Meta-Language. The framework is developed using the compiler writing tools, Flex and Yacc to design the front end of the compiler. The lexer and parser have been developed to accommodate the complete keyword set and syntax set of both the languages. Two intermediate representations have been used in between the translation of the source program to machine code. Abstract Syntax Tree has been used as a high level intermediate representation that preserves the hierarchical properties of the source program. A new machine-independent stack-based byte-code has also been devised to act as a low level intermediate representation. The byte-code is essentially organised into an output class file that can be used to produce an interpreted output. The results especially in the spheres of providing C++ concepts in Java have given an insight regarding the potential strong features of the resultant meta-language.

Performance evaluation of Ormat unit at Wabuska, Nevada. Final report

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

Culver, G.

1986-07-01

Three nominal 24 hour tests under summer, winter and spring weather conditions, were run on an Ormat geothermal binary power generation machine. The machine, located at TAD's Enterprises in Wabuska, Nevada is supplied with approximately 830 gpm of geothermal water at 221/sup 0/F and has two spray cooling ponds. During the tests, temperature, pressure, and flows of geothermal water, freon, cooling water and instantaneous electrical production were recorded hourly. At least once during each test, energy consumption of the well pump, freon feed pump and cooling water pumps were made. Power output of the machine is limited by spray pondmore » capacity. Net output ranged from 410.2 kW during summer conditions when cooling water was 65/sup 0/F to 610.4 kW during winter conditions when cooling water was 55/sup 0/F. Net resource utilization ranged from 1.005 Whr/lb during the summer test to 1.55 Whr/lb during the winter test. Spray pond performance averaged 63% for the fall and winter tests. Availability of the Ormat unit itself during the eight month test period was generally good, averaging 95.5%. Overall system availability, including well pumps, cooling system and electric grid was somewhat less - averaging 83%.« less

Determination of plasma pinch time and effective current radius of double planar wire array implosions from current measurements on a 1-MA linear transformer driver

NASA Astrophysics Data System (ADS)

Steiner, Adam M.; Yager-Elorriaga, David A.; Patel, Sonal G.; Jordan, Nicholas M.; Gilgenbach, Ronald M.; Safronova, Alla S.; Kantsyrev, Victor L.; Shlyaptseva, Veronica V.; Shrestha, Ishor; Schmidt-Petersen, Maximillian T.

2016-10-01

Implosions of planar wire arrays were performed on the Michigan Accelerator for Inductive Z-pinch Experiments, a linear transformer driver (LTD) at the University of Michigan. These experiments were characterized by lower than expected peak currents and significantly longer risetimes compared to studies performed on higher impedance machines. A circuit analysis showed that the load inductance has a significant impact on the current output due to the comparatively low impedance of the driver; the long risetimes were also attributed to high variability in LTD switch closing times. A circuit model accounting for these effects was employed to measure changes in load inductance as a function of time to determine plasma pinch timing and calculate a minimum effective current-carrying radius. These calculations showed good agreement with available shadowgraphy and x-ray diode measurements.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

CIVET: Continuous Integration, Verification, Enhancement, and Testing

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

Alger, Brian; Gaston, Derek R.; Permann, Cody J

A Git server (GitHub, GitLab, BitBucket) sends event notifications to the Civet server. These are either a " Pull Request" or a "Push" notification. Civet then checks the database to determine what tests need to be run and marks them as ready to run. Civet clients, running on dedicated machines, query the server for available jobs that are ready to run. When a client gets a job it executes the scripts attached to the job and report back to the server the output and exit status. When the client updates the server, the server will also update the Git servermore » with the result of the job, as well as updating the main web page.« less

34 CFR 80.41 - Financial reporting.

Code of Federal Regulations, 2011 CFR

2011-07-01

... under this part. (5) Federal agencies may provide computer outputs to grantees to expedite or contribute... machine usable format or computer printouts instead of prescribed forms. (6) Federal agencies may waive... project or program. However, the report will not be required more frequently than quarterly. If the...

Enhancing Three-dimensional Movement Control System for Assemblies of Machine-Building Facilities

NASA Astrophysics Data System (ADS)

Kuzyakov, O. N.; Andreeva, M. A.

2018-01-01

Aspects of enhancing three-dimensional movement control system are given in the paper. Such system is to be used while controlling assemblies of machine-building facilities, which is a relevant issue. The base of the system known is three-dimensional movement control device with optical principle of action. The device consists of multi point light emitter and light receiver matrix. The processing of signals is enhanced to increase accuracy of measurements by switching from discrete to analog signals. Light receiver matrix is divided into four areas, and the output value of each light emitter in each matrix area is proportional to its luminance level. Thus, determing output electric signal value of each light emitter in corresponding area leads to determing position of multipoint light emitter and position of object tracked. This is done by using Case-based reasoning method, the precedent in which is described as integral signal value of each matrix area, coordinates of light receivers, which luminance level is high, and decision to be made in this situation.

Modeling safety requirements of an FMS using Petri-nets

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.; Buck, A. A.; Smith, R.

1993-08-01

This paper is concerned with the modelling of safety requirements using Petri nets as a tool to model and simulate a Flexible Manufacturing System (FMS). The FMS cell described comprises a pick and place robot, a multi-head drilling machine together with a vision system and illustrates how the hierarchical structure of Petri nets can be used to ensure that all fail- safe requirements are satisfied; block diagrams together with fully detailed example Petri nets are given. The work demonstrates the use of cell and robot control Petro nets together with robot subnets for the x, y and z axes and associated output nets; the control and output nets are linked together with a safety net. Individual machines are linked with the control and safety nets of an FMS at cell level. The paper also illustrates how a Petri net can act as a decision maker during image inspection and identifies the unsafe conditions that can arise within an FMS.

Ranking support vector machine for multiple kernels output combination in protein-protein interaction extraction from biomedical literature.

PubMed

Yang, Zhihao; Lin, Yuan; Wu, Jiajin; Tang, Nan; Lin, Hongfei; Li, Yanpeng

2011-10-01

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight combination and optimal weight combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

XRF inductive bead fusion and PLC based control system

NASA Astrophysics Data System (ADS)

Zhu, Jin-hong; Wang, Ying-jie; Shi, Hong-xin; Chen, Qing-ling; Chen, Yu-xi

2009-03-01

In order to ensure high-quality X-ray fluorescence spectrometry (XRF) analysis, an inductive bead fusion machine was developed. The prototype consists of super-audio IGBT induction heating power supply, rotation and swing mechanisms, and programmable logic controller (PLC). The system can realize sequence control, mechanical movement control, output current and temperature control. Experimental results show that the power supply can operate at an ideal quasi-resonant state, in which the expected power output and the required temperature can be achieved for rapid heating and the uniform formation of glass beads respectively.

Visual Inference Programming

NASA Technical Reports Server (NTRS)

Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter

2002-01-01

The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.

SU-F-T-367: Using PRIMO, a PENELOPE-Based Software, to Improve the Small Field Dosimetry of Linear Accelerators

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

Benmakhlouf, H; Andreo, P; Brualla, L

2016-06-15

Purpose: To calculate output correction factors for Varian Clinac 2100iX beams for seven small field detectors and use the values to determine the small field output factors for the linacs at Karolinska university hospital. Methods: Phase space files (psf) for square fields between 0.25cm and 10cm were calculated using the PENELOPE-based PRIMO software. The linac MC-model was tuned by comparing PRIMO-estimated and experimentally determined depth doses and lateral dose-profiles for 40cmx40cm fields. The calculated psf were used as radiation sources to calculate the correction factors of IBA and PTW detectors with the code penEasy/PENELOPE. Results: The optimal tuning parameters ofmore » the MClinac model in PRIMO were 5.4 MeV incident electron energy and zero energy spread, focal spot size and beam divergence. Correction factors obtained for the liquid ion chamber (PTW-T31018) are within 1% down to 0.5 cm fields. For unshielded diodes (IBA-EFD, IBA-SFD, PTW-T60017 and PTW-T60018) the corrections are up to 2% at intermediate fields (>1cm side), becoming down to −11% for fields smaller than 1cm. The shielded diode (IBA-PFD and PTW-T60016) corrections vary with field size from 0 to −4%. Volume averaging effects are found for most detectors in the presence of 0.25cm fields. Conclusion: Good agreement was found between correction factors based on PRIMO-generated psf and those from other publications. The calculated factors will be implemented in output factor measurements (using several detectors) in the clinic. PRIMO is a userfriendly general code capable of generating small field psf and can be used without having to code own linac geometries. It can therefore be used to improve the clinical dosimetry, especially in the commissioning of linear accelerators. Important dosimetry data, such as dose-profiles and output factors can be determined more accurately for a specific machine, geometry and setup by using PRIMO and having a MC-model of the detector used.« less

A Pervasive Parallel Processing Framework for Data Visualization and Analysis at Extreme Scale

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

Moreland, Kenneth; Geveci, Berk

2014-11-01

The evolution of the computing world from teraflop to petaflop has been relatively effortless, with several of the existing programming models scaling effectively to the petascale. The migration to exascale, however, poses considerable challenges. All industry trends infer that the exascale machine will be built using processors containing hundreds to thousands of cores per chip. It can be inferred that efficient concurrency on exascale machines requires a massive amount of concurrent threads, each performing many operations on a localized piece of data. Currently, visualization libraries and applications are based off what is known as the visualization pipeline. In the pipelinemore » model, algorithms are encapsulated as filters with inputs and outputs. These filters are connected by setting the output of one component to the input of another. Parallelism in the visualization pipeline is achieved by replicating the pipeline for each processing thread. This works well for today’s distributed memory parallel computers but cannot be sustained when operating on processors with thousands of cores. Our project investigates a new visualization framework designed to exhibit the pervasive parallelism necessary for extreme scale machines. Our framework achieves this by defining algorithms in terms of worklets, which are localized stateless operations. Worklets are atomic operations that execute when invoked unlike filters, which execute when a pipeline request occurs. The worklet design allows execution on a massive amount of lightweight threads with minimal overhead. Only with such fine-grained parallelism can we hope to fill the billions of threads we expect will be necessary for efficient computation on an exascale machine.« less

Two-qubit quantum cloning machine and quantum correlation broadcasting

NASA Astrophysics Data System (ADS)

Kheirollahi, Azam; Mohammadi, Hamidreza; Akhtarshenas, Seyed Javad

2016-11-01

Due to the axioms of quantum mechanics, perfect cloning of an unknown quantum state is impossible. But since imperfect cloning is still possible, a question arises: "Is there an optimal quantum cloning machine?" Buzek and Hillery answered this question and constructed their famous B-H quantum cloning machine. The B-H machine clones the state of an arbitrary single qubit in an optimal manner and hence it is universal. Generalizing this machine for a two-qubit system is straightforward, but during this procedure, except for product states, this machine loses its universality and becomes a state-dependent cloning machine. In this paper, we propose some classes of optimal universal local quantum state cloners for a particular class of two-qubit systems, more precisely, for a class of states with known Schmidt basis. We then extend our machine to the case that the Schmidt basis of the input state is deviated from the local computational basis of the machine. We show that more local quantum coherence existing in the input state corresponds to less fidelity between the input and output states. Also we present two classes of a state-dependent local quantum copying machine. Furthermore, we investigate local broadcasting of two aspects of quantum correlations, i.e., quantum entanglement and quantum discord, defined, respectively, within the entanglement-separability paradigm and from an information-theoretic perspective. The results show that although quantum correlation is, in general, very fragile during the broadcasting procedure, quantum discord is broadcasted more robustly than quantum entanglement.

Cortical activity predicts good variation in human motor output.

PubMed

Babikian, Sarine; Kanso, Eva; Kutch, Jason J

2017-04-01

Human movement patterns have been shown to be particularly variable if many combinations of activity in different muscles all achieve the same task goal (i.e., are goal-equivalent). The nervous system appears to automatically vary its output among goal-equivalent combinations of muscle activity to minimize muscle fatigue or distribute tissue loading, but the neural mechanism of this "good" variation is unknown. Here we use a bimanual finger task, electroencephalography (EEG), and machine learning to determine if cortical signals can predict goal-equivalent variation in finger force output. 18 healthy participants applied left and right index finger forces to repeatedly perform a task that involved matching a total (sum of right and left) finger force. As in previous studies, we observed significantly more variability in goal-equivalent muscle activity across task repetitions compared to variability in muscle activity that would not achieve the goal: participants achieved the task in some repetitions with more right finger force and less left finger force (right > left) and in other repetitions with less right finger force and more left finger force (left > right). We found that EEG signals from the 500 milliseconds (ms) prior to each task repetition could make a significant prediction of which repetitions would have right > left and which would have left > right. We also found that cortical maps of sites contributing to the prediction contain both motor and pre-motor representation in the appropriate hemisphere. Thus, goal-equivalent variation in motor output may be implemented at a cortical level.

System and method for characterizing synthesizing and/or canceling out acoustic signals from inanimate sound sources

DOEpatents

Holzrichter, John F.; Burnett, Greg C.; Ng, Lawrence C.

2003-01-01

A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

Characterizing, synthesizing, and/or canceling out acoustic signals from sound sources

DOEpatents

Holzrichter, John F [Berkeley, CA; Ng, Lawrence C [Danville, CA

2007-03-13

A system for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate and animate sound sources. Electromagnetic sensors monitor excitation sources in sound producing systems, such as animate sound sources such as the human voice, or from machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The systems disclosed enable accurate calculation of transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

System and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources

DOEpatents

Holzrichter, John F; Burnett, Greg C; Ng, Lawrence C

2013-05-21

A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

System and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources

DOEpatents

Holzrichter, John F.; Burnett, Greg C.; Ng, Lawrence C.

2007-10-16

A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

SU-E-T-636: ProteusONE Machine QA Procedure and Stabiity Study: Half Year Clinical Operation

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

Freund, D; Ding, X; Wu, H

2015-06-15

Purpose: The objective of this study is to evaluate the stability of ProteusOne, the 1st commercial PBS proton system, throughout the daily QA and monthly over 6 month clinical operation. Method: Daily QA test includes IGRT position/repositioning, output in the middle of SOBP, beam flatness, symmetry, inplane and crossplane dimensions as well as energy range check. Daily range shifter QA consist of output, symmetry and field size checks to make sure its integrity. In 30 mins Daily QA test, all the measurements are performed using the MatriXXPT (IBA dosimetry). The data from these measurement was collected and compare over themore » first 6 month of clinical operation. In addition to the items check in daily QA, the summary also includes the monthly QA gantry star shots, absolute position check using a novel device, XRV-100. Results: Average machine output at the center of the spread out bragg peak was 197.5±.8 cGy and was within 1%of the baseline at 198.4 cGy. Beam flatness was within 1% cross plane with an average of 0.67±0.12% and 2% in-plane with an average of 1.08±0.17% compared to baseline measurements of 0.6 and 1.03, respectively. In all cases the radiation isocenter shift was less than or equal to 1mm. Output for the range shifter was within 2% for each individual measurement and averaged 34.4±.2cGy compare to a baseline reading of 34.5cGy. The average range shifter in and cross plane field size measurements were 19.8±0.5cm and 20.5±0.4cm compared with baseline values of 20.19cm and 20.79cm, respectively. Range shifter field symmetry had an average of less 1% for both in-plane and cross plane measurements. Conclusion: All machine metrics over the past 6 months have proved to be stable. Although, some averages are outside the baseline measurement they are within 1% tolerance and the deviation across all measurements is minimal.« less

Mathematical modelling and numerical simulation of forces in milling process

NASA Astrophysics Data System (ADS)

Turai, Bhanu Murthy; Satish, Cherukuvada; Prakash Marimuthu, K.

2018-04-01

Machining of the material by milling induces forces, which act on the work piece material, tool and which in turn act on the machining tool. The forces involved in milling process can be quantified, mathematical models help to predict these forces. A lot of research has been carried out in this area in the past few decades. The current research aims at developing a mathematical model to predict forces at different levels which arise machining of Aluminium6061 alloy. Finite element analysis was used to develop a FE model to predict the cutting forces. Simulation was done for varying cutting conditions. Different experiments was designed using Taguchi method. A L9 orthogonal array was designed and the output was measure for the different experiments. The same was used to develop the mathematical model.

Multilevel-Dc-Bus Inverter For Providing Sinusoidal And Pwm Electrical Machine Voltages

DOEpatents

Su, Gui-Jia [Knoxville, TN

2005-11-29

A circuit for controlling an ac machine comprises a full bridge network of commutation switches which are connected to supply current for a corresponding voltage phase to the stator windings, a plurality of diodes, each in parallel connection to a respective one of the commutation switches, a plurality of dc source connections providing a multi-level dc bus for the full bridge network of commutation switches to produce sinusoidal voltages or PWM signals, and a controller connected for control of said dc source connections and said full bridge network of commutation switches to output substantially sinusoidal voltages to the stator windings. With the invention, the number of semiconductor switches is reduced to m+3 for a multi-level dc bus having m levels. A method of machine control is also disclosed.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

Hydroclimatic sustainability assessment of changing climate on cholera in the Ganges-Brahmaputra basin

NASA Astrophysics Data System (ADS)

Nasr-Azadani, Fariborz; Khan, Rakibul; Rahimikollu, Javad; Unnikrishnan, Avinash; Akanda, Ali; Alam, Munirul; Huq, Anwar; Jutla, Antarpreet; Colwell, Rita

2017-10-01

The association of cholera and climate has been extensively documented. However, determining the effects of changing climate on the occurrence of disease remains a challenge. Bimodal peaks of cholera in Bengal Delta are hypothesized to be linked to asymmetric flow of the Ganges and Brahmaputra rivers. Spring cholera is related to intrusion of bacteria-laden coastal seawater during low flow seasons, while autumn cholera results from cross-contamination of water resources when high flows in the rivers cause massive inundation. Coarse resolution of General Circulation Model (GCM) output (usually at 100 - 300 km)cannot be used to evaluate variability at the local scale(10-20 km),hence the goal of this study was to develop a framework that could be used to understand impacts of climate change on occurrence of cholera. Instead of a traditional approach of downscaling precipitation, streamflow of the two rivers was directly linked to GCM outputs, achieving reasonable accuracy (R2 = 0.89 for the Ganges and R2 = 0.91 for the Brahmaputra)using machine learning algorithms (Support Vector Regression-Particle Swarm Optimization). Copula methods were used to determine probabilistic risks of cholera under several discharge conditions. Key results, using model outputs from ECHAM5, GFDL, andHadCM3for A1B and A2 scenarios, suggest that the combined low flow of the two rivers may increase in the future, with high flows increasing for first half of this century, decreasing thereafter. Spring and autumn cholera, assuming societal conditions remain constant e.g., at the current rate, may decrease. However significant shifts were noted in the magnitude of river discharge suggesting that cholera dynamics of the delta may well demonstrate an uncertain predictable pattern of occurrence over the next century.

Development of machine-vision system for gap inspection of muskmelon grafted seedlings.

PubMed

Liu, Siyao; Xing, Zuochang; Wang, Zifan; Tian, Subo; Jahun, Falalu Rabiu

2017-01-01

Grafting robots have been developed in the world, but some auxiliary works such as gap-inspecting for grafted seedlings still need to be done by human. An machine-vision system of gap inspection for grafted muskmelon seedlings was developed in this study. The image acquiring system consists of a CCD camera, a lens and a front white lighting source. The image of inspected gap was processed and analyzed by software of HALCON 12.0. The recognition algorithm for the system is based on principle of deformable template matching. A template should be created from an image of qualified grafted seedling gap. Then the gap image of the grafted seedling will be compared with the created template to determine their matching degree. Based on the similarity between the gap image of grafted seedling and the template, the matching degree will be 0 to 1. The less similar for the grafted seedling gap with the template the smaller of matching degree. Thirdly, the gap will be output as qualified or unqualified. If the matching degree of grafted seedling gap and the template is less than 0.58, or there is no match is found, the gap will be judged as unqualified; otherwise the gap will be qualified. Finally, 100 muskmelon seedlings were grafted and inspected to test the gap inspection system. Results showed that the gap inspection machine-vision system could recognize the gap qualification correctly as 98% of human vision. And the inspection speed of this system can reach 15 seedlings·min-1. The gap inspection process in grafting can be fully automated with this developed machine-vision system, and the gap inspection system will be a key step of a fully-automatic grafting robots.

