Induction motor control

NASA Technical Reports Server (NTRS)

Hansen, Irving G.

1990-01-01

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

Induction motor control

NASA Technical Reports Server (NTRS)

Hansen, Irving G.

1990-01-01

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

A machine learning approach to computer-aided molecular design

NASA Astrophysics Data System (ADS)

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

1991-12-01

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

Determination of efficiencies, loss mechanisms, and performance degradation factors in chopper controlled dc vehical motors. Section 2: The time dependent finite element modeling of the electromagnetic field in electrical machines: Methods and applications. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Hamilton, H. B.; Strangas, E.

1980-01-01

The time dependent solution of the magnetic field is introduced as a method for accounting for the variation, in time, of the machine parameters in predicting and analyzing the performance of the electrical machines. The method of time dependent finite element was used in combination with an also time dependent construction of a grid for the air gap region. The Maxwell stress tensor was used to calculate the airgap torque from the magnetic vector potential distribution. Incremental inductances were defined and calculated as functions of time, depending on eddy currents and saturation. The currents in all the machine circuits were calculated in the time domain based on these inductances, which were continuously updated. The method was applied to a chopper controlled DC series motor used for electric vehicle drive, and to a salient pole sychronous motor with damper bars. Simulation results were compared to experimentally obtained ones.

Induced electric fields in workers near low-frequency induction heating machines.

PubMed

Kos, Bor; Valič, Blaž; Kotnik, Tadej; Gajšek, Peter

2014-04-01

Published data on occupational exposure to induction heating equipment are scarce, particularly in terms of induced quantities in the human body. This article provides some additional information by investigating exposure to two such machines-an induction furnace and an induction hardening machine. Additionally, a spatial averaging algorithm for measured fields we developed in a previous publication is tested on new data. The human model was positioned at distances where measured values of magnetic flux density were above the reference levels. All human exposure was below the basic restriction-the lower bound of the 0.1 top percentile induced electric field in the body of a worker was 0.193 V/m at 30 cm from the induction furnace. © 2013 Wiley Periodicals, Inc.

Block-Module Electric Machines of Alternating Current

NASA Astrophysics Data System (ADS)

Zabora, I.

2018-03-01

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

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

NASA Astrophysics Data System (ADS)

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

1980-09-01

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

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

On-line diagnosis of defaults on squirrel cage motors using FEM

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

Bentounsi, A.; Nicolas, A.

1998-09-01

In industry, the predictive maintenance has become a strategic concept. Economic interest of on-line diagnosis of faults in electric machines gave rise to various researches in that field. This paper proposes a local approach to tackle the problem of breaking bars and end-rings of squirrel cage in induction machines based mainly on the signature of the local variables, such as the normal flux density. This allows a finer analysis, by use of a finite element based simulation.

Method and device for determining bond separation strength using induction heating

NASA Technical Reports Server (NTRS)

Coultrip, Robert H. (Inventor); Johnson, Samuel D. (Inventor); Copeland, Carl E. (Inventor); Phillips, W. Morris (Inventor); Fox, Robert L. (Inventor)

1994-01-01

An induction heating device includes an induction heating gun which includes a housing, a U-shaped pole piece having two spaced apart opposite ends defining a gap there between, the U-shaped pole piece being mounted in one end of the housing, and a tank circuit including an induction coil wrapped around the pole piece and a capacitor connected to the induction coil. A power source is connected to the tank circuit. A pull test machine is provided having a stationary chuck and a movable chuck, the two chucks holding two test pieces bonded together at a bond region. The heating gun is mounted on the pull test machine in close proximity to the bond region of the two test pieces, whereby when the tank circuit is energized, the two test pieces are heated by induction heating while a tension load is applied to the two test pieces by the pull test machine to determine separation strength of the bond region.

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.

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

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

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

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

PubMed

Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

2012-09-01

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Grid-connected in-stream hydroelectric generation based on the doubly fed induction machine

NASA Astrophysics Data System (ADS)

Lenberg, Timothy J.

Within the United States, there is a growing demand for new environmentally friendly power generation. This has led to a surge in wind turbine development. Unfortunately, wind is not a stable prime mover, but water is. Why not apply the advances made for wind to in-stream hydroelectric generation? One important advancement is the creation of the Doubly Fed Induction Machine (DFIM). This thesis covers the application of a gearless DFIM topology for hydrokinetic generation. After providing background, this thesis presents many of the options available for the mechanical portion of the design. A mechanical turbine is then specified. Next, a method is presented for designing a DFIM including the actual design for this application. In Chapter 4, a simulation model of the system is presented, complete with a control system that maximizes power generation based on water speed. This section then goes on to present simulation results demonstrating proper operation.

Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

PubMed Central

Gutierrez-Villalobos, Jose M.; Rodriguez-Resendiz, Juvenal; Rivas-Araiza, Edgar A.; Martínez-Hernández, Moisés A.

2015-01-01

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor. PMID:26131677

Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive.

PubMed

Gutierrez-Villalobos, Jose M; Rodriguez-Resendiz, Juvenal; Rivas-Araiza, Edgar A; Martínez-Hernández, Moisés A

2015-06-29

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor.

Soft Computing Application in Fault Detection of Induction Motor

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

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

NASA Astrophysics Data System (ADS)

Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

2017-11-01

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

Electro-mechanical energy conversion system having a permanent magnet machine with stator, resonant transfer link and energy converter controls

DOEpatents

Skeist, S. Merrill; Baker, Richard H.

2006-01-10

An electro-mechanical energy conversion system coupled between an energy source and an energy load comprising an energy converter device including a permanent magnet induction machine coupled between the energy source and the energy load to convert the energy from the energy source and to transfer the converted energy to the energy load and an energy transfer multiplexer to control the flow of power or energy through the permanent magnetic induction machine.

High Velocity Linear Induction Launcher with Exit-Edge Compensation for Testing of Aerospace Components

NASA Technical Reports Server (NTRS)

Kuznetsov, Stephen; Marriott, Darin

2008-01-01

Advances in ultra high speed linear induction electromagnetic launchers over the past decade have focused on magnetic compensation of the exit and entry-edge transient flux wave to produce efficient and compact linear electric machinery. The paper discusses two approaches to edge compensation in long-stator induction catapults with typical end speeds of 150 to 1,500 m/s. In classical linear induction machines, the exit-edge effect is manifest as two auxiliary traveling waves that produce a magnetic drag on the projectile and a loss of magnetic flux over the main surface of the machine. In the new design for the Stator Compensated Induction Machine (SCIM) high velocity launcher, the exit-edge effect is nulled by a dual wavelength machine or alternately the airgap flux is peaked at a location prior to the exit edge. A four (4) stage LIM catapult is presently being constructed for 180 m/s end speed operation using double-sided longitudinal flux machines. Advanced exit and entry edge compensation is being used to maximize system efficiency, and minimize stray heating of the reaction armature. Each stage will output approximately 60 kN of force and produce over 500 G s of acceleration on the armature. The advantage of this design is there is no ablation to the projectile and no sliding contacts, allowing repeated firing of the launcher without maintenance of any sort. The paper shows results of a parametric study for 500 m/s and 1,500 m/s linear induction launchers incorporating two of the latest compensation techniques for an air-core stator primary and an iron-core primary winding. Typical thrust densities for these machines are in the range of 150 kN/sq.m. to 225 kN/sq.m. and these compete favorably with permanent magnet linear synchronous machines. The operational advantages of the high speed SCIM launcher are shown by eliminating the need for pole-angle position sensors as would be required by synchronous systems. The stator power factor is also improved.

Optimal Control of Induction Machines to Minimize Transient Energy Losses

NASA Astrophysics Data System (ADS)

Plathottam, Siby Jose

Induction machines are electromechanical energy conversion devices comprised of a stator and a rotor. Torque is generated due to the interaction between the rotating magnetic field from the stator, and the current induced in the rotor conductors. Their speed and torque output can be precisely controlled by manipulating the magnitude, frequency, and phase of the three input sinusoidal voltage waveforms. Their ruggedness, low cost, and high efficiency have made them ubiquitous component of nearly every industrial application. Thus, even a small improvement in their energy efficient tend to give a large amount of electrical energy savings over the lifetime of the machine. Hence, increasing energy efficiency (reducing energy losses) in induction machines is a constrained optimization problem that has attracted attention from researchers. The energy conversion efficiency of induction machines depends on both the speed-torque operating point, as well as the input voltage waveform. It also depends on whether the machine is in the transient or steady state. Maximizing energy efficiency during steady state is a Static Optimization problem, that has been extensively studied, with commercial solutions available. On the other hand, improving energy efficiency during transients is a Dynamic Optimization problem that is sparsely studied. This dissertation exclusively focuses on improving energy efficiency during transients. This dissertation treats the transient energy loss minimization problem as an optimal control problem which consists of a dynamic model of the machine, and a cost functional. The rotor field oriented current fed model of the induction machine is selected as the dynamic model. The rotor speed and rotor d-axis flux are the state variables in the dynamic model. The stator currents referred to as d-and q-axis currents are the control inputs. A cost functional is proposed that assigns a cost to both the energy losses in the induction machine, as well as the deviations from desired speed-torque-magnetic flux setpoints. Using Pontryagin's minimum principle, a set of necessary conditions that must be satisfied by the optimal control trajectories are derived. The conditions are in the form a two-point boundary value problem, that can be solved numerically. The conjugate gradient method that was modified using the Hestenes-Stiefel formula was used to obtain the numerical solution of both the control and state trajectories. Using the distinctive shape of the numerical trajectories as inspiration, analytical expressions were derived for the state, and control trajectories. It was shown that the trajectory could be fully described by finding the solution of a one-dimensional optimization problem. The sensitivity of both the optimal trajectory and the optimal energy efficiency to different induction machine parameters were analyzed. A non-iterative solution that can use feedback for generating optimal control trajectories in real time was explored. It was found that an artificial neural network could be trained using the numerical solutions and made to emulate the optimal control trajectories with a high degree of accuracy. Hence a neural network along with a supervisory logic was implemented and used in a real-time simulation to control the Finite Element Method model of the induction machine. The results were compared with three other control regimes and the optimal control system was found to have the highest energy efficiency for the same drive cycle.

Mask induction despite circuit obstruction: an unrecognized hazard of relying on automated machine check technology.

PubMed

Yang, Kamie K; Lewis, Ian H

2014-06-15

Various equipment malfunctions of anesthesia gas delivery systems have been previously reported. Our profession increasingly uses technology as a means to prevent these errors. We report a case of a near-total anesthesia circuit obstruction that went undetected before the induction of anesthesia despite the use of automated machine check technology. This case highlights that automated machine check modules can fail to detect severe equipment failure and demonstrates how, even in this era of expanding technology, manual checks still remain essential components of safe care.

The reflection of evolving bearing faults in the stator current's extended park vector approach for induction machines

NASA Astrophysics Data System (ADS)

Corne, Bram; Vervisch, Bram; Derammelaere, Stijn; Knockaert, Jos; Desmet, Jan

2018-07-01

Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.

Harmonic reduction of Direct Torque Control of six-phase induction motor.

PubMed

Taheri, A

2016-07-01

In this paper, a new switching method in Direct Torque Control (DTC) of a six-phase induction machine for reduction of current harmonics is introduced. Selecting a suitable vector in each sampling period is an ordinal method in the ST-DTC drive of a six-phase induction machine. The six-phase induction machine has 64 voltage vectors and divided further into four groups. In the proposed DTC method, the suitable voltage vectors are selected from two vector groups. By a suitable selection of two vectors in each sampling period, the harmonic amplitude is decreased more, in and various comparison to that of the ST-DTC drive. The harmonics loss is greater reduced, while the electromechanical energy is decreased with switching loss showing a little increase. Spectrum analysis of the phase current in the standard and new switching table DTC of the six-phase induction machine and determination for the amplitude of each harmonics is proposed in this paper. The proposed method has a less sampling time in comparison to the ordinary method. The Harmonic analyses of the current in the low and high speed shows the performance of the presented method. The simplicity of the proposed method and its implementation without any extra hardware is other advantages of the proposed method. The simulation and experimental results show the preference of the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Control Demonstration of Multiple Doubly-Fed Induction Motors for Hybrid Electric Propulsion

NASA Technical Reports Server (NTRS)

Sadey, David J.; Bodson, Marc; Csank, Jeffrey T.; Hunker, Keith R.; Theman, Casey J.; Taylor, Linda M.

2017-01-01

The Convergent Aeronautics Solutions (CAS) High Voltage-Hybrid Electric Propulsion (HVHEP) task was formulated to support the move into future hybrid-electric aircraft. The goal of this project is to develop a new AC power architecture to support the needs of higher efficiency and lower emissions. This proposed architecture will adopt the use of the doubly-fed induction machine (DFIM) for propulsor drive motor application.The Convergent Aeronautics Solutions (CAS) High Voltage-Hybrid Electric Propulsion (HVHEP) task was formulated to support the move into future hybrid-electric aircraft. The goal of this project is to develop a new AC power architecture to support the needs of higher efficiency and lower emissions. This proposed architecture will adopt the use of the doubly-fed induction machine (DFIM) for propulsor drive motor application. DFIMs are attractive for several reasons, including but not limited to the ability to self-start, ability to operate sub- and super-synchronously, and requiring only fractionally rated power converters on a per-unit basis depending on the required range of operation. The focus of this paper is based specifically on the presentation and analysis of a novel strategy which allows for independent operation of each of the aforementioned doubly-fed induction motors. This strategy includes synchronization, soft-start, and closed loop speed control of each motor as a means of controlling output thrust; be it concurrently or differentially. The demonstration of this strategy has recently been proven out on a low power test bed using fractional horsepower machines. Simulation and hardware test results are presented in the paper.

Comparisons between designs for single-sided linear electric motors: Homopolar synchronous and induction

NASA Astrophysics Data System (ADS)

Nondahl, T. A.; Richter, E.

1980-09-01

A design study of two types of single sided (with a passive rail) linear electric machine designs, namely homopolar linear synchronous machines (LSM's) and linear induction machines (LIM's), is described. It is assumed the machines provide tractive effort for several types of light rail vehicles and locomotives. These vehicles are wheel supported and require tractive powers ranging from 200 kW to 3735 kW and top speeds ranging from 112 km/hr to 400 km/hr. All designs are made according to specified magnetic and thermal criteria. The LSM advantages are a higher power factor, much greater restoring forces for track misalignments, and less track heating. The LIM advantages are no need to synchronize the excitation frequency precisely to vehicle speed, simpler machine construction, and a more easily anchored track structure. The relative weights of the two machine types vary with excitation frequency and speed; low frequencies and low speeds favor the LSM.

Energy saving concepts relating to induction generators

NASA Technical Reports Server (NTRS)

Nola, F. J.

1980-01-01

Energy saving concepts relating to induction generators are presented. The first describes a regenerative scheme using an induction generator as a variable load for prime movers under test is described. A method for reducing losses in induction machines used specifically as wind driven generators is also described.

Theory-Based Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2009-01-01

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

EPA Science Inventory

The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

EPA Science Inventory

The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

Induction machine

DOEpatents

Owen, Whitney H.

1980-01-01

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

Direct Torque Control of a Three-Phase Voltage Source Inverter-Fed Induction Machine

DTIC Science & Technology

2013-12-01

factors, FOC acquires all advantages of DC machine control and frees itself from the mechanical commutation drawbacks. Furthermore, FOC leads to high...of three-phase induction motor using microcontroller,” S.R.M Engineering College, Tamil Nadu, India , June/July 2006. [5] Texas Instruments Europe...loop. Direct flux control is possible through the constant magnetic field orientation achieved through commutator action. These two primary factors

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.

New Technique of High-Performance Torque Control Developed for Induction Machines

NASA Technical Reports Server (NTRS)

Kenny, Barbara H.

2003-01-01

Two forms of high-performance torque control for motor drives have been described in the literature: field orientation control and direct torque control. Field orientation control has been the method of choice for previous NASA electromechanical actuator research efforts with induction motors. Direct torque control has the potential to offer some advantages over field orientation, including ease of implementation and faster response. However, the most common form of direct torque control is not suitable for the highspeed, low-stator-flux linkage induction machines designed for electromechanical actuators with the presently available sample rates of digital control systems (higher sample rates are required). In addition, this form of direct torque control is not suitable for the addition of a high-frequency carrier signal necessary for the "self-sensing" (sensorless) position estimation technique. This technique enables low- and zero-speed position sensorless operation of the machine. Sensorless operation is desirable to reduce the number of necessary feedback signals and transducers, thus improving the reliability and reducing the mass and volume of the system. This research was directed at developing an alternative form of direct torque control known as a "deadbeat," or inverse model, solution. This form uses pulse-width modulation of the voltage applied to the machine, thus reducing the necessary sample and switching frequency for the high-speed NASA motor. In addition, the structure of the deadbeat form allows the addition of the high-frequency carrier signal so that low- and zero-speed sensorless operation is possible. The new deadbeat solution is based on using the stator and rotor flux as state variables. This choice of state variables leads to a simple graphical representation of the solution as the intersection of a constant torque line with a constant stator flux circle. Previous solutions have been expressed only in complex mathematical terms without a method to clearly visualize the solution. The graphical technique allows a more insightful understanding of the operation of the machine under various conditions.

Base drive and overlap protection circuit

DOEpatents

Gritter, David J.

1983-01-01

An inverter (34) which provides power to an A. C. machine (28) is controlled by a circuit (36) employing PWM control strategy whereby A. C. power is supplied to the machine at a preselectable frequency and preselectable voltage. This is accomplished by the technique of waveform notching in which the shapes of the notches are varied to determine the average energy content of the overall waveform. Through this arrangement, the operational efficiency of the A. C. machine is optimized. The control circuit includes a microcomputer and memory element which receive various parametric inputs and calculate optimized machine control data signals therefrom. The control data is asynchronously loaded into the inverter through an intermediate buffer (38). A base drive and overlap protection circuit is included to insure that both transistors of a complimentary pair are not conducting at the same time. In its preferred embodiment, the present invention is incorporated within an electric vehicle (10) employing a 144 VDC battery pack (32) and a three-phase induction motor (18).

Magnetic properties of Fe-Si steel depending on compressive and tensile stresses under sinusoidal and distorted excitations

NASA Astrophysics Data System (ADS)

Permiakov, V.; Pulnikov, A.; Dupré, L.; De Wulf, M.; Melkebeek, J.

2003-05-01

In this article, the magnetic properties of nonoriented electrical steel under sinusoidal and distorted excitations are investigated for the whole range of unidirectional mechanical stresses. The distorted flux obtained from the tooth tip of 3 kW induction machine at no-load test was put into the measurement system. The total losses increase for compressive stress both under sinusoidal and distorted excitations. For tensile elastic stresses, the total losses first decrease and then increase in a very similar way for both excitations. In contrast, the difference between total losses under sinusoidal and distorted magnetic fluxes becomes smaller with increase of the plastic strain. This work is a serious step toward complete characterization of the magnetic properties of electrical steel in the teeth area of induction machines. A deeper insight of that problem can improve the design of induction machines and other electromagnetic devices.

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

PubMed Central

Burriel-Valencia, Jordi; Martinez-Roman, Javier; Sapena-Bano, Angel

2018-01-01

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources. PMID:29316650

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window.

PubMed

Burriel-Valencia, Jordi; Puche-Panadero, Ruben; Martinez-Roman, Javier; Sapena-Bano, Angel; Pineda-Sanchez, Manuel

2018-01-06

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

Acoustic sensor for real-time control for the inductive heating process

DOEpatents

Kelley, John Bruce; Lu, Wei-Yang; Zutavern, Fred J.

2003-09-30

Disclosed is a system and method for providing closed-loop control of the heating of a workpiece by an induction heating machine, including generating an acoustic wave in the workpiece with a pulsed laser; optically measuring displacements of the surface of the workpiece in response to the acoustic wave; calculating a sub-surface material property by analyzing the measured surface displacements; creating an error signal by comparing an attribute of the calculated sub-surface material properties with a desired attribute; and reducing the error signal below an acceptable limit by adjusting, in real-time, as often as necessary, the operation of the inductive heating machine.

Deployment of an inductance-based quasi-digital sensor to detect metallic wear debris in lubricant oil of rotating machinery

NASA Astrophysics Data System (ADS)

Sanga, Ramesh; Srinivasan, V. S.; Sivaramakrishna, M.; Prabhakara Rao, G.

2018-07-01

In rotating machinery due to continuous rotational induced wear and tear, metallic debris will be produced and mixes with the in-service lubricant oil over the course of time. This debris gives the sign of potential machine failure due to the aging of critical parts like gears and bearings. The size and type of wear debris has a direct relationship with the degree of wear in the machine and gives information about the healthiness of equipment. This article presents an inductive quasi-digital sensor to detect the metallic debris, its type; size in the lubrication oil of rotating machinery. A microcontroller based low cost, low power, high resolution and high precise instrument with alarm indication and LCD is developed to detect ferrous debris of sizes from 30 µm and non-ferrous debris of 50 µm. It is thoroughly tested and calibrated with ferrous, non-ferrous debris of different sizes in the air environment. Finally, an experiment is conducted to check the performance of the instrument by circulating lubricant oil containing ferrous, non-ferrous debris through the sensor.

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

NASA Astrophysics Data System (ADS)

Datta, Jinia; Chowdhuri, Sumana; Bera, Jitendranath

2016-12-01

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

Field-circuit analysis and measurements of a single-phase self-excited induction generator

NASA Astrophysics Data System (ADS)

Makowski, Krzysztof; Leicht, Aleksander

2017-12-01

The paper deals with a single-phase induction machine operating as a stand-alone self-excited single-phase induction generator for generation of electrical energy from renewable energy sources. By changing number of turns and size of wires in the auxiliary stator winding, an improvement of performance characteristics of the generator were obtained as regards no-load and load voltage of the stator windings as well as stator winding currents of the generator. Field-circuit simulation models of the generator were developed using Flux2D software package for the generator with shunt capacitor in the main stator winding. The obtained results have been validated experimentally at the laboratory setup using the single-phase capacitor induction motor of 1.1 kW rated power and 230 V voltage as a base model of the generator.

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

NASA Technical Reports Server (NTRS)

Bose, Bimal K.; Kim, Min-Huei

1995-01-01

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

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Automatic inference of multicellular regulatory networks using informative priors.

PubMed

Sun, Xiaoyun; Hong, Pengyu

2009-01-01

To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

Torque shudder protection device and method

DOEpatents

King, Robert D.; De Doncker, Rik W. A. A.; Szczesny, Paul M.

1997-01-01

A torque shudder protection device for an induction machine includes a flux command generator for supplying a steady state flux command and a torque shudder detector for supplying a status including a negative status to indicate a lack of torque shudder and a positive status to indicate a presence of torque shudder. A flux adapter uses the steady state flux command and the status to supply a present flux command identical to the steady state flux command for a negative status and different from the steady state flux command for a positive status. A limiter can receive the present flux command, prevent the present flux command from exceeding a predetermined maximum flux command magnitude, and supply the present flux command to a field oriented controller. After determining a critical electrical excitation frequency at which a torque shudder occurs for the induction machine, a flux adjuster can monitor the electrical excitation frequency of the induction machine and adjust a flux command to prevent the monitored electrical excitation frequency from reaching the critical electrical excitation frequency.

Torque shudder protection device and method

DOEpatents

King, R.D.; Doncker, R.W.A.A. De.; Szczesny, P.M.

1997-03-11

A torque shudder protection device for an induction machine includes a flux command generator for supplying a steady state flux command and a torque shudder detector for supplying a status including a negative status to indicate a lack of torque shudder and a positive status to indicate a presence of torque shudder. A flux adapter uses the steady state flux command and the status to supply a present flux command identical to the steady state flux command for a negative status and different from the steady state flux command for a positive status. A limiter can receive the present flux command, prevent the present flux command from exceeding a predetermined maximum flux command magnitude, and supply the present flux command to a field oriented controller. After determining a critical electrical excitation frequency at which a torque shudder occurs for the induction machine, a flux adjuster can monitor the electrical excitation frequency of the induction machine and adjust a flux command to prevent the monitored electrical excitation frequency from reaching the critical electrical excitation frequency. 5 figs.

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.

Pole-phase modulated toroidal winding for an induction machine

DOEpatents

Miller, John Michael; Ostovic, Vlado

1999-11-02

A stator (10) for an induction machine for a vehicle has a cylindrical core (12) with inner and outer slots (26, 28) extending longitudinally along the inner and outer peripheries between the end faces (22, 24). Each outer slot is associated with several adjacent inner slots. A plurality of toroidal coils (14) are wound about the core and laid in the inner and outer slots. Each coil occupies a single inner slot and is laid in the associated outer slot thereby minimizing the distance the coil extends from the end faces and minimizing the length of the induction machine. The toroidal coils are configured for an arbitrary pole phase modulation wherein the coils are configured with variable numbers of phases and poles for providing maximum torque for cranking and switchable to a another phase and pole configuration for alternator operation. An adaptor ring (36) circumferentially positioned about the stator improves mechanical strength, and provides a coolant channel manifold (34) for removing heat produced in stator windings during operation.

The eXperience Induction Machine: A New Paradigm for Mixed-Reality Interaction Design and Psychological Experimentation

NASA Astrophysics Data System (ADS)

Bernardet, Ulysses; Bermúdez I Badia, Sergi; Duff, Armin; Inderbitzin, Martin; Le Groux, Sylvain; Manzolli, Jônatas; Mathews, Zenon; Mura, Anna; Väljamäe, Aleksander; Verschure, Paul F. M. J.

The eXperience Induction Machine (XIM) is one of the most advanced mixed-reality spaces available today. XIM is an immersive space that consists of physical sensors and effectors and which is conceptualized as a general-purpose infrastructure for research in the field of psychology and human-artifact interaction. In this chapter, we set out the epistemological rational behind XIM by putting the installation in the context of psychological research. The design and implementation of XIM are based on principles and technologies of neuromorphic control. We give a detailed description of the hardware infrastructure and software architecture, including the logic of the overall behavioral control. To illustrate the approach toward psychological experimentation, we discuss a number of practical applications of XIM. These include the so-called, persistent virtual community, the application in the research of the relationship between human experience and multi-modal stimulation, and an investigation of a mixed-reality social interaction paradigm.

Numerical analysis method for linear induction machines.

NASA Technical Reports Server (NTRS)

Elliott, D. G.

1972-01-01

A numerical analysis method has been developed for linear induction machines such as liquid metal MHD pumps and generators and linear motors. Arbitrary phase currents or voltages can be specified and the moving conductor can have arbitrary velocity and conductivity variations from point to point. The moving conductor is divided into a mesh and coefficients are calculated for the voltage induced at each mesh point by unit current at every other mesh point. Combining the coefficients with the mesh resistances yields a set of simultaneous equations which are solved for the unknown currents.

Extending the Constant Power Speed Range of the Brushless DC Motor through Dual Mode Inverter Control -- Part I: Theory and Simulation

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

Lawler, J.S.

2001-10-29

An inverter topology and control scheme has been developed that can drive low-inductance, surface-mounted permanent magnet motors over the wide constant power speed range required in electric vehicle applications. This new controller is called the dual-mode inverter control (DMIC) [1]. The DMIC can drive either the Permanent Magnet Synchronous Machine (PMSM) with sinusoidal back emf, or the brushless dc machine (BDCM) with trapezoidal emf in the motoring and regenerative braking modes. In this paper we concentrate on the BDCM under high-speed motoring conditions. Simulation results show that if all motor and inverter loss mechanisms are neglected, the constant power speedmore » range of the DMIC is infinite. The simulation results are supported by closed form expressions for peak and rms motor current and average power derived from analytical solution to the differential equations governing the DMIC/BDCM drive for the lossless case. The analytical solution shows that the range of motor inductance that can be accommodated by the DMIC is more than an order of magnitude such that the DMIC is compatible with both low- and high-inductance BDCMs. Finally, method is given for integrating the classical hysteresis band current control, used for motor control below base speed, with the phase advance of DMIC that is applied above base speed. The power versus speed performance of the DMIC is then simulated across the entire speed range.« less

State reference design and saturated control of doubly-fed induction generators under voltage dips

NASA Astrophysics Data System (ADS)

Tilli, Andrea; Conficoni, Christian; Hashemi, Ahmad

2017-04-01

In this paper, the stator/rotor currents control problem of doubly-fed induction generator under faulty line voltage is carried out. Common grid faults cause a steep decline in the line voltage profile, commonly denoted as voltage dip. This point is critical for such kind of machines, having their stator windings directly connected to the grid. In this respect, solid methodological nonlinear control theory arguments are exploited and applied to design a novel controller, whose main goal is to improve the system behaviour during voltage dips, endowing it with low voltage ride through capability, a fundamental feature required by modern Grid Codes. The proposed solution exploits both feedforward and feedback actions. The feedforward part relies on suitable reference trajectories for the system internal dynamics, which are designed to prevent large oscillations in the rotor currents and command voltages, excited by line perturbations. The feedback part uses state measurements and is designed according to Linear Matrix Inequalities (LMI) based saturated control techniques to further reduce oscillations, while explicitly accounting for the system constraints. Numerical simulations verify the benefits of the internal dynamics trajectory planning, and the saturated state feedback action, in crucially improving the Doubly-Fed Induction Machine response under severe grid faults.

Effect of electric arc, gas oxygen torch and induction melting techniques on the marginal accuracy of cast base-metal and noble metal-ceramic crowns.

PubMed

Gómez-Cogolludo, Pablo; Castillo-Oyagüe, Raquel; Lynch, Christopher D; Suárez-García, María-Jesús

2013-09-01

The aim of this study was to identify the most appropriate alloy composition and melting technique by evaluating the marginal accuracy of cast metal-ceramic crowns. Seventy standardised stainless-steel abutments were prepared to receive metal-ceramic crowns and were randomly divided into four alloy groups: Group 1: palladium-gold (Pd-Au), Group 2: nickel-chromium-titanium (Ni-Cr-Ti), Group 3: nickel-chromium (Ni-Cr) and Group 4: titanium (Ti). Groups 1, 2 and 3 were in turn subdivided to be melted and cast using: (a) gas oxygen torch and centrifugal casting machine (TC) or (b) induction and centrifugal casting machine (IC). Group 4 was melted and cast using electric arc and vacuum/pressure machine (EV). All of the metal-ceramic crowns were luted with glass-ionomer cement. The marginal fit was measured under an optical microscope before and after cementation using image analysis software. All data was subjected to two-way analysis of variance (ANOVA). Duncan's multiple range test was run for post-hoc comparisons. The Student's t-test was used to investigate the influence of cementation (α=0.05). Uncemented Pd-Au/TC samples achieved the best marginal adaptation, while the worst fit corresponded to the luted Ti/EV crowns. Pd-Au/TC, Ni-Cr and Ti restorations demonstrated significantly increased misfit after cementation. The Ni-Cr-Ti alloy was the most predictable in terms of differences in misfit when either torch or induction was applied before or after cementation. Cemented titanium crowns exceeded the clinically acceptable limit of 120μm. The combination of alloy composition, melting technique, casting method and luting process influences the vertical seal of cast metal-ceramic crowns. An accurate use of the gas oxygen torch may overcome the results attained with the induction system concerning the marginal adaptation of fixed dental prostheses. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analysis of laser-induction hybrid cladding processing conditions

NASA Astrophysics Data System (ADS)

Huang, Yongjun; Zeng, Xiaoyan; Hu, Qianwu

2007-12-01

A new cladding approach based on laser-induction hybrid technique on flat sheets is presented in this paper. Coating is produced by means of 5kw cw CO II laser equipped with 100kw high frequent inductor, and the experiments set-up, involving a special machining-head, which can provide laser-induction hybrid heat resources simultaneously. The formation of thick NiCrSiB coating on a steel substrate by off-axial powder feeding is studied from an experimental point of view. A substrate melting energy model is developed to describe the energy relationship between laser-induction hybrid cladding and laser cladding alone quantitatively. By comparing the experimental results with the calculational ones, it is shown that the tendency of fusion zone height of theoretical calculation is in agreement with that of tests in laser-induction hybrid cladding. Via analyses and tests, the conclusions can be lead to that the fusion zone height can be increased easily and the good bond of cladding track can be achieved within wide cladding processing window in laser-induction hybrid processing. It shows that the induction heating has an obvious effect on substrate melting and metallurgical bond.

Comparison of effects of overload on parameters and performance of samarium-cobalt and strontium-ferrite radially oriented permanent magnet brushless DC motors

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

Demerdash, N.A.; Nehl, T.W.; Nyamusa, T.A.

1985-08-01

Effects of high momentary overloads on the samarium-cobalt and strontium-ferrite permanent magnets and the magnetic field in electronically commutated brushless dc machines, as well as their impact on the associated machine parameters were studied. The effect of overload on the machine parameters, and subsequently on the machine system performance was also investigated. This was accomplished through the combined use of finite element analysis of the magnetic field in such machines, perturbation of the magnetic energies to determine machine inductances, and dynamic simulation of the performance of brushless dc machines, when energized from voltage source inverters. These effects were investigated throughmore » application of the above methods to two equivalent 15 hp brushless dc motors, one of which was built with samarium-cobalt magnets, while the other was built with strontium- ferrite magnets. For momentary overloads as high as 4.5 p.u. magnet flux reductions of 29% and 42% of the no load flux were obtained in the samarium-cobalt and strontiumferrite machines, respectively. Corresponding reductions in the line to line armature inductances of 52% and 46% of the no load values were reported for the samarium-cobalt and strontium-ferrite cases, respectively. The overload affected the profiles and magnitudes of armature induced back emfs. Subsequently, the effects of overload on machine parameters were found to have significant impact on the performance of the machine systems, where findings indicate that the samarium-cobalt unit is more suited for higher overload duties than the strontium-ferrite machine.« less

Study on magnetic force of electromagnetic levitation circular knitting machine

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Four quadrant control of induction motors

NASA Technical Reports Server (NTRS)

Hansen, Irving G.

1991-01-01

Induction motors are the nation's workhorse, being the motor of choice in most applications due to their simple rugged construction. It has been estimated that 14 to 27 percent of the country's total electricity use could be saved with adjustable speed drives. Until now, induction motors have not been suited well for variable speed or servo-drives, due to the inherent complexity, size, and inefficiency of their variable speed controls. Work at NASA Lewis Research Center on field oriented control of induction motors using pulse population modulation method holds the promise for the desired drive electronics. The system allows for a variable voltage to frequency ratio which enables the user to operate the motor at maximum efficiency, while having independent control of both the speed and torque of an induction motor in all four quadrants of the speed torque map. Multiple horsepower machine drives were demonstrated, and work is on-going to develop a 20 hp average, 40 hp peak class of machine. The pulse population technique, results to date, and projections for implementation of this existing new motor control technology are discussed.

Comparative analysis of expert and machine-learning methods for classification of body cavity effusions in companion animals.

PubMed

Hotz, Christine S; Templeton, Steven J; Christopher, Mary M

2005-03-01

A rule-based expert system using CLIPS programming language was created to classify body cavity effusions as transudates, modified transudates, exudates, chylous, and hemorrhagic effusions. The diagnostic accuracy of the rule-based system was compared with that produced by 2 machine-learning methods: Rosetta, a rough sets algorithm and RIPPER, a rule-induction method. Results of 508 body cavity fluid analyses (canine, feline, equine) obtained from the University of California-Davis Veterinary Medical Teaching Hospital computerized patient database were used to test CLIPS and to test and train RIPPER and Rosetta. The CLIPS system, using 17 rules, achieved an accuracy of 93.5% compared with pathologist consensus diagnoses. Rosetta accurately classified 91% of effusions by using 5,479 rules. RIPPER achieved the greatest accuracy (95.5%) using only 10 rules. When the original rules of the CLIPS application were replaced with those of RIPPER, the accuracy rates were identical. These results suggest that both rule-based expert systems and machine-learning methods hold promise for the preliminary classification of body fluids in the clinical laboratory.

DIRECT SIMULATION OF A-C MACHINERY.

DTIC Science & Technology

show the application of the simulation to both induction and synchronous machines. The fundamental space harmonic only, the fundamental and third ... space harmonic only, or all the space harmonics are considered. The report concludes that: (1) Successful direct simulation of the 2-phase induction

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Impact of wind generator infed on dynamic performance of a power system

NASA Astrophysics Data System (ADS)

Alam, Md. Ahsanul

Wind energy is one of the most prominent sources of electrical energy in the years to come. A tendency to increase the amount of electricity generation from wind turbine can be observed in many countries. One of the major concerns related to the high penetration level of the wind energy into the existing power grid is its influence on power system dynamic performance. In this thesis, the impact of wind generation system on power system dynamic performance is investigated through detailed dynamic modeling of the entire wind generator system considering all the relevant components. Nonlinear and linear models of a single machine as well as multimachine wind-AC system have been derived. For the dynamic model of integrated wind-AC system, a general transformation matrix is determined for the transformation of machine and network quantities to a common reference frame. Both time-domain and frequency domain analyses on single machine and multimachine systems have been carried out. The considered multimachine systems are---A 4 machine 12 bus system, and 10 machine 39 bus New England system. Through eigenvalue analysis, impact of asynchronous wind system on overall network damping has been quantified and modes responsible for the instability have been identified. Over with a number of simulation studies it is observed that for a induction generator based wind generation system, the fixed capacitor located at the generator terminal cannot normally cater for the reactive power demand during the transient disturbances like wind gust and fault on the system. For weak network connection, system instability may be initiated because of induction generator terminal voltage collapse under certain disturbance conditions. Incorporation of dynamic reactive power compensation scheme through either variable susceptance control or static compensator (STATCOM) is found to improve the dynamic performance significantly. Further improvement in transient profile has been brought in by supporting STATCOM with bulk energy storage devices. Two types of energy storage system (ESS) have been considered---battery energy storage system, and supercapacitor based energy storage system. A decoupled P -- Q control strategy has been implemented on STATCOM/ESS. It is observed that wind generators when supported by STATCOM/ESS can achieve significant withstand capability in the presence of grid fault of reasonable duration. It experiences almost negligible rotor speed variation, maintains constant terminal voltage, and resumes delivery of smoothed (almost transient free) power to the grid immediately after the fault is cleared. Keywords: Wind energy, induction generator, dynamic performance of wind generators, energy storage system, decoupled P -- Q control, multimachine system.

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

Inductive System Health Monitoring

NASA Technical Reports Server (NTRS)

Iverson, David L.

2004-01-01

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Pinch current limitation effect in plasma focus

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

Lee, S.; Saw, S. H.; INTI International University College, 71800 Nilai

The Lee model couples the electrical circuit with plasma focus dynamics, thermodynamics, and radiation. It is used to design and simulate experiments. A beam-target mechanism is incorporated, resulting in realistic neutron yield scaling with pinch current and increasing its versatility for investigating all Mather-type machines. Recent runs indicate a previously unsuspected 'pinch current limitation' effect. The pinch current does not increase beyond a certain value however low the static inductance is reduced to. The results indicate that decreasing the present static inductance of the PF1000 machine will neither increase the pinch current nor the neutron yield, contrary to expectations.

Diagnosis of the three-phase induction motor using thermal imaging

NASA Astrophysics Data System (ADS)

Glowacz, Adam; Glowacz, Zygfryd

2017-03-01

Three-phase induction motors are used in the industry commonly for example woodworking machines, blowers, pumps, conveyors, elevators, compressors, mining industry, automotive industry, chemical industry and railway applications. Diagnosis of faults is essential for proper maintenance. Faults may damage a motor and damaged motors generate economic losses caused by breakdowns in production lines. In this paper the authors develop fault diagnostic techniques of the three-phase induction motor. The described techniques are based on the analysis of thermal images of three-phase induction motor. The authors analyse thermal images of 3 states of the three-phase induction motor: healthy three-phase induction motor, three-phase induction motor with 2 broken bars, three-phase induction motor with faulty ring of squirrel-cage. In this paper the authors develop an original method of the feature extraction of thermal images MoASoID (Method of Areas Selection of Image Differences). This method compares many training sets together and it selects the areas with the biggest changes for the recognition process. Feature vectors are obtained with the use of mentioned MoASoID and image histogram. Next 3 methods of classification are used: NN (the Nearest Neighbour classifier), K-means, BNN (the back-propagation neural network). The described fault diagnostic techniques are useful for protection of three-phase induction motor and other types of rotating electrical motors such as: DC motors, generators, synchronous motors.