Biologically based machine vision: signal analysis of monopolar cells in the visual system of Musca domestica.

PubMed

Newton, Jenny; Barrett, Steven F; Wilcox, Michael J; Popp, Stephanie

2002-01-01

Machine vision for navigational purposes is a rapidly growing field. Many abilities such as object recognition and target tracking rely on vision. Autonomous vehicles must be able to navigate in dynamic enviroments and simultaneously locate a target position. Traditional machine vision often fails to react in real time because of large computational requirements whereas the fly achieves complex orientation and navigation with a relatively small and simple brain. Understanding how the fly extracts visual information and how neurons encode and process information could lead us to a new approach for machine vision applications. Photoreceptors in the Musca domestica eye that share the same spatial information converge into a structure called the cartridge. The cartridge consists of the photoreceptor axon terminals and monopolar cells L1, L2, and L4. It is thought that L1 and L2 cells encode edge related information relative to a single cartridge. These cells are thought to be equivalent to vertebrate bipolar cells, producing contrast enhancement and reduction of information sent to L4. Monopolar cell L4 is thought to perform image segmentation on the information input from L1 and L2 and also enhance edge detection. A mesh of interconnected L4's would correlate the output from L1 and L2 cells of adjacent cartridges and provide a parallel network for segmenting an object's edges. The focus of this research is to excite photoreceptors of the common housefly, Musca domestica, with different visual patterns. The electrical response of monopolar cells L1, L2, and L4 will be recorded using intracellular recording techniques. Signal analysis will determine the neurocircuitry to detect and segment images.

Ultrasonic drilling apparatus

DOEpatents

Duran, Edward L.; Lundin, Ralph L.

1989-01-01

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

Ultrasonic drilling apparatus

DOEpatents

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

1988-06-20

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

A Practical Torque Estimation Method for Interior Permanent Magnet Synchronous Machine in Electric Vehicles.

PubMed

Wu, Zhihong; Lu, Ke; Zhu, Yuan

2015-01-01

The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary for the safety of the vehicle. In this paper, a torque estimation method based on flux estimator with a modified low pass filter is presented. Moreover, by taking into account the non-ideal characteristic of the inverter, the torque estimation accuracy is improved significantly. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulation and experiment.

Methods for the design and analysis of power optimized finite-state machines using clock gating

NASA Astrophysics Data System (ADS)

Chodorowski, Piotr

2017-11-01

The paper discusses two methods of design of power optimized FSMs. Both methods use clock gating techniques. The main objective of the research was to write a program capable of generating automatic hardware description of finite-state machines in VHDL and testbenches to help power analysis. The creation of relevant output files is detailed step by step. The program was tested using the LGSynth91 FSM benchmark package. An analysis of the generated circuits shows that the second method presented in this paper leads to significant reduction of power consumption.

A Practical Torque Estimation Method for Interior Permanent Magnet Synchronous Machine in Electric Vehicles

PubMed Central

Zhu, Yuan

2015-01-01

The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary for the safety of the vehicle. In this paper, a torque estimation method based on flux estimator with a modified low pass filter is presented. Moreover, by taking into account the non-ideal characteristic of the inverter, the torque estimation accuracy is improved significantly. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulation and experiment. PMID:26114557

Stimulation at Desert Peak -modeling with the coupled THM code FEHM

DOE Data Explorer

kelkar, sharad

2013-04-30

Numerical modeling of the 2011 shear stimulation at the Desert Peak well 27-15. This submission contains the FEHM executable code for a 64-bit PC Windows-7 machine, and the input and output files for the results presented in the included paper from ARMA-213 meeting.

REST: a computer system for estimating logging residue by using the line-intersect method

Treesearch

A. Jeff Martin

1975-01-01

A computer program was designed to accept logging-residue measurements obtained by line-intersect sampling and transform them into summaries useful for the land manager. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

Techniques for decoding speech phonemes and sounds: A concept

NASA Technical Reports Server (NTRS)

Lokerson, D. C.; Holby, H. G.

1975-01-01

Techniques studied involve conversion of speech sounds into machine-compatible pulse trains. (1) Voltage-level quantizer produces number of output pulses proportional to amplitude characteristics of vowel-type phoneme waveforms. (2) Pulses produced by quantizer of first speech formants are compared with pulses produced by second formants.

Efficiently Ranking Hyphotheses in Machine Learning

NASA Technical Reports Server (NTRS)

Chien, Steve

1997-01-01

This paper considers the problem of learning the ranking of a set of alternatives based upon incomplete information (e.g. a limited number of observations). At each decision cycle, the system can output a complete ordering on the hypotheses or decide to gather additional information (e.g. observation) at some cost.

Obtaining Accurate Probabilities Using Classifier Calibration

ERIC Educational Resources Information Center

Pakdaman Naeini, Mahdi

2016-01-01

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

A low-cost contact system to assess load displacement velocity in a resistance training machine.

PubMed

Buscà, Bernat; Font, Anna

2011-01-01

This study sought to determine the validity of a new system for assessing the displacement and average velocity within machine-based resistance training exercise using the Chronojump System. The new design is based on a contact bar and a simple, low-cost mechanism that detects the conductivity of electrical potentials with a precision chronograph. This system allows coaches to assess velocity to control the strength training process. A validation study was performed by assessing the concentric phase parameters of a leg press exercise. Output time data from the Chronojump System in combination with the pre-established range of movement was compared with data from a position sensor connected to a Biopac System. A subset of 87 actions from 11 professional tennis players was recorded and, using the two methods, average velocity and displacement variables in the same action were compared. A t-test for dependent samples and a correlation analysis were undertaken. The r value derived from the correlation between the Biopac System and the contact Chronojump System was >0.94 for all measures of displacement and velocity on all loads (p < 0.01). The Effect Size (ES) was 0.18 in displacement and 0.14 in velocity and ranged from 0.09 to 0.31 and from 0.07 to 0.34, respectively. The magnitude of the difference between the two methods in all parameters and the correlation values provided certain evidence of validity of the Chronojump System to assess the average displacement velocity of loads in a resistance training machine. Key pointsThe assessment of speed in resistance machines is a valuable source of information for strength training.Many commercial systems used to assess velocity, power and force are expensive thereby preventing widespread use by coaches and athletes.The system is intended to be a low-cost device for assessing and controlling the velocity exerted on each repetition in any resistance training machine.The system could be easily adapted in any vertical displacement barbell exercise.

Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features

NASA Astrophysics Data System (ADS)

Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.

2017-12-01

Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.

The study on force, surface integrity, tool life and chip on laser assisted machining of inconel 718 using Nd:YAG laser source.

PubMed

Venkatesan, K

2017-07-01

Inconel 718, a high-temperature alloy, is a promising material for high-performance aerospace gas turbine engines components. However, the machining of the alloy is difficult owing to immense shear strength, rapid work hardening rate during turning, and less thermal conductivity. Hence, like ceramics and composites, the machining of this alloy is considered as difficult-to-turn materials. Laser assisted turning method has become a promising solution in recent years to lessen cutting stress when materials that are considered difficult-to-turn, such as Inconel 718 is employed. This study investigated the influence of input variables of laser assisted machining on the machinability aspect of the Inconel 718. The comparison of machining characteristics has been carried out to analyze the process benefits with the variation of laser machining variables. The laser assisted machining variables are cutting speeds of 60-150 m/min, feed rates of 0.05-0.125 mm/rev with a laser power between 1200 W and 1300 W. The various output characteristics such as force, roughness, tool life and geometrical characteristic of chip are investigated and compared with conventional machining without application of laser power. From experimental results, at a laser power of 1200 W, laser assisted turning outperforms conventional machining by 2.10 times lessening in cutting force, 46% reduction in surface roughness as well as 66% improvement in tool life when compared that of conventional machining. Compared to conventional machining, with the application of laser, the cutting speed of carbide tool has increased to a cutting condition of 150 m/min, 0.125 mm/rev. Microstructural analysis shows that no damage of the subsurface of the workpiece.

On the Use of Machine Learning Techniques for the Mechanical Characterization of Soft Biological Tissues.

PubMed

Cilla, M; Pérez-Rey, I; Martínez, M A; Peña, Estefania; Martínez, Javier

2018-06-23

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the SEF and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted, thus the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues. This article is protected by copyright. All rights reserved.

Precise on-machine extraction of the surface normal vector using an eddy current sensor array

NASA Astrophysics Data System (ADS)

Wang, Yongqing; Lian, Meng; Liu, Haibo; Ying, Yangwei; Sheng, Xianjun

2016-11-01

To satisfy the requirements of on-machine measurement of the surface normal during complex surface manufacturing, a highly robust normal vector extraction method using an Eddy current (EC) displacement sensor array is developed, the output of which is almost unaffected by surface brightness, machining coolant and environmental noise. A precise normal vector extraction model based on a triangular-distributed EC sensor array is first established. Calibration of the effects of object surface inclination and coupling interference on measurement results, and the relative position of EC sensors, is involved. A novel apparatus employing three EC sensors and a force transducer was designed, which can be easily integrated into the computer numerical control (CNC) machine tool spindle and/or robot terminal execution. Finally, to test the validity and practicability of the proposed method, typical experiments were conducted with specified testing pieces using the developed approach and system, such as an inclined plane and cylindrical and spherical surfaces.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, Jones B.; Burleson, Robert R.; Pardue, Robert M.

1982-01-01

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of position interferometers and part contour description data inputs to calculate error components for each axis of movement and output them to corresponding axis drives with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, J.B.; Burleson, R.R.; Pardue, R.M.

1980-09-12

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of positions interferometers and part contour description data input to calculate error components for each axis of movement and output them to corresponding axis driven with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Chi-square-based scoring function for categorization of MEDLINE citations.

PubMed

Kastrin, A; Peterlin, B; Hristovski, D

2010-01-01

Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine-learning algorithms (support vector machines, decision trees, naïve Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine-learning algorithms. We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.

Super short term forecasting of photovoltaic power generation output in micro grid

NASA Astrophysics Data System (ADS)

Gong, Cheng; Ma, Longfei; Chi, Zhongjun; Zhang, Baoqun; Jiao, Ran; Yang, Bing; Chen, Jianshu; Zeng, Shuang

2017-01-01

The prediction model combining data mining and support vector machine (SVM) was built. Which provide information of photovoltaic (PV) power generation output for economic operation and optimal control of micro gird, and which reduce influence of power system from PV fluctuation. Because of the characteristic which output of PV rely on radiation intensity, ambient temperature, cloudiness, etc., so data mining was brought in. This technology can deal with large amounts of historical data and eliminate superfluous data, by using fuzzy classifier of daily type and grey related degree. The model of SVM was built, which can dock with information from data mining. Based on measured data from a small PV station, the prediction model was tested. The numerical example shows that the prediction model is fast and accurate.

Fuzzy Petri nets to model vision system decisions within a flexible manufacturing system

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.; Buck, A. A.; Smith, R.

1994-10-01

The paper presents a Petri net approach to modelling, monitoring and control of the behavior of an FMS cell. The FMS cell described comprises a pick and place robot, vision system, CNC-milling machine and 3 conveyors. The work illustrates how the block diagrams in a hierarchical structure can be used to describe events at different levels of abstraction. It focuses on Fuzzy Petri nets (Fuzzy logic with Petri nets) including an artificial neural network (Fuzzy Neural Petri nets) to model and control vision system decisions and robot sequences within an FMS cell. This methodology can be used as a graphical modelling tool to monitor and control the imprecise, vague and uncertain situations, and determine the quality of the output product of an FMS cell.

SU-F-T-467: A Cross-Checking Approach for Dosimetric Verification of Beam- Matched Elekta Linear Accelerators

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

Zheng, Y; Yuan, J; Geis, P

2016-06-15

Purpose: To verify the similarity of the dosimetric characteristics between two Elekta linear accelerators (linacs) in order to treat patients interchangeably on these two machines without re-planning. Methods: To investigate the viability of matching the 6 MV flattened beam on an existing linac (Elekta Synergy with Agility head) with a recently installed new linca (Elekta Versa HD), percent depth doses (PDD), flatness and symmetry output factors were compared for both machines. To validate the beam matching among machines, we carried out two approaches to cross-check the dosimetrical equivalence: 1) the prior treatment plans were re-computed based on the newly builtmore » Versa HD treatment planning system (TPS) model without changing the beam control points; 2) The same plans were delivered on both machines and the radiation dose measurements on a MapCheck2 were compared with TPS calculations. Three VMAT plans (Head and neck, lung, and prostate) were used in the study. Results: The difference between the PDDs for 10×10 cm{sup 2} field at all depths was less than 0.8%. The difference of flatness and symmetry for 30×30 cm{sup 2} field was less than 0.8%, and the measured output factors varies by less than 1% for each field size ranging from 2×2 cm2 to 40×40 cm{sup 2}. For the same plans, the maximum difference of the two calculated dose distributions is 2% of prescription. For the QA measurements, the gamma index passing rates were above 99% for 3%/3mm criteria with 10% threshold for all three clinical plans. Conclusion: A beam modality matching between two Elekta linacs is demonstrated with a cross-checking approach.« less

Lawrence Livermore National Laboratory ULTRA-350 Test Bed

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

Hopkins, D J; Wulff, T A; Carlisle, K

2001-04-10

LLNL has many in-house designed high precision machine tools. Some of these tools include the Large Optics Diamond Turning Machine (LODTM) [1], Diamond Turning Machine No.3 (DTM-3) and two Precision Engineering Research Lathes (PERL-1 and PERL-11). These machines have accuracy in the sub-micron range and in most cases position resolution in the couple of nanometers range. All of these machines are built with similar underlying technologies. The machines use capstan drive technology, laser interferometer position feedback, tachometer velocity feedback, permanent magnet (PM) brush motors and analog velocity and position loop servo compensation [2]. The machine controller does not perform anymore » servo compensation it simply computes the differences between the commanded position and the actual position (the following error) and sends this to a D/A for the analog servo position loop. LLNL is designing a new high precision diamond turning machine. The machine is called the ULTRA 350 [3]. In contrast to many of the proven technologies discussed above, the plan for the new machine is to use brushless linear motors, high precision linear scales, machine controller motor commutation and digital servo compensation for the velocity and position loops. Although none of these technologies are new and have been in use in industry, applications of these technologies to high precision diamond turning is limited. To minimize the risks of these technologies in the new machine design, LLNL has established a test bed to evaluate these technologies for application in high precision diamond turning. The test bed is primarily composed of commercially available components. This includes the slide with opposed hydrostatic bearings, the oil system, the brushless PM linear motor, the two-phase input three-phase output linear motor amplifier and the system controller. The linear scales are not yet commercially available but use a common electronic output format. As of this writing, the final verdict for the use of these technologies is still out but the first part of the work has been completed with promising results. The goal of this part of the work was to close a servo position loop around a slide incorporating these technologies and to measure the performance. This paper discusses the tests that were setup for system evaluation and the results of the measurements made. Some very promising results include; slide positioning to nanometer level and slow speed slide direction reversal at less than 100nm/min with no observed discontinuities. This is very important for machine contouring in diamond turning. As a point of reference, at 100 nm/min it would take the slide almost 7 years to complete the full designed travel of 350 mm. This speed has been demonstrated without the use of a velocity sensor. The velocity is derived from the position sensor. With what has been learned on the test bed, the paper finishes with a brief comparison of the old and new technologies. The emphasis of this comparison will be on the servo performance as illustrated with bode plot diagrams.« less

Lawrence Livermore National Laboratory ULTRA-350 Test Bed

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

Hopkins, D J; Wulff, T A; Carlisle, K

2001-04-10

LLNL has many in-house designed high precision machine tools. Some of these tools include the Large Optics Diamond Turning Machine (LODTM) [1], Diamond Turning Machine No.3 (DTM-3) and two Precision Engineering Research Lathes (PERL-I and PERL-II). These machines have accuracy in the sub-micron range and in most cases position resolution in the couple of nanometers range. All of these machines are built with similar underlying technologies. The machines use capstan drive technology, laser interferometer position feedback, tachometer velocity feedback, permanent magnet (PM) brush motors and analog velocity and position loop servo compensation [2]. The machine controller does not perform anymore » servo compensation it simply computes the differences between the commanded position and the actual position (the following error) and sends this to a D/A for the analog servo position loop. LLNL is designing a new high precision diamond turning machine. The machine is called the ULTRA 350 [3]. In contrast to many of the proven technologies discussed above, the plan for the new machine is to use brushless linear motors, high precision linear scales, machine controller motor commutation and digital servo compensation for the velocity and position loops. Although none of these technologies are new and have been in use in industry, applications of these technologies to high precision diamond turning is limited. To minimize the risks of these technologies in the new machine design, LLNL has established a test bed to evaluate these technologies for application in high precision diamond turning. The test bed is primarily composed of commercially available components. This includes the slide with opposed hydrostatic bearings, the oil system, the brushless PM linear motor, the two-phase input three-phase output linear motor amplifier and the system controller. The linear scales are not yet commercially available but use a common electronic output format. As of this writing, the final verdict for the use of these technologies is still out but the first part of the work has been completed with promising results. The goal of this part of the work was to close a servo position loop around a slide incorporating these technologies and to measure the performance. This paper discusses the tests that were setup for system evaluation and the results of the measurements made. Some very promising results include; slide positioning to nanometer level and slow speed slide direction reversal at less than 100nm/min with no observed discontinuities. This is very important for machine contouring in diamond turning. As a point of reference, at 100 nm/min it would take the slide almost 7 years to complete the full designed travel of 350 mm. This speed has been demonstrated without the use of a velocity sensor. The velocity is derived from the position sensor. With what has been learned on the test bed, the paper finishes with a brief comparison of the old and new technologies. The emphasis of this comparison will be on the servo performance as illustrated with bode plot diagrams.« less

Pre-resistance-welding resistance check

DOEpatents

Destefan, Dennis E.; Stompro, David A.

1991-01-01

A preweld resistance check for resistance welding machines uses an open circuited measurement to determine the welding machine resistance, a closed circuit measurement to determine the parallel resistance of a workpiece set and the machine, and a calculation to determine the resistance of the workpiece set. Any variation in workpiece set or machine resistance is an indication that the weld may be different from a control weld.

Accessible engineering drawings for visually impaired machine operators.

PubMed

Ramteke, Deepak; Kansal, Gayatri; Madhab, Benu

2014-01-01

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

Support vector machine for the diagnosis of malignant mesothelioma

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A new wind energy conversion system

NASA Technical Reports Server (NTRS)

Smetana, F. O.

1975-01-01

It is presupposed that vertical axis wind energy machines will be superior to horizontal axis machines on a power output/cost basis and the design of a new wind energy machine is presented. The design employs conical cones with sharp lips and smooth surfaces to promote maximum drag and minimize skin friction. The cones are mounted on a vertical axis in such a way as to assist torque development. Storing wind energy as compressed air is thought to be optimal and reasons are: (1) the efficiency of compression is fairly high compared to the conversion of mechanical energy to electrical energy in storage batteries; (2) the release of stored energy through an air motor has high efficiency; and (3) design, construction, and maintenance of an all-mechanical system is usually simpler than for a mechanical to electrical conversion system.

Development of a wearable measurement and control unit for personal customizing machine-supported exercise.