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.

Electronically commutated motors for vehicle applications

NASA Astrophysics Data System (ADS)

Echolds, E. F.

1980-02-01

Two permanent magnet electronically commutated motors for electric vehicle traction are discussed. One, based on existing technology, produces 23 kW (peak) at 26,000 rpm, and 11 kW continuous at 18,000 rpm. The motor has a conventional design: a four-pole permanent magnet rotor and a three-phase stator similar to those used on ordinary induction motors. The other, advanced technology motor, is rated at 27 kW (peak) at 14,000 rpm, and 11 kW continuous at 10,500 rpm. The machine employs a permanent magnet rotor and a novel ironless stator design in an axial air gap, homopolar configuration. Comparison of the new motors with conventional brush type machines indicates potential for substantial cost savings.

Cage-rotor induction motor inter-turn short circuit fault detection with and without saturation effect by MEC model.

PubMed

Naderi, Peyman

2016-09-01

The inter-turn short fault for the Cage-Rotor-Induction-Machine (CRIM) is studied in this paper and its local saturation is taken into account. However, in order to observe the exact behavior of machine, the Magnetic-Equivalent-Circuit (MEC) and nonlinear B-H curve are proposed to provide an insight into the machine model and saturation effect respectively. The electrical machines are generally operated near to their saturation zone due to some design necessities. Hence, when the machine is exposed to a fault such as short circuit or eccentricities, it is operated within its saturation zone and thus, time and space harmonics are integrated and as a result, current and torque harmonics are generated which the phenomenon cannot be explored when saturation is dismissed. Nonetheless, inter-turn short circuit may lead to local saturation and this occurrence is studied in this paper using MEC model. In order to achieve the mentioned objectives, two and also four-pole machines are modeled as two samples and the machines performances are analyzed in healthy and faulty cases with and without saturation effect. A novel strategy is proposed to precisely detect inter-turn short circuit fault according to the stator׳s lines current signatures and the accuracy of the proposed method is verified by experimental results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Induction motor speed drive improvement using fuzzy IP-self-tuning controller. A real time implementation.

PubMed

Lokriti, Abdesslam; Salhi, Issam; Doubabi, Said; Zidani, Youssef

2013-05-01

An IP-self-tuning controller tuned by a fuzzy adjustor, is proposed to improve induction machine speed control. The interest of such controller is the possibility to adjust only one gain, instead of two gains for the case of the PI-self-tuning controllers commonly used in the literature. This paper presents simulation and experimental results. These latter were obtained by practical implementation on a DSPace 1104 board of three different speed controllers (the classical IP, the fuzzy-like-PI and the IP-self-tuning), for a 1.5KW induction machine. The paper presents different tests used to compare the performances of the proposed controller to the two others in terms of computation time, tracking performances and disturbances rejection. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

PubMed

Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

2010-09-17

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Miniature Gas-Circulating Machine

NASA Technical Reports Server (NTRS)

Swift, Walter L.; Valenzuela, Javier A.; Sixsmith, Herbert; Nutt, William E.

1993-01-01

Proposed gas-circulating machine consists essentially of centrifugal pump driven by induction motor. Noncontact bearings suppress wear and contamination. Used to circulate helium (or possibly hydrogen or another gas) in regeneration sorption-compressor refrigeration system aboard spacecraft. Also proves useful in terrestrial applications in which long life, reliability, and low contamination essential.

Design synthesis and optimization of permanent magnet synchronous machines based on computationally-efficient finite element analysis

NASA Astrophysics Data System (ADS)

Sizov, Gennadi Y.

In this dissertation, a model-based multi-objective optimal design of permanent magnet ac machines, supplied by sine-wave current regulated drives, is developed and implemented. The design procedure uses an efficient electromagnetic finite element-based solver to accurately model nonlinear material properties and complex geometric shapes associated with magnetic circuit design. Application of an electromagnetic finite element-based solver allows for accurate computation of intricate performance parameters and characteristics. The first contribution of this dissertation is the development of a rapid computational method that allows accurate and efficient exploration of large multi-dimensional design spaces in search of optimum design(s). The computationally efficient finite element-based approach developed in this work provides a framework of tools that allow rapid analysis of synchronous electric machines operating under steady-state conditions. In the developed modeling approach, major steady-state performance parameters such as, winding flux linkages and voltages, average, cogging and ripple torques, stator core flux densities, core losses, efficiencies and saturated machine winding inductances, are calculated with minimum computational effort. In addition, the method includes means for rapid estimation of distributed stator forces and three-dimensional effects of stator and/or rotor skew on the performance of the machine. The second contribution of this dissertation is the development of the design synthesis and optimization method based on a differential evolution algorithm. The approach relies on the developed finite element-based modeling method for electromagnetic analysis and is able to tackle large-scale multi-objective design problems using modest computational resources. Overall, computational time savings of up to two orders of magnitude are achievable, when compared to current and prevalent state-of-the-art methods. These computational savings allow one to expand the optimization problem to achieve more complex and comprehensive design objectives. The method is used in the design process of several interior permanent magnet industrial motors. The presented case studies demonstrate that the developed finite element-based approach practically eliminates the need for using less accurate analytical and lumped parameter equivalent circuit models for electric machine design optimization. The design process and experimental validation of the case-study machines are detailed in the dissertation.

Performance Analysis of Saturated Induction Motors by Virtual Tests

ERIC Educational Resources Information Center

Ojaghi, M.; Faiz, J.; Kazemi, M.; Rezaei, M.

2012-01-01

Many undergraduate-level electrical machines textbooks give detailed treatments of the performance of induction motors. Students can deepen this understanding of motor performance by performing the appropriate practical work in laboratories or in simulation using proper software packages. This paper considers various common and less-common tests…

Steels with controlled hardenability for induction hardening

NASA Astrophysics Data System (ADS)

Shepelyakovskii, K. Z.

1980-07-01

Steels of the CH and LH type developed in the Soviet Union permit the use of a new method of induction hardening — bulk-surface hardening — and efficient utilization of the high-strength conditions (σb = 230-250 kgf/mm2). These steels make it possible to improve the structural strength, operating characteristics, service life, and reliability of critical heavily loaded machine parts. At the same time, CH steels make it possible to reduce by a factor of 2-3 the quantity of alloying elements, reduce the electrical energy for heat treatment, and completely exclude the cost of quenching oil for heat treatment in automatic equipment with high labor productivity, while retaining good working conditions. All this leads to substantial savings in production and operation. For example, when transmission gears (cylindrical and conical) are manufactured from LH steels the annual savings amount to more than 700,000 rubles at two automobile plants. Machine parts of CH steels — half axles and bearings in railway cars —have saved respectively six and four million rubles annually. The introduction of controlled-hardenability steels for induction hardening is a necessary condition for technological progress in machine construction and metallurgy.

Structural Design Optimization of Doubly-Fed Induction Generators Using GeneratorSE

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

Sethuraman, Latha; Fingersh, Lee J; Dykes, Katherine L

2017-11-13

A wind turbine with a larger rotor swept area can generate more electricity, however, this increases costs disproportionately for manufacturing, transportation, and installation. This poster presents analytical models for optimizing doubly-fed induction generators (DFIGs), with the objective of reducing the costs and mass of wind turbine drivetrains. The structural design for the induction machine includes models for the casing, stator, rotor, and high-speed shaft developed within the DFIG module in the National Renewable Energy Laboratory's wind turbine sizing tool, GeneratorSE. The mechanical integrity of the machine is verified by examining stresses, structural deflections, and modal properties. The optimization results aremore » then validated using finite element analysis (FEA). The results suggest that our analytical model correlates with the FEA in some areas, such as radial deflection, differing by less than 20 percent. But the analytical model requires further development for axial deflections, torsional deflections, and stress calculations.« less

Investigation of Combined Motor/Magnetic Bearings for Flywheel Energy Storage Systems

NASA Technical Reports Server (NTRS)

Hofmann, Heath

2003-01-01

Dr. Hofmann's work in the summer of 2003 consisted of two separate projects. In the first part of the summer, Dr. Hofmann prepared and collected information regarding rotor losses in synchronous machines; in particular, machines with low rotor losses operating in vacuum and supported by magnetic bearings, such as the motor/generator for flywheel energy storage systems. This work culminated in a presentation at NASA Glenn Research Center on this topic. In the second part, Dr. Hofmann investigated an approach to flywheel energy storage where the phases of the flywheel motor/generator are connected in parallel with the phases of an induction machine driving a mechanical actuator. With this approach, additional power electronics for driving the flywheel unit are not required. Simulations of the connection of a flywheel energy storage system to a model of an electromechanical actuator testbed at NASA Glenn were performed that validated the proposed approach. A proof-of-concept experiment using the D1 flywheel unit at NASA Glenn and a Sundstrand induction machine connected to a dynamometer was successfully conducted.

Public Data Set: Non-inductively Driven Tokamak Plasmas at Near-Unity βt in the Pegasus Toroidal Experiment

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

Reusch, Joshua A.; Bodner, Grant M.; Bongard, Michael W.

This public data set contains openly-documented, machine readable digital research data corresponding to figures published in J.A. Reusch et al., 'Non-inductively Driven Tokamak Plasmas at Near-Unity βt in the Pegasus Toroidal Experiment,' Phys. Plasmas 25, 056101 (2018).

Fundamental studies on initiation and evolution of multi-channel discharges and their application to next generation pulsed power machines.

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

Schwarz, Jens; Savage, Mark E.; Lucero, Diego Jose

Future pulsed power systems may rely on linear transformer driver (LTD) technology. The LTD's will be the building blocks for a driver that can deliver higher current than the Z-Machine. The LTD's would require tens of thousands of low inductance ( %3C 85nH), high voltage (200 kV DC) switches with high reliability and long lifetime ( 10 4 shots). Sandia's Z-Machine employs 36 megavolt class switches that are laser triggered by a single channel discharge. This is feasible for tens of switches but the high inductance and short switch life- time associated with the single channel discharge are undesirable formore » future machines. Thus the fundamental problem is how to lower inductance and losses while increasing switch life- time and reliability. These goals can be achieved by increasing the number of current-carrying channels. The rail gap switch is ideal for this purpose. Although those switches have been extensively studied during the past decades, each effort has only characterized a particular switch. There is no comprehensive understanding of the underlying physics that would allow predictive capability for arbitrary switch geometry. We have studied rail gap switches via an extensive suite of advanced diagnostics in synergy with theoretical physics and advanced modeling capability. Design and topology of multichannel switches as they relate to discharge dynamics are investigated. This involves electrically and optically triggered rail gaps, as well as discrete multi-site switch concepts.« less

Nonlinear Control of the Doubly Fed Induction Motor with Copper Losses Minimization for Electrical Vehicle

NASA Astrophysics Data System (ADS)

Drid, S.; Nait-Said, M.-S.; Tadjine, M.; Makouf, A.

2008-06-01

There is an increasing interest in electric vehicles due to environmental concerns. Recent efforts are directed toward developing an improved propulsion system for electric vehicles applications with minimal power losses. This paper deals with the high efficient vector control for the reduction of copper losses of the doubly fed motor. Firstly, the feedback linearization control based on Lyapunov approach is employed to design the underlying controller achieving the double fluxes orientation. The fluxes controllers are designed independently of the speed. The speed controller is designed using the Lyapunov method especially employed to the unknown load torques. The global asymptotic stability of the overall system is theoretically proven. Secondly, a new Torque Copper Losses Factor is proposed to deal with the problem of the machine copper losses. Its main function is to optimize the torque in keeping the machine saturation at an acceptable level. This leads to a reduction in machine currents and therefore their accompanied copper losses guaranteeing improved machine efficiency. The simulation results in comparative presentation confirm largely the effectiveness of the proposed DFIM control with a very interesting energy saving contribution.

Integrated Inverter For Driving Multiple Electric Machines

DOEpatents

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

2006-04-04

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

Power-Factor and Torque Calculation under Consideration of Cross Saturation of the Interior Permanent Magnet Synchronous Motor with Brushless Field Excitation

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

Lee, Seong T; Burress, Timothy A; Tolbert, Leon M

2009-01-01

This paper introduces a new method for calculating the power factor and output torque by considering the cross saturation between direct-axis (d-axis) and quadrature-axis (q-axis) of an interior permanent magnet synchronous motor (IPMSM). The conventional two-axis IPMSM model is modified to include the cross saturation effect by adding the cross-coupled inductance terms. This paper also contains the new method of calculating the cross-coupled inductance values as well as self-inductance values in d- and q-axes. The analyzed motor is a high-speed brushless field excitation machine that offers high torque per ampere per core length at low speed and weakened flux atmore » high speed, which was developed for the traction motor of a hybrid electric vehicle. The conventional two-axis IPMSM model was modified to include the cross-saturation effect by adding the cross-coupled inductance terms Ldq and Lqd. By the advantage of the excited structure of the experimental IPMSM, the analyzing works were performed under two conditions, the highest and lowest excited conditions. Therefore, it is possible to investigate the cross-saturation effect when a machine has higher magnetic flux from its rotor. The following is a summary of conclusions that may be drawn from this work: (1) Considering cross saturation of an IPMSM offers more accurate expected values of motor parameters in output torque calculation, especially when negative d-axis current is high; (2) A less saturated synchronous machine could be more affected by the cross-coupled saturation effect; (3) Both cross-coupled inductances, L{sub qd} and L{sub dq}, are mainly governed by d-axis current rather than q-axis current; (4) The modified torque equation, can be used for the dynamic model of an IPMSM for developing a better control model or control strategy; and (5) It is possible that the brushless field excitation structure has a common magnetic flux path on both d- and q-axis, and as a result, the reluctance torque of the machine could be reduced.« less

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products.

PubMed

Sun, Yunan; Zhou, Hui; Zhu, Hongmei; Leung, Siu-wai

2016-01-25

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

NASA Astrophysics Data System (ADS)

Sun, Yunan; Zhou, Hui; Zhu, Hongmei; Leung, Siu-Wai

2016-01-01

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

Using a PC and External Media to Quantitatively Investigate Electromagnetic Induction

ERIC Educational Resources Information Center

Bonanno, A.; Bozzo, G.; Camarca, M.; Sapia, P.

2011-01-01

In this article we describe an experimental learning path about electromagnetic induction which uses an Atwood machine where one of the two hanging bodies is a cylindrical magnet falling through a plexiglass guide, surrounded either by a coil or by a copper pipe. The first configuration (magnet falling across a coil) allows students to…

Inductive displacement sensors with a notch filter for an active magnetic bearing system.

PubMed

Chen, Seng-Chi; Le, Dinh-Kha; Nguyen, Van-Sum

2014-07-15

Active magnetic bearing (AMB) systems support rotating shafts without any physical contact, using electromagnetic forces. Each radial AMB uses two pairs of electromagnets at opposite sides of the rotor. This allows the rotor to float in the air gap, and the machine to operate without frictional losses. In active magnetic suspension, displacement sensors are necessary to detect the radial and axial movement of the suspended object. In a high-speed rotating machine equipped with an AMB, the rotor bending modes may be limited to the operating range. The natural frequencies of the rotor can cause instability. Thus, notch filters are a useful circuit for stabilizing the system. In addition, commercial displacement sensors are sometimes not suitable for AMB design, and cannot filter the noise caused by the natural frequencies of rotor. Hence, implementing displacement sensors based on the AMB structure is necessary to eliminate noises caused by natural frequency disturbances. The displacement sensor must be highly sensitive in the desired working range, and also exhibit a low interference noise, high stability, and low cost. In this study, we used the differential inductive sensor head and lock-in amplifier for synchronous demodulation. In addition, an active low-pass filter and a notch filter were used to eliminate disturbances, which caused by natural frequencies. As a consequence, the inductive displacement sensor achieved satisfactory linearity, high sensitivity, and disturbance elimination. This sensor can be easily produced for AMB applications. A prototype of these displacement sensors was built and tested.

Offline detection of broken rotor bars in AC induction motors

NASA Astrophysics Data System (ADS)

Powers, Craig Stephen

ABSTRACT. OFFLINE DETECTION OF BROKEN ROTOR BARS IN AC INDUCTION MOTORS. The detection of the broken rotor bar defect in medium- and large-sized AC induction machines is currently one of the most difficult tasks for the motor condition and monitoring industry. If a broken rotor bar defect goes undetected, it can cause a catastrophic failure of an expensive machine. If a broken rotor bar defect is falsely determined, it wastes time and money to physically tear down and inspect the machine only to find an incorrect diagnosis. Previous work in 2009 at Baker/SKF-USA in collaboration with the Korea University has developed a prototype instrument that has been highly successful in correctly detecting the broken rotor bar defect in ACIMs where other methods have failed. Dr. Sang Bin and his students at the Korea University have been using this prototype instrument to help the industry save money in the successful detection of the BRB defect. A review of the current state of motor conditioning and monitoring technology for detecting the broken rotor bar defect in ACIMs shows improved detection of this fault is still relevant. An analysis of previous work in the creation of this prototype instrument leads into the refactoring of the software and hardware into something more deployable, cost effective and commercially viable.

A new capacitive long-range displacement nanometer sensor with differential sensing structure based on time-grating

NASA Astrophysics Data System (ADS)

Yu, Zhicheng; Peng, Kai; Liu, Xiaokang; Pu, Hongji; Chen, Ziran

2018-05-01

High-precision displacement sensors, which can measure large displacements with nanometer resolution, are key components in many ultra-precision fabrication machines. In this paper, a new capacitive nanometer displacement sensor with differential sensing structure is proposed for long-range linear displacement measurements based on an approach denoted time grating. Analytical models established using electric field coupling theory and an area integral method indicate that common-mode interference will result in a first-harmonic error in the measurement results. To reduce the common-mode interference, the proposed sensor design employs a differential sensing structure, which adopts a second group of induction electrodes spatially separated from the first group of induction electrodes by a half-pitch length. Experimental results based on a prototype sensor demonstrate that the measurement accuracy and the stability of the sensor are substantially improved after adopting the differential sensing structure. Finally, a prototype sensor achieves a measurement accuracy of  ±200 nm over the full 200 mm measurement range of the sensor.

Implementation and comparative study of control strategies for an isolated DFIG based WECS

NASA Astrophysics Data System (ADS)

Bouchiba, Nouha; Barkia, Asma; Sallem, Souhir; Chrifi-Alaoui, Larbi; Drid, Saïd; Kammoun, M. B. A.

2017-10-01

Nowadays, a global interest for renewable energy sources has been growing intensely. In particular, a wind energy has become the most popular. In case of autonomous systems, wind energy conversion system (WECS) based on a double fed induction generator (DFIG) is widely used. In this paper, in order to control the stand-alone system outputs under wind speed and load variations, three kinds of nonlinear control strategies have been proposed, applied and compared, such as: Classical PI controller, Back-Stepping and Sliding Mode controllers. A series of experiments have been conducted to evaluate and to compare the developed controllers' dynamic performances under load demand and speed variations. The design and the implementation of different control strategies to a 1.5kW doubly fed induction machine is carried out using a dSpace DS1104 card based on MATLAB/Simulink environment. Experimental results are presented to show the validity of the implemented controllers and demonstrate the effectiveness of each controller compared with others.

Induced sadness increases persistence in a simulated slot machine task among recreational gamblers.

PubMed

Devos, Gaëtan; Clark, Luke; Maurage, Pierre; Billieux, Joël

2018-05-01

Gambling may constitute a strategy for coping with depressive mood, but a direct influence of depressive mood on gambling behaviors has never been tested via realistic experimental designs in gamblers. The current study tested whether experimentally induced sadness increases persistence on a simulated slot machine task using real monetary reinforcement in recreational gamblers. Sixty participants were randomly assigned to an experimental (sadness induction) or control (no emotional induction) condition, and then performed a slot machine task consisting of a mandatory phase followed by a persistence phase. Potential confounding variables (problem gambling symptoms, impulsivity traits, gambling cognitions) were measured to ensure that the experimental and control groups were comparable. The study showed that participants in the sadness condition displayed greater gambling persistence than control participants (p = .011). These data support the causal role of negative affect in decisions to gamble and persistence, which bears important theoretical and clinical implications. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Investigation of self-excited induction generators for wind turbine applications

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

Muljadi, E.; Butterfield, C.P.; Sallan, J.

2000-02-28

The use of squirrel-cage induction machines in wind generation is widely accepted as a generator of choice. The squirrel-cage induction machine is simple, reliable, cheap, lightweight, and requires very little maintenance. Generally, the induction generator is connected to the utility at constant frequency. With a constant frequency operation, the induction generator operates at practically constant speed (small range of slip). The wind turbine operates in optimum efficiency only within a small range of wind speed variation. The variable-speed operation allows an increase in energy captured and reduces both the torque peaks in the drive train and the power fluctuations sentmore » to the utility. In variable-speed operation, an induction generator needs an interface to convert the variable frequency output of the generator to the fixed frequency at the utility. This interface can be simplified by using a self-excited generator because a simple diode bridge is required to perform the ac/dc conversion. The subsequent dc/ac conversion can be performed using different techniques. The use of a thyristor bridge is readily available for large power conversion and has a lower cost and higher reliability. The firing angle of the inverter bridge can be controlled to track the optimum power curve of the wind turbine. With only diodes and thyristors used in power conversion, the system can be scaled up to a very high voltage and high power applications. This paper analyzes the operation of such a system applied to a 1/3-hp self-excited induction generator. It includes the simulations and tests performed for the different excitation configurations.« less

Non Invasive Sensors for Monitoring the Efficiency of AC Electrical Rotating Machines

PubMed Central

Zidat, Farid; Lecointe, Jean-Philippe; Morganti, Fabrice; Brudny, Jean-François; Jacq, Thierry; Streiff, Frédéric

2010-01-01

This paper presents a non invasive method for estimating the energy efficiency of induction motors used in industrial applications. This method is innovative because it is only based on the measurement of the external field emitted by the motor. The paper describes the sensors used, how they should be placed around the machine in order to decouple the external field components generated by both the air gap flux and the winding end-windings. The study emphasizes the influence of the eddy currents flowing in the yoke frame on the sensor position. A method to estimate the torque from the external field use is proposed. The measurements are transmitted by a wireless module (Zig-Bee) and they are centralized and stored on a PC computer. PMID:22163631

Non invasive sensors for monitoring the efficiency of AC electrical rotating machines.

PubMed

Zidat, Farid; Lecointe, Jean-Philippe; Morganti, Fabrice; Brudny, Jean-François; Jacq, Thierry; Streiff, Frédéric

2010-01-01

This paper presents a non invasive method for estimating the energy efficiency of induction motors used in industrial applications. This method is innovative because it is only based on the measurement of the external field emitted by the motor. The paper describes the sensors used, how they should be placed around the machine in order to decouple the external field components generated by both the air gap flux and the winding end-windings. The study emphasizes the influence of the eddy currents flowing in the yoke frame on the sensor position. A method to estimate the torque from the external field use is proposed. The measurements are transmitted by a wireless module (Zig-Bee) and they are centralized and stored on a PC computer.

A Novelty Design Of Minimization Of Electrical Losses In A Vector Controlled Induction Machine Drive

NASA Astrophysics Data System (ADS)

Aryza, Solly; Irwanto, M.; Lubis, Zulkarnain; Putera Utama Siahaan, Andysah; Rahim, Robbi; Furqan, Mhd.

2018-01-01

The induction motor has in the industry . More attention has been a focus to develop and design of induction motor drive. With the method of vector control novelty prove the efficiency of induction motor over their entire speed range. In this paper desirable to design a loss minimization controller which can improve the efficiency. Also, this research described Modeling of an induction motor with core loss included. Realization of methods vector control for an induction motor drive with loss element included. The case of the loss minimization condition. The procedure was successful to calculate the gains of a PI controller. Though the problem of obtaining a robust and sensorless induction motor drive is by no means completely solved, the results obtained as part of this work point in a promising direction.

Simulation of three-phase induction motor drives using indirect field oriented control in PSIM environment

NASA Astrophysics Data System (ADS)

Aziri, Hasif; Patakor, Fizatul Aini; Sulaiman, Marizan; Salleh, Zulhisyam

2017-09-01

This paper presents the simulation of three-phase induction motor drives using Indirect Field Oriented Control (IFOC) in PSIM environment. The asynchronous machine is well known about natural limitations fact of highly nonlinearity and complexity of motor model. In order to resolve these problems, the IFOC is applied to control the instantaneous electrical quantities such as torque and flux component. As FOC is controlling the stator current that represented by a vector, the torque component is aligned with d coordinate while the flux component is aligned with q coordinate. There are five levels of the incremental system are gradually built up to verify and testing the software module in the system. Indeed, all of system build levels are verified and successfully tested in PSIM environment. Moreover, the corresponding system of five build levels are simulated in PSIM environment which is user-friendly for simulation studies in order to explore the performance of speed responses based on IFOC algorithm for three-phase induction motor drives.

NASA Tech Briefs, December 2013

NASA Technical Reports Server (NTRS)

2013-01-01

Topics include: Microwave Kinetic Inductance Detector With; Selective Polarization Coupling; Flexible Microstrip Circuits for; Superconducting Electronics; CFD Extraction Tool for TecPlot From DPLR Solutions; RECOVIR Software for Identifying Viruses; Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model; Orbital Debris Engineering Model (ORDEM) v.3; Scatter-Reducing Sounding Filtration Using a Genetic Algorithm and Mean Monthly Standard Deviation; Thermo-Mechanical Methodology for Stabilizing Shape Memory Alloy Response; Hermetic Seal Designs for Sample Return Sample Tubes; Silicon Alignment Pins: An Easy Way To Realize a Wafer-to-Wafer Alignment; Positive-Buoyancy Rover for Under Ice Mobility; Electric Machine With Boosted Inductance to Stabilize Current Control; International Space Station-Based Electromagnetic Launcher for Space Science Payloads; Advanced Hybrid Spacesuit Concept Featuring Integrated Open Loop and Closed Loop Ventilation Systems; Data Quality Screening Service.

Influence of winding construction on starter-generator thermal processes

NASA Astrophysics Data System (ADS)

Grachev, P. Yu; Bazarov, A. A.; Tabachinskiy, A. S.

2018-01-01

Dynamic processes in starter-generators features high winding are overcurrent. It can lead to insulation overheating and fault operation mode. For hybrid and electric vehicles, new high efficiency construction of induction machines windings is proposed. Stator thermal processes need be considered in the most difficult operation modes. The article describes construction features of new compact stator windings, electromagnetic and thermal models of processes in stator windings and explains the influence of innovative construction on thermal processes. Models are based on finite element method.

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Machine learning of fault characteristics from rocket engine simulation data

NASA Technical Reports Server (NTRS)

Ke, Min; Ali, Moonis

1990-01-01

Transformation of data into knowledge through conceptual induction has been the focus of our research described in this paper. We have developed a Machine Learning System (MLS) to analyze the rocket engine simulation data. MLS can provide to its users fault analysis, characteristics, and conceptual descriptions of faults, and the relationships of attributes and sensors. All the results are critically important in identifying faults.

Induction linear accelerators

NASA Astrophysics Data System (ADS)

Birx, Daniel

1992-03-01

Among the family of particle accelerators, the Induction Linear Accelerator is the best suited for the acceleration of high current electron beams. Because the electromagnetic radiation used to accelerate the electron beam is not stored in the cavities but is supplied by transmission lines during the beam pulse it is possible to utilize very low Q (typically<10) structures and very large beam pipes. This combination increases the beam breakup limited maximum currents to of order kiloamperes. The micropulse lengths of these machines are measured in 10's of nanoseconds and duty factors as high as 10-4 have been achieved. Until recently the major problem with these machines has been associated with the pulse power drive. Beam currents of kiloamperes and accelerating potentials of megavolts require peak power drives of gigawatts since no energy is stored in the structure. The marriage of liner accelerator technology and nonlinear magnetic compressors has produced some unique capabilities. It now appears possible to produce electron beams with average currents measured in amperes, peak currents in kiloamperes and gradients exceeding 1 MeV/meter, with power efficiencies approaching 50%. The nonlinear magnetic compression technology has replaced the spark gap drivers used on earlier accelerators with state-of-the-art all-solid-state SCR commutated compression chains. The reliability of these machines is now approaching 1010 shot MTBF. In the following paper we will briefly review the historical development of induction linear accelerators and then discuss the design considerations.

Fuzzy – PI controller to control the velocity parameter of Induction Motor

NASA Astrophysics Data System (ADS)

Malathy, R.; Balaji, V.

2018-04-01

The major application of Induction motor includes the usage of the same in industries because of its high robustness, reliability, low cost, highefficiency and good self-starting capability. Even though it has the above mentioned advantages, it also have some limitations: (1) the standard motor is not a true constant-speed machine, itsfull-load slip varies less than 1 % (in high-horsepower motors).And (2) it is not inherently capable of providing variable-speedoperation. In order to solve the above mentioned problem smart motor controls and variable speed controllers are used. Motor applications involve non linearity features, which can be controlled by Fuzzy logic controller as it is capable of handling those features with high efficiency and it act similar to human operator. This paper presents individuality of the plant modelling. The fuzzy logic controller (FLC)trusts on a set of linguistic if-then rules, a rule-based Mamdani for closed loop Induction Motor model. Themotor model is designed and membership functions are chosenaccording to the parameters of the motor model. Simulation results contains non linearity in induction motor model. A conventional PI controller iscompared practically to fuzzy logic controller using Simulink.

Comparative investigation of diagnosis media for induction machine mechanical unbalance fault.

PubMed

Salah, Mohamed; Bacha, Khmais; Chaari, Abdelkader

2013-11-01

For an induction machine, we suggest a theoretical development of the mechanical unbalance effect on the analytical expressions of radial vibration and stator current. Related spectra are described and characteristic defect frequencies are determined. Moreover, the stray flux expressions are developed for both axial and radial sensor coil positions and a substitute diagnosis technique is proposed. In addition, the load torque effect on the detection efficiency of these diagnosis media is discussed and a comparative investigation is performed. The decisive factor of comparison is the fault sensitivity. Experimental results show that spectral analysis of the axial stray flux can be an alternative solution to cover effectiveness limitation of the traditional stator current technique and to substitute the classical vibration practice. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Application de la methode de la reponse frequentielle a l'arret "SSFR", sur une machine synchrone a poles saillants de grande puissance

NASA Astrophysics Data System (ADS)

Belqorchi, Abdelghafour

Forty years after Watson and Manchur conducted the Stand-Still Frequency Response (SSFR) test on a large turbogenerator, the applicability of this technic on a powerful salient pole synchronous generator has yet to be confirmed. The scientific literature on the subject is rare and very few have attempted to compare SSFR parameter results with those deduced by classical tests. The validity of SSFR on large salient pole machines has still to be proven. The present work aims in participating to fill this knowledge gap. It can be used to build a database of measurements highly needed to draw the validity of the technic. Also, the author hopes to demonstrate the potential of SSFR model to represent the machine, not only in cases of weak disturbances but also strong ones such as instantaneous three-phase short-circuit faults. The difficulties raised by previous searchers are: The lack of accuracy in very low frequency measurements; The difficulty in rotor positioning, according to d and q axes, in case of salient pole machines; The measurement current level influence on magnetizing inductances, in axes-d and; The rotation impact on damper circuits for some rotors design. Aware of the above difficulties, the author conducted an SSFR test on a large salient pole machine (285 MVA). The generator under test has laminated non isolated rotor and an integral slot number. The damper windings in adjacent poles are connected together, via the polar core and the rotor rim. Finally, the damping circuit is unaffected by rotation. To improve the measurement accuracy, in very low frequencies, the most precise frequency response analyser available on the market was used. Besides, the frequency responses of the signals conditioning modules (i.e., isolation, amplification...) were accounted for to correct the four measured SSFR transfer functions. Immunization against noise and use of instrumentation in their optimum range, were other technics rigorously applied. Magnetizing inductances, being influenced by the measurement current magnitude, the latter was maintained constant in the range 1mHz-20Hz. Other problems such as the rotation impact on damper circuits or the difficulty of rotor positioning are eliminated or attenuated by the intrinsic characteristics of the machine. Regarding the data analysis, the Maximum Likelihood Estimation (MLE) method was used to determine the third and second order equivalent circuit from SSFR measurements. In d-axis, the approaches of adjustment to two and three transfer functions (Ld(s), sG(s) and Lafo(s)) were explored. The second order model, derived from (Ld( s) and G(s)), was used to deduce the machine standard parameters. The latter were compared with the values given by the manufacturer and by conventional on-site tests: Instantaneous three-phase short-circuit, Dalton-Cameron and the d-axis transient time constant at open stator (T'do). The comparison showed the good accuracy of SSFR values. Subsequently, a machine model was built in EMTP-RV based on SSFR standard parameters. The model was able to reproduce stator and rotor currents measured during instantaneous three-phase short-circuit test. Some adjustments, to SSFR parameters, were needed to reproduce stator voltage and rotor current acquired during load rejection d-axis test. It is worthwhile noting that the load rejection d-axis test, recently added to IEEE 115-2009 annex, must be modified to take into account the saturation and excitation impedance impact on deduced parameters. Regarding this issue, some suggestions are proposed by the author. The obtained SSFR results, contribute to raise confidence on SSFR application on large salient pole machines. In addition, it shows the aptitude of the SSFR model to represent the machine in both cases of weak and strong disturbances, at least on machines similar the one studied. Index Terms: Salient pole, frequency response, SSFR, equivalent circuit, operational inductance.

Increased Resistance to Flow and Ventilator Failure Secondary to Faulty CO2 Absorbent Insert Not Detected During Automated Anesthesia Machine Check: A Case Report.

PubMed

Moreno-Duarte, Ingrid; Montenegro, Julio; Balonov, Konstantin; Schumann, Roman

2017-04-15

Most modern anesthesia workstations provide automated checkout, which indicates the readiness of the anesthesia machine. In this case report, an anesthesia machine passed the automated machine checkout. Minutes after the induction of general anesthesia, we observed a mismatch between the selected and delivered tidal volumes in the volume auto flow mode with increased inspiratory resistance during manual ventilation. Endotracheal tube kinking, circuit obstruction, leaks, and patient-related factors were ruled out. Further investigation revealed a broken internal insert within the CO2 absorbent canister that allowed absorbent granules to cause a partial obstruction to inspiratory and expiratory flow triggering contradictory alarms. We concluded that even when the automated machine checkout indicates machine readiness, unforeseen equipment failure due to unexpected events can occur and require providers to remain vigilant.

Finite Element Analysis in the Estimation of Air-Gap Torque and Surface Temperature of Induction Machine

NASA Astrophysics Data System (ADS)

Mr., J. Ravi Kumar; Banakara, Basavaraja, Dr.

2017-08-01

This paper presents electromagnetic and thermal behavior of Induction Motor (IM) through the modeling and analysis by applying multiphysics coupled Finite Element Analysis (FEA). Therefore prediction of the magnetic flux, electromagnetic torque, stator and rotor losses and temperature distribution inside an operating electric motor are the most important issues during its design. Prediction and estimation of these parameters allows design engineers to decide capability of the machine for the proposed load, temperature rating and its application for which it is being designed ensuring normal motor operation at rated conditions. In this work, multiphysics coupled electromagnetic - thermal modeling and analysis of induction motor at rated and high frequency has carried out applying Arkkio’s torque method. COMSOL Multiphysics software is used for modeling and finite element analysis of IM. Transient electromagnetic torque, magnetic field distribution, speed-torque characteristics of IM were plotted and studied at different frequencies. This proposed work helps in the design and prediction of accurate performance of induction motor specific to various industrial drive applications. Results obtained are also validated with experimental analysis. The main purpose of this model is to use it as an integral part of the design aiming to system optimization of Variable Speed Drive (VSD) and its components using coupled simulations.

Computer-aided design studies of the homopolar linear synchronous motor

NASA Astrophysics Data System (ADS)

Dawson, G. E.; Eastham, A. R.; Ong, R.

1984-09-01

The linear induction motor (LIM), as an urban transit drive, can provide good grade-climbing capabilities and propulsion/braking performance that is independent of steel wheel-rail adhesion. In view of its 10-12 mm airgap, the LIM is characterized by a low power factor-efficiency product of order 0.4. A synchronous machine offers high efficiency and controllable power factor. An assessment of the linear homopolar configuration of this machine is presented as an alternative to the LIM. Computer-aided design studies using the finite element technique have been conducted to identify a suitable machine design for urban transit propulsion.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

PubMed

Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

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

Schlossberg, David J.; Bodner, Grant M.; Bongard, Michael W.

This public data set contains openly-documented, machine readable digital research data corresponding to figures published in D.J. Schlossberg et al., 'Non-Inductively Driven Tokamak Plasmas at Near-Unity Toroidal Beta,' Phys. Rev. Lett. 119, 035001 (2017).

Design and application of electromechanical actuators for deep space missions

NASA Technical Reports Server (NTRS)

Haskew, Tim A.; Wander, John

1993-01-01

The annual report Design and Application of Electromechanical Actuators for Deep Space Missions is presented. The reporting period is 16 Aug. 1992 to 15 Aug. 1993. However, the primary focus will be work performed since submission of our semi-annual progress report in Feb. 1993. Substantial progress was made. We currently feel confident in providing guidelines for motor and control strategy selection in electromechanical actuators to be used in thrust vector control (TVC) applications. A small portion was presented in the semi-annual report. At this point, we have implemented highly detailed simulations of various motor/drive systems. The primary motor candidates were the brushless dc machine, permanent magnet synchronous machine, and the induction machine. The primary control implementations were pulse width modulation and hysteresis current control. Each of the two control strategies were applied to each of the three motor choices. With either pulse width modulation or hysteresis current control, the induction machine was always vector controlled. A standard test position command sequence for system performance evaluation is defined. Currently, we are gathering all of the necessary data for formal presentation of the results. Briefly stated for TVC application, we feel that the brushless dc machine operating under PWM current control is the best option. Substantial details on the topic, with supporting simulation results, will be provided later, in the form of a technical paper prepared for submission and also in the next progress report with more detail than allowed for paper publication.

Opportunistic Constructive Induction: Using Fragments of Domain Knowledge to Guide Construction

DTIC Science & Technology

1991-01-01

contribute to its success in ways which they are often unaware of and, unfortunately, which go by unacknowledged. At the risk of omitting some people I am...ference on Machine Learning, pages 322 -329, Austin, TX, June 1990. [Blumer e_1 al... 19871 Anselm Blurner, Andrzej Ehrenfeucht, David Haussler, and...and Ryszard S. Michalki. A Comparative Review of Selected Methods fur Learning from Examples. In- Machine Learning: An A rtificialIntelligence

Superconductivity for Electromagnetic Guns

DTIC Science & Technology

1984-03-01

greater than that for a pulsed homopolar machine when the time constant is less than 0.1 sec (ref 32) (See fig. 18). Since the energy density in a...transferred from the capacitor to the induct- or. If the capacitor is replaced by a homopolar machine, then, as is well-known, the kinetic energy of the...rotor plays the role of an "electrical" capacitance and the two arrangements (capacitance and homopolar ) are functionally equivalent. Group 3. In

Electric field prediction for a human body-electric machine system.

PubMed

Ioannides, Maria G; Papadopoulos, Peter J; Dimitropoulou, Eugenia

2004-01-01

A system consisting of an electric machine and a human body is studied and the resulting electric field is predicted. A 3-phase induction machine operating at full load is modeled considering its geometry, windings, and materials. A human model is also constructed approximating its geometry and the electric properties of tissues. Using the finite element technique the electric field distribution in the human body is determined for a distance of 1 and 5 m from the machine and its effects are studied. Particularly, electric field potential variations are determined at specific points inside the human body and for these points the electric field intensity is computed and compared to the limit values for exposure according to international standards.