PubMed

Wang, Zhihui; Tamura, Naoki; Kiryu, Tohru

2005-01-01

Wearable technology has been used in various health-related fields to develop advanced monitoring solutions. However, the monitoring function alone cannot meet all the requirements of personal customizing machine-supported exercise that have biosignal-based controls. In this paper, we propose a new wearable unit design equipped with measurement and control functions to support the personal customization process. The wearable unit can measure the heart rate and electromyogram signals during exercise and output workload control commands to the exercise machines. We then applied a prototype of the wearable unit to an Internet-based cycle ergometer system. The wearable unit was examined using twelve young people to check its feasibility. The results verified that the unit could successfully adapt to the control of the workload and was effective for continuously supporting gradual changes in physical activities.

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

Yale, S H

A survey was conducted of x-ray facilities in 2000 dental offices under actual operating conditions. Each of 10 dental schools in the United States collected data on 200 local dental offices to implement geographic analysis of the status of radiation hygiene in the offices. The data provided records of roentgen (r) output of each machine, relative r dose to patient, and dose to operator. In addition, specific information relating to both operator and machine was coiiected and evaluated. Some dentists were found to be operating under unsafe conditions, but the average dentist covered in the survey was statistically safe. Onmore » the basis of the survey, it was concluded that the probiem of radiation hazards in dentistry will be resolved when all dental x-ray machines are properly filtered and collimated and high-speed dental x-ray film is used. (P.C.H.)« less

Design Comparison of Inner and Outer Rotor of Permanent Magnet Flux Switching Machine for Electric Bicycle Application

NASA Astrophysics Data System (ADS)

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

2017-08-01

Recent advancements have led to the development of flux switching machines (FSMs) with flux sources within the stators. The advantage of being a single-piece machine with a robust rotor structure makes FSM an excellent choice for speed applications. There are three categories of FSM, namely, the permanent magnet (PM) FSM, the field excitation (FE) FSM, and the hybrid excitation (HE) FSM. The PMFSM and the FEFSM have their respective PM and field excitation coil (FEC) as their key flux sources. Meanwhile, as the name suggests, the HEFSM has a combination of PM and FECs as the flux sources. The PMFSM is a simple and cheap machine, and it has the ability to control variable flux, which would be suitable for an electric bicycle. Thus, this paper will present a design comparison between an inner rotor and an outer rotor for a single-phase permanent magnet flux switching machine with 8S-10P, designed specifically for an electric bicycle. The performance of this machine was validated using the 2D- FEA. As conclusion, the outer-rotor has much higher torque approximately at 54.2% of an innerrotor PMFSM. From the comprehensive analysis of both designs it can be conclude that output performance is lower than the SRM and IPMSM design machine. But, it shows that the possibility to increase the design performance by using “deterministic optimization method”.

Real-Time Deflection Monitoring for Milling of a Thin-Walled Workpiece by Using PVDF Thin-Film Sensors with a Cantilevered Beam as a Case Study

PubMed Central

Luo, Ming; Liu, Dongsheng; Luo, Huan

2016-01-01

Thin-walled workpieces, such as aero-engine blisks and casings, are usually made of hard-to-cut materials. The wall thickness is very small and it is easy to deflect during milling process under dynamic cutting forces, leading to inaccurate workpiece dimensions and poor surface integrity. To understand the workpiece deflection behavior in a machining process, a new real-time nonintrusive method for deflection monitoring is presented, and a detailed analysis of workpiece deflection for different machining stages of the whole machining process is discussed. The thin-film polyvinylidene fluoride (PVDF) sensor is attached to the non-machining surface of the workpiece to copy the deflection excited by the dynamic cutting force. The relationship between the input deflection and the output voltage of the monitoring system is calibrated by testing. Monitored workpiece deflection results show that the workpiece experiences obvious vibration during the cutter entering the workpiece stage, and vibration during the machining process can be easily tracked by monitoring the deflection of the workpiece. During the cutter exiting the workpiece stage, the workpiece experiences forced vibration firstly, and free vibration exists until the amplitude reduces to zero after the cutter exits the workpiece. Machining results confirmed the suitability of the deflection monitoring system for machining thin-walled workpieces with the application of PVDF sensors. PMID:27626424

Short guide to SDI profiling at ORNL

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

Pomerance, H.S.

1976-06-01

ORNL has machine-searchable data bases that correspond to printed indexes and abstracts. This guide describes the peculiarities of those several data bases and the conventions of the ORNL search system so that users can write their own queries or search profiles and can interpret the part of the output that is encoded.

Wireless Computing Architecture III

DTIC Science & Technology

2013-09-01

MIMO Multiple-Input and Multiple-Output MIMO /CON MIMO with concurrent hannel access and estimation MU- MIMO Multiuser MIMO OFDM Orthogonal...compressive sensing \\; a design for concurrent channel estimation in scalable multiuser MIMO networking; and novel networking protocols based on machine...Network, Antenna Arrays, UAV networking, Angle of Arrival, Localization MIMO , Access Point, Channel State Information, Compressive Sensing 16

Magnetostrictive Vibration Damper and Energy Harvester for Rotating Machinery

NASA Technical Reports Server (NTRS)

Deng, Zhangxian; Asnani, Vivake M.; Dapino, Marcelo J.

2015-01-01

Vibrations generated by machine driveline components can cause excessive noise and structural damage. Magnetostrictive materials, including Galfenol (iron-gallium alloys) and Terfenol-D (terbium-iron-dysprosium alloys), are able to convert mechanical energy to magnetic energy. A magnetostrictive vibration ring is proposed, which generates electrical energy and dampens vibration, when installed in a machine driveline. A 2D axisymmetric finite element (FE) model incorporating magnetic, mechanical, and electrical dynamics is constructed in COMSOL Multiphysics. Based on the model, a parametric study considering magnetostrictive material geometry, pickup coil size, bias magnet strength, flux path design, and electrical load is conducted to maximize loss factor and average electrical output power. By connecting various resistive loads to the pickup coil, the maximum loss factors for Galfenol and Terfenol-D due to electrical energy loss are identified as 0.14 and 0.34, respectively. The maximum average electrical output power for Galfenol and Terfenol-D is 0.21 W and 0.58 W, respectively. The loss factors for Galfenol and Terfenol-D are increased to 0.59 and 1.83, respectively, by using an L-C resonant circuit.

Prediction of Francis Turbine Prototype Part Load Pressure and Output Power Fluctuations with Hydroelectric Model

NASA Astrophysics Data System (ADS)

Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.

2017-04-01

The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

A T-Type Capacitive Sensor Capable of Measuring 5-DOF Error Motions of Precision Spindles

PubMed Central

Xiang, Kui; Qiu, Rongbo; Mei, Deqing; Chen, Zichen

2017-01-01

The precision spindle is a core component of high-precision machine tools, and the accurate measurement of its error motions is important for improving its rotation accuracy as well as the work performance of the machine. This paper presents a T-type capacitive sensor (T-type CS) with an integrated structure. The proposed sensor can measure the 5-degree-of-freedom (5-DOF) error motions of a spindle in-situ and simultaneously by integrating electrode groups in the cylindrical bore of the stator and the outer end face of its flange, respectively. Simulation analysis and experimental results show that the sensing electrode groups with differential measurement configuration have near-linear output for the different types of rotor displacements. What’s more, the additional capacitance generated by fringe effects has been reduced about 90% with the sensing electrode groups fabricated based on flexible printed circuit board (FPCB) and related processing technologies. The improved signal processing circuit has also been increased one times in the measuring performance and makes the measured differential output capacitance up to 93% of the theoretical values. PMID:28846631

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Design and market considerations for axial flux superconducting electric machine design

NASA Astrophysics Data System (ADS)

Ainslie, M. D.; George, A.; Shaw, R.; Dawson, L.; Winfield, A.; Steketee, M.; Stockley, S.

2014-05-01

In this paper, the authors investigate a number of design and market considerations for an axial flux superconducting electric machine design that uses high temperature superconductors. The axial flux machine design is assumed to utilise high temperature superconductors in both wire (stator winding) and bulk (rotor field) forms, to operate over a temperature range of 65-77 K, and to have a power output in the range from 10s of kW up to 1 MW (typical for axial flux machines), with approximately 2-3 T as the peak trapped field in the bulk superconductors. The authors firstly investigate the applicability of this type of machine as a generator in small- and medium-sized wind turbines, including the current and forecasted market and pricing for conventional turbines. Next, a study is also carried out on the machine's applicability as an in-wheel hub motor for electric vehicles. Some recommendations for future applications are made based on the outcome of these two studies. Finally, the cost of YBCO-based superconducting (2G HTS) wire is analysed with respect to competing wire technologies and compared with current conventional material costs and current wire costs for both 1G and 2G HTS are still too great to be economically feasible for such superconducting devices.

Abstract quantum computing machines and quantum computational logics

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Scale effects and a method for similarity evaluation in micro electrical discharge machining

NASA Astrophysics Data System (ADS)

Liu, Qingyu; Zhang, Qinhe; Wang, Kan; Zhu, Guang; Fu, Xiuzhuo; Zhang, Jianhua

2016-08-01

Electrical discharge machining(EDM) is a promising non-traditional micro machining technology that offers a vast array of applications in the manufacturing industry. However, scale effects occur when machining at the micro-scale, which can make it difficult to predict and optimize the machining performances of micro EDM. A new concept of "scale effects" in micro EDM is proposed, the scale effects can reveal the difference in machining performances between micro EDM and conventional macro EDM. Similarity theory is presented to evaluate the scale effects in micro EDM. Single factor experiments are conducted and the experimental results are analyzed by discussing the similarity difference and similarity precision. The results show that the output results of scale effects in micro EDM do not change linearly with discharge parameters. The values of similarity precision of machining time significantly increase when scaling-down the capacitance or open-circuit voltage. It is indicated that the lower the scale of the discharge parameter, the greater the deviation of non-geometrical similarity degree over geometrical similarity degree, which means that the micro EDM system with lower discharge energy experiences more scale effects. The largest similarity difference is 5.34 while the largest similarity precision can be as high as 114.03. It is suggested that the similarity precision is more effective in reflecting the scale effects and their fluctuation than similarity difference. Consequently, similarity theory is suitable for evaluating the scale effects in micro EDM. This proposed research offers engineering values for optimizing the machining parameters and improving the machining performances of micro EDM.

Acoustical Detection Of Leakage In A Combustor

NASA Technical Reports Server (NTRS)

Puster, Richard L.; Petty, Jeffrey L.

1993-01-01

Abnormal combustion excites characteristic standing wave. Acoustical leak-detection system gives early warning of failure, enabling operating personnel to stop combustion process and repair spray bar before leak grows large enough to cause damage. Applicable to engines, gas turbines, furnaces, and other machines in which acoustic emissions at known frequencies signify onset of damage. Bearings in rotating machines monitored for emergence of characteristic frequencies shown in previous tests associated with incipient failure. Also possible to monitor for signs of trouble at multiple frequencies by feeding output of transducer simultaneously to multiple band-pass filters and associated circuitry, including separate trigger circuit set to appropriate level for each frequency.

FPGA-based fused smart-sensor for tool-wear area quantitative estimation in CNC machine inserts.

PubMed

Trejo-Hernandez, Miguel; Osornio-Rios, Roque Alfredo; de Jesus Romero-Troncoso, Rene; Rodriguez-Donate, Carlos; Dominguez-Gonzalez, Aurelio; Herrera-Ruiz, Gilberto

2010-01-01

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used.

Relationship between sprint times and the strength/power outputs of a machine squat jump.

PubMed

Harris, Nigel K; Cronin, John B; Hopkins, Will G; Hansen, Keir T

2008-05-01

Strength testing is often used with team-sport athletes, but some measures of strength may have limited prognostic/diagnostic value in terms of the physical demands of the sport. The purpose of this study was to investigate relationships between sprint ability and the kinetic and kinematic outputs of a machine squat jump. Thirty elite level rugby union and league athletes with an extensive resistance-training background performed bilateral concentric-only machine squat jumps across loads of 20% to 90% 1 repetition maximum (1RM), and sprints over 10 meters and 30 or 40 meters. The magnitudes of the relationships were interpreted using Pearson correlation coefficients, which had uncertainty (90% confidence limits) of approximately +/-0.3. Correlations of 10-meter sprint time with kinetic and kinematic variables (force, velocity, power, and impulse) were generally positive and of moderate to strong magnitude (r = 0.32-0.53). The only negative correlations observed were for work, although the magnitude was small (r = -0.18 to -0.26). The correlations for 30- or 40-meter sprint times were similar to those for 10-meter times, although the correlation with work was positive and moderate (r = 0.35-0.40). Correlations of 10-meter time with kinetic variables expressed relative to body mass were generally positive and of trivial to small magnitude (r = 0.01-0.29), with the exceptions of work (r = -0.31 to -0.34), and impulse (r = -0.34 to -0.39). Similar correlations were observed for 30- and 40-meter times with kinetic measures expressed relative to body mass. Although correlations do not imply cause and effect, the preoccupation with maximizing power output in this particular resistance exercise to improve sprint ability appears problematic. Work and impulse are potentially important strength qualities to develop in the pursuit of improved sprinting performance.

Fast metabolite identification with Input Output Kernel Regression.

PubMed

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-06-15

An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. celine.brouard@aalto.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Fast metabolite identification with Input Output Kernel Regression

PubMed Central

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-01-01

Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628

A variable-mode stator consequent pole memory machine

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory

NASA Astrophysics Data System (ADS)

Khanna, Rajesh; Kumar, Anish; Garg, Mohinder Pal; Singh, Ajit; Sharma, Neeraj

2015-12-01

Electric discharge drill machine (EDDM) is a spark erosion process to produce micro-holes in conductive materials. This process is widely used in aerospace, medical, dental and automobile industries. As for the performance evaluation of the electric discharge drilling machine, it is very necessary to study the process parameters of machine tool. In this research paper, a brass rod 2 mm diameter was selected as a tool electrode. The experiments generate output responses such as tool wear rate (TWR). The best parameters such as pulse on-time, pulse off-time and water pressure were studied for best machining characteristics. This investigation presents the use of Taguchi approach for better TWR in drilling of Al-7075. A plan of experiments, based on L27 Taguchi design method, was selected for drilling of material. Analysis of variance (ANOVA) shows the percentage contribution of the control factor in the machining of Al-7075 in EDDM. The optimal combination levels and the significant drilling parameters on TWR were obtained. The optimization results showed that the combination of maximum pulse on-time and minimum pulse off-time gives maximum MRR.

Testing of Anesthesia Machines and Defibrillators in Healthcare Institutions.

PubMed

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

2017-09-01

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

Spirolit-2 instrument used to test pulmonary ventilation

NASA Astrophysics Data System (ADS)

Zhuravlev, V. V.

1985-02-01

At the present time, the Spirolit-2 automatic analyzer of main respiratory gases, of the Junkalor Dessau firm, is used to examine parameters of gas exchange, levels of energy expended by man and animals with different degrees of activity. However, the capabilities of this model of the instrument are limited. A method of determining pulmonary ventilation with use of the Spirolit-2 is described. An additional exhalation valve is built into a valve box to which an anesthesia machine rubber bag is attached. Samples are collected into another bag concurrently with the usual tests on the Spirolit-2 instrument. Four to five minutes are sufficient to obtain stable parameters at relative rest of oxygen uptake, determine carbon dioxide output per minute and collect samples in for analysis of exhaled air. The proposed method can furnish information about the dynamics of development of respiratory function of the lungs at virtually any moment with a constant physical load. For this, there must be spare bags to collect samples. Stage-by-stage data can be obtained analogously as to ventilation volume during a step test while determining maximum oxygen uptake.

Accelerometer Method and Apparatus for Integral Display and Control Functions

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1996-01-01

Method and apparatus for detecting mechanical vibrations and outputting a signal in response thereto. Art accelerometer package having integral display and control functions is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine conditions over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase in amplitude over a selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated.

Strain gauge validation experiments for the Sandia 34-meter VAWT (Vertical Axis Wind Turbine) test bed

NASA Astrophysics Data System (ADS)

Sutherland, Herbert J.

1988-08-01

Sandia National Laboratories has erected a research oriented, 34- meter diameter, Darrieus vertical axis wind turbine near Bushland, Texas. This machine, designated the Sandia 34-m VAWT Test Bed, is equipped with a large array of strain gauges that have been placed at critical positions about the blades. This manuscript details a series of four-point bend experiments that were conducted to validate the output of the blade strain gauge circuits. The output of a particular gauge circuit is validated by comparing its output to equivalent gauge circuits (in this stress state) and to theoretical predictions. With only a few exceptions, the difference between measured and predicted strain values for a gauge circuit was found to be of the order of the estimated repeatability for the measurement system.

Accelerometer Method and Apparatus for Integral Display and Control Functions

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1998-01-01

Method and apparatus for detecting mechanical vibrations and outputting a signal in response thereto is discussed. An accelerometer package having integral display and control functions is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine conditions over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase in amplitude over a selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Simulation of dynamic processes when machining transition surfaces of stepped shafts

NASA Astrophysics Data System (ADS)

Maksarov, V. V.; Krasnyy, V. A.; Viushin, R. V.

2018-03-01

The paper addresses the characteristics of stepped surfaces of parts categorized as "solids of revolution". It is noted that in the conditions of transition modes during the switch to end surface machining, there is cutting with varied load intensity in the section of the cut layer, which leads to change in cutting force, onset of vibrations, an increase in surface layer roughness, a decrease of size precision, and increased wear of a tool's cutting edge. This work proposes a method that consists in developing a CNC program output code that allows one to process complex forms of stepped shafts with only one machine setup. The authors developed and justified a mathematical model of a technological system for mechanical processing with consideration for the resolution of tool movement at the stages of transition processes to assess the dynamical stability of a system in the process of manufacturing stepped surfaces of parts of “solid of revolution” type.

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

NASA Astrophysics Data System (ADS)

Zhu, Kunpeng; Wong, Yoke San; Hong, Geok Soon

2009-02-01

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.

The Laser MicroJet (LMJ): a multi-solution technology for high quality micro-machining

NASA Astrophysics Data System (ADS)

Mai, Tuan Anh; Richerzhagen, Bernold; Snowdon, Paul C.; Wood, David; Maropoulos, Paul G.

2007-02-01

The field of laser micromachining is highly diverse. There are many different types of lasers available in the market. Due to their differences in irradiating wavelength, output power and pulse characteristic they can be selected for different applications depending on material and feature size [1]. The main issues by using these lasers are heat damages, contamination and low ablation rates. This report examines on the application of the Laser MicroJet(R) (LMJ), a unique combination of a laser beam with a hair-thin water jet as a universal tool for micro-machining of MEMS substrates, as well as ferrous and non-ferrous materials. The materials include gallium arsenide (GaAs) & silicon wafers, steel, tantalum and alumina ceramic. A Nd:YAG laser operating at 1064 nm (infra red) and frequency doubled 532 nm (green) were employed for the micro-machining of these materials.

Electromechanical millimotor

DOEpatents

Garcia, E.J.; Christenson, T.R.; Polosky, M.A.

1999-06-29

A millimeter-sized machine, including electromagnetic circuits adapted to convert electromagnetic energy to mechanical energy, for engaging and operating external mechanical loads. A plurality of millimeter-sized magnetic actuators operate out of phase with each other to control a plurality of millimeter-sized structural elements to drive an external mechanical load. Each actuator is connected to a link. Each link, in turn, is connected to a drive pinion at another similar pivoting joint. When the magnetic actuators are energized, each drive pinion is then capable of driving a larger output gear in gear-like fashion to produce positive torque about the drive pinion center at all angular positions of the output gear. 29 figs.

Electromechanical millimotor

DOEpatents

Garcia, Ernest J.; Christenson, Todd R.; Polosky, Marc A.

1999-01-01

A millimeter-sized machine, including electromagnetic circuits adapted to convert electromagnetic energy to mechanical energy, for engaging and operating external mechanical loads. A plurality of millimeter-sized magnetic actuators operate out of phase with each other to control a plurality of millimeter-sized structural elements to drive an external mechanical load. Each actuator is connected to a link. Each link, in turn, is connected to a drive pinion at another similar pivoting joint. When the magnetic actuators are energized, each drive pinion is then capable of driving a larger output gear in gear-like fashion to produce positive torque about the drive pinion center at all angular positions of the output gear.