Three dimensional magnetic fields in extra high speed modified Lundell alternators computed by a combined vector-scalar magnetic potential finite element method

NASA Technical Reports Server (NTRS)

Demerdash, N. A.; Wang, R.; Secunde, R.

1992-01-01

A 3D finite element (FE) approach was developed and implemented for computation of global magnetic fields in a 14.3 kVA modified Lundell alternator. The essence of the new method is the combined use of magnetic vector and scalar potential formulations in 3D FEs. This approach makes it practical, using state of the art supercomputer resources, to globally analyze magnetic fields and operating performances of rotating machines which have truly 3D magnetic flux patterns. The 3D FE-computed fields and machine inductances as well as various machine performance simulations of the 14.3 kVA machine are presented in this paper and its two companion papers.

Pole-zero form fractional model identification in frequency domain

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

Mansouri, R.; Djamah, T.; Djennoune, S.

2009-03-05

This paper deals with system identification in the frequency domain using non integer order models given in the pole-zero form. The usual identification techniques cannot be used in this case because of the non integer orders of differentiation which makes the problem strongly nonlinear. A general identification method based on Levenberg-Marquardt algorithm is developed and allows to estimate the (2n+2m+1) parameters of the model. Its application to identify the ''skin effect'' of a squirrel cage induction machine modeling is then presented.

Wireless Monitoring of Induction Machine Rotor Physical Variables

PubMed Central

Doolan Fernandes, Jefferson; Carvalho Souza, Francisco Elvis; de Paiva, José Alvaro

2017-01-01

With the widespread use of electric machines, there is a growing need to extract information from the machines to improve their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of the rotor physical variables of a squirrel cage induction motor. The system is comprised of: a circuit to acquire desirable rotor variable(s) and value(s) that send it to the computer; a rectifier and power storage circuit that converts an alternating current in a continuous current but also stores energy for a certain amount of time to wait for the motor’s shutdown; and a magnetic generator that harvests energy from the rotating field to power the circuits mentioned above. The embedded system is set on the rotor of a 5 HP squirrel cage induction motor, making it difficult to power the system because it is rotating. This problem can be solved with the construction of a magnetic generator device to avoid the need of using batteries or collector rings and will send data to the computer using a wireless NRF24L01 module. For the proposed system, initial validation tests were made using a temperature sensor (DS18b20), as this variable is known as the most important when identifying the need for maintenance and control systems. Few tests have shown promising results that, with further improvements, can prove the feasibility of using sensors in the rotor. PMID:29156564

Wireless Monitoring of Induction Machine Rotor Physical Variables.

PubMed

Doolan Fernandes, Jefferson; Carvalho Souza, Francisco Elvis; Cipriano Maniçoba, Glauco George; Salazar, Andrés Ortiz; de Paiva, José Alvaro

2017-11-18

With the widespread use of electric machines, there is a growing need to extract information from the machines to improve their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of the rotor physical variables of a squirrel cage induction motor. The system is comprised of: a circuit to acquire desirable rotor variable(s) and value(s) that send it to the computer; a rectifier and power storage circuit that converts an alternating current in a continuous current but also stores energy for a certain amount of time to wait for the motor's shutdown; and a magnetic generator that harvests energy from the rotating field to power the circuits mentioned above. The embedded system is set on the rotor of a 5 HP squirrel cage induction motor, making it difficult to power the system because it is rotating. This problem can be solved with the construction of a magnetic generator device to avoid the need of using batteries or collector rings and will send data to the computer using a wireless NRF24L01 module. For the proposed system, initial validation tests were made using a temperature sensor (DS18b20), as this variable is known as the most important when identifying the need for maintenance and control systems. Few tests have shown promising results that, with further improvements, can prove the feasibility of using sensors in the rotor.

Static air-gap eccentricity fault diagnosis using rotor slot harmonics in line neutral voltage of three-phase squirrel cage induction motor

NASA Astrophysics Data System (ADS)

Oumaamar, Mohamed El Kamel; Maouche, Yassine; Boucherma, Mohamed; Khezzar, Abdelmalek

2017-02-01

The mixed eccentricity fault detection in a squirrel cage induction motor has been thoroughly investigated. However, a few papers have been related to pure static eccentricity fault and the authors focused on the RSH harmonics presented in stator current. The main objective of this paper is to present an alternative method based on the analysis of line neutral voltage taking place between the supply and the stator neutrals in order to detect air-gap static eccentricity, and to highlight the classification of all RSH harmonics in line neutral voltage. The model of squirrel cage induction machine relies on the rotor geometry and winding layout. Such developed model is used to analyze the impact of the pure static air-gap eccentricity by predicting the related frequencies in the line neutral voltage spectrum. The results show that the line neutral voltage spectrum are more sensitive to the air-gap static eccentricity fault compared to stator current one. The theoretical analysis and simulated results are confirmed by experiments.

Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions.

PubMed

Yahia, K; Cardoso, A J M; Ghoggal, A; Zouzou, S E

2014-03-01

Fast Fourier transform (FFT) analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always provide good results for the cases of load torque, speed and voltages variation, leading to a variation of the motor-slip and the consequent FFT problems that appear due to the non-stationary nature of the involved signals. In this paper, the discrete wavelet transform (DWT) of the apparent-power signal for the airgap-eccentricity fault detection in three-phase induction motors is presented in order to overcome the above FFT problems. The proposed method is based on the decomposition of the apparent-power signal from which wavelet approximation and detail coefficients are extracted. The energy evaluation of a known bandwidth permits to define a fault severity factor (FSF). Simulation as well as experimental results are provided to illustrate the effectiveness and accuracy of the proposed method presented even for the case of load torque variations. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

Fundamental Study on Saving Energy for Electrified Railway System Applying High Temperature Superconductor Motor and Energy Storage System

NASA Astrophysics Data System (ADS)

Konishi, Takeshi; Nakamura, Taketsune; Amemiya, Naoyuki

Induction motor instead of dc one has been applied widely for dc electric rolling stock because of the advantage of its utility and efficiency. However, further improvement of motor characteristics will be required to realize environment-friendly dc railway system in the future. It is important to study more efficient machine applying dc electric rolling stock for next generation high performance system. On the other hand, the methods to reuse regenerative energy produced by motors effectively are also important. Therefore, we carried out fundamental study on saving energy for electrified railway system. For the first step, we introduced the energy storage system applying electric double-layer capacitors (EDLC), and its control system. And then, we tried to obtain the specification of high temperature superconductor induction/synchronous motor (HTS-ISM), which performance is similar with that of the conventional induction motors. Furthermore, we tried to evaluate an electrified railway system applying energy storage system and HTS-ISM based on simulation. We succeeded in showing the effectiveness of the introductions of energy storage system and HTS-ISM in DC electrified railway system.

Punching influence on magnetic properties of the stator teeth of an induction motor

NASA Astrophysics Data System (ADS)

Kedous-Lebouc, A.; Cornut, B.; Perrier, J. C.; Manfé, Ph.; Chevalier, Th.

2003-01-01

In order to study the effects of punching of electrical steel sheets, a suitable geometrical structure able to characterize the stator teeth behavior of an induction motor is proposed and validated. The influence of the punching on a fully processed M330-65A is then characterized. A spectacular degradation of loss and B( H) curves is observed. This leads to a perceptible increase of the no-load machine current.

Thermocouple and infrared sensor-based measurement of temperature distribution in metal cutting.

PubMed

Kus, Abdil; Isik, Yahya; Cakir, M Cemal; Coşkun, Salih; Özdemir, Kadir

2015-01-12

In metal cutting, the magnitude of the temperature at the tool-chip interface is a function of the cutting parameters. This temperature directly affects production; therefore, increased research on the role of cutting temperatures can lead to improved machining operations. In this study, tool temperature was estimated by simultaneous temperature measurement employing both a K-type thermocouple and an infrared radiation (IR) pyrometer to measure the tool-chip interface temperature. Due to the complexity of the machining processes, the integration of different measuring techniques was necessary in order to obtain consistent temperature data. The thermal analysis results were compared via the ANSYS finite element method. Experiments were carried out in dry machining using workpiece material of AISI 4140 alloy steel that was heat treated by an induction process to a hardness of 50 HRC. A PVD TiAlN-TiN-coated WNVG 080404-IC907 carbide insert was used during the turning process. The results showed that with increasing cutting speed, feed rate and depth of cut, the tool temperature increased; the cutting speed was found to be the most effective parameter in assessing the temperature rise. The heat distribution of the cutting tool, tool-chip interface and workpiece provided effective and useful data for the optimization of selected cutting parameters during orthogonal machining.

Thermocouple and Infrared Sensor-Based Measurement of Temperature Distribution in Metal Cutting

PubMed Central

Kus, Abdil; Isik, Yahya; Cakir, M. Cemal; Coşkun, Salih; Özdemir, Kadir

2015-01-01

In metal cutting, the magnitude of the temperature at the tool-chip interface is a function of the cutting parameters. This temperature directly affects production; therefore, increased research on the role of cutting temperatures can lead to improved machining operations. In this study, tool temperature was estimated by simultaneous temperature measurement employing both a K-type thermocouple and an infrared radiation (IR) pyrometer to measure the tool-chip interface temperature. Due to the complexity of the machining processes, the integration of different measuring techniques was necessary in order to obtain consistent temperature data. The thermal analysis results were compared via the ANSYS finite element method. Experiments were carried out in dry machining using workpiece material of AISI 4140 alloy steel that was heat treated by an induction process to a hardness of 50 HRC. A PVD TiAlN-TiN-coated WNVG 080404-IC907 carbide insert was used during the turning process. The results showed that with increasing cutting speed, feed rate and depth of cut, the tool temperature increased; the cutting speed was found to be the most effective parameter in assessing the temperature rise. The heat distribution of the cutting tool, tool-chip interface and workpiece provided effective and useful data for the optimization of selected cutting parameters during orthogonal machining. PMID:25587976

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.

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

NASA Astrophysics Data System (ADS)

Patel, Raj Kumar; Giri, V. K.

2017-06-01

One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

PubMed

Nassif, Houssam; Al-Ali, Hassan; Khuri, Sawsan; Keirouz, Walid; Page, David

2010-01-01

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.

Using a PC and external media to quantitatively investigate electromagnetic induction

NASA Astrophysics Data System (ADS)

Bonanno, A.; Bozzo, G.; Camarca, M.; Sapia, P.

2011-07-01

In this article we describe an experimental learning path about electromagnetic induction which uses an Atwood machine where one of the two hanging bodies is a cylindrical magnet falling through a plexiglass guide, surrounded either by a coil or by a copper pipe. The first configuration (magnet falling across a coil) allows students to quantitatively study the Faraday-Neumann-Lenz law, while the second configuration (falling through a copper pipe) permits learners to investigate the complex phenomena of induction by quantifying the amount of electric power dissipated through the pipe as a result of Foucault eddy currents, when the magnet travels through the pipe. The magnet's fall acceleration can be set by adjusting the counterweight of the Atwood machine so that both the kinematic quantities associated with it and the electromotive force induced within the coil are continuously and quantitatively monitored (respectively, by a common personal computer (PC) equipped with a webcam and by freely available software that makes it possible to use the audio card to convert the PC into an oscilloscope). Measurements carried out when the various experimental parameters are changed provide a useful framework for a thorough understanding and clarification of the conceptual nodes related to electromagnetic induction. The proposed learning path is under evaluation in various high schools participating in the project 'Lauree Scientifiche' promoted by the Italian Department of Education.

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

Signal injection as a fault detection technique.

PubMed

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.

Signal Injection as a Fault Detection Technique

PubMed Central

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies. PMID:22163801

Stator and Rotor Flux Based Deadbeat Direct Torque Control of Induction Machines

NASA Technical Reports Server (NTRS)

Kenny, Barbara H.; Lorenz, Robert D.

2001-01-01

A new, deadbeat type of direct torque control is proposed, analyzed, and experimentally verified in this paper. The control is based on stator and rotor flux as state variables. This choice of state variables allows a graphical representation which is transparent and insightful. The graphical solution shows the effects of realistic considerations such as voltage and current limits. A position and speed sensorless implementation of the control, based on the self-sensing signal injection technique, is also demonstrated experimentally for low speed operation. The paper first develops the new, deadbeat DTC methodology and graphical representation of the new algorithm. It then evaluates feasibility via simulation and experimentally demonstrates performance of the new method with a laboratory prototype including the sensorless methods.

Stator and Rotor Flux Based Deadbeat Direct Torque Control of Induction Machines

NASA Technical Reports Server (NTRS)

Kenny, Barbara H.; Lorenz, Robert D.

2003-01-01

A new, deadbeat type of direct torque control is proposed, analyzed and experimentally verified in this paper. The control is based on stator and rotor flux as state variables. This choice of state variables allows a graphical representation which is transparent and insightful. The graphical solution shows the effects of realistic considerations such as voltage and current limits. A position and speed sensorless implementation of the control, based on the self-sensing signal injection technique, is also demonstrated experimentally for low speed operation. The paper first develops the new, deadbeat DTC methodology and graphical representation of the new algorithm. It then evaluates feasibility via simulation and experimentally demonstrates performance of the new method with a laboratory prototype including the sensorless methods.

Stator and Rotor Flux Based Deadbeat Direct Torque Control of Induction Machines. Revision 1

NASA Technical Reports Server (NTRS)

Kenny, Barbara H.; Lorenz, Robert D.

2002-01-01

A new, deadbeat type of direct torque control is proposed, analyzed, and experimentally verified in this paper. The control is based on stator and rotor flux as state variables. This choice of state variables allows a graphical representation which is transparent and insightful. The graphical solution shows the effects of realistic considerations such as voltage and current limits. A position and speed sensorless implementation of the control, based on the self-sensing signal injection technique, is also demonstrated experimentally for low speed operation. The paper first develops the new, deadbeat DTC methodology and graphical representation of the new algorithm. It then evaluates feasibility via simulation and experimentally demonstrates performance of the new method with a laboratory prototype including the sensorless methods.

An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis

NASA Astrophysics Data System (ADS)

Gu, Fengshou; Yesilyurt, Isa; Li, Yuhua; Harris, Georgina; Ball, Andrew

2006-08-01

In order to discriminate small changes for early fault diagnosis of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried out on an electric induction motor driving system using IAS measurement. The diagnosis results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of motor rotor bar defects and shaft misalignment.

Performance Analysis of Three-Phase Induction Motor with AC Direct and VFD

NASA Astrophysics Data System (ADS)

Kumar, Dinesh

2018-03-01

The electrical machine analysis and performance calculation is a very important aspect of efficient drive system design. The development of power electronics devices and power converters provide smooth speed control of Induction Motors by changing the frequency of input supply. These converters, on one hand are providing a more flexible speed control that also leads to problems of harmonics and their associated ailments like pulsating torque, distorted current and voltage waveforms, increasing losses etc. This paper includes the performance analysis of three phase induction motor with three-phase AC direct and variable frequency drives (VFD). The comparison has been concluded with respect to various parameters. MATLAB-SIMULINKTM is used for the analysis.

Applying machine learning classification techniques to automate sky object cataloguing

NASA Astrophysics Data System (ADS)

Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

1993-08-01

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is consistency of classification. The classification rules which are the product of the inductive learning techniques will form an objective, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically catalogued data.

Study of Various Slanted Air-Gap Structures of Interior Permanent Magnet Synchronous Motor with Brushless Field Excitation

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

Tolbert, Leon M; Lee, Seong T

2010-01-01

This paper shows how to maximize the effect of the slanted air-gap structure of an interior permanent magnet synchronous motor with brushless field excitation (BFE) for application in a hybrid electric vehicle. The BFE structure offers high torque density at low speed and weakened flux at high speed. The unique slanted air-gap is intended to increase the output torque of the machine as well as to maximize the ratio of the back-emf of a machine that is controllable by BFE. This irregularly shaped air-gap makes a flux barrier along the d-axis flux path and decreases the d-axis inductance; as amore » result, the reluctance torque of the machine is much higher than a uniform air-gap machine, and so is the output torque. Also, the machine achieves a higher ratio of the magnitude of controllable back-emf. The determination of the slanted shape was performed by using magnetic equivalent circuit analysis and finite element analysis (FEA).« less

Comparison of Solid State Inverters for AC Induction Motor Traction Propulsion Systems

DOT National Transportation Integrated Search

1980-12-01

This report is one of a series concerned with the application of ac machines as traction motors for railroad motive power. It presents results of a laboratory evaluation and computer analysis of different inverter systems. Three inverter systems, sin...

Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming

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

King, R.D.; Srinivasan, A.

1996-10-01

The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set ofmore » compounds that have been widely predicted by other SAR methods (the compounds used in the NTP`s first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). The Progol predictions were based solely on chemical structure and the results of tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% ({+-}3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigated-these structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP`s second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds. 29 refs., 2 figs., 4 tabs.« less

Emotion recognition from EEG using higher order crossings.

PubMed

Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

2010-03-01

Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).

Comparaison de méthodes d'identification des paramètres d'une machine asynchrone

NASA Astrophysics Data System (ADS)

Bellaaj-Mrabet, N.; Jelassi, K.

1998-07-01

Interests, in Genetic Algorithms (G.A.) expands rapidly. This paper consists initially to apply G.A. for identifying induction motor parameters. Next, we compare the performances with classical methods like Maximum Likelihood and classical electrotechnical methods. These methods are applied on three induction motors of different powers to compare results following a set of criteria. Les algorithmes génétiques sont des méthodes adaptatives de plus en plus utilisée pour la résolution de certains problèmes d'optimisation. Le présent travail consiste d'une part, à mettre en œuvre un A.G sur des problèmes d'identification des machines électriques, et d'autre part à comparer ses performances avec les méthodes classiques tels que la méthode du maximum de vraisemblance et la méthode électrotechnique basée sur des essais à vides et en court-circuit. Ces méthodes sont appliquées sur des machines asynchrones de différentes puissances. Les résultats obtenus sont comparés selon certains critères, permettant de conclure sur la validité et la performance de chaque méthode.

Faraday's first dynamo: A retrospective

NASA Astrophysics Data System (ADS)

Smith, Glenn S.

2013-12-01

In the early 1830s, Michael Faraday performed his seminal experimental research on electromagnetic induction, in which he created the first electric dynamo—a machine for continuously converting rotational mechanical energy into electrical energy. His machine was a conducting disc, rotating between the poles of a permanent magnet, with the voltage/current obtained from brushes contacting the disc. In his first dynamo, the magnetic field was asymmetric with respect to the axis of the disc. This is to be contrasted with some of his later symmetric designs, which are the ones almost invariably discussed in textbooks on electromagnetism. In this paper, a theoretical analysis is developed for Faraday's first dynamo. From this analysis, the eddy currents in the disc and the open-circuit voltage for arbitrary positioning of the brushes are determined. The approximate analysis is verified by comparing theoretical results with measurements made on an experimental recreation of the dynamo. Quantitative results from the analysis are used to elucidate Faraday's qualitative observations, from which he learned so much about electromagnetic induction. For the asymmetric design, the eddy currents in the disc dissipate energy that makes the dynamo inefficient, prohibiting its use as a practical generator of electric power. Faraday's experiments with his first dynamo provided valuable insight into electromagnetic induction, and this insight was quickly used by others to design practical generators.

Superconducting-electromagnetic hybrid bearing using YBCO bulk blocks for passive axial levitation

NASA Astrophysics Data System (ADS)

Nicolsky, R.; de Andrade, R., Jr.; Ripper, A.; David, D. F. B.; Santisteban, J. A.; Stephan, R. M.; Gawalek, W.; Habisreuther, T.; Strasser, T.

2000-06-01

A superconducting/electromagnetic hybrid bearing has been designed using active radial electromagnetic positioning and a superconducting passive axial levitator. This bearing has been tested for an induction machine with a vertical shaft. The prototype was conceived as a four-pole, two-phase induction machine using specially designed stator windings for delivering torque and radial positioning simultaneously. The radial bearing uses four eddy-current sensors, displaced 90° from each other, for measuring the shaft position and a PID control system for feeding back the currents. The stator windings have been adapted from the ones of a standard induction motor. The superconducting axial bearing has been assembled with commercial NdFeB permanent magnets and a set of seven top-seeded-melt-textured YBCO large-grain cylindrical blocks. The bearing set-up was previously simulated by a finite element method for different permanent magnet-superconductor block configurations. The stiffness of the superconducting axial bearing has been investigated by measuring by a dynamic method the vertical and transversal elastic constants for different field cooling processes. The resulting elastic constants show a linear dependence on the air gap, i.e. the clearance between the permanent magnet assembly and the set of superconducting large-grain blocks, which is dependent on cooling distance.

Evaluation of resonating Si cantilevers sputter-deposited with AlN piezoelectric thin films for mass sensing applications

NASA Astrophysics Data System (ADS)

Sökmen, Ü.; Stranz, A.; Waag, A.; Ababneh, A.; Seidel, H.; Schmid, U.; Peiner, E.

2010-06-01

We report on a micro-machined resonator for mass sensing applications which is based on a silicon cantilever excited with a sputter-deposited piezoelectric aluminium nitride (AlN) thin film actuator. An inductively coupled plasma (ICP) cryogenic dry etching process was applied for the micro-machining of the silicon substrate. A shift in resonance frequency was observed, which was proportional to a mass deposited in an e-beam evaporation process on top. We had a mass sensing limit of 5.2 ng. The measurements from the cantilevers of the two arrays revealed a quality factor of 155-298 and a mass sensitivity of 120.34 ng Hz-1 for the first array, and a quality factor of 130-137 and a mass sensitivity of 104.38 ng Hz-1 for the second array. Furthermore, we managed to fabricate silicon cantilevers, which can be improved for the detection in the picogram range due to a reduction of the geometrical dimensions.

Correlation of Noise Signature to Pulsed Power Events at the HERMES III Accelerator.

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

Lewis, Barbara; Joseph, Nathan Ryan; Salazar, Juan Diego

2016-11-01

The HERMES III accelerator, which is located at Sandia National Laboratories' Tech Area IV, is the largest pulsed gamma X-ray source in the world. The accelerator is made up of 20 inductive cavities that are charged to 1 MV each by complex pulsed power circuitry. The firing time of the machine components ranges between the microsecond and nanosecond timescales. This results in a variety of electromagnetic frequencies when the accelerator fires. Testing was done to identify the HERMES electromagnetic noise signal and to map it to the various accelerator trigger events. This report will show the measurement methods used tomore » capture the noise spectrum produced from the machine and correlate this noise signature with machine events.« less

PWM Inverter control and the application thereof within electric vehicles

DOEpatents

Geppert, Steven

1982-01-01

An inverter (34) which provides power to an A.C. machine (28) is controlled by a circuit (36) employing PWM control strategy whereby A.C. power is supplied to the machine at a preselectable frequency and preselectable voltage. This is accomplished by the technique of waveform notching in which the shapes of the notches are varied to determine the average energy content of the overall waveform. Through this arrangement, the operational efficiency of the A.C. machine is optimized. The control circuit includes a micro-computer and memory element which receive various parametric inputs and calculate optimized machine control data signals therefrom. The control data is asynchronously loaded into the inverter through an intermediate buffer (38). In its preferred embodiment, the present invention is incorporated within an electric vehicle (10) employing a 144 VDC battery pack (32) and a three-phase induction motor (18).

EV drivetrain inverter with V/HZ optimization

DOEpatents

Gritter, David J.; O'Neil, Walter K.

1986-01-01

An inverter (34) which provides power to an A.C. machine (28) is controlled by a circuit (36) employing PWM control strategy whereby A.C. power is supplied to the machine at a preselectable frequency and preselectable voltage. This is accomplished by the technique of waveform notching in which the shapes of the notches are varied to determine the average energy content of the overall waveform. Through this arrangement, the operational efficiency of the A.C. machine is optimized. The control circuit includes a micro-computer which calculates optimized machine control data signals from various parametric inputs and during steady state load conditions, seeks a best V/HZ ratio to minimize battery current drawn (system losses) from a D.C. power source (32). In the preferred embodiment, the present invention is incorporated within an electric vehicle (10) employing a 144 VDC battery pack and a three-phase induction motor (18).

Universal Parameter Measurement and Sensorless Vector Control of Induction and Permanent Magnet Synchronous Motors

NASA Astrophysics Data System (ADS)

Yamamoto, Shu; Ara, Takahiro

Recently, induction motors (IMs) and permanent-magnet synchronous motors (PMSMs) have been used in various industrial drive systems. The features of the hardware device used for controlling the adjustable-speed drive in these motors are almost identical. Despite this, different techniques are generally used for parameter measurement and speed-sensorless control of these motors. If the same technique can be used for parameter measurement and sensorless control, a highly versatile adjustable-speed-drive system can be realized. In this paper, the authors describe a new universal sensorless control technique for both IMs and PMSMs (including salient pole and nonsalient pole machines). A mathematical model applicable for IMs and PMSMs is discussed. Using this model, the authors derive the proposed universal sensorless vector control algorithm on the basis of estimation of the stator flux linkage vector. All the electrical motor parameters are determined by a unified test procedure. The proposed method is implemented on three test machines. The actual driving test results demonstrate the validity of the proposed method.

Energy optimization for a wind DFIG with flywheel energy storage

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

Hamzaoui, Ihssen, E-mail: hamzaoui-ihssen2000@yahoo.fr; Laboratory of Instrumentation, Faculty of Electronics and Computer, University of Khemis Miliana, Ain Defla; Bouchafaa, Farid, E-mail: fbouchafa@gmail.com

2016-07-25

The type of distributed generation unit that is the subject of this paper relates to renewable energy sources, especially wind power. The wind generator used is based on a double fed induction Generator (DFIG). The stator of the DFIG is connected directly to the network and the rotor is connected to the network through the power converter with three levels. The objective of this work is to study the association a Flywheel Energy Storage System (FESS) in wind generator. This system is used to improve the quality of electricity provided by wind generator. It is composed of a flywheel; anmore » induction machine (IM) and a power electronic converter. A maximum power tracking technique « Maximum Power Point Tracking » (MPPT) and a strategy for controlling the pitch angle is presented. The model of the complete system is developed in Matlab/Simulink environment / to analyze the results from simulation the integration of wind chain to networks.« less

Detection of broken rotor bar faults in induction motor at low load using neural network.

PubMed

Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

2016-09-01

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Bending Distortion Analysis of a Steel Shaft Manufacturing Chain from Cold Drawing to Grinding

NASA Astrophysics Data System (ADS)

Dias, Vinicius Waechter; da Silva Rocha, Alexandre; Zottis, Juliana; Dong, Juan; Epp, Jérémy; Zoch, Hans Werner

2017-04-01

Shafts are usually manufactured from bars that are cold drawn, cut machined, induction hardened, straightened, and finally ground. The main distortion is characterized by bending that appears after induction hardening and is corrected by straightening and/or grinding. In this work, the consequence of the variation of manufacturing parameters on the distortion was analyzed for a complete manufacturing route for production of induction hardened shafts made of Grade 1045 steel. A DoE plan was implemented varying the drawing angle, cutting method, induction hardening layer depth, and grinding penetration depth. The distortion was determined by calculating curvature vectors from dimensional analysis by 3D coordinate measurements. Optical microscopy, microhardness testing, residual stress analysis, and FEM process simulation were used to evaluate and understand effects of the main carriers of distortion potential. The drawing process was identified as the most significant influence on the final distortion of the shafts.

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

NASA Astrophysics Data System (ADS)

Aouabdi, Salim; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

2017-06-01

This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.

Modes of mechanical ventilation for the operating room.

PubMed

Ball, Lorenzo; Dameri, Maddalena; Pelosi, Paolo

2015-09-01

Most patients undergoing surgical procedures need to be mechanically ventilated, because of the impact of several drugs administered at induction and during maintenance of general anaesthesia on respiratory function. Optimization of intraoperative mechanical ventilation can reduce the incidence of post-operative pulmonary complications and improve the patient's outcome. Preoxygenation at induction of general anaesthesia prolongs the time window for safe intubation, reducing the risk of hypoxia and overweighs the potential risk of reabsorption atelectasis. Non-invasive positive pressure ventilation delivered through different interfaces should be considered at the induction of anaesthesia morbidly obese patients. Anaesthesia ventilators are becoming increasingly sophisticated, integrating many functions that were once exclusive to intensive care. Modern anaesthesia machines provide high performances in delivering the desired volumes and pressures accurately and precisely, including assisted ventilation modes. Therefore, the physicians should be familiar with the potential and pitfalls of the most commonly used intraoperative ventilation modes: volume-controlled, pressure-controlled, dual-controlled and assisted ventilation. Although there is no clear evidence to support the advantage of any one of these ventilation modes over the others, protective mechanical ventilation with low tidal volume and low levels of positive end-expiratory pressure (PEEP) should be considered in patients undergoing surgery. The target tidal volume should be calculated based on the predicted or ideal body weight rather than on the actual body weight. To optimize ventilation monitoring, anaesthesia machines should include end-inspiratory and end-expiratory pause as well as flow-volume loop curves. The routine administration of high PEEP levels should be avoided, as this may lead to haemodynamic impairment and fluid overload. Higher PEEP might be considered during surgery longer than 3 h, laparoscopy in the Trendelenburg position and in patients with body mass index >35 kg/m(2). Large randomized trials are warranted to identify subgroups of patients and the type of surgery that can potentially benefit from specific ventilation modes or ventilation settings. Copyright © 2015 Elsevier Ltd. All rights reserved.

Design and Performance Improvement of AC Machines Sharing a Common Stator

NASA Astrophysics Data System (ADS)

Guo, Lusu

With the increasing demand on electric motors in various industrial applications, especially electric powered vehicles (electric cars, more electric aircrafts and future electric ships and submarines), both synchronous reluctance machines (SynRMs) and interior permanent magnet (IPM) machines are recognized as good candidates for high performance variable speed applications. Developing a single stator design which can be used for both SynRM and IPM motors is a good way to reduce manufacturing and maintenance cost. SynRM can be used as a low cost solution for many electric driving applications and IPM machines can be used in power density crucial circumstances or work as generators to meet the increasing demand for electrical power on board. In this research, SynRM and IPM machines are designed sharing a common stator structure. The prototype motors are designed with the aid of finite element analysis (FEA). Machine performances with different stator slot and rotor pole numbers are compared by FEA. An 18-slot, 4-pole structure is selected based on the comparison for this prototype design. Sometimes, torque pulsation is the major drawback of permanent magnet synchronous machines. There are several sources of torque pulsations, such as back-EMF distortion, inductance variation and cogging torque due to presence of permanent magnets. To reduce torque pulsations in permanent magnet machines, all the efforts can be classified into two categories: one is from the design stage, the structure of permanent magnet machines can be optimized with the aid of finite element analysis. The other category of reducing torque pulsation is after the permanent magnet machine has been manufactured or the machine structure cannot be changed because of other reasons. The currents fed into the permanent magnet machine can be controlled to follow a certain profile which will make the machine generate a smoother torque waveform. Torque pulsation reduction methods in both categories will be discussed in this dissertation. In the design stage, an optimization method based on orthogonal experimental design will be introduced. Besides, a universal current profiling technique is proposed to minimize the torque pulsation along with the stator copper losses in modular interior permanent magnet motors. Instead of sinusoidal current waveforms, this algorithm will calculate the proper currents which can minimize the torque pulsation. Finite element analysis and Matlab programing will be used to develop this optimal current profiling algorithm. Permanent magnet machines are becoming more attractive in some modern traction applications, such as traction motors and generators for an electrified vehicle. The operating speed or the load condition in these applications may be changing all the time. Compared to electric machines used to operate at a constant speed and constant load, better control performance is required. In this dissertation, a novel model reference adaptive control (MRAC) used on five-phase interior permanent magnet motor drives is presented. The primary controller is designed based on artificial neural network (ANN) to simulate the nonlinear characteristics of the system without knowledge of accurate motor model or parameters. The proposed motor drive decouples the torque and flux components of five-phase IPM motors by applying a multiple reference frame transformation. Therefore, the motor can be easily driven below the rated speed with the maximum torque per ampere (MTPA) operation or above the rated speed with the flux weakening operation. The ANN based primary controller consists of a radial basis function (RBF) network which is trained on-line to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment and implemented experimentally utilizing dSPACE DS1104 DSP board on a five-phase prototype IPM motor. The proposed model reference adaptive control method has been applied on the commons stator SynRM and IPM machine as well.

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

NASA Astrophysics Data System (ADS)

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

2002-08-01

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

Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization.

PubMed

Shima, Hideaki; Masuda, Shizuka; Date, Yasuhiro; Shino, Amiu; Tsuboi, Yuuri; Kajikawa, Mizuho; Inoue, Yoshihiro; Kanamoto, Taisei; Kikuchi, Jun

2017-12-01

Prebiotics and probiotics strongly impact the gut ecosystem by changing the composition and/or metabolism of the microbiota to improve the health of the host. However, the composition of the microbiota constantly changes due to the intake of daily diet. This shift in the microbiota composition has a considerable impact; however, non-pre/probiotic foods that have a low impact are ignored because of the lack of a highly sensitive evaluation method. We performed comprehensive acquisition of data using existing measurements (nuclear magnetic resonance, next-generation DNA sequencing, and inductively coupled plasma-optical emission spectroscopy) and analyses based on a combination of machine learning and network visualization, which extracted important factors by the Random Forest approach, and applied these factors to a network module. We used two pteridophytes, Pteridium aquilinum and Matteuccia struthiopteris , for the representative daily diet. This novel analytical method could detect the impact of a small but significant shift associated with Matteuccia struthiopteris but not Pteridium aquilinum intake, using the functional network module. In this study, we proposed a novel method that is useful to explore a new valuable food to improve the health of the host as pre/probiotics.

System and component design and test of a 10 hp, 18,000 rpm AC dynamometer utilizing a high frequency AC voltage link, part 1

NASA Technical Reports Server (NTRS)

Lipo, Thomas A.; Alan, Irfan

1991-01-01

Hard and soft switching test results conducted with one of the samples of first generation MOS-controlled thyristor (MCTs) and similar test results with several different samples of second generation MCT's are reported. A simple chopper circuit is used to investigate the basic switching characteristics of MCT under hard switching and various types of resonant circuits are used to determine soft switching characteristics of MCT under both zero voltage and zero current switching. Next, operation principles of a pulse density modulated converter (PDMC) for three phase (3F) to 3F two-step power conversion via parallel resonant high frequency (HF) AC link are reviewed. The details for the selection of power switches and other power components required for the construction of the power circuit for the second generation 3F to 3F converter system are discussed. The problems encountered in the first generation system are considered. Design and performance of the first generation 3F to 3F power converter system and field oriented induction moter drive based upon a 3 kVA, 20 kHz parallel resonant HF AC link are described. Low harmonic current at the input and output, unity power factor operation of input, and bidirectional flow capability of the system are shown via both computer and experimental results. The work completed on the construction and testing of the second generation converter and field oriented induction motor drive based upon specifications for a 10 hp squirrel cage dynamometer and a 20 kHz parallel resonant HF AC link is discussed. The induction machine is designed to deliver 10 hp or 7.46 kW when operated as an AC-dynamo with power fed back to the source through the converter. Results presented reveal that the proposed power level requires additional energy storage elements to overcome difficulties with a peak link voltage variation problem that limits reaching to the desired power level. The power level test of the second generation converter after the addition of extra energy storage elements to the HF link are described. The importance of the source voltage level to achieve a better current regulation for the source side PDMC is also briefly discussed. The power levels achieved in the motoring mode of operation show that the proposed power levels achieved in the generating mode of operation can also be easily achieved provided that no mechanical speed limitation were present to drive the induction machine at the proposed power level.

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation. [applied to Shuttle Mission Simulator

NASA Technical Reports Server (NTRS)

Nieten, Joseph; Burke, Roger

1993-01-01

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

Incremental Inductive Learning in a Constructivist Agent

NASA Astrophysics Data System (ADS)

Perotto, Filipo Studzinski; Älvares, Luís Otávio

The constructivist paradigm in Artificial Intelligence has been definitively inaugurated in the earlier 1990's by Drescher's pioneer work [10]. He faces the challenge of design an alternative model for machine learning, founded in the human cognitive developmental process described by Piaget [x]. His effort has inspired many other researchers.

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

DTIC Science & Technology

1981-03-01

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

Analysis of four dental alloys following torch/centrifugal and induction/ vacuum-pressure casting procedures.

PubMed

Thompson, Geoffrey A; Luo, Qing; Hefti, Arthur

2013-12-01

Previous studies have shown casting methodology to influence the as-cast properties of dental casting alloys. It is important to consider clinically important mechanical properties so that the influence of casting can be clarified. The purpose of this study was to evaluate how torch/centrifugal and inductively cast and vacuum-pressure casting machines may affect the castability, microhardness, chemical composition, and microstructure of 2 high noble, 1 noble, and 1 base metal dental casting alloys. Two commonly used methods for casting were selected for comparison: torch/centrifugal casting and inductively heated/ vacuum-pressure casting. One hundred and twenty castability patterns were fabricated and divided into 8 groups. Four groups were torch/centrifugally cast in Olympia (O), Jelenko O (JO), Genesis II (G), and Liberty (L) alloys. Similarly, 4 groups were cast in O, JO, G, and L by an inductively induction/vacuum-pressure casting machine. Each specimen was evaluated for casting completeness to determine a castability value, while porosity was determined by standard x-ray techniques. Each group was metallographically prepared for further evaluation that included chemical composition, Vickers microhardness, and grain analysis of microstructure. Two-way ANOVA was used to determine significant differences among the main effects. Statistically significant effects were examined further with the Tukey HSD procedure for multiple comparisons. Data obtained from the castability experiments were non-normal and the variances were unequal. They were analyzed statistically with the Kruskal-Wallis rank sum test. Significant results were further investigated statistically with the Steel-Dwass method for multiple comparisons (α=.05). The alloy type had a significant effect on surface microhardness (P<.001). In contrast, the technique used for casting did not affect the microhardness of the test specimen (P=.465). Similarly, the interaction between the alloy and casting technique was not significant (P=.119). A high level of castability (98.5% on average) was achieved overall. The frequency of casting failures as a function of alloy type and casting method was determined. Failure was defined as a castability index score of <100%. Three of 28 possible comparisons between alloy and casting combinations were statistically significant. The results suggested that casting technique affects the castability index of alloys. Radiographic analysis detected large porosities in regions near the edge of the castability pattern and infrequently adjacent to noncast segments. All castings acquired traces of elements found in the casting crucibles. The grain size for each dental casting alloy was generally finer for specimens produced by the induction/vacuum-pressure method. The difference was substantial for JO and L. This study demonstrated a relation between casting techniques and some physical properties of metal ceramic casting alloys. Copyright © 2013 Editorial Council for the Journal of Prosthetic Dentistry. Published by Mosby, Inc. All rights reserved.

The effect of sample size and disease prevalence on supervised machine learning of narrative data.

PubMed Central

McKnight, Lawrence K.; Wilcox, Adam; Hripcsak, George

2002-01-01

This paper examines the independent effects of outcome prevalence and training sample sizes on inductive learning performance. We trained 3 inductive learning algorithms (MC4, IB, and Naïve-Bayes) on 60 simulated datasets of parsed radiology text reports labeled with 6 disease states. Data sets were constructed to define positive outcome states at 4 prevalence rates (1, 5, 10, 25, and 50%) in training set sizes of 200 and 2,000 cases. We found that the effect of outcome prevalence is significant when outcome classes drop below 10% of cases. The effect appeared independent of sample size, induction algorithm used, or class label. Work is needed to identify methods of improving classifier performance when output classes are rare. PMID:12463878

Electric motor-transformer aggregate in hermetic objects of transport vehicles

NASA Astrophysics Data System (ADS)

Zabora, Igor

2017-10-01

The construction and features of operation for new electrical unit - electric motor-transformer aggregate (DTA) are considered. Induction motors are intended for operation in hermetic plants with extreme conditions surrounding gas, steam-to-gas and liquid environment at a high temperature (to several hundred of degrees). Main objective of spent researches is the substantiation of possibility reliable and effective electric power transform with electric machine means directly in hermetic objects with extreme conditions environment by means of new DTA. The principle and job analysis of new disk induction motors of block-module type are observed.