Phase-space interference in extensive and nonextensive quantum heat engines

NASA Astrophysics Data System (ADS)

Hardal, Ali Ü. C.; Paternostro, Mauro; Müstecaplıoǧlu, Özgür E.

2018-04-01

Quantum interference is at the heart of what sets the quantum and classical worlds apart. We demonstrate that quantum interference effects involving a many-body working medium is responsible for genuinely nonclassical features in the performance of a quantum heat engine. The features with which quantum interference manifests itself in the work output of the engine depends strongly on the extensive nature of the working medium. While identifying the class of work substances that optimize the performance of the engine, our results shed light on the optimal size of such media of quantum workers to maximize the work output and efficiency of quantum energy machines.

A 3D stand generator for central Appalachian hardwood forests

Treesearch

Jingxin Wang; Yaoxiang Li; Gary W. Miller

2002-01-01

A 3-dimensional (3D) stand generator was developed for central Appalachian hardwood forests. It was designed for a harvesting simulator to examine the interactions of stand, harvest, and machine. The Component Object Model (COM) was used to design and implement the program. Input to the generator includes species composition, stand density, and spatial pattern. Output...

Software Independent Verification and Validation (SIV&V) Simplified

DTIC Science & Technology

2006-12-01

Configuration Item I/O Input/Output I2V2 Independent Integrated Verification and Validation IBM International Business Machines ICD Interface...IPT Integrated Product Team IRS Interface Requirements Specification ISD Integrated System Diagram ITD Integrated Test Description ITP ...programming languages such as COBOL (Common Business Oriented Language) (Codasyl committee 1960), and FORTRAN (FORmula TRANslator) ( IBM 1952) (Robat 11

Methods and systems for micro transmissions

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

Stalford, Harold L

2014-12-23

Methods and systems for micro transmissions for a micro machine may comprise an input shaft assembly coupled to a micro actuator, an output shaft assembly coupled to a micro shaft, and one or more power conversion elements operable to convert a first type of movement from the micro actuator into a second, disparate type of movement for the micro shaft.

76 FR 81518 - Notice of Issuance of Final Determination Concerning Laser-Based Multi-Function Office Machines

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-28

... Determination Concerning Laser-Based Multi-Function Office Machines AGENCY: U.S. Customs and Border Protection... country of origin of laser-based multi-function office machines. Based upon the facts presented, CBP has... essential character of the laser-based multi-function office machine, and it is at their assembly and...

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

Effects of potassium titanate fiber on the wear of automotive brake linings

NASA Technical Reports Server (NTRS)

Halberstadt, M. L.; Mansfield, J. A.; Rhee, S. K.

1977-01-01

Asbestos reinforcing fiber in an automotive friction material was replaced by an experimental ingredient having better thermal stability, and the effects on wear and friction were studied. A friction materials test machine (SAE J661a) was used to determine friction and wear, under constant energy output conditions, as a function of temperature between 121 and 343 C (250 and 650 F). When potassium titanate fiber replaced one half of the asbestos in a standard commercial lining, with a 40 percent upward adjustment of phenolic resin content, wear above 204 C (400 F) was improved by 40% and friction by 30%. Tests on a full-scale inertial dynamometer supported the findings of the sample dynamometer tests. It was demonstrated that the potassium titanate fiber contributes directly to the improvement in wear and friction.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Cell-cycle research with synchronous cultures: an evaluation

NASA Technical Reports Server (NTRS)

Helmstetter, C. E.; Thornton, M.; Grover, N. B.

2001-01-01

The baby-machine system, which produces new-born Escherichia coli cells from cultures immobilized on a membrane, was developed many years ago in an attempt to attain optimal synchrony with minimal disturbance of steady-state growth. In the present article, we put forward a model to describe the behaviour of cells produced by this method, and provide quantitative evaluation of the parameters involved, at each of four different growth rates. Considering the high level of selection achievable with this technique and the natural dispersion in interdivision times, we believe that the output of the baby machine is probably close to optimal in terms of both quality and persistence of synchrony. We show that considerable information on events in the cell cycle can be obtained from populations with age distributions very much broader than those achieved with the baby machine and differing only modestly from steady state. The data presented here, together with the long and fruitful history of findings employing the baby-machine technique, suggest that minimisation of stress on cells is the single most important factor for successful cell-cycle analysis.

Design and FPGA Implementation of a Universal Chaotic Signal Generator Based on the Verilog HDL Fixed-Point Algorithm and State Machine Control

NASA Astrophysics Data System (ADS)

Qiu, Mo; Yu, Simin; Wen, Yuqiong; Lü, Jinhu; He, Jianbin; Lin, Zhuosheng

In this paper, a novel design methodology and its FPGA hardware implementation for a universal chaotic signal generator is proposed via the Verilog HDL fixed-point algorithm and state machine control. According to continuous-time or discrete-time chaotic equations, a Verilog HDL fixed-point algorithm and its corresponding digital system are first designed. In the FPGA hardware platform, each operation step of Verilog HDL fixed-point algorithm is then controlled by a state machine. The generality of this method is that, for any given chaotic equation, it can be decomposed into four basic operation procedures, i.e. nonlinear function calculation, iterative sequence operation, iterative values right shifting and ceiling, and chaotic iterative sequences output, each of which corresponds to only a state via state machine control. Compared with the Verilog HDL floating-point algorithm, the Verilog HDL fixed-point algorithm can save the FPGA hardware resources and improve the operation efficiency. FPGA-based hardware experimental results validate the feasibility and reliability of the proposed approach.

Mechanistic models versus machine learning, a fight worth fighting for the biological community?

PubMed

Baker, Ruth E; Peña, Jose-Maria; Jayamohan, Jayaratnam; Jérusalem, Antoine

2018-05-01

Ninety per cent of the world's data have been generated in the last 5 years ( Machine learning: the power and promise of computers that learn by example Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input-output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome? © 2018 The Author(s).

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

PubMed

Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

2016-12-16

As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

Non-symmetric approach to single-screw expander and compressor modeling

NASA Astrophysics Data System (ADS)

Ziviani, Davide; Groll, Eckhard A.; Braun, James E.; Horton, W. Travis; De Paepe, M.; van den Broek, M.

2017-08-01

Single-screw type volumetric machines are employed both as compressors in refrigeration systems and, more recently, as expanders in organic Rankine cycle (ORC) applications. The single-screw machine is characterized by having a central grooved rotor and two mating toothed starwheels that isolate the working chambers. One of the main features of such machine is related to the simultaneous occurrence of the compression or expansion processes on both sides of the main rotor which results in a more balanced loading on the main shaft bearings with respect to twin-screw machines. However, the meshing between starwheels and main rotor is a critical aspect as it heavily affects the volumetric performance of the machine. To allow flow interactions between the two sides of the rotor, a non-symmetric modelling approach has been established to obtain a more comprehensive model of the single-screw machine. The resulting mechanistic model includes in-chamber governing equations, leakage flow models, heat transfer mechanisms, viscous and mechanical losses. Forces and moments balances are used to estimate the loads on the main shaft bearings as well as on the starwheel bearings. An 11 kWe single-screw expander (SSE) adapted from an air compressor operating with R245fa as working fluid is used to validate the model. A total of 60 steady-steady points at four different rotational speeds have been collected to characterize the performance of the machine. The maximum electrical power output and overall isentropic efficiency measured were 7.31 kW and 51.91%, respectively.

Support vector machine-based facial-expression recognition method combining shape and appearance

NASA Astrophysics Data System (ADS)

Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

2010-11-01

Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

Computer Simulation Of An In-Process Surface Finish Sensor.

NASA Astrophysics Data System (ADS)

Rakels, Jan H.

1987-01-01

It is generally accepted, that optical methods are the most promising for the in-process measurement of surface finish. These methods have the advantages of being non-contacting and fast data acquisition. Furthermore, these optical instruments can be easily retrofitted on existing machine-tools. In the Micro-Engineering Centre at the University of Warwick, an optical sensor has been developed which can measure the rms roughness, slope and wavelength of turned and precision ground surfaces during machining. The operation of this device is based upon the Kirchhoff-Fresnel diffraction integral. Application of this theory to ideal turned and ground surfaces is straightforward, and indeed the calculated diffraction patterns are in close agreement with patterns produced by an actual optical instrument. Since it is mathematically difficult to introduce real machine-tool behaviour into the diffraction integral, a computer program has been devised, which simulates the operation of the optical sensor. The program produces a diffraction pattern as a graphical output. Comparison between computer generated and actual diffraction patterns of the same surfaces show a high correlation. The main aim of this program is to construct an atlas, which maps known machine-tool errors versus optical diffraction patterns. This atlas can then be used for machine-tool condition diagnostics. It has been found that optical monitoring is very sensitive to minor defects. Therefore machine-tool detoriation can be detected before it is detrimental.

Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances

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

Qi, Junjian; Wang, Jianhui; Liu, Hui

Abstract: In this paper, nonlinear model reduction for power systems is performed by the balancing of empirical controllability and observability covariances that are calculated around the operating region. Unlike existing model reduction methods, the external system does not need to be linearized but is directly dealt with as a nonlinear system. A transformation is found to balance the controllability and observability covariances in order to determine which states have the greatest contribution to the input-output behavior. The original system model is then reduced by Galerkin projection based on this transformation. The proposed method is tested and validated on a systemmore » comprised of a 16-machine 68-bus system and an IEEE 50-machine 145-bus system. The results show that by using the proposed model reduction the calculation efficiency can be greatly improved; at the same time, the obtained state trajectories are close to those for directly simulating the whole system or partitioning the system while not performing reduction. Compared with the balanced truncation method based on a linearized model, the proposed nonlinear model reduction method can guarantee higher accuracy and similar calculation efficiency. It is shown that the proposed method is not sensitive to the choice of the matrices for calculating the empirical covariances.« less

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine.

PubMed

Silitonga, Arridina Susan; Hassan, Masjuki Haji; Ong, Hwai Chyuan; Kusumo, Fitranto

2017-11-01

The purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805-0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259-2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy.

Noise radiation characteristics of the Westinghouse WWG-0600 (600kW) wind turbine generator

NASA Technical Reports Server (NTRS)

Shepherd, Kevin P.; Hubbard, Harvey H.

1989-01-01

Acoustic data are presented from five different WWG-0600 machines for the wind speed range 6.7 to 13.4 m/s, for a power output range of 51 to 600 kW and for upwind, downwind and crosswind locations. Both broadband and narrowband data are presented and are compared with calculations and with similar data from other machines. Predicted broadband spectra are in good agreement with measurements at high power and underestimate them at low power. Discrete frequency rotational noise components are present in all measurements and are believed due to terrain induced wind gradients. Predictions are in general agreement with measurements upwind and downwind but underestimate them in the crosswind direction.

FPGA-Based Fused Smart-Sensor for Tool-Wear Area Quantitative Estimation in CNC Machine Inserts

PubMed Central

Trejo-Hernandez, Miguel; Osornio-Rios, Roque Alfredo; de Jesus Romero-Troncoso, Rene; Rodriguez-Donate, Carlos; Dominguez-Gonzalez, Aurelio; Herrera-Ruiz, Gilberto

2010-01-01

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used. PMID:22319304

Pileup Mitigation with Machine Learning (PUMML)

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Method of operating a thermoelectric generator

DOEpatents

Reynolds, Michael G; Cowgill, Joshua D

2013-11-05

A method for operating a thermoelectric generator supplying a variable-load component includes commanding the variable-load component to operate at a first output and determining a first load current and a first load voltage to the variable-load component while operating at the commanded first output. The method also includes commanding the variable-load component to operate at a second output and determining a second load current and a second load voltage to the variable-load component while operating at the commanded second output. The method includes calculating a maximum power output of the thermoelectric generator from the determined first load current and voltage and the determined second load current and voltage, and commanding the variable-load component to operate at a third output. The commanded third output is configured to draw the calculated maximum power output from the thermoelectric generator.

Classification

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.

2011-01-01

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

High speed operation of permanent magnet machines

NASA Astrophysics Data System (ADS)

El-Refaie, Ayman M.

This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been investigated. A 6kW, 36slot/30pole prototype SPM machine has been designed and built. Experimental measurements have been used to verify the analytical and FEA results. These test results have demonstrated that wide constant-power speed range can be achieved. Other important machine features such as the near-sinusoidal back-emf, high efficiency, and low cogging torque have also been demonstrated.

Self-powered vision electronic-skin basing on piezo-photodetecting Ppy/PVDF pixel-patterned matrix for mimicking vision.

PubMed

Han, Wuxiao; Zhang, Linlin; He, Haoxuan; Liu, Hongmin; Xing, Lili; Xue, Xinyu

2018-06-22

The development of multifunctional electronic-skin that establishes human-machine interfaces, enhances perception abilities or has other distinct biomedical applications is the key to the realization of artificial intelligence. In this paper, a new self-powered (battery-free) flexible vision electronic-skin has been realized from pixel-patterned matrix of piezo-photodetecting PVDF/Ppy film. The electronic-skin under applied deformation can actively output piezoelectric voltage, and the outputting signal can be significantly influenced by UV illumination. The piezoelectric output can act as both the photodetecting signal and electricity power. The reliability is demonstrated over 200 light on-off cycles. The sensing unit matrix of 6 × 6 pixels on the electronic-skin can realize image recognition through mapping multi-point UV stimuli. This self-powered vision electronic-skin that simply mimics human retina may have potential application in vision substitution.

Self-powered vision electronic-skin basing on piezo-photodetecting Ppy/PVDF pixel-patterned matrix for mimicking vision

NASA Astrophysics Data System (ADS)

Han, Wuxiao; Zhang, Linlin; He, Haoxuan; Liu, Hongmin; Xing, Lili; Xue, Xinyu

2018-06-01

The development of multifunctional electronic-skin that establishes human-machine interfaces, enhances perception abilities or has other distinct biomedical applications is the key to the realization of artificial intelligence. In this paper, a new self-powered (battery-free) flexible vision electronic-skin has been realized from pixel-patterned matrix of piezo-photodetecting PVDF/Ppy film. The electronic-skin under applied deformation can actively output piezoelectric voltage, and the outputting signal can be significantly influenced by UV illumination. The piezoelectric output can act as both the photodetecting signal and electricity power. The reliability is demonstrated over 200 light on–off cycles. The sensing unit matrix of 6 × 6 pixels on the electronic-skin can realize image recognition through mapping multi-point UV stimuli. This self-powered vision electronic-skin that simply mimics human retina may have potential application in vision substitution.

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.

PubMed

Knijnenburg, Theo A; Klau, Gunnar W; Iorio, Francesco; Garnett, Mathew J; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F A

2016-11-23

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present 'Logic Optimization for Binary Input to Continuous Output' (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

Characterizing output bottlenecks in a supercomputer

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

Xie, Bing; Chase, Jeffrey; Dillow, David A

2012-01-01

Supercomputer I/O loads are often dominated by writes. HPC (High Performance Computing) file systems are designed to absorb these bursty outputs at high bandwidth through massive parallelism. However, the delivered write bandwidth often falls well below the peak. This paper characterizes the data absorption behavior of a center-wide shared Lustre parallel file system on the Jaguar supercomputer. We use a statistical methodology to address the challenges of accurately measuring a shared machine under production load and to obtain the distribution of bandwidth across samples of compute nodes, storage targets, and time intervals. We observe and quantify limitations from competing traffic,more » contention on storage servers and I/O routers, concurrency limitations in the client compute node operating systems, and the impact of variance (stragglers) on coupled output such as striping. We then examine the implications of our results for application performance and the design of I/O middleware systems on shared supercomputers.« less

Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming.

PubMed

Wang, Jun-Sheng; Yang, Guang-Hong

2017-07-25

This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where the terms depending on the innovation covariance matrix are available with the aid of the innovation covariance matrix identified beforehand. Therefore, by iterating the Bellman equation, the resulting value function can converge to the optimal one in the presence of the aforementioned noise, and the nearly optimal control laws are delivered. To show the effectiveness and the advantages of the proposed approach, a simulation example and a velocity control experiment on a dc machine are employed.

NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

NASA Technical Reports Server (NTRS)

Baffes, P. T.

1994-01-01

NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS allows the user to generate C code to implement the network loaded into the system. This permits the placement of networks as components, or subroutines, in other systems. In short, once a network performs satisfactorily, the Generate C Code option provides the means for creating a program separate from NETS to run the network. Other features: files may be stored in binary or ASCII format; multiple input propagation is permitted; bias values may be included; capability to scale data without writing scaling code; quick interactive testing of network from the main menu; and several options that allow the user to manipulate learning efficiency. NETS is written in ANSI standard C language to be machine independent. The Macintosh version (MSC-22108) includes code for both a graphical user interface version and a command line interface version. The machine independent version (MSC-21588) only includes code for the command line interface version of NETS 3.0. The Macintosh version requires a Macintosh II series computer and has been successfully implemented under System 7. Four executables are included on these diskettes, two for floating point operations and two for integer arithmetic. It requires Think C 5.0 to compile. A minimum of 1Mb of RAM is required for execution. Sample input files and executables for both the command line version and the Macintosh user interface version are provided on the distribution medium. The Macintosh version is available on a set of three 3.5 inch 800K Macintosh format diskettes. The machine independent version has been successfully implemented on an IBM PC series compatible running MS-DOS, a DEC VAX running VMS, a SunIPC running SunOS, and a CRAY Y-MP running UNICOS. Two executables for the IBM PC version are included on the MS-DOS distribution media, one compiled for floating point operations and one for integer arithmetic. The machine independent version is available on a set of three 5.25 inch 360K MS-DOS format diskettes (standard distribution medium) or a .25 inch streaming magnetic tape cartridge in UNIX tar format. NETS was developed in 1989 and updated in 1992. IBM PC is a registered trademark of International Business Machines. MS-DOS is a registered trademark of Microsoft Corporation. DEC, VAX, and VMS are trademarks of Digital Equipment Corporation. SunIPC and SunOS are trademarks of Sun Microsystems, Inc. CRAY Y-MP and UNICOS are trademarks of Cray Research, Inc.

Detections of Propellers in Saturn's Rings using Machine Learning: Preliminary Results

NASA Astrophysics Data System (ADS)

Gordon, Mitchell K.; Showalter, Mark R.; Odess, Jennifer; Del Villar, Ambi; LaMora, Andy; Paik, Jin; Lakhani, Karim; Sergeev, Rinat; Erickson, Kristen; Galica, Carol; Grayzeck, Edwin; Morgan, Thomas; Knopf, William

2015-11-01

We report on the initial analysis of the output of a tool designed to identify persistent, non-axisymmetric features in the rings of Saturn. This project introduces a new paradigm for scientific software development. The preliminary results include what appear to be new detections of propellers in the rings of Saturn.The Planetary Data System (PDS), working with the NASA Tournament Lab (NTL), Crowd Innovation Lab at Harvard University, and the Topcoder community at Appirio, Inc., under the umbrella “Cassini Rings Challenge”, sponsored a set of competitions employing crowd sourcing and machine learning to develop a tool which could be made available to the community at large. The Challenge was tackled by running a series of separate contests to solve individual tasks prior to the major machine learning challenge. Each contest was comprised of a set of requirements, a timeline, one or more prizes, and other incentives, and was posted by Appirio to the Topcoder Community. In the case of the machine learning challenge (a “Marathon Challenge” on the Topcoder platform), members competed against each other by submitting solutions that were scored in real time and posted to a public leader-board by a scoring algorithm developed by Appirio for this contest.The current version of the algorithm was run against ~30,000 of the highest resolution Cassini ISS images. That set included 668 images with a total of 786 features previously identified as propellers in the main rings. The tool identified 81% of those previously identified propellers. In a preliminary, close examination of 130 detections identified by the tool, we determined that of the 130 detections, 11 were previously identified propeller detections, 5 appear to be new detections of known propellers, and 4 appear to be detections of propellers which have not been seen previously. A total of 20 valid detections from 130 candidates implies a relatively high false positive rate which we hope to reduce by further algorithm development. The machine learning aspect of the algorithm means that as our set of verified detections increases so does the pool of “ground-truth” data used to train the algorithm for future use.

Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

PubMed Central

Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

2010-01-01

Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis. PMID:15790898

A Low-Cost Contact System to Assess Load Displacement Velocity in a Resistance Training Machine

PubMed Central

Buscà, Bernat; Font, Anna

2011-01-01

This study sought to determine the validity of a new system for assessing the displacement and average velocity within machine-based resistance training exercise using the Chronojump System. The new design is based on a contact bar and a simple, low-cost mechanism that detects the conductivity of electrical potentials with a precision chronograph. This system allows coaches to assess velocity to control the strength training process. A validation study was performed by assessing the concentric phase parameters of a leg press exercise. Output time data from the Chronojump System in combination with the pre-established range of movement was compared with data from a position sensor connected to a Biopac System. A subset of 87 actions from 11 professional tennis players was recorded and, using the two methods, average velocity and displacement variables in the same action were compared. A t-test for dependent samples and a correlation analysis were undertaken. The r value derived from the correlation between the Biopac System and the contact Chronojump System was >0.94 for all measures of displacement and velocity on all loads (p < 0.01). The Effect Size (ES) was 0.18 in displacement and 0.14 in velocity and ranged from 0.09 to 0.31 and from 0.07 to 0.34, respectively. The magnitude of the difference between the two methods in all parameters and the correlation values provided certain evidence of validity of the Chronojump System to assess the average displacement velocity of loads in a resistance training machine. Key points The assessment of speed in resistance machines is a valuable source of information for strength training. Many commercial systems used to assess velocity, power and force are expensive thereby preventing widespread use by coaches and athletes. The system is intended to be a low-cost device for assessing and controlling the velocity exerted on each repetition in any resistance training machine. The system could be easily adapted in any vertical displacement barbell exercise. PMID:24150620

Electronic data processing codes for California wildland plants

Treesearch

Merton J. Reed; W. Robert Powell; Bur S. Bal

1963-01-01

Systematized codes for plant names are helpful to a wide variety of workers who must record the identity of plants in the field. We have developed such codes for a majority of the vascular plants encountered on California wildlands and have published the codes in pocket size, using photo-reductions of the output from data processing machines. A limited number of the...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Laser machining of southern pine

Treesearch

C. W. McMillin; J. E. Harry

1971-01-01

When cutting with an air-jet-assisted carbon-dioxide laser of 240 watts output power, maximum feed speed at the point of full penetration of the beam decreased with increasing workpiece thickness in both wet and dry samples; the trend was curvilinear. Feed speeds averaged 99.1 and 14.6 inches per minute for samples 0.25 and 1.00 inch thick, respectively. Somewhat...

Processing woody biomass with a modified horizontal grinder

Treesearch

Dana Mitchell; John Klepac

2008-01-01

This study documents the production rate and cost of producing woody biomass chips for use in a power plant. The power plant has specific raw material handling requirements. Output from a 3-knife chipper, a tub grinder, and a horizontal grinder was considered. None of the samples from these machines met the specifications needed. A horizontal grinder was modified to...

SCSI Communication Test Bus

NASA Technical Reports Server (NTRS)

Hua, Chanh V.; D'Ambrose, John J.; Jaworski, Richard C.; Halula, Elaine M.; Thornton, David N.; Heligman, Robert L.; Turner, Michael R.

1990-01-01

Small Computer System Interface (SCSI) communication test bus provides high-data-rate, standard interconnection enabling communication among International Business Machines (IBM) Personal System/2 Micro Channel, other devices connected to Micro Channel, test equipment, and host computer. Serves primarily as nonintrusive input/output attachment to PS/2 Micro Channel bus, providing rapid communication for debugger. Opens up possibility of using debugger in real-time applications.

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Advanced single permanent magnet axipolar ironless stator ac motor for electric passenger vehicles

NASA Technical Reports Server (NTRS)

Beauchamp, E. D.; Hadfield, J. R.; Wuertz, K. L.

1983-01-01

A program was conducted to design and develop an advanced-concept motor specifically created for propulsion of electric vehicles with increased range, reduced energy consumption, and reduced life-cycle costs in comparison with conventional systems. The motor developed is a brushless, dc, rare-earth cobalt, permanent magnet, axial air gap inductor machine that uses an ironless stator. Air cooling is inherent provided by the centrifugal-fan action of the rotor poles. An extensive design phase was conducted, which included analysis of the system performance versus the SAE J227a(D) driving cycle. A proof-of-principle model was developed and tested, and a functional model was developed and tested. Full generator-level testing was conducted on the functional model, recording electromagnetic, thermal, aerodynamic, and acoustic noise data. The machine demonstrated 20.3 kW output at 1466 rad/s and 160 dc. The novel ironless stator demonstated the capability to continuously operate at peak current. The projected system performance based on the use of a transistor inverter is 23.6 kW output power at 1466 rad/s and 83.3 percent efficiency. Design areas of concern regarding electric vehicle applications include the inherently high windage loss and rotor inertia.

Unsupervised machine learning account of magnetic transitions in the Hubbard model

NASA Astrophysics Data System (ADS)

Ch'ng, Kelvin; Vazquez, Nick; Khatami, Ehsan

2018-01-01

We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and t -distributed stochastic neighboring ensemble (t -SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary) spin configurations generated, through Monte Carlo simulations of small clusters, for the Ising and Fermi-Hubbard models at finite temperatures. Results from a convolutional autoencoder for the three-dimensional Ising model can be shown to produce the magnetization and the susceptibility as a function of temperature with a high degree of accuracy. Quantum fluctuations distort this picture and prevent us from making such connections between the output of the autoencoder and physical observables for the Hubbard model. However, we are able to define an indicator based on the output of the t -SNE algorithm that shows a near perfect agreement with the antiferromagnetic structure factor of the model in two and three spatial dimensions in the weak-coupling regime. t -SNE also predicts a transition to the canted antiferromagnetic phase for the three-dimensional model when a strong magnetic field is present. We show that these techniques cannot be expected to work away from half filling when the "sign problem" in quantum Monte Carlo simulations is present.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

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

NASA Astrophysics Data System (ADS)

Hafiz Mohamad, Mohamad; Nafis Osman Zahid, Muhammed

2018-03-01

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

Reliability Centred Maintenance (RCM) Analysis of Laser Machine in Filling Lithos at PT X

NASA Astrophysics Data System (ADS)

Suryono, M. A. E.; Rosyidi, C. N.

2018-03-01

PT. X used automated machines which work for sixteen hours per day. Therefore, the machines should be maintained to keep the availability of the machines. The aim of this research is to determine maintenance tasks according to the cause of component’s failure using Reliability Centred Maintenance (RCM) and determine the amount of optimal inspection frequency which must be performed to the machine at filling lithos process. In this research, RCM is used as an analysis tool to determine the critical component and find optimal inspection frequencies to maximize machine’s reliability. From the analysis, we found that the critical machine in filling lithos process is laser machine in Line 2. Then we proceed to determine the cause of machine’s failure. Lastube component has the highest Risk Priority Number (RPN) among other components such as power supply, lens, chiller, laser siren, encoder, conveyor, and mirror galvo. Most of the components have operational consequences and the others have hidden failure consequences and safety consequences. Time-directed life-renewal task, failure finding task, and servicing task can be used to overcome these consequences. The results of data analysis show that the inspection must be performed once a month for laser machine in the form of preventive maintenance to lowering the downtime.

VLSI implementation of a bio-inspired olfactory spiking neural network.

PubMed

Hsieh, Hung-Yi; Tang, Kea-Tiong

2012-07-01

This paper presents a low-power, neuromorphic spiking neural network (SNN) chip that can be integrated in an electronic nose system to classify odor. The proposed SNN takes advantage of sub-threshold oscillation and onset-latency representation to reduce power consumption and chip area, providing a more distinct output for each odor input. The synaptic weights between the mitral and cortical cells are modified according to an spike-timing-dependent plasticity learning rule. During the experiment, the odor data are sampled by a commercial electronic nose (Cyranose 320) and are normalized before training and testing to ensure that the classification result is only caused by learning. Measurement results show that the circuit only consumed an average power of approximately 3.6 μW with a 1-V power supply to discriminate odor data. The SNN has either a high or low output response for a given input odor, making it easy to determine whether the circuit has made the correct decision. The measurement result of the SNN chip and some well-known algorithms (support vector machine and the K-nearest neighbor program) is compared to demonstrate the classification performance of the proposed SNN chip.The mean testing accuracy is 87.59% for the data used in this paper.

Automatic ball bar for a coordinate measuring machine

DOEpatents

Jostlein, H.

1997-07-15

An automatic ball bar for a coordinate measuring machine determines the accuracy of a coordinate measuring machine having at least one servo drive. The apparatus comprises a first and second gauge ball connected by a telescoping rigid member. The rigid member includes a switch such that inward radial movement of the second gauge ball relative to the first gauge ball causes activation of the switch. The first gauge ball is secured in a first magnetic socket assembly in order to maintain the first gauge ball at a fixed location with respect to the coordinate measuring machine. A second magnetic socket assembly secures the second gauge ball to the arm or probe holder of the coordinate measuring machine. The second gauge ball is then directed by the coordinate measuring machine to move radially inward from a point just beyond the length of the ball bar until the switch is activated. Upon switch activation, the position of the coordinate measuring machine is determined and compared to known ball bar length such that the accuracy of the coordinate measuring machine can be determined. 5 figs.

Automatic ball bar for a coordinate measuring machine

DOEpatents

Jostlein, Hans

1997-01-01

An automatic ball bar for a coordinate measuring machine determines the accuracy of a coordinate measuring machine having at least one servo drive. The apparatus comprises a first and second gauge ball connected by a telescoping rigid member. The rigid member includes a switch such that inward radial movement of the second gauge ball relative to the first gauge ball causes activation of the switch. The first gauge ball is secured in a first magnetic socket assembly in order to maintain the first gauge ball at a fixed location with respect to the coordinate measuring machine. A second magnetic socket assembly secures the second gauge ball to the arm or probe holder of the coordinate measuring machine. The second gauge ball is then directed by the coordinate measuring machine to move radially inward from a point just beyond the length of the ball bar until the switch is activated. Upon switch activation, the position of the coordinate measuring machine is determined and compared to known ball bar length such that the accuracy of the coordinate measuring machine can be determined.

Two models for identification and predicting behaviour of an induction motor system

NASA Astrophysics Data System (ADS)

Kuo, Chien-Hsun

2018-01-01

System identification or modelling is the process of building mathematical models of dynamical systems based on the available input and output data from the systems. This paper introduces system identification by using ARX (Auto Regressive with eXogeneous input) and ARMAX (Auto Regressive Moving Average with eXogeneous input) models. Through the identified system model, the predicted output could be compared with the measured one to help prevent the motor faults from developing into a catastrophic machine failure and avoid unnecessary costs and delays caused by the need to carry out unscheduled repairs. The induction motor system is illustrated as an example. Numerical and experimental results are shown for the identified induction motor system.

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

Petersen, T.; Diamond, J.; Liu, N.

The readout electronics for the resonant beam position monitors (BPMs) in the Fermilab Switchyard (SY) have been upgraded, utilizing a low noise amplifier transition board and Fermilab designed digitizer boards. The stripline BPMs are estimated to have an average signal output of between -110 dBm and -80 dBm, with an estimated peak output of -70 dBm. The external resonant circuit is tuned to the SY machine frequency of 53.10348 MHz. Both the digitizer and transition boards have variable gain in order to accommodate the large dynamic range and irregularity of the resonant extraction spill. These BPMs will aid in auto-tuningmore » of the SY beamline as well as enabling operators to monitor beam position through the spill.« less

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

PubMed Central

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

2005-01-01

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

Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

NASA Astrophysics Data System (ADS)

Samanta, B.; Al-Balushi, K. R.

2003-03-01

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

PubMed

Webb, Samuel J; Hanser, Thierry; Howlin, Brendan; Krause, Paul; Vessey, Jonathan D

2014-03-25

A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query. Models have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity. This methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.

Smoothing Control of Wind Farm Output by Using Kinetic Energy of Variable Speed Wind Power Generators

NASA Astrophysics Data System (ADS)

Sato, Daiki; Saitoh, Hiroumi

This paper proposes a new control method for reducing fluctuation of power system frequency through smoothing active power output of wind farm. The proposal is based on the modulation of rotaional kinetic energy of variable speed wind power generators through power converters between permanent magnet synchronous generators (PMSG) and transmission lines. In this paper, the proposed control is called Fluctuation Absorption by Flywheel Characteristics control (FAFC). The FAFC can be easily implemented by adding wind farm output signal to Maximum Power Point Tracking control signal through a feedback control loop. In order to verify the effectiveness of the FAFC control, a simulation study was carried out. In the study, it was assumed that the wind farm consisting of PMSG type wind power generator and induction machine type wind power generaotors is connected with a power sysem. The results of the study show that the FAFC control is a useful method for reducing the impacts of wind farm output fluctuation on system frequency without additional devices such as secondary battery.

Miga Aero Actuator and 2D Machined Mechanical Binary Latch

NASA Technical Reports Server (NTRS)

Gummin, Mark A.

2013-01-01

Shape memory alloy (SMA) actuators provide the highest force-to-weight ratio of any known actuator. They can be designed for a wide variety of form factors from flat, thin packages, to form-matching packages for existing actuators. SMA actuators can be operated many thousands of times, so that ground testing is possible. Actuation speed can be accurately controlled from milliseconds to position and hold, and even electronic velocity-profile control is possible. SMA actuators provide a high degree of operational flexibility, and are truly smart actuators capable of being accurately controlled by onboard microprocessors across a wide range of voltages. The Miga Aero actuator is a SMA actuator designed specifically for spaceflight applications. Providing 13 mm of stroke with either 20- or 40-N output force in two different models, the Aero actuator is made from low-outgassing PEEK (polyether ether ketone) plastic, stainless steel, and nickel-titanium SMA wires. The modular actuator weighs less than 28 grams. The dorsal output attachment allows the Aero to be used in either PUSH or PULL modes by inverting the mounting orientation. The SPA1 actuator utilizes commercially available SMA actuator wire to provide 3/8-in. (approx. =.1 cm) of stroke at a force of over 28 lb (approx. = .125 N). The force is provided by a unique packaging of the single SMA wire that provides the output force of four SMA wires mechanically in parallel. The output load is shared by allowing the SMA wire to slip around the output attachment end to adjust or balance the load, preventing any individual wire segment from experiencing high loads during actuation. A built-in end limit switch prevents overheating of the SMA element following actuation when used in conjunction with the Miga Analog Driver [a simple MOSFET (metal oxide semiconductor field-effect transistor) switching circuit]. A simple 2D machined mechanical binary latch has been developed to complement the capabilities of SMA wire actuators. SMA actuators typically perform ideally as latch-release devices, wherein a spring-loaded device is released when the SMA actuator actuates in one direction. But many applications require cycling between two latched states open and closed.

Contour scanning of textile preforms using a light-section sensor for the automated manufacturing of fibre-reinforced plastics

NASA Astrophysics Data System (ADS)

Schmitt, R.; Niggemann, C.; Mersmann, C.

2008-04-01

Fibre-reinforced plastics (FRP) are particularly suitable for components where light-weight structures with advanced mechanical properties are required, e.g. for aerospace parts. Nevertheless, many manufacturing processes for FRP include manual production steps without an integrated quality control. A vital step in the process chain is the lay-up of the textile preform, as it greatly affects the geometry and the mechanical performance of the final part. In order to automate the FRP production, an inline machine vision system is needed for a closed-loop control of the preform lay-up. This work describes the development of a novel laser light-section sensor for optical inspection of textile preforms and its integration and validation in a machine vision prototype. The proposed method aims at the determination of the contour position of each textile layer through edge scanning. The scanning route is automatically derived by using texture analysis algorithms in a preliminary step. As sensor output a distinct stage profile is computed from the acquired greyscale image. The contour position is determined with sub-pixel accuracy using a novel algorithm based on a non-linear least-square fitting to a sigmoid function. The whole contour position is generated through data fusion of the measured edge points. The proposed method provides robust process automation for the FRP production improving the process quality and reducing the scrap quota. Hence, the range of economically feasible FRP products can be increased and new market segments with cost sensitive products can be addressed.

A novel evaluation of two related and two independent algorithms for eye movement classification during reading.

PubMed

Friedman, Lee; Rigas, Ioannis; Abdulin, Evgeny; Komogortsev, Oleg V

2018-05-15

Nystrӧm and Holmqvist have published a method for the classification of eye movements during reading (ONH) (Nyström & Holmqvist, 2010). When we applied this algorithm to our data, the results were not satisfactory, so we modified the algorithm (now the MNH) to better classify our data. The changes included: (1) reducing the amount of signal filtering, (2) excluding a new type of noise, (3) removing several adaptive thresholds and replacing them with fixed thresholds, (4) changing the way that the start and end of each saccade was determined, (5) employing a new algorithm for detecting PSOs, and (6) allowing a fixation period to either begin or end with noise. A new method for the evaluation of classification algorithms is presented. It was designed to provide comprehensive feedback to an algorithm developer, in a time-efficient manner, about the types and numbers of classification errors that an algorithm produces. This evaluation was conducted by three expert raters independently, across 20 randomly chosen recordings, each classified by both algorithms. The MNH made many fewer errors in determining when saccades start and end, and it also detected some fixations and saccades that the ONH did not. The MNH fails to detect very small saccades. We also evaluated two additional algorithms: the EyeLink Parser and a more current, machine-learning-based algorithm. The EyeLink Parser tended to find more saccades that ended too early than did the other methods, and we found numerous problems with the output of the machine-learning-based algorithm.

EDM machinability of SiCw/Al composites

NASA Technical Reports Server (NTRS)

Ramulu, M.; Taya, M.

1989-01-01

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

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud[OPEN

PubMed Central

Merchant, Nirav

2016-01-01

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today’s pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant’s Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching. PMID:27020957

A sandwiched piezoelectric transducer with flex end-caps for energy harvesting in large force environments

NASA Astrophysics Data System (ADS)

Kuang, Yang; Daniels, Alice; Zhu, Meiling

2017-08-01

This paper presents a sandwiched piezoelectric transducer (SPT) for energy harvesting in large force environments with increased load capacity and electric power output. The SPT uses (1) flex end-caps to amplify the applied load force so as to increase its power output and (2) a sandwiched piezoelectric-substrate structure to reduce the stress concentration in the piezoelectric material so as to increase the load capacity. A coupled piezoelectric-circuit finite element model (CPC-FEM) was developed, which is able to directly predict the electric power output of the SPT connected to a load resistor. The CPC-FEM was used to study the effects of various parameters of the SPT on the performance to obtain an optimal design. These parameters included the substrate thickness, the end-cap material and thickness, the electrode length, the joint length, the end-cap internal angle and the PZT thickness. A prototype with optimised parameters was tested on a loading machine, and the experimental results were compared with simulation. A good agreement was observed between simulation and experiment. When subjected to a 1 kN 2 Hz sinusoidal force applied by the loading machine, the SPT produced an average power of 4.68 mW. The application of the SPT as a footwear energy harvester was demonstrated by fitting the SPT into a boot and performing the tests on a treadmill, and the SPT generated an average power of 2.5 mW at a walking speed of 4.8 km h-1.

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud.

PubMed

Duvick, Jon; Standage, Daniel S; Merchant, Nirav; Brendel, Volker P

2016-04-01

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching. © 2016 American Society of Plant Biologists. All rights reserved.