Numerical investigation and electro-acoustic modeling of measurement methods for the in-duct acoustical source parameters.

PubMed

Jang, Seung-Ho; Ih, Jeong-Guon

2003-02-01

It is known that the direct method yields different results from the indirect (or load) method in measuring the in-duct acoustic source parameters of fluid machines. The load method usually comes up with a negative source resistance, although a fairly accurate prediction of radiated noise can be obtained from any method. This study is focused on the effect of the time-varying nature of fluid machines on the output results of two typical measurement methods. For this purpose, a simplified fluid machine consisting of a reservoir, a valve, and an exhaust pipe is considered as representing a typical periodic, time-varying system and the measurement situations are simulated by using the method of characteristics. The equivalent circuits for such simulations are also analyzed by considering the system as having a linear time-varying source. It is found that the results from the load method are quite sensitive to the change of cylinder pressure or valve profile, in contrast to those from the direct method. In the load method, the source admittance turns out to be predominantly dependent on the valve admittance at the calculation frequency as well as the valve and load admittances at other frequencies. In the direct method, however, the source resistance is always positive and the source admittance depends mainly upon the zeroth order of valve admittance.

Obtaining of High Cr Content Cast Iron Materials

NASA Astrophysics Data System (ADS)

Florea, C.; Bejinariu, C.; Carcea, I.; Cimpoesu, N.; Chicet, D. L.; Savin, C.

2017-06-01

We have obtained, through the classic casting process, 3 highly chromium-based experimental alloys proposed for replacing the FC 250 classical cast iron in braking applications. Casting was carried out in an induction furnace and cast into moulds made of KALHARTZ 8500 resin casting mixture and HARTER hardener at SC RanCon SRL Iasi. It is known that the microstructure of the cast iron is a combination of martensite with a small amount of residual austenite after the heat treatment of the ingot. In the case of high-alloy chromium alloys, the performance of the material is due to the presence of M7C3 carbides distributed in the iron matrix Resistance to machining and deformation is based on alloy composition and microstructure, while abrasion resistance will depend on properties and wear conditions.

Design and analysis of a toroidal tester for the measurement of core losses under axial compressive stress

NASA Astrophysics Data System (ADS)

Alatawneh, Natheer; Rahman, Tanvir; Lowther, David A.; Chromik, Richard

2017-06-01

Electric machine cores are subjected to mechanical stresses due to manufacturing processes. These stresses include radial, circumferential and axial components that may have significant influences on the magnetic properties of the electrical steel and hence, on the output and efficiencies of electrical machines. Previously, most studies of iron losses due to mechanical stress have considered only radial and circumferential components. In this work, an improved toroidal tester has been designed and developed to measure the core losses and the magnetic properties of electrical steel under a compressive axial stress. The shape of the toroidal ring has been verified using 3D stress analysis. Also, 3D electromagnetic simulations show a uniform flux density distribution in the specimen with a variation of 0.03 T and a maximum average induction level of 1.5 T. The developed design has been prototyped, and measurements were carried out using a steel sample of grade 35WW300. Measurements show that applying small mechanical stresses normal to the sample thickness rises the delivered core losses, then the losses decrease continuously as the stress increases. However, the drop in core losses at high stresses does not go lower than the free-stress condition. Physical explanations for the observed trend of core losses as a function of stress are provided based on core loss separation to the hysteresis and eddy current loss components. The experimental results show that the effect of axial compressive stress on magnetic properties of electrical steel at high level of inductions becomes less pronounced.

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

Effects of pole flux distribution in a homopolar linear synchronous machine

NASA Astrophysics Data System (ADS)

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

1994-05-01

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

Reactive power compensating system

DOEpatents

Williams, Timothy J.; El-Sharkawi, Mohamed A.; Venkata, Subrahmanyam S.

1987-01-01

The reactive power of an induction machine is compensated by providing fixed capacitors on each phase line for the minimum compensation required, sensing the current on one line at the time its voltage crosses zero to determine the actual compensation required for each phase, and selecting switched capacitors on each line to provide the balance of the compensation required.

Real time PI-backstepping induction machine drive with efficiency optimization.

PubMed

Farhani, Fethi; Ben Regaya, Chiheb; Zaafouri, Abderrahmen; Chaari, Abdelkader

2017-09-01

This paper describes a robust and efficient speed control of a three phase induction machine (IM) subjected to load disturbances. First, a Multiple-Input Multiple-Output (MIMO) PI-Backstepping controller is proposed for a robust and highly accurate tracking of the mechanical speed and rotor flux. Asymptotic stability of the control scheme is proven by Lyapunov Stability Theory. Second, an active online optimization algorithm is used to optimize the efficiency of the drive system. The efficiency improvement approach consists of adjusting the rotor flux with respect to the load torque in order to minimize total losses in the IM. A dSPACE DS1104 R&D board is used to implement the proposed solution. The experimental results released on 3kW squirrel cage IM, show that the reference speed as well as the rotor flux are rapidly achieved with a fast transient response and without overshoot. A good load disturbances rejection response and IM parameters variation are fairly handled. The improvement of drive system efficiency reaches up to 180% at light load. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Learning to predict chemical reactions.

PubMed

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

Learning to Predict Chemical Reactions

PubMed Central

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section. PMID:21819139

Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

NASA Astrophysics Data System (ADS)

Climente-Alarcon, V.; Antonino-Daviu, J.; Riera-Guasp, M.; Pons-Llinares, J.; Roger-Folch, J.; Jover-Rodriguez, P.; Arkkio, A.

2011-02-01

The present work is focused on the diagnosis of mixed eccentricity faults in induction motors via the study of currents demanded by the machine. Unlike traditional methods, based on the analysis of stationary currents (Motor Current Signature Analysis (MCSA)), this work provides new findings regarding the diagnosis approach proposed by the authors in recent years, which is mainly focused on the fault diagnosis based on the analysis of transient quantities, such as startup or plug stopping currents (Transient Motor Current Signature Analysis (TMCSA)), using suitable time-frequency decomposition (TFD) tools. The main novelty of this work is to prove the usefulness of tracking the transient evolution of high-order eccentricity-related harmonics in order to diagnose the condition of the machine, complementing the information obtained with the low-order components, whose transient evolution was well characterised in previous works. Tracking of high-order eccentricity-related harmonics during the transient, through their associated patterns in the time-frequency plane, may significantly increase the reliability of the diagnosis, since the set of fault-related patterns arising after application of the corresponding TFD tool is very unlikely to be caused by other faults or phenomena. Although there are different TFD tools which could be suitable for the transient extraction of these harmonics, this paper makes use of a Wigner-Ville distribution (WVD)-based algorithm in order to carry out the time-frequency decomposition of the startup current signal, since this is a tool showing an excellent trade-off between frequency resolution at both high and low frequencies. Several simulation results obtained with a finite element-based model and experimental results show the validity of this fault diagnosis approach under several faulty and operating conditions. Also, additional signals corresponding to the coexistence of the eccentricity and other non-fault related phenomena making difficult the diagnosis (fluctuating load torque) are included in the paper. Finally, a comparison with an alternative TFD tool - the discrete wavelet transform (DWT) - applied in previous papers, is also carried out in the contribution. The results are promising regarding the usefulness of the methodology for the reliable diagnosis of eccentricities and for their discrimination against other phenomena.

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).

Mining emotional profiles using e-mail messages for earlier warnings of potential terrorist activities

NASA Astrophysics Data System (ADS)

Galitsky, Boris; Kovalerchuk, Boris

2006-04-01

We develop a software system Text Scanner for Emotional Distress (TSED) for helping to detect email messages which are suspicious of coming from people under strong emotional distress. It has been confirmed by multiple studies that terrorist attackers have experienced a substantial emotional distress at some points before committing a terrorist attack. Therefore, if an individual in emotional distress can be detected on the basis of email texts, some preventive measures can be taken. The proposed detection machinery is based on extraction and classification of emotional profiles from emails. An emotional profile is a formal representation of a sequence of emotional states through a textual discourse where communicative actions are attached to these emotional states. The issues of extraction of emotional profiles from text and reasoning about it are discussed and illustrated. We then develop an inductive machine learning and reasoning framework to relate an emotional profile to the class "Emotional distress" or "No emotional distress", given a training dataset where the class is assigned by an expert. TSED's machine learning is evaluated using the database of structured customer complaints.

Improved Creep Measurements for Ultra-High Temperature Materials

NASA Technical Reports Server (NTRS)

Hyers, Robert W.; Ye, X.; Rogers, Jan R.

2010-01-01

Our team has developed a novel approach to measuring creep at extremely high temperatures using electrostatic levitation (ESL). This method has been demonstrated on niobium up to 2300 C, while ESL has melted tungsten (3400 C). This method has been extended to lower temperatures and higher stresses and applied to new materials, including a niobium-based superalloy, MASC. High-precision machined spheres of the sample are levitated in the NASA MSFC ESL, a national user facility and heated with a laser. The samples are rotated with an induction motor at up to 30,000 revolutions per second. The rapid rotation loads the sample through centripetal acceleration, producing a shear stress of about 60 MPa at the center, causing the sample to deform. The deformation of the sample is captured on high-speed video, which is analyzed by machine-vision software from the University of Massachusetts. The deformations are compared to finite element models to determine the constitutive constants in the creep relation. Furthermore, the non-contact method exploits stress gradients within the sample to determine the stress exponent in a single test.

Integrative relational machine-learning for understanding drug side-effect profiles

PubMed Central

2013-01-01

Background Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. Results In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Conclusions Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs. PMID:23802887

Integrative relational machine-learning for understanding drug side-effect profiles.

PubMed

Bresso, Emmanuel; Grisoni, Renaud; Marchetti, Gino; Karaboga, Arnaud Sinan; Souchet, Michel; Devignes, Marie-Dominique; Smaïl-Tabbone, Malika

2013-06-26

Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Evaluation of half wave induction motor drive for use in passenger vehicles

NASA Technical Reports Server (NTRS)

Hoft, R. G.; Kawamura, A.; Goodarzi, A.; Yang, G. Q.; Erickson, C. L.

1985-01-01

Research performed at the University of Missouri-Columbia to devise and design a lower cost inverter induction motor drive for electrical propulsion of passenger vehicles is described. A two phase inverter motor system is recommended. The new design is predicted to provide comparable vehicle performance, improved reliability and a cost advantage for a high production vehicle, decreased total rating of the power semiconductor switches, and a somewhat simpler control hardware compared to the conventional three phase bridge inverter motor drive system. The major disadvantages of the two phase inverter motor drive are that it is larger and more expensive than a three phase machine, the design of snubbers for the power leakage inductances produce higher transient voltages, and the torque pulsations are relatively large because of the necessity to limit the inverter switching frequency to achieve high efficiency.

Power-Factor Calculation under Consideration of Cross Saturation of the Interior Permanent Magnet Synchronous Motor with Brushless Field Excitation

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

Lee, Seong T; Burress, Timothy A; Hsu, John S

2009-01-01

This paper introduces a new method for calculating the power factor with consideration of the cross saturation between the direct-axis (d-axis) and the quadrature-axis (q-axis) of an interior permanent magnet synchronous motor (IPMSM). The conventional two-axis IPMSM model is modified to include the cross-saturation effect by adding the cross-coupled inductance terms. This paper also contains the new method of calculating the cross-coupled inductance values as well as self-inductance values in d- and q-axes. The analyzed motor is a high-speed brushless field excitation machine that offers high torque per ampere per core length at low speed and weakened flux at highmore » speed, which was developed for the traction motor of a hybrid electric vehicle.« less

Intense X-ray machine for penetrating radiography

NASA Astrophysics Data System (ADS)

Lucht, Roy A.; Eckhouse, Shimon

Penetrating radiography has been used for many years in the nuclear weapons research programs. Infrequently penetrating radiography has been used in conventional weapons research programs. For example the Los Alamos PHERMEX machine was used to view uranium rods penetrating steel for the GAU-8 program, and the Ector machine was used to see low density regions in forming metal jets. The armor/anti-armor program at Los Alamos has created a need for an intense flash X-ray machine that can be dedicated to conventional weapons research. The Balanced Technology Initiative, through DARPA, has funded the design and construction of such a machine at Los Alamos. It will be an 8- to 10-MeV diode machine capable of delivering a dose of 500 R at 1 m with a spot size of less than 5 mm. The machine used an 87.5-stage low inductance Marx generator that charges up a 7.4-(Omega), 32-ns water line. The water line is discharged through a self-breakdown oil switch into a 12.4-(Omega) water line that rings up the voltage into the high impendance X-ray diode. A long (233-cm) vacuum drift tube is used to separate the large diameter oil-insulated diode region from the X-ray source area that may be exposed to high overpressures by the explosive experiments. The electron beam is selffocused at the target area using a low pressure background gas.

A New Apparatus for Measuring the Temperature at Machine Parts Rotating at High Speeds

NASA Technical Reports Server (NTRS)

Gnam, E.

1945-01-01

After a brief survey of the available methods for measuring the temperatures of machine parts at high speed, in particular turbine blades and rotors, an apparatus is described which is constructed on the principle of induction. Transmission of the measuring current by sliding contacts therefore is avoided. Up-to-date experiments show that it is possible to give the apparatus a high degree of sensitivity and accuracy. In comparison with sliding contact types, the present apparatus shows the important advantage that it operates for any length of time without wear, and that the contact difficulties, particularly occurring at high sliding speeds,are avoided.

Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive.

PubMed

Ameid, Tarek; Menacer, Arezki; Talhaoui, Hicham; Azzoug, Youness

2018-05-03

This paper presents a methodology for the broken rotor bars fault detection is considered when the rotor speed varies continuously and the induction machine is controlled by Field-Oriented Control (FOC). The rotor fault detection is obtained by analyzing a several mechanical and electrical quantities (i.e., rotor speed, stator phase current and output signal of the speed regulator) by the Discrete Wavelet Transform (DWT) in variable speed drives. The severity of the fault is obtained by stored energy calculation for active power signal. Hence, it can be a useful solution as fault indicator. The FOC is implemented in order to preserve a good performance speed control; to compensate the broken rotor bars effect in the mechanical speed and to ensure the operation continuity and to investigate the fault effect in the variable speed. The effectiveness of the technique is evaluated in simulation and in a real-time implementation by using Matlab/Simulink with the real-time interface (RTI) based on dSpace 1104 board. Copyright © 2018. Published by Elsevier Ltd.

Influence of cutting strains and magnetic anisotropy of electrical steel on the air gap flux distribution of an induction motor

NASA Astrophysics Data System (ADS)

Hribernik, Božo

1984-02-01

This paper describes an iterative algorithm for the simulation of various real magnetic materials in a small induction motor and their influence on the flux distribution in the air gap. Two standard materials, fully-, and semi-processed steel strips were used. The nonlinearity of the magnetization curve, the influence of cutting strains and magnetic anisotropy are also considered. All these influences bring out the facts that the uniformly rotated and sine form exitation causes a nonuniformly rotated and deformed magnetic field in the air gap of the machine and that the magnetization current is winding place dependent.

Fractography of induction-hardened steel fractured in fatigue and overload

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

Santos, C.G.; Laird, C.

1997-07-01

The fracture surfaces of induction-hardened steel specimens obtained from an auto axle were characterized, macroscopically and microscopically, after being fractured in fatigue and monotonic overload. Specimens were tested in cyclic three-point bending under load control, and the S-N curve was established for specimens that had been notched by spark machining to facilitate fractography. Scanning electron microscopy of the fractured surfaces obtained for lives spanning the range 17,000 to 418,000 cycles revealed diverse fracture morphologies, including intergranular fracture and transgranular fatigue fracture. The results are being offered to assist in the analysis of complex field failures in strongly hardened steel.

MRAS state estimator for speed sensorless ISFOC induction motor drives with Luenberger load torque estimation.

PubMed

Zorgani, Youssef Agrebi; Koubaa, Yassine; Boussak, Mohamed

2016-03-01

This paper presents a novel method for estimating the load torque of a sensorless indirect stator flux oriented controlled (ISFOC) induction motor drive based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to inter-connect a speed estimator with the load torque observer. For this purpose, a MRAS has been applied to estimate the rotor speed with tuned load torque in order to obtain a high performance ISFOC induction motor drive. The reference and adjustable models, developed in the stationary stator reference frame, are used in the MRAS scheme in an attempt to estimate the speed of the measured terminal voltages and currents. The load torque is estimated by means of a Luenberger observer defined throughout the mechanical equation. Every observer state matrix depends on the mechanical characteristics of the machine taking into account the vicious friction coefficient and inertia moment. Accordingly, some simulation results are presented to validate the proposed method and to highlight the influence of the variation of the inertia moment and the friction coefficient on the speed and the estimated load torque. The experimental results, concerning to the sensorless speed with a load torque estimation, are elaborated in order to validate the effectiveness of the proposed method. The complete sensorless ISFOC with load torque estimation is successfully implemented in real time using a digital signal processor board DSpace DS1104 for a laboratory 3 kW induction motor. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Burner Rig in the Material and Stresses Building

NASA Image and Video Library

1969-11-21

A burner rig heats up a material sample in the Materials and Stresses Building at the National Aeronautics and Space Administration (NASA) Lewis Research Center. Materials technology is an important element in the successful development of advanced airbreathing and rocket propulsion systems. Different types of engines operate in different environments so an array of dependable materials is needed. NASA Lewis began investigating the characteristics of different materials shortly after World War II. In 1949 the materials group was expanded into its own division. The Lewis researchers sought to study and test materials in environments that simulate the environment in which they would operate. The Materials and Stresses Building, built in 1949, contained a number of laboratories to analyze the materials. They are subjected to high temperatures, high stresses, corrosion, irradiation, and hot gasses. The Physics of Solids Laboratory included a cyclotron, cloud chamber, helium cryostat, and metallurgy cave. The Metallographic Laboratory possessed six x-ray diffraction machines, two metalloscopes, and other equipment. The Furnace Room had two large induction machines, a 4500⁰ F graphite furnace, and heat treating equipment. The Powder Laboratory included 60-ton and 3000-ton presses. The Stresses Laboratory included stress rupture machines, fatigue machines, and tensile strength machines.

2005 AG20/20 Annual Review

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; McKellip, Rodney D.

2005-01-01

Topics covered include: Implementation and Validation of Sensor-Based Site-Specific Crop Management; Enhanced Management of Agricultural Perennial Systems (EMAPS) Using GIS and Remote Sensing; Validation and Application of Geospatial Information for Early Identification of Stress in Wheat; Adapting and Validating Precision Technologies for Cotton Production in the Mid-Southern United States - 2004 Progress Report; Development of a System to Automatically Geo-Rectify Images; Economics of Precision Agriculture Technologies in Cotton Production-AG 2020 Prescription Farming Automation Algorithms; Field Testing a Sensor-Based Applicator for Nitrogen and Phosphorus Application; Early Detection of Citrus Diseases Using Machine Vision and DGPS; Remote Sensing of Citrus Tree Stress Levels and Factors; Spectral-based Nitrogen Sensing for Citrus; Characterization of Tree Canopies; In-field Sensing of Shallow Water Tables and Hydromorphic Soils with an Electromagnetic Induction Profiler; Maintaining the Competitiveness of Tree Fruit Production Through Precision Agriculture; Modeling and Visualizing Terrain and Remote Sensing Data for Research and Education in Precision Agriculture; Thematic Soil Mapping and Crop-Based Strategies for Site-Specific Management; and Crop-Based Strategies for Site-Specific Management.