Performance Assessment of Flashed Steam Geothermal Power Plant

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

Alt, Theodore E.

1980-12-01

Five years of operating experience at the Comision Federal de Electricidad (CFE) Cerro Prieto flashed steam geothermal power plant are evaluated from the perspective of U. S. utility operations. We focus on the design and maintenance of the power plant that led to the achievement of high plant capacity factors for Units No. 1 and 2 since commercial operation began in 1973. For this study, plant capacity factor is the ratio of the average load on the machines or equipment for the period of time considered to the capacity rating of the machines or equipment. The plant capacity factor ismore » the annual gross output in GWh compared to 657 GWh (2 x 37.5 MW x 8760 h). The CFE operates Cerro Prieto at base load consistent with the system connected electrical demand of the Baja California Division. The plant output was curtailed during the winter months of 1973-1975 when the system electric demand was less than the combined output capability of Cerro Prieto and the fossil fuel plant near Tijuana. Each year the system electric demand has increased and the Cerro Prieto units now operate at full load all the time. The CFE added Units 3 and 4 to Cerro Prieto in 1979 which increased the plant name plate capacity to 150 MW. Part of this additional capacity will supply power to San Diego Gas and Electric Company through an interconnection across the border. The achievement of a high capacity factor over an extensive operating period was influenced by operation, design, and maintenance of the geothermal flash steam power plant.« less

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

PubMed Central

Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong

2012-01-01

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181

Detection and analysis of part load and full load instabilities in a real Francis turbine prototype

NASA Astrophysics Data System (ADS)

Presas, Alexandre; Valentin, David; Egusquiza, Eduard; Valero, Carme

2017-04-01

Francis turbines operate in many cases out of its best efficiency point, in order to regulate their output power according to the instantaneous energy demand of the grid. Therefore, it is of paramount importance to analyse and determine the unstable operating points for these kind of units. In the framework of the HYPERBOLE project (FP7-ENERGY-2013-1; Project number 608532) a large Francis unit was investigated numerically, experimentally in a reduced scale model and also experimentally and numerically in the real prototype. This paper shows the unstable operating points identified during the experimental tests on the real Francis unit and the analysis of the main characteristics of these instabilities. Finally, it is shown that similar phenomena have been identified on previous research in the LMH (Laboratory for Hydraulic Machines, Lausanne) with the reduced scale model.

Modeling the low-velocity impact characteristics of woven glass epoxy composite laminates using artificial neural networks

NASA Astrophysics Data System (ADS)

Mathivanan, N. Rajesh; Mouli, Chandra

2012-12-01

In this work, a new methodology based on artificial neural networks (ANN) has been developed to study the low-velocity impact characteristics of woven glass epoxy laminates of EP3 grade. To train and test the networks, multiple impact cases have been generated using statistical analysis of variance (ANOVA). Experimental tests were performed using an instrumented falling-weight impact-testing machine. Different impact velocities and impact energies on different thicknesses of laminates were considered as the input parameters of the ANN model. This model is a feed-forward back-propagation neural network. Using the input/output data of the experiments, the model was trained and tested. Further, the effects of the low-velocity impact response of the laminates at different energy levels were investigated by studying the cause-effect relationship among the influential factors using response surface methodology. The most significant parameter is determined from the other input variables through ANOVA.

Determining the optimal load for jump squats: a review of methods and calculations.

PubMed

Dugan, Eric L; Doyle, Tim L A; Humphries, Brendan; Hasson, Christopher J; Newton, Robert U

2004-08-01

There has been an increasing volume of research focused on the load that elicits maximum power output during jump squats. Because of a lack of standardization for data collection and analysis protocols, results of much of this research are contradictory. The purpose of this paper is to examine why differing methods of data collection and analysis can lead to conflicting results for maximum power and associated optimal load. Six topics relevant to measurement and reporting of maximum power and optimal load are addressed: (a) data collection equipment, (b) inclusion or exclusion of body weight force in calculations of power, (c) free weight versus Smith machine jump squats, (d) reporting of average versus peak power, (e) reporting of load intensity, and (f) instructions given to athletes/ participants. Based on this information, a standardized protocol for data collection and reporting of jump squat power and optimal load is presented.

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Evaluation of a disposable diesel exhaust filter for permissible mining machines

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

Ambs, J.L.; Cantrell, B.K.; Watts, W.F.

1994-01-01

The US Bureau of Mines (USBM) Diesel Research Program emphasizes the development and evaluation of emission control devices to reduce exposure of miners to diesel exhaust pollutants. Studies by the USBM have shown that diesel exhaust aerosol (DEA) contributes a substantial portion of the respirable aerosol in underground coal mines using diesel equipment not equipped with emission controls. The USBM and the Donaldson Co., Inc., Minneapolis, MN, have developed a low-temperature, disposable diesel exhaust filter (DDEF) for use on permissible diesel haulage vehicles equipped with waterbath exhaust conditioners. These were evaluated in three underground mines to determine their effectiveness inmore » reducing DEA concentrations. The DDEF reduced DEA concentrations from 70 to 90% at these mines. The usable life of the filter ranged from 10 to 32 h, depending on factors that affect DEA output, such as mine altitude, engine type, and duty-cycle. Cost per filter is approximately $40.« less

Half Moon Bay, Grays Harbor, Washington: Movable-Bed Physical Model Study

DTIC Science & Technology

2006-09-01

wave machine used in Half Moon Bay physical model.................................50 Figure 28. Wave analysis output from model wave measurements...Point Chehalis used to reduce strong longshore current................82 Figure 46. Analysis of irregular waves measured at model wave Gauge 4...required several reconstruction efforts between origi- nal construction and present day due to the harsh wave climate on the Washington coast. After

The Great American Degree Machine. An Economic Analysis of the Human Resource Output of Higher Education.

ERIC Educational Resources Information Center

Adkins, Douglas L.

This document studies the changes in the total number of holders of bachelor's and more advance degrees from 1930 to 1971 and provides detailed annual estimates of degree holders in 44 fields. Considered are four possible models that might explain the steady growth in the number of degrees awarded and the changes that occurred in their…

Development of an Electric Motor Powered Low Cost Coconut Deshelling Machine

NASA Astrophysics Data System (ADS)

Mondal, Imdadul Hoque; Prasanna Kumar, G. V.

2016-06-01

An electric motor powered coconut deshelling machine was developed in line with the commercially available unit, but with slight modifications. The machine worked on the principle that the coconut shell can be caused to fail in shear and compressive forces. It consisted of a toothed wheel, a deshelling rod, an electric motor, and a compound chain drive. A bevelled 16 teeth sprocket with 18 mm pitch was used as the toothed wheel. Mild steel round bar of 18 mm diameter was used as the deshelling rod. The sharp edge tip of the deshelling rod was inserted below the shell to apply shear force on the shell, and the fruit was tilted toward the rotary toothed wheel to apply the compressive force on the shell. The speed of rotation of the toothed wheel was set at 34 ± 2 rpm. The output capacity of the machine was found to be 24 coconuts/h with 95 % of the total time effectively used for deshelling. The labour requirement was found to be 43 man-h/1000 nuts. About 13 % of the kernels got scraped and about 7 % got sliced during the operation. The developed coconut deshelling machine was recommended for the minimum annual use of 200 h or deshelling of 4700 coconuts per year. The cost of operation for 200 h of annual use was found to be about ` 47/h. The developed machine was found to be simple, easy to operate, energy efficient, safe and reduce drudgery involved in deshelling by conventional methods.

Evaluation of the TrueBeam machine performance check (MPC) beam constancy checks for flattened and flattening filter-free (FFF) photon beams.

PubMed

Barnes, Michael P; Greer, Peter B

2017-01-01

Machine Performance Check (MPC) is an automated and integrated image-based tool for verification of beam and geometric performance of the TrueBeam linac. The aims of the study were to evaluate the MPC beam performance tests against current daily quality assurance (QA) methods, to compare MPC performance against more accurate monthly QA tests and to test the sensitivity of MPC to changes in beam performance. The MPC beam constancy checks test the beam output, uniformity, and beam center against the user defined baseline. MPC was run daily over a period of 5 months (n = 115) in parallel with the Daily QA3 device. Additionally, IC Profiler, in-house EPID tests, and ion chamber measurements were performed biweekly and results presented in a form directly comparable to MPC. The sensitivity of MPC was investigated using controlled adjustments of output, beam angle, and beam position steering. Over the period, MPC output agreed with ion chamber to within 0.6%. For an output adjustment of 1.2%, MPC was found to agree with ion chamber to within 0.17%. MPC beam center was found to agree with the in-house EPID method within 0.1 mm. A focal spot position adjustment of 0.4 mm (at isocenter) was measured with MPC beam center to within 0.01 mm. An average systematic offset of 0.5% was measured in the MPC uniformity and agreement of MPC uniformity with symmetry measurements was found to be within 0.9% for all beams. MPC uniformity detected a change in beam symmetry of 1.5% to within 0.3% and 0.9% of IC Profiler for flattened and FFF beams, respectively. © 2016 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Modeling the control of the central nervous system over the cardiovascular system using support vector machines.

PubMed

Díaz, José; Acosta, Jesús; González, Rafael; Cota, Juan; Sifuentes, Ernesto; Nebot, Àngela

2018-02-01

The control of the central nervous system (CNS) over the cardiovascular system (CS) has been modeled using different techniques, such as fuzzy inductive reasoning, genetic fuzzy systems, neural networks, and nonlinear autoregressive techniques; the results obtained so far have been significant, but not solid enough to describe the control response of the CNS over the CS. In this research, support vector machines (SVMs) are used to predict the response of a branch of the CNS, specifically, the one that controls an important part of the cardiovascular system. To do this, five models are developed to emulate the output response of five controllers for the same input signal, the carotid sinus blood pressure (CSBP). These controllers regulate parameters such as heart rate, myocardial contractility, peripheral and coronary resistance, and venous tone. The models are trained using a known set of input-output response in each controller; also, there is a set of six input-output signals for testing each proposed model. The input signals are processed using an all-pass filter, and the accuracy performance of the control models is evaluated using the percentage value of the normalized mean square error (MSE). Experimental results reveal that SVM models achieve a better estimation of the dynamical behavior of the CNS control compared to others modeling systems. The main results obtained show that the best case is for the peripheral resistance controller, with a MSE of 1.20e-4%, while the worst case is for the heart rate controller, with a MSE of 1.80e-3%. These novel models show a great reliability in fitting the output response of the CNS which can be used as an input to the hemodynamic system models in order to predict the behavior of the heart and blood vessels in response to blood pressure variations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

NASA Astrophysics Data System (ADS)

Rudrapati, R.; Sahoo, P.; Bandyopadhyay, A.

2016-09-01

The main aim of the present work is to analyse the significance of turning parameters on surface roughness in computer numerically controlled (CNC) turning operation while machining of aluminium alloy material. Spindle speed, feed rate and depth of cut have been considered as machining parameters. Experimental runs have been conducted as per Box-Behnken design method. After experimentation, surface roughness is measured by using stylus profile meter. Factor effects have been studied through analysis of variance. Mathematical modelling has been done by response surface methodology, to made relationships between the input parameters and output response. Finally, process optimization has been made by teaching learning based optimization (TLBO) algorithm. Predicted turning condition has been validated through confirmatory experiment.

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

PubMed Central

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

2017-01-01

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

Single molecule thermodynamics in biological motors.

PubMed

Taniguchi, Yuichi; Karagiannis, Peter; Nishiyama, Masayoshi; Ishii, Yoshiharu; Yanagida, Toshio

2007-04-01

Biological molecular machines use thermal activation energy to carry out various functions. The process of thermal activation has the stochastic nature of output events that can be described according to the laws of thermodynamics. Recently developed single molecule detection techniques have allowed each distinct enzymatic event of single biological machines to be characterized providing clues to the underlying thermodynamics. In this study, the thermodynamic properties in the stepping movement of a biological molecular motor have been examined. A single molecule detection technique was used to measure the stepping movements at various loads and temperatures and a range of thermodynamic parameters associated with the production of each forward and backward step including free energy, enthalpy, entropy and characteristic distance were obtained. The results show that an asymmetry in entropy is a primary factor that controls the direction in which the motor will step. The investigation on single molecule thermodynamics has the potential to reveal dynamic properties underlying the mechanisms of how biological molecular machines work.

Noise measurements for single and multiple operation of 50 kw wind turbine generators

NASA Technical Reports Server (NTRS)

Hubbard, H. H.; Shepherd, K. P.

1982-01-01

The noise characteristics of the U.S. Windpower Inc., 50 kw wind turbine generator were measured at various distances from 30 m to 1100 m and for a range of output power. The generated noise is affected by the aerodynamic wakes of the tower legs at frequencies below about 120 Hz and the blade trailing edge thickness at frequencies of about 2 kHz. Rope strakes and airfoil fairings on the legs did not result in substantial noise reductions. Sharpening the blade trailing edges near the tip was effective in reducing broad band noise near 2 kHz. For multiple machines the sound fields are superposed. A three-fold increase in number of machines (from 1 to 3) results in a predicted increase in he sound pressure level of about 5 dB. The detection threshold for 14 machines operating in a 13 - 20 mph wind is observed to be at approximately 1160 m in the downwind direction.

A solid-state controller for a wind-driven slip-ring induction generator

NASA Astrophysics Data System (ADS)

Velayudhan, C.; Bundell, J. H.; Leary, B. G.

1984-08-01

The three-phase induction generator appears to become the preferred choice for wind-powered systems operated in parallel with existing power systems. A problem arises in connection with the useful operating speed range of the squirrel-cage machine, which is relatively narrow, as, for instance, in the range from 1 to 1.15. Efficient extraction of energy from a wind turbine, on the other hand, requires a speed range, perhaps as large as 1 to 3. One approach for 'matching' the generator to the turbine for the extraction of maximum power at any usable wind speed involves the use of a slip-ring induction machine. The power demand of the slip-ring machine can be matched to the available output from the wind turbine by modifying the speed-torque characteristics of the generator. A description is presented of a simple electronic rotor resistance controller which can optimize the power taken from a wind turbine over the full speed range.

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

PubMed

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

2017-11-18

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

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Application of Metamorphic Testing to Supervised Classifiers

PubMed Central

Xie, Xiaoyuan; Ho, Joshua; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2010-01-01

Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no “test oracle” to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called “metamorphic testing”, which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well. PMID:21243103

A machine learning system to improve heart failure patient assistance.

PubMed

Guidi, Gabriele; Pettenati, Maria Chiara; Melillo, Paolo; Iadanza, Ernesto

2014-11-01

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

Determination of The Mechanical Power in Belt Conveyor's Drive System in Industrial Conditions

NASA Astrophysics Data System (ADS)

Król, Robert; Kaszuba, Damian; Kisielewski, Waldemar

2016-10-01

Mechanical power is a value which carries a significant amount of information on the properties of the operating status of the machine analysed. The value of mechanical power reflects the degree of load of the drive system and of the entire machine. It is essential to determine the actual efficiency of the drive system η [%], which is the key parameter of the energy efficiency of the drive system. In the case of a single drive of a belt conveyor the actual efficiency is expressed as the ratio of mechanical output power Pm [W] at the drive pulley shaft to active electrical power drawn by the motor Pe [W]. Furthermore, the knowledge about the mechanical power from all drives of the multiple driven belt conveyor allows for the analysis of load distribution between the drives. In case of belt conveyor, the mechanical power Pm [W] generated by the drive at the drive pulley's shaft is equal to its angular velocity ω [rad / s] multiplied by the torque T [Nm]. The measurement of angular velocity is relatively easy and can be realized with the use of a tachometer or can be determined on the basis of linear velocity of the conveyor belt during belt conveyor's steady state operation. Significantly more difficult to perform in industrial conditions is the measurement of the torque. This is due to the operational conditions of belt conveyors (e.g. dustiness, high humidity, high temperature) and tight assembly of the drive components without the possibility of their disassembly. It makes it difficult or even impossible to measure the torque using a number of the techniques available, causing an individual approach to each object of research. The paper proposes a measurement methodology allowing to determine the mechanical power in belt conveyors drives which are commonly used in underground and surface mining. The paper presents result of the research into mechanical power in belt conveyor's drive carried out in underground mine conditions.

Effective seat-to-head transmissibility in whole-body vibration: Effects of posture and arm position

NASA Astrophysics Data System (ADS)

Rahmatalla, Salam; DeShaw, Jonathan

2011-12-01

Seat-to-head transmissibility is a biomechanical measure that has been widely used for many decades to evaluate seat dynamics and human response to vibration. Traditionally, transmissibility has been used to correlate single-input or multiple-input with single-output motion; it has not been effectively used for multiple-input and multiple-output scenarios due to the complexity of dealing with the coupled motions caused by the cross-axis effect. This work presents a novel approach to use transmissibility effectively for single- and multiple-input and multiple-output whole-body vibrations. In this regard, the full transmissibility matrix is transformed into a single graph, such as those for single-input and single-output motions. Singular value decomposition and maximum distortion energy theory were used to achieve the latter goal. Seat-to-head transmissibility matrices for single-input/multiple-output in the fore-aft direction, single-input/multiple-output in the vertical direction, and multiple-input/multiple-output directions are investigated in this work. A total of ten subjects participated in this study. Discrete frequencies of 0.5-16 Hz were used for the fore-aft direction using supported and unsupported back postures. Random ride files from a dozer machine were used for the vertical and multiple-axis scenarios considering two arm postures: using the armrests or grasping the steering wheel. For single-input/multiple-output, the results showed that the proposed method was very effective in showing the frequencies where the transmissibility is mostly sensitive for the two sitting postures and two arm positions. For multiple-input/multiple-output, the results showed that the proposed effective transmissibility indicated higher values for the armrest-supported posture than for the steering-wheel-supported posture.

Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

PubMed

Anam, Khairul; Al-Jumaily, Adel

2017-01-01

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features. Different from other studies on the implementation of ELM in the myoelectric controller, this paper presents a complete and thorough investigation of various types of ELMs including the node-based and kernel-based ELM. Furthermore, this paper provides comparisons of ELMs and other well-known classifiers such as linear discriminant analysis (LDA), k-nearest neighbour (kNN), support vector machine (SVM) and least-square SVM (LS-SVM). The experimental results show the most accurate ELM classifier is radial basis function ELM (RBF-ELM). The comparison of RBF-ELM and other well-known classifiers shows that RBF-ELM is as accurate as SVM and LS-SVM but faster than the SVM family; it is superior to LDA and kNN. The experimental results also indicate that the accuracy gap of the M-PR on the amputees and non-amputees is not too much with the accuracy of 98.55% on amputees and 99.5% on the non-amputees using six electromyography (EMG) channels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determining the Effect of Material Hardness During the Hard Turning of AISI4340 Steel

NASA Astrophysics Data System (ADS)

Kambagowni, Venkatasubbaiah; Chitla, Raju; Challa, Suresh

2018-05-01

In the present manufacturing industries hardened steels are most widely used in the applications like tool design and mould design. It enhances the application range of hard turning of hardened steels in manufacturing industries. This study discusses the impact of workpiece hardness, feed and depth of cut on Arithmetic mean roughness (Ra), root mean square roughness (Rq), mean depth of roughness (Rz) and total roughness (Rt) during the hard turning. Experiments have been planned according to the Box-Behnken design and conducted on hardened AISI4340 steel at 45, 50 and 55 HRC with wiper ceramic cutting inserts. Cutting speed is kept constant during this study. The analysis of variance was used to determine the effects of the machining parameters. 3-D response surface plots drawn based on RSM were utilized to set up the input-output relationships. The results indicated that the feed rate has the most significant parameter for Ra, Rq and Rz and hardness has the most critical parameter for the Rt. Further, hardness shows its influence over all the surface roughness characteristics.

Hydraulic Fatigue-Testing Machine

NASA Technical Reports Server (NTRS)

Hodo, James D.; Moore, Dennis R.; Morris, Thomas F.; Tiller, Newton G.

1987-01-01

Fatigue-testing machine applies fluctuating tension to number of specimens at same time. When sample breaks, machine continues to test remaining specimens. Series of tensile tests needed to determine fatigue properties of materials performed more rapidly than in conventional fatigue-testing machine.

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.

Review of Cuttability Indices and A New Rockmass Classification Approach for Selection of Surface Miners

NASA Astrophysics Data System (ADS)

Dey, Kaushik; Ghose, A. K.

2011-09-01

Rock excavation is carried out either by drilling and blasting or using rock-cutting machines like rippers, bucket wheel excavators, surface miners, road headers etc. Economics of mechanised rock excavation by rock-cutting machines largely depends on the achieved production rates. Thus, assessment of the performance (productivity) is important prior to deploying a rock-cutting machine. In doing so, several researchers have classified rockmass in different ways and have developed cuttability indices to correlate machine performance directly. However, most of these indices were developed to assess the performance of road headers/tunnel-boring machines apart from a few that were developed in the earlier days when the ripper was a popular excavating equipment. Presently, around 400 surface miners are in operation around the world amongst which, 105 are in India. Until now, no rockmass classification system is available to assess the performance of surface miners. Surface miners are being deployed largely on trial and error basis or based on the performance charts provided by the manufacturer. In this context, it is logical to establish a suitable cuttability index to predict the performance of surface miners. In this present paper, the existing cuttability indices are reviewed and a new cuttability indexes proposed. A new relationship is also developed to predict the output from surface miners using the proposed cuttability index.

Ergonomic evaluation of conventional and improved methods of aonla pricking with women workers.

PubMed

Rai, Arpana; Gandhi, Sudesh; Sharma, D K

2012-01-01

Conventional and improved methods of aonla pricking were evaluated ergonomically on an experiment conducted for 20 minute with women workers. The working heart rate, energy expenditure rate, total cardiac cost of work and physiological cost of work with conventional tools varied from 93-102 beats.min-1, 6-7.5 kJ.min-1, 285-470 beats, 14 -23 beats.min-1 while with machine varied from 96-105 beats.min-1, 6.5-8 kJ.min-1 , 336-540 beats, 16-27 beats.min-1 respectively. OWAS score for conventional method was 2 indicating corrective measures in near future while with machine was 1 indicating no corrective measures. Result of Nordic Musculoskeletal Questionnaire revealed that subjects complaint of pain in back, neck, right shoulder and right hand due to unnatural body posture and repetitive movement with hand tool. Moreover pricking was carried out in improper lighting conditions (200-300 lux) resulting into finger injuries from sharp edges of hand tool, whereas with machine no such problems were observed. Output with machine increased thrice than hand pricking in a given time. Machine was found useful in terms of saving time, increased productivity, enhanced safety and comfort as involved improved posture, was easy to handle and operate, thus increasing efficiency of the worker leading to better quality of life.

Humidity detection using chitosan film based sensor

NASA Astrophysics Data System (ADS)

Nasution, T. I.; Nainggolan, I.; Dalimunthe, D.; Balyan, M.; Cuana, R.; Khanifah, S.

2018-02-01

A humidity sensor made of the natural polymer chitosan has been successfully fabricated in the film form by a solution casting method. Humidity testing was performed by placing a chitosan film sensor in a cooling machine room, model KT-2000 Ahu. The testing results showed that the output voltage values of chitosan film sensor increased with the increase in humidity percentage. For the increase in humidity percentage from 30 to 90% showed that the output voltage of chitosan film sensor increased from 32.19 to 138.75 mV. It was also found that the sensor evidenced good repeatability and stability during the testing. Therefore, chitosan has a great potential to be used as new sensing material for the humidity detection of which was cheaper and environmentally friendly.

Theoretical and experimental power from large horizontal-axis wind turbines

NASA Technical Reports Server (NTRS)

Viterna, L. A.; Janetzke, D. C.

1982-01-01

A method for calculating the output power from large horizontal-axis wind turbines is presented. Modifications to the airfoil characteristics and the momentum portion of classical blade element-momentum theory are given that improve correlation with measured data. Improvement is particularly evident at low tip-speed ratios where aerodynamic stall can occur as the blade experiences high angles of attack. Output power calculated using the modified theory is compared with measured data for several large wind turbines. These wind turbines range in size from the DOE/NASA 100 kW Mod-0 (38 m rotor diameter) to the 2000 kW Mod-1 (61 m rotor diameter). The calculated results are in good agreement with measured data from these machines.

New design environment for defect detection in web inspection systems

NASA Astrophysics Data System (ADS)

Hajimowlana, S. Hossain; Muscedere, Roberto; Jullien, Graham A.; Roberts, James W.

1997-09-01

One of the aims of industrial machine vision is to develop computer and electronic systems destined to replace human vision in the process of quality control of industrial production. In this paper we discuss the development of a new design environment developed for real-time defect detection using reconfigurable FPGA and DSP processor mounted inside a DALSA programmable CCD camera. The FPGA is directly connected to the video data-stream and outputs data to a low bandwidth output bus. The system is targeted for web inspection but has the potential for broader application areas. We describe and show test results of the prototype system board, mounted inside a DALSA camera and discuss some of the algorithms currently simulated and implemented for web inspection applications.

Axial calibration methods of piezoelectric load sharing dynamometer

NASA Astrophysics Data System (ADS)

Zhang, Jun; Chang, Qingbing; Ren, Zongjin; Shao, Jun; Wang, Xinlei; Tian, Yu

2018-06-01

The relationship between input and output of load sharing dynamometer is seriously non-linear in different loading points of a plane, so it's significant for accutately measuring force to precisely calibrate the non-linear relationship. In this paper, firstly, based on piezoelectric load sharing dynamometer, calibration experiments of different loading points are performed in a plane. And then load sharing testing system is respectively calibrated based on BP algorithm and ELM (Extreme Learning Machine) algorithm. Finally, the results show that the calibration result of ELM is better than BP for calibrating the non-linear relationship between input and output of loading sharing dynamometer in the different loading points of a plane, which verifies that ELM algorithm is feasible in solving force non-linear measurement problem.

Output calculation of electron therapy at extended SSD using an improved LBR method.

PubMed

Alkhatib, Hassaan A; Gebreamlak, Wondesen T; Tedeschi, David J; Mihailidis, Dimitris; Wright, Ben W; Neglia, William J; Sobash, Philip T; Fontenot, Jonas D

2015-02-01

To calculate the output factor (OPF) of any irregularly shaped electron beam at extended SSD. Circular cutouts were prepared from 2.0 cm diameter to the maximum possible size for 15 × 15 applicator cone. In addition, two irregular cutouts were prepared. For each cutout, percentage depth dose (PDD) at the standard SSD and doses at different SSD values were measured using 6, 9, 12, and 16 MeV electron beam energies on a Varian 2100C LINAC and the distance at which the central axis electron fluence becomes independent of cutout size was determined. The measurements were repeated with an ELEKTA Synergy LINAC using 14 × 14 applicator cone and electron beam energies of 6, 9, 12, and 15 MeV. The PDD measurements were performed using a scanning system and two diodes-one for the signal and the other a stationary reference outside the tank. The doses of the circular cutouts at different SSDs were measured using PTW 0.125 cm(3) Semiflex ion-chamber and EDR2 films. The electron fluence was measured using EDR2 films. For each circular cutout, the lateral buildup ratio (LBR) was calculated from the measured PDD curve using the open applicator cone as the reference field. The effective SSD (SSDeff) of each circular cutout was calculated from the measured doses at different SSD values. Using the LBR value and the radius of the circular cutout, the corresponding lateral spread parameter [σR(z)] was calculated. Taking the cutout size dependence of σR(z) into account, the PDD curves of the irregularly shaped cutouts at the standard SSD were calculated. Using the calculated PDD curve of the irregularly shaped cutout along with the LBR and SSDeff values of the circular cutouts, the output factor of the irregularly shaped cutout at extended SSD was calculated. Finally, both the calculated PDD curves and output factor values were compared with the measured values. The improved LBR method has been generalized to calculate the output factor of electron therapy at extended SSD. The percentage difference between the calculated and the measured output factors of irregularly shaped cutouts in a clinical useful SSD region was within 2%. Similar results were obtained for all available electron energies of both Varian 2100C and ELEKTA Synergy machines.

SU-F-BRE-14: Uncertainty Analysis for Dose Measurements Using OSLD NanoDots

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

Kry, S; Alvarez, P; Stingo, F

2014-06-15

Purpose: Optically stimulated luminescent dosimeters (OSLD) are an increasingly popular dosimeter for research and clinical applications. It is also used by the Radiological Physics Center for remote auditing of machine output. In this work we robustly calculated the reproducibility and uncertainty of the OSLD nanoDot. Methods: For the RPC dose calculation, raw readings are corrected for depletion, element sensitivity, fading, linearity, and energy. System calibration is determined for the experimental OSLD irradiated at different institutions by using OSLD irradiated by the RPC under reference conditions (i.e., standards): 1 Gy in a Cobalt beam. The intra-dot and inter-dot reproducibilities (coefficient ofmore » variation) were determined from the history of RPC readings of these standards. The standard deviation of the corrected OSLD signal was then calculated analytically using a recursive formalism that did not rely on the normality assumption of the underlying uncertainties, or on any type of mathematical approximation. This analytical uncertainty was compared to that empirically estimated from >45,000 RPC beam audits. Results: The intra-dot variability was found to be 0.59%, with only a small variation between readers. Inter-dot variability was found to be 0.85%. The uncertainty in each of the individual correction factors was empirically determined. When the raw counts from each OSLD were adjusted for the appropriate correction factors, the analytically determined coefficient of variation was 1.8% over a range of institutional irradiation conditions that are seen at the RPC. This is reasonably consistent with the empirical observations of the RPC, where the coefficient of variation of the measured beam outputs is 1.6% (photons) and 1.9% (electrons). Conclusion: OSLD nanoDots provide sufficiently good precision for a wide range of applications, including the RPC remote monitoring program for megavoltage beams. This work was supported by PHS grant CA10953 awarded by the NIH (DHHS)« less

Method for Operating a Sensor to Differentiate Between Analytes in a Sample

DOEpatents

Kunt, Tekin; Cavicchi, Richard E; Semancik, Stephen; McAvoy, Thomas J

1998-07-28

Disclosed is a method for operating a sensor to differentiate between first and second analytes in a sample. The method comprises the steps of determining a input profile for the sensor which will enhance the difference in the output profiles of the sensor as between the first analyte and the second analyte; determining a first analyte output profile as observed when the input profile is applied to the sensor; determining a second analyte output profile as observed when the temperature profile is applied to the sensor; introducing the sensor to the sample while applying the temperature profile to the sensor, thereby obtaining a sample output profile; and evaluating the sample output profile as against the first and second analyte output profiles to thereby determine which of the analytes is present in the sample.

Use of IT platform in determination of efficiency of mining machines

NASA Astrophysics Data System (ADS)

Brodny, Jarosław; Tutak, Magdalena

2018-01-01

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

Detecting Cavitation Pitting Without Disassembly

NASA Technical Reports Server (NTRS)

Barkhoudarian, S.

1986-01-01

Technique for detecting cavitation pitting in pumps, turbines, and other machinery uses low-level nuclear irradiation. Isotopes concentrated below surface emit gamma radiation, a portion of which is attenuated by overlying material. Where there are cavitation pits, output of gamma-ray detector fluctuates as detector is scanned near pits. Important to detect cavitation pits because nozzle, turbine blade, or other pump component weakened by cavitation could fail catastrophically and cause machine to explode.

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

PubMed Central

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

2007-01-01

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

Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

ATLAS@AWS

NASA Astrophysics Data System (ADS)

Gehrcke, Jan-Philip; Kluth, Stefan; Stonjek, Stefan

2010-04-01

We show how the ATLAS offline software is ported on the Amazon Elastic Compute Cloud (EC2). We prepare an Amazon Machine Image (AMI) on the basis of the standard ATLAS platform Scientific Linux 4 (SL4). Then an instance of the SLC4 AMI is started on EC2 and we install and validate a recent release of the ATLAS offline software distribution kit. The installed software is archived as an image on the Amazon Simple Storage Service (S3) and can be quickly retrieved and connected to new SL4 AMI instances using the Amazon Elastic Block Store (EBS). ATLAS jobs can then configure against the release kit using the ATLAS configuration management tool (cmt) in the standard way. The output of jobs is exported to S3 before the SL4 AMI is terminated. Job status information is transferred to the Amazon SimpleDB service. The whole process of launching instances of our AMI, starting, monitoring and stopping jobs and retrieving job output from S3 is controlled from a client machine using python scripts implementing the Amazon EC2/S3 API via the boto library working together with small scripts embedded in the SL4 AMI. We report our experience with setting up and operating the system using standard ATLAS job transforms.

A high reliability module with thermoelectric device by molding technology for M2M wireless sensor network

NASA Astrophysics Data System (ADS)

Nakagawa, K.; Tanaka, T.; Suzuki, T.

2015-10-01

This paper presents the fabrication of a new energy harvesting module that uses a thermoelectric device (TED) by using molding technology. Through molding technology, the TED and circuit board can be properly protected and a heat-radiating fin structure can be simultaneously constructed. The output voltage per heater temperature of the TED module at 20 °C ambient temperature is 8 mV K-1, similar to the result with the aluminum heat sink which is almost the same fin size as the TED module. The accelerated environmental tests are performed on a damp heat test, which is an aging test under high temperature and high humidity, highly accelerated temperature, and humidity stress test (HAST) for the purpose of evaluating the electrical reliability in harsh environments, cold test and thermal cycle test to evaluate degrading characteristics by cycling through two temperatures. All test results indicate that the TED and circuit board can be properly protected from harsh temperature and humidity by using molding technology because the output voltage of after-tested modules is reduced by less than 5%. This study presents a novel fabrication method for a high reliability TED-installed module appropriate for Machine to Machine wireless sensor networks.

Modeling and simulation of a hybrid ship power system

NASA Astrophysics Data System (ADS)

Doktorcik, Christopher J.

2011-12-01

Optimizing the performance of naval ship power systems requires integrated design and coordination of the respective subsystems (sources, converters, and loads). A significant challenge in the system-level integration is solving the Power Management Control Problem (PMCP). The PMCP entails deciding on subsystem power usages for achieving a trade-off between the error in tracking a desired position/velocity profile, minimizing fuel consumption, and ensuring stable system operation, while at the same time meeting performance limitations of each subsystem. As such, the PMCP naturally arises at a supervisory level of a ship's operation. In this research, several critical steps toward the solution of the PMCP for surface ships have been undertaken. First, new behavioral models have been developed for gas turbine engines, wound rotor synchronous machines, DC super-capacitors, induction machines, and ship propulsion systems. Conventional models describe system inputs and outputs in terms of physical variables such as voltage, current, torque, and force. In contrast, the behavioral models developed herein express system inputs and outputs in terms of power whenever possible. Additionally, the models have been configured to form a hybrid system-level power model (HSPM) of a proposed ship electrical architecture. Lastly, several simulation studies have been completed to expose the capabilities and limitations of the HSPM.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

PubMed

Reid, Colleen E; Jerrett, Michael; Petersen, Maya L; Pfister, Gabriele G; Morefield, Philip E; Tager, Ira B; Raffuse, Sean M; Balmes, John R

2015-03-17

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

PubMed

Cheng, Ching-An; Huang, Han-Pang

2016-12-01

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.

Predicting human protein function with multi-task deep neural networks.

PubMed

Fa, Rui; Cozzetto, Domenico; Wan, Cen; Jones, David T

2018-01-01

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning techniques to further enhance prediction ability.

Expected energy-based restricted Boltzmann machine for classification.

PubMed

Elfwing, S; Uchibe, E; Doya, K

2015-04-01

In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

NASA Astrophysics Data System (ADS)

Yang, G.; Lin, Y.; Bhattacharya, P.

2007-12-01

To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Compensated pulsed alternator

DOEpatents

Weldon, William F.; Driga, Mircea D.; Woodson, Herbert H.

1980-01-01

This invention relates to an electromechanical energy converter with inertial energy storage. The device, a single phase, two or multi-pole alternator with stationary field coils, and a rotating armature is provided. The rotor itself may be of laminated steel for slower pulses or for faster pulses should be nonmagnetic and electrically nonconductive in order to allow rapid penetration of the field as the armature coil rotates. The armature coil comprises a plurality of power generating conductors mounted on the rotor. The alternator may also include a stationary or counterrotating compensating coil to increase the output voltage thereof and to reduce the internal impedance of the alternator at the moment of peak outout. As the machine voltage rises sinusoidally, an external trigger switch is adapted to be closed at the appropriate time to create the desired output current from said alternator to an external load circuit, and as the output current passes through zero a self-commutating effect is provided to allow the switch to disconnect the generator from the external circuit.

Demonstration of quantum superiority in learning parity with noise with superconducting qubits

NASA Astrophysics Data System (ADS)

Ristè, Diego; da Silva, Marcus; Ryan, Colm; Cross, Andrew; Smolin, John; Gambetta, Jay; Chow, Jerry; Johnson, Blake

A problem in machine learning is to identify the function programmed in an unknown device, or oracle, having only access to its output. In particular, a parity function computes the parity of a subset of a bit register. We implement an oracle executing parity functions in a five-qubit superconducting processor and compare the performance of a classical and a quantum learner. The classical learner reads the output of multiple oracle calls and uses the results to infer the hidden function. In addition to querying the oracle, the quantum learner can apply coherent rotations on the output register before the readout. We show that, given a target success probability, the quantum approach outperforms the classical one in the number of queries needed. Moreover, this gap increases with readout noise and with the size of the qubit register. This result shows that quantum advantage can already emerge in current systems with a few, noisy qubits. We acknowledge support from IARPA under Contract W911NF-10-1-0324.

Bubbler: A Novel Ultra-High Power Density Energy Harvesting Method Based on Reverse Electrowetting

PubMed Central

Hsu, Tsung-Hsing; Manakasettharn, Supone; Taylor, J. Ashley; Krupenkin, Tom

2015-01-01

We have proposed and successfully demonstrated a novel approach to direct conversion of mechanical energy into electrical energy using microfluidics. The method combines previously demonstrated reverse electrowetting on dielectric (REWOD) phenomenon with the fast self-oscillating process of bubble growth and collapse. Fast bubble dynamics, used in conjunction with REWOD, provides a possibility to increase the generated power density by over an order of magnitude, as compared to the REWOD alone. This energy conversion approach is particularly well suited for energy harvesting applications and can enable effective coupling to a broad array of mechanical systems including such ubiquitous but difficult to utilize low-frequency energy sources as human and machine motion. The method can be scaled from a single micro cell with 10−6 W output to power cell arrays with a total power output in excess of 10 W. This makes the fabrication of small light-weight energy harvesting devices capable of producing a wide range of power outputs feasible. PMID:26567850

Bubbler: A Novel Ultra-High Power Density Energy Harvesting Method Based on Reverse Electrowetting.

PubMed

Hsu, Tsung-Hsing; Manakasettharn, Supone; Taylor, J Ashley; Krupenkin, Tom

2015-11-16

We have proposed and successfully demonstrated a novel approach to direct conversion of mechanical energy into electrical energy using microfluidics. The method combines previously demonstrated reverse electrowetting on dielectric (REWOD) phenomenon with the fast self-oscillating process of bubble growth and collapse. Fast bubble dynamics, used in conjunction with REWOD, provides a possibility to increase the generated power density by over an order of magnitude, as compared to the REWOD alone. This energy conversion approach is particularly well suited for energy harvesting applications and can enable effective coupling to a broad array of mechanical systems including such ubiquitous but difficult to utilize low-frequency energy sources as human and machine motion. The method can be scaled from a single micro cell with 10(-6) W output to power cell arrays with a total power output in excess of 10 W. This makes the fabrication of small light-weight energy harvesting devices capable of producing a wide range of power outputs feasible.

An artificial neural network model for periodic trajectory generation

NASA Astrophysics Data System (ADS)

Shankar, S.; Gander, R. E.; Wood, H. C.

A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

Driver compliance to take-over requests with different auditory outputs in conditional automation.

PubMed

Forster, Yannick; Naujoks, Frederik; Neukum, Alexandra; Huestegge, Lynn

2017-12-01

Conditionally automated driving (CAD) systems are expected to improve traffic safety. Whenever the CAD system exceeds its limit of operation, designers of the system need to ensure a safe and timely enough transition from automated to manual mode. An existing visual Human-Machine Interface (HMI) was supplemented by different auditory outputs. The present work compares the effects of different auditory outputs in form of (1) a generic warning tone and (2) additional semantic speech output on driver behavior for the announcement of an upcoming take-over request (TOR). We expect the information carried by means of speech output to lead to faster reactions and better subjective evaluations by the drivers compared to generic auditory output. To test this assumption, N=17 drivers completed two simulator drives, once with a generic warning tone ('Generic') and once with additional speech output ('Speech+generic'), while they were working on a non-driving related task (NDRT; i.e., reading a magazine). Each drive incorporated one transition from automated to manual mode when yellow secondary lanes emerged. Different reaction time measures, relevant for the take-over process, were assessed. Furthermore, drivers evaluated the complete HMI regarding usefulness, ease of use and perceived visual workload just after experiencing the take-over. They gave comparative ratings on usability and acceptance at the end of the experiment. Results revealed that reaction times, reflecting information processing time (i.e., hands on the steering wheel, termination of NDRT), were shorter for 'Speech+generic' compared to 'Generic' while reaction time, reflecting allocation of attention (i.e., first glance ahead), did not show this difference. Subjective ratings were in favor of the system with additional speech output. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tekscan pressure sensor output changes in the presence of liquid exposure.

PubMed

Jansson, Kyle S; Michalski, Max P; Smith, Sean D; LaPrade, Robert F; Wijdicks, Coen A

2013-02-01

The purpose of the study was to evaluate the load output of a pressure sensor in the presence of liquid saturation in a controlled environment. We hypothesized that a calibrated pressure sensor would provide diminishing load outputs over time in controlled environments of both humidified air and while submerged in saline and the sensors would reach a steady state output once saturated. A consistent compressive load was repeatedly applied to pressure sensors over time (Model 4000, Tekscan, Inc., South Boston, MA) with a tensile testing machine (Instron ElectroPuls E10000, Norwood, MA). All sensors were initially calibrated in a dry environment and were tested in three groups: humid air, submerged in 0.9% saline solution, and dry. Linear regression of load output over time for the pressure sensors exposed to humidity and submerged showed a 4.6% and 4.7% decline in load output each hour for the initial 6h, respectively (β=-0.046, 95% CI: [-0.053 to -0.039]; p<0.001) (β=-0.047, 95% CI: [-0.053 to -0.042; p<0.001). Tests after 72 h of exposure had linear regression decline in load output over time of 0.40% and 0.47% per hour for humidified and submerged sensors, respectively (β=-0.004, 95% CI: [-0.006 to -0.003]; p<0.001) (β=-0.047, 95% CI: [-0.053 to -0.042]; p<0.001). Because outcomes in biomedical research can affect clinical practices and treatments, the diminishing load output of the sensor in the presence of liquids should be accounted for. We recommend soaking sensors for more than 48 h prior to testing in a moist environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Design and optimization of G-band extended interaction klystron with high output power

NASA Astrophysics Data System (ADS)

Li, Renjie; Ruan, Cunjun; Zhang, Huafeng

2018-03-01

A ladder-type Extended Interaction Klystron (EIK) with unequal-length slots in the G-band is proposed and designed. The key parameters of resonance cavities working in the π mode are obtained based on the theoretical analysis and 3D simulation. The influence of the device fabrication tolerance on the high-frequency performance is analyzed in detail, and it is found that at least 5 μm of machining precision is required. Thus, the dynamic tuning is required to compensate for the frequency shift and increase the bandwidth. The input and output coupling hole dimensions are carefully designed to achieve high output power along with a broad bandwidth. The effect of surface roughness of the metallic material on the output power has been investigated, and it is proposed that lower surface roughness leads to higher output power. The focusing magnetic field is also optimized to 0.75 T in order to maintain the beam transportation and achieve high output power. With 16.5 kV operating voltage and 0.30 A beam current, the output power of 360 W, the efficiency of 7.27%, the gain of 38.6 dB, and the 3 dB bandwidth of 500 MHz are predicted. The output properties of the EIK show great stability with the effective suppression of oscillation and mode competition. Moreover, small-signal theory analysis and 1D code AJDISK calculations are carried out to verify the results of 3D PIC simulations. A close agreement among the three methods proves the relative validity and the reliability of the designed EIK. Thus, it is indicated that the EIK with unequal-length slots has potential for power improvement and bandwidth extension.

Reproducibility and Transparency in Ocean-Climate Modeling

NASA Astrophysics Data System (ADS)

Hannah, N.; Adcroft, A.; Hallberg, R.; Griffies, S. M.

2015-12-01

Reproducibility is a cornerstone of the scientific method. Within geophysical modeling and simulation achieving reproducibility can be difficult, especially given the complexity of numerical codes, enormous and disparate data sets, and variety of supercomputing technology. We have made progress on this problem in the context of a large project - the development of new ocean and sea ice models, MOM6 and SIS2. Here we present useful techniques and experience.We use version control not only for code but the entire experiment working directory, including configuration (run-time parameters, component versions), input data and checksums on experiment output. This allows us to document when the solutions to experiments change, whether due to code updates or changes in input data. To avoid distributing large input datasets we provide the tools for generating these from the sources, rather than provide raw input data.Bugs can be a source of non-determinism and hence irreproducibility, e.g. reading from or branching on uninitialized memory. To expose these we routinely run system tests, using a memory debugger, multiple compilers and different machines. Additional confidence in the code comes from specialised tests, for example automated dimensional analysis and domain transformations. This has entailed adopting a code style where we deliberately restrict what a compiler can do when re-arranging mathematical expressions.In the spirit of open science, all development is in the public domain. This leads to a positive feedback, where increased transparency and reproducibility makes using the model easier for external collaborators, who in turn provide valuable contributions. To facilitate users installing and running the model we provide (version controlled) digital notebooks that illustrate and record analysis of output. This has the dual role of providing a gross, platform-independent, testing capability and a means to documents model output and analysis.

Classification of autistic individuals and controls using cross-task characterization of fMRI activity

PubMed Central

Chanel, Guillaume; Pichon, Swann; Conty, Laurence; Berthoz, Sylvie; Chevallier, Coralie; Grèzes, Julie

2015-01-01

Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations. PMID:26793434

Technical Note: PRESAGE three-dimensional dosimetry accurately measures Gamma Knife output factors

PubMed Central

Klawikowski, Slade J.; Yang, James N.; Adamovics, John; Ibbott, Geoffrey S.

2014-01-01

Small-field output factor measurements are traditionally very difficult because of steep dose gradients, loss of lateral electronic equilibrium, and dose volume averaging in finitely sized detectors. Three-dimensional (3D) dosimetry is ideal for measuring small output factors and avoids many of these potential challenges of point and two-dimensional detectors. PRESAGE 3D polymer dosimeters were used to measure the output factors for the 4 mm and 8 mm collimators of the Leksell Perfexion Gamma Knife radiosurgery treatment system. Discrepancies between the planned and measured distance between shot centers were also investigated. A Gamma Knife head frame was mounted onto an anthropomorphic head phantom. Special inserts were machined to hold 60 mm diameter, 70 mm tall cylindrical PRESAGE dosimeters. The phantom was irradiated with one 16 mm shot and either one 4 mm or one 8 mm shot, to a prescribed dose of either 3 Gy or 4 Gy to the 50% isodose line. The two shots were spaced between 30 mm and 60 mm apart and aligned along the central axis of the cylinder. The Presage dosimeters were measured using the DMOS-RPC optical CT scanning system. Five independent 4 mm output factor measurements fell within 2% of the manufacturer’s Monte Carlo simulation-derived nominal value, as did two independent 8 mm output factor measurements. The measured distances between shot centers varied by ± 0.8 mm with respect to the planned shot displacements. On the basis of these results, we conclude that PRESAGE dosimetry is excellently suited to quantify the difficult-to-measure Gamma Knife output factors. PMID:25368961

Method and apparatus for executing an asynchronous clutch-to-clutch shift in a hybrid transmission

DOEpatents

Demirovic, Besim; Gupta, Pinaki; Kaminsky, Lawrence A.; Naqvi, Ali K.; Heap, Anthony H.; Sah, Jy-Jen F.

2014-08-12

A hybrid transmission includes first and second electric machines. A method for operating the hybrid transmission in response to a command to execute a shift from an initial continuously variable mode to a target continuously variable mode includes increasing torque of an oncoming clutch associated with operating in the target continuously variable mode and correspondingly decreasing a torque of an off-going clutch associated with operating in the initial continuously variable mode. Upon deactivation of the off-going clutch, torque outputs of the first and second electric machines and the torque of the oncoming clutch are controlled to synchronize the oncoming clutch. Upon synchronization of the oncoming clutch, the torque for the oncoming clutch is increased and the transmission is operated in the target continuously variable mode.

Surface Texturing of Polyimide Composite by Micro-Ultrasonic Machining

NASA Astrophysics Data System (ADS)

Qu, N. S.; Zhang, T.; Chen, X. L.

2018-03-01

In this study, micro-dimples were prepared on a polyimide composite surface to obtain the dual benefits of polymer materials and surface texture. Micro-ultrasonic machining is employed for the first time for micro-dimple fabrication on polyimide composite surfaces. Surface textures of simple patterns were fabricated successfully with dimple depths of 150 μm, side lengths of 225-425 μm, and area ratios of 10-30%. The friction coefficient of the micro-dimple surfaces with side lengths of 325 or 425 μm could be increased by up to 100% of that of non-textured surfaces, alongside a significant enhancement of wear resistance. The results show that surface texturing of polyimide composite can be applied successfully to increase the friction coefficient and reduce wear, thereby contributing to a large output torque.

Multi-model blending

DOEpatents

Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

2016-10-18

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

XeCl excimer laser with new prism resonator configurations and its performance characteristics

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

Benerji, N. S., E-mail: nsb@rrcat.gov.in, E-mail: bsingh@rrcat.gov.in; Singh, A.; Varshnay, N.

2015-07-15

New resonator cavity configurations, namely, the prism resonator and unstable prism resonator, are demonstrated for the first time in an excimer (XeCl) laser with interesting and novel results. High misalignment tolerance ∼50 mrad is achieved with considerably reduced beam divergence of less than ∼1 mrad without reduction in output power capabilities of the laser. The misalignment tolerance of ∼50 mrad is a dramatic improvement of ∼25 times compared to ∼2 mrad normally observed in standard excimer laser with plane-plane cavity. Increase in depth of focus from 3 mm to 5.5 mm was also achieved in case of prism resonator configurationmore » with an improvement of about 60%. Unstable prism resonator configuration is demonstrated here in this paper with further reduction in beam divergence to about 0.5 mrad using plano-convex lens as output coupler. The misalignment tolerance in case of unstable prism resonator was retained at about 30 mrad which is a high value compared to standard unstable resonators. The output beam spot was completely filled with flat-top profile with prism resonator configurations, which is desired for various material processing applications. Focusing properties and beam divergence in case of prism resonator have been investigated using SEM (scanning electron microscope) images. SEM images of the focused spot size (∼20 μm holes) on metal sheet indicate beam divergence of about 0.05 mrad which is about 1.5 times diffraction limit. Energy contained in this angle is thus sufficient for micro-machining applications. Clean and sharp edges of the micro-holes show high pointing stability with multiple shot exposures. Such characteristics of the excimer laser system will be extremely useful in micro-machining and other field applications.« less

XeCl excimer laser with new prism resonator configurations and its performance characteristics.

PubMed

Benerji, N S; Singh, A; Varshnay, N; Singh, Bijendra

2015-07-01

New resonator cavity configurations, namely, the prism resonator and unstable prism resonator, are demonstrated for the first time in an excimer (XeCl) laser with interesting and novel results. High misalignment tolerance ∼50 mrad is achieved with considerably reduced beam divergence of less than ∼1 mrad without reduction in output power capabilities of the laser. The misalignment tolerance of ∼50 mrad is a dramatic improvement of ∼25 times compared to ∼2 mrad normally observed in standard excimer laser with plane-plane cavity. Increase in depth of focus from 3 mm to 5.5 mm was also achieved in case of prism resonator configuration with an improvement of about 60%. Unstable prism resonator configuration is demonstrated here in this paper with further reduction in beam divergence to about 0.5 mrad using plano-convex lens as output coupler. The misalignment tolerance in case of unstable prism resonator was retained at about 30 mrad which is a high value compared to standard unstable resonators. The output beam spot was completely filled with flat-top profile with prism resonator configurations, which is desired for various material processing applications. Focusing properties and beam divergence in case of prism resonator have been investigated using SEM (scanning electron microscope) images. SEM images of the focused spot size (∼20 μm holes) on metal sheet indicate beam divergence of about 0.05 mrad which is about 1.5 times diffraction limit. Energy contained in this angle is thus sufficient for micro-machining applications. Clean and sharp edges of the micro-holes show high pointing stability with multiple shot exposures. Such characteristics of the excimer laser system will be extremely useful in micro-machining and other field applications.

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

PubMed

Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

2017-08-01

This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

Unitary Shaft-Angle and Shaft-Speed Sensor Assemblies

NASA Technical Reports Server (NTRS)

Alhorn, Dean C.; Howard, David E.; Smith, Dennis A.

2006-01-01

The figure depicts a unit that contains a rotary-position or a rotary-speed sensor, plus electronic circuitry necessary for its operation, all enclosed in a single housing with a shaft for coupling to an external rotary machine. This rotation sensor unit is complete: when its shaft is mechanically connected to that of the rotary machine and it is supplied with electric power, it generates an output signal directly indicative of the rotary position or speed, without need for additional processing by other circuitry. The incorporation of all of the necessary excitatory and readout circuitry into the housing (in contradistinction to using externally located excitatory and/or readout circuitry) in a compact arrangement is the major difference between this unit and prior rotation-sensor units. The sensor assembly inside the housing includes excitatory and readout integrated circuits mounted on a circular printed-circuit board. In a typical case in which the angle or speed transducer(s) utilize electromagnetic induction, the assembly also includes another circular printed-circuit board on which the transducer windings are mounted. A sheet of high-magnetic permeability metal ("mu metal") is placed between the winding board and the electronic-circuit board to prevent spurious coupling of excitatory signals from the transducer windings to the readout circuits. The housing and most of the other mechanical hardware can be common to a variety of different sensor designs. Hence, the unit can be configured to generate any of variety of outputs by changing the interior sensor assembly. For example, the sensor assembly could contain an analog tachometer circuit that generates an output proportional (in both magnitude and sign or in magnitude only) to the speed of rotation.

A flexible Monte Carlo tool for patient or phantom specific calculations: comparison with preliminary validation measurements

NASA Astrophysics Data System (ADS)

Davidson, S.; Cui, J.; Followill, D.; Ibbott, G.; Deasy, J.

2008-02-01

The Dose Planning Method (DPM) is one of several 'fast' Monte Carlo (MC) computer codes designed to produce an accurate dose calculation for advanced clinical applications. We have developed a flexible machine modeling process and validation tests for open-field and IMRT calculations. To complement the DPM code, a practical and versatile source model has been developed, whose parameters are derived from a standard set of planning system commissioning measurements. The primary photon spectrum and the spectrum resulting from the flattening filter are modeled by a Fatigue function, cut-off by a multiplying Fermi function, which effectively regularizes the difficult energy spectrum determination process. Commonly-used functions are applied to represent the off-axis softening, increasing primary fluence with increasing angle ('the horn effect'), and electron contamination. The patient dependent aspect of the MC dose calculation utilizes the multi-leaf collimator (MLC) leaf sequence file exported from the treatment planning system DICOM output, coupled with the source model, to derive the particle transport. This model has been commissioned for Varian 2100C 6 MV and 18 MV photon beams using percent depth dose, dose profiles, and output factors. A 3-D conformal plan and an IMRT plan delivered to an anthropomorphic thorax phantom were used to benchmark the model. The calculated results were compared to Pinnacle v7.6c results and measurements made using radiochromic film and thermoluminescent detectors (TLD).

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Montazeri, A.; West, C.; Monk, S. D.; Taylor, C. J.

2017-04-01

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

MULTIPLE SHAFT TOOL HEAD

DOEpatents

Colbert, H.P.

1962-10-23

An improved tool head arrangement is designed for the automatic expanding of a plurality of ferruled tubes simultaneously. A plurality of output shafts of a multiple spindle drill head are driven in unison by a hydraulic motor. A plurality of tube expanders are respectively coupled to the shafts through individual power train arrangements. The axial or thrust force required for the rolling operation is provided by a double acting hydraulic cylinder having a hollow through shaft with the shaft cooperating with an internally rotatable splined shaft slidably coupled to a coupling rigidly attached to the respectlve output shaft of the drill head, thereby transmitting rotary motion and axial thrust simultaneously to the tube expander. A hydraulic power unit supplies power to each of the double acting cylinders through respective two-position, four-way valves, under control of respective solenoids for each of the cylinders. The solenoids are in turn selectively controlled by a tool selection control unit which in turn is controlled by signals received from a programmed, coded tape from a tape reader. The number of expanders that are extended in a rolling operation, which may be up to 42 expanders, is determined by a predetermined program of operations depending upon the arrangement of the ferruled tubes to be expanded in the tube bundle. The tape reader also supplies dimensional information to a machine tool servo control unit for imparting selected, horizontal and/or vertical movement to the tool head assembly. (AEC)

41 CFR 101-25.106 - Servicing of office machines.

Code of Federal Regulations, 2011 CFR

2011-07-01

... inventory in relation to operating needs; i.e., availability of reserve machine in case of breakdown; (9... machines. 101-25.106 Section 101-25.106 Public Contracts and Property Management Federal Property...-General Policies § 101-25.106 Servicing of office machines. (a) The determination as to whether office...

41 CFR 109-25.104 - Acquisition of office furniture and office machines.

Code of Federal Regulations, 2011 CFR

2011-01-01

... furniture and office machines. 109-25.104 Section 109-25.104 Public Contracts and Property Management... furniture and office machines. DOE offices and designated contractors shall make the determination as to whether requirements can be met through the utilization of DOE owned furniture and office machines. ...

Irrelevance of the Power Stroke for the Directionality, Stopping Force, and Optimal Efficiency of Chemically Driven Molecular Machines

PubMed Central

Astumian, R. Dean

2015-01-01

A simple model for a chemically driven molecular walker shows that the elastic energy stored by the molecule and released during the conformational change known as the power-stroke (i.e., the free-energy difference between the pre- and post-power-stroke states) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Further, the apportionment of the dependence on the externally applied force between the forward and reverse rate constants of the power-stroke (or indeed among all rate constants) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Arguments based on the principle of microscopic reversibility demonstrate that this result is general for all chemically driven molecular machines, and even more broadly that the relative energies of the states of the motor have no role in determining the directionality, stopping force, or optimal efficiency of the machine. Instead, the directionality, stopping force, and optimal efficiency are determined solely by the relative heights of the energy barriers between the states. Molecular recognition—the ability of a molecular machine to discriminate between substrate and product depending on the state of the machine—is far more important for determining the intrinsic directionality and thermodynamics of chemo-mechanical coupling than are the details of the internal mechanical conformational motions of the machine. In contrast to the conclusions for chemical driving, a power-stroke is very important for the directionality and efficiency of light-driven molecular machines and for molecular machines driven by external modulation of thermodynamic parameters. PMID:25606678