Electric Machine with Boosted Inductance to Stabilize Current Control

NASA Technical Reports Server (NTRS)

Abel, Steve

2013-01-01

High-powered motors typically have very low resistance and inductance (R and L) in their windings. This makes the pulse-width modulated (PWM) control of the current very difficult, especially when the bus voltage (V) is high. These R and L values are dictated by the motor size, torque (Kt), and back-emf (Kb) constants. These constants are in turn set by the voltage and the actuation torque-speed requirements. This problem is often addressed by placing inductive chokes within the controller. This approach is undesirable in that space is taken and heat is added to the controller. By keeping the same motor frame, reducing the wire size, and placing a correspondingly larger number of turns in each slot, the resistance, inductance, torque constant, and back-emf constant are all increased. The increased inductance aids the current control but ruins the Kt and Kb selections. If, however, a fraction of the turns is moved from their "correct slot" to an "incorrect slot," the increased R and L values are retained, but the Kt and Kb values are restored to the desired values. This approach assumes that increased resistance is acceptable to a degree. In effect, the heat allocated to the added inductance has been moved from the controller to the motor body, which in some cases is preferred.

Evaluating the Toxicity of Cigarette Whole Smoke Solutions in an Air-Liquid-Interface Human In Vitro Airway Tissue Model.

PubMed

Cao, Xuefei; Muskhelishvili, Levan; Latendresse, John; Richter, Patricia; Heflich, Robert H

2017-03-01

Exposure to cigarette smoke causes a multitude of pathological changes leading to tissue damage and disease. Quantifying such changes in highly differentiated in vitro human tissue models may assist in evaluating the toxicity of tobacco products. In this methods development study, well-differentiated human air-liquid-interface (ALI) in vitro airway tissue models were used to assess toxicological endpoints relevant to tobacco smoke exposure. Whole mainstream smoke solutions (WSSs) were prepared from 2 commercial cigarettes (R60 and S60) that differ in smoke constituents when machine-smoked under International Organization for Standardization conditions. The airway tissue models were exposed apically to WSSs 4-h per day for 1-5 days. Cytotoxicity, tissue barrier integrity, oxidative stress, mucin secretion, and matrix metalloproteinase (MMP) excretion were measured. The treatments were not cytotoxic and had marginal effects on tissue barrier properties; however, other endpoints responded in time- and dose-dependent manners, with the R60 resulting in higher levels of response than the S60 for many endpoints. Based on the lowest effect dose, differences in response to the WSSs were observed for mucin induction and MMP secretion. Mitigation of mucin induction by cotreatment of cultures with N-acetylcysteine suggests that oxidative stress contributes to mucus hypersecretion. Overall, these preliminary results suggest that quantifying disease-relevant endpoints using ALI airway models is a potential tool for tobacco product toxicity evaluation. Additional research using tobacco samples generated under smoking machine conditions that more closely approximate human smoking patterns will inform further methods development. Published by Oxford University Press on behalf of the Society of Toxicology 2017. This work is written by US Government employees and is in the public domain in the US.

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class.

PubMed

Aguiar, G F M; Batista, B L; Rodrigues, J L; Silva, L R S; Campiglia, A D; Barbosa, R M; Barbosa, F

2012-12-01

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The classification of explosion-proof protected induction motor into adequate temperature and efficiency class

NASA Astrophysics Data System (ADS)

Brinovar, Iztok; Srpčič, Gregor; Seme, Sebastijan; Štumberger, Bojan; Hadžiselimović, Miralem

2017-07-01

This article deals with the classification of explosion-proof protected induction motors, which are used in hazardous areas, into adequate temperature and efficiency class. Hazardous areas are defined as locations with a potentially explosive atmosphere where explosion may occur due to present of flammable gasses, liquids or combustible dusts (industrial plants, mines, etc.). Electric motors and electrical equipment used in such locations must be specially designed and tested to prevent electrical initiation of explosion due to high surface temperature and arcing contacts. This article presents the basic tests of three-phase explosion-proof protected induction motor with special emphasis on the measuring system and temperature rise test. All the measurements were performed with high-accuracy instrumentation and accessory equipment and carried out at the Institute of energy technology in the Electric machines and drives laboratory and Applied electrical engineering laboratory.

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.

Analysis of field-oriented controlled induction motor drives under sensor faults and an overview of sensorless schemes.

PubMed

Arun Dominic, D; Chelliah, Thanga Raj

2014-09-01

To obtain high dynamic performance on induction motor drives (IMD), variable voltage and variable frequency operation has to be performed by measuring speed of rotation and stator currents through sensors and fed back them to the controllers. When the sensors are undergone a fault, the stability of control system, may be designed for an industrial process, is disturbed. This paper studies the negative effects on a 12.5 hp induction motor drives when the field oriented control system is subjected to sensor faults. To illustrate the importance of this study mine hoist load diagram is considered as shaft load of the tested machine. The methods to recover the system from sensor faults are discussed. In addition, the various speed sensorless schemes are reviewed comprehensively. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Field-Oriented Control Of Induction Motors

NASA Technical Reports Server (NTRS)

Burrows, Linda M.; Roth, Mary Ellen; Zinger, Don S.

1993-01-01

Field-oriented control system provides for feedback control of torque or speed or both. Developed for use with commercial three-phase, 400-Hz, 208-V, 5-hp motor. Systems include resonant power supply operating at 20 kHz. Pulse-population-modulation subsystem selects individual pulses of 20-kHz single-phase waveform as needed to synthesize three waveforms of appropriate lower frequency applied to three phase windings of motor. Electric actuation systems using technology currently being built to peak powers of 70 kW. Amplitude of voltage of effective machine-frequency waveform determined by momentary frequency of pulses, while machine frequency determined by rate of repetition of overall temporal pattern of pulses. System enables independent control of both voltage and frequency.

Method of forming shrink-fit compression seal

NASA Technical Reports Server (NTRS)

Podgorski, T. J. (Inventor)

1977-01-01

A method for making a glass-to-metal seal is described. A domed metal enclosure having a machined seal ring is fitted to a glass post machined to a slight taper and to a desired surface finish. The metal part is then heated by induction in a vacuum. As the metal part heats and expands relative to the glass post, the metal seal ring, possessing a higher coefficient of expansion than the glass post, slides down the tapered post. Upon cooling, the seal ring crushes against the glass post forming the seal. The method results in a glass-to-metal seal possessing extremely good leak resistance, while the parts are kept clean and free of the contaminants.

Closed-loop torque feedback for a universal field-oriented controller

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

De Doncker, R.W.A.A.; King, R.D.; Sanza, P.C.

A torque feedback system is employed in a universal field-oriented (UFO) controller to tune a torque-producing current command and a slip frequency command in order to achieve robust torque control of an induction machine even in the event of current regulator errors and during transitions between pulse width modulated (PWM) and square wave modes of operation. 1 figure.

Closed-loop torque feedback for a universal field-oriented controller

DOEpatents

De Doncker, R.W.A.A.; King, R.D.; Sanza, P.C.; Haefner, K.B.

1992-11-24

A torque feedback system is employed in a universal field-oriented (UFO) controller to tune a torque-producing current command and a slip frequency command in order to achieve robust torque control of an induction machine even in the event of current regulator errors and during transitions between pulse width modulated (PWM) and square wave modes of operation. 1 figure.

Closed-loop torque feedback for a universal field-oriented controller

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

De Doncker, Rik W. A. A.; King, Robert D.; Sanza, Peter C.

A torque feedback system is employed in a universal field-oriented (UFO) controller to tune a torque-producing current command and a slip frequency command in order to achieve robust torque control of an induction machine even in the event of current regulator errors and during transitions between pulse width modulated (PWM) and square wave modes of operation.

Compensation for Harmonic Currents and Reactive Power in Wind Power Generation System using PWM Inverter

NASA Astrophysics Data System (ADS)

Shinohara, Katsuji; Shinhatsubo, Kurato; Iimori, Kenichi; Yamamoto, Kichiro; Saruban, Takamichi; Yamaemori, Takahiro

In recent year, consciousness of environmental problems is enhancing, and the price of the electric power purchased by an electric power company is established expensive for the power plant utilizing the natural energy. So, the introduction of the wind power generation is promoted in Japan. Generally, squirrel-cage induction machines are widely used as a generator in wind power generation system because of its small size, lightweight and low-cost. However, the induction machines do not have a source of excitation. Thus, it causes the inrush currents and the instantaneous voltage drop when the generator is directly connected to a power grid. To reduce the inrush currents, an AC power regulator is used. Wind power generations are frequently connected to and disconnected from the power grid. However, when the inrush currents are reduced, harmonic currents are caused by phase control of the AC power regulator. And the phase control of AC power regulator cannot control the power factor. Therefore, we propose the use of the AC power regulator to compensate for the harmonic currents and reactive power in the wind power generation system, and demonstrate the validity of its system by simulated and experimental results.

Integrating machine learning and physician knowledge to improve the accuracy of breast biopsy.

PubMed

Dutra, I; Nassif, H; Page, D; Shavlik, J; Strigel, R M; Wu, Y; Elezaby, M E; Burnside, E

2011-01-01

In this work we show that combining physician rules and machine learned rules may improve the performance of a classifier that predicts whether a breast cancer is missed on percutaneous, image-guided breast core needle biopsy (subsequently referred to as "breast core biopsy"). Specifically, we show how advice in the form of logical rules, derived by a sub-specialty, i.e. fellowship trained breast radiologists (subsequently referred to as "our physicians") can guide the search in an inductive logic programming system, and improve the performance of a learned classifier. Our dataset of 890 consecutive benign breast core biopsy results along with corresponding mammographic findings contains 94 cases that were deemed non-definitive by a multidisciplinary panel of physicians, from which 15 were upgraded to malignant disease at surgery. Our goal is to predict upgrade prospectively and avoid surgery in women who do not have breast cancer. Our results, some of which trended toward significance, show evidence that inductive logic programming may produce better results for this task than traditional propositional algorithms with default parameters. Moreover, we show that adding knowledge from our physicians into the learning process may improve the performance of the learned classifier trained only on data.

Condition monitoring of Electric Components

NASA Astrophysics Data System (ADS)

Zaman, Ishtiaque

A universal non-intrusive model of a flexible antenna array is presented in this paper to monitor and identify the failures in electric machines. This adjustable antenna is designed to serve the purpose of condition monitoring of a vast range of electrical components including Induction Motor (IM), Printed Circuit Board (PCB), Synchronous Reluctance Motor (SRM), Permanent Magnet Synchronous Machine (PMSM) etc. by capturing the low frequency magnetic field radiated around these machines. The basic design and specification of the proposed antenna array for low frequency components is portrayed first. The design of the antenna is adjustable to fit for an extensive variety of segments. Subsequent to distinguishing the design and specifications of the antenna, the ideal area of the most delicate stray field has been identified for healthy current streaming around the machineries. Following this, short circuit representing faulty situation has been introduced and compared with the healthy cases. Precision has been found recognizing the faults using this one generic model of Antenna and the results are presented for three different machines i.e. IM, SRM and PMSM. Finite element method has been used to design the antenna and detect the optimum location and faults in the machines. Finally, a 3D Printer is proposed to be employed to build the antenna as per the details tended to in this paper contingent upon the power segments.

A novel semi-transductive learning framework for efficient atypicality detection in chest radiographs

NASA Astrophysics Data System (ADS)

Alzubaidi, Mohammad; Balasubramanian, Vineeth; Patel, Ameet; Panchanathan, Sethuraman; Black, John A., Jr.

2012-03-01

Inductive learning refers to machine learning algorithms that learn a model from a set of training data instances. Any test instance is then classified by comparing it to the learned model. When the set of training instances lend themselves well to modeling, the use of a model substantially reduces the computation cost of classification. However, some training data sets are complex, and do not lend themselves well to modeling. Transductive learning refers to machine learning algorithms that classify test instances by comparing them to all of the training instances, without creating an explicit model. This can produce better classification performance, but at a much higher computational cost. Medical images vary greatly across human populations, constituting a data set that does not lend itself well to modeling. Our previous work showed that the wide variations seen across training sets of "normal" chest radiographs make it difficult to successfully classify test radiographs with an inductive (modeling) approach, and that a transductive approach leads to much better performance in detecting atypical regions. The problem with the transductive approach is its high computational cost. This paper develops and demonstrates a novel semi-transductive framework that can address the unique challenges of atypicality detection in chest radiographs. The proposed framework combines the superior performance of transductive methods with the reduced computational cost of inductive methods. Our results show that the proposed semitransductive approach provides both effective and efficient detection of atypical regions within a set of chest radiographs previously labeled by Mayo Clinic expert thoracic radiologists.

Semi-supervised and unsupervised extreme learning machines.

PubMed

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Effective switching frequency multiplier inverter

DOEpatents

Su, Gui-Jia [Oak Ridge, TN; Peng, Fang Z [Okemos, MI

2007-08-07

A switching frequency multiplier inverter for low inductance machines that uses parallel connection of switches and each switch is independently controlled according to a pulse width modulation scheme. The effective switching frequency is multiplied by the number of switches connected in parallel while each individual switch operates within its limit of switching frequency. This technique can also be used for other power converters such as DC/DC, AC/DC converters.

Construction and Initial Tests of MAIZE: 1 MA LTD-Driven Z-Pinch *

NASA Astrophysics Data System (ADS)

Gilgenbach, R. M.; Gomez, M. R.; Zier, J. C.; Tang, W.; French, D. M.; Lau, Y. Y.; Mazarakis, M. G.; Cuneo, M. E.; Johnston, M. D.; Oliver, B. V.; Mehlhorn, T. A.; Kim, A. A.; Sinebryukhov, V. A.

2008-11-01

We report construction and initial testing of a 1-MA Linear Transformer Driver (LTD), The Michigan Accelerator for Inductive Z-pinch Experiments, (MAIZE). This machine, the first of its type to reach the USA, is based on the joint HCEI, Sandia Laboratories, and UM development effort. The compact LTD uses 80 capacitors and 40 spark gap switches, in 40 ``bricks'', to deliver 1 MA, 100 kV pulses with 70 ns risetime into a matched resistive load. Test results will be presented for a single brick and the full LTD. Design and construction will be presented of a low-inductance MITL. Experimental research programs under design and construction at UM include: a) Studies of Magneto-Raleigh-Taylor Instability of planar foils, and b) Vacuum convolute studies including cathode and anode plasma. Theory and simulation results will be presented for these planned experiments. Initial experimental designs and moderate-current feasibility experiments will be discussed. *Research supported by U. S. DoE through Sandia National Laboratories award document numbers 240985, 768225, 790791 and 805234 to the UM. MRG supported by NNSA Fellowship and JCZ supported by NPSC Fellowship / Sandia National Labs.

Dynamic characteristics of motor-gear system under load saltations and voltage transients

NASA Astrophysics Data System (ADS)

Bai, Wenyu; Qin, Datong; Wang, Yawen; Lim, Teik C.

2018-02-01

In this paper, a dynamic model of a motor-gear system is proposed. The model combines a nonlinear permeance network model (PNM) of a squirrel-cage induction motor and a coupled lateral-torsional dynamic model of a planetary geared rotor system. The external excitations including voltage transients and load saltations, as well as the internal excitations such as spatial effects, magnetic circuits topology and material nonlinearity in the motor, and time-varying mesh stiffness and damping in the planetary gear system are considered in the proposed model. Then, the simulation results are compared with those predicted by the electromechanical model containing a dynamic motor model with constant inductances. The comparison showed that the electromechanical system model with the PNM motor model yields more reasonable results than the electromechanical system model with the lumped-parameter electric machine. It is observed that electromechanical coupling effect can induce additional and severe gear vibrations. In addition, the external conditions, especially the voltage transients, will dramatically affect the dynamic characteristics of the electromechanical system. Finally, some suggestions are offered based on this analysis for improving the performance and reliability of the electromechanical system.

When Induction Meets Memory: Evidence for Gradual Transition from Similarity-Based to Category-Based Induction

ERIC Educational Resources Information Center

Fisher, Anna V.; Sloutsky, Vladimir M.

2005-01-01

The ability to perform induction appears early; however, underlying mechanisms remain unclear. Some argue that early induction is category based, whereas others suggest that early induction is similarity based. Category- and similarity-based induction should result in different memory traces and thus in different memory accuracy. Performing…

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.

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

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

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

1980-11-01

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

Microstructure and Mechanical Properties of Stainless Steel/Brass Joints Brazed by Sn-Electroplated Ag Brazing Filler Metals

NASA Astrophysics Data System (ADS)

Wang, Xingxing; Peng, Jin; Cui, Datian

2018-05-01

To develop a high-Sn-content AgCuZnSn brazing filler metal, the BAg50CuZn was used as the base filler metal and a Sn layer was electroplated upon it. Then, the 304 stainless steel and the H62 brass were induction-brazed with the Sn-plated brazing filler metals. The microstructures of the joints were examined with an optical microscope, a scanning electron microscope and an x-ray diffractometer. The corresponding mechanical properties were obtained with a universal tensile testing machine. The results indicated that the induction brazed joints consisted of the Ag phase, the Cu phase and the CuZn phase. When the content of Sn in the Sn-plated Ag brazing filler metal was 6.0 or 7.2 wt.%, the Cu5Zn8, the Cu41Sn11 and the Ag3Sn phases appeared in the brazed joint. The tensile strength of the joints brazed with the Sn-plated filler metal was higher compared to the joints with the base filler metal. When the content of Sn was 6.0 wt.%, the highest tensile strength of the joint reached to 395 MPa. The joint fractures presented a brittle mode, mixed with a low amount of ductile fracture, when the content of Sn exceeded 6.0 wt.%.

Design and performance tests of a distributed power-driven wheel loader

NASA Astrophysics Data System (ADS)

Jin, Xiaolin; Shi, Laide; Bian, Yongming

2010-03-01

An improved ZLM15B distributed power-driven wheel loader was designed, whose travel and brake system was accomplished by two permanent magnet synchronous motorized-wheels instead of traditional mechanical components, and whose hydraulic systems such as the working device system and steering system were both actuated by an induction motor. All above systems were flexibly coupled with 3-phase 380VAC electric power with which the diesel engine power is replaced. On the level cement road, traveling, braking, traction and steering tests were carried out separately under non-load and heavy-load conditions. Data show that machine speed is 5 km/h around and travel efficiency of motorized-wheels is above 95%; that machine braking deceleration is between 0.5 and 0.64 m/s2 but efficiency of motorized-wheels is less than 10%; that maximum machine traction is above 2t while efficiency of motorized-wheels is more than 90% and that adaptive differential steering can be smoothly achieved by motorized-wheels.

Design and performance tests of a distributed power-driven wheel loader

NASA Astrophysics Data System (ADS)

Jin, Xiaolin; Shi, Laide; Bian, Yongming

2009-12-01

An improved ZLM15B distributed power-driven wheel loader was designed, whose travel and brake system was accomplished by two permanent magnet synchronous motorized-wheels instead of traditional mechanical components, and whose hydraulic systems such as the working device system and steering system were both actuated by an induction motor. All above systems were flexibly coupled with 3-phase 380VAC electric power with which the diesel engine power is replaced. On the level cement road, traveling, braking, traction and steering tests were carried out separately under non-load and heavy-load conditions. Data show that machine speed is 5 km/h around and travel efficiency of motorized-wheels is above 95%; that machine braking deceleration is between 0.5 and 0.64 m/s2 but efficiency of motorized-wheels is less than 10%; that maximum machine traction is above 2t while efficiency of motorized-wheels is more than 90% and that adaptive differential steering can be smoothly achieved by motorized-wheels.

Objective Assessment of an Ionic Footbath (IonCleanse): Testing Its Ability to Remove Potentially Toxic Elements from the Body

PubMed Central

Kennedy, Deborah A.; Cooley, Kieran; Einarson, Thomas R.; Seely, Dugald

2012-01-01

Ionic footbaths are often used in holistic health centres and spas to aid in detoxification; however, claims that these machines eliminate toxins from the body have not been rigorously evaluated. In this proof-of-principle study, we sought to measure the release of potentially toxic elements from ionic footbaths into distilled and tap water with and without feet. Water samples were collected and analyzed following 30-minute ionic footbath sessions without feet using both distilled (n = 1) and tap water (n = 6) and following four ionic footbaths using tap water (once/week for 4 weeks) in six healthy participants. Urine collection samples were analyzed at four points during the study. Hair samples were analyzed for element concentrations at baseline and study conclusion. Contrary to claims made for the machine, there does not appear to be any specific induction of toxic element release through the feet when running the machine according to specifications. PMID:22174728

Cavallo's multiplier for in situ generation of high voltage

NASA Astrophysics Data System (ADS)

Clayton, S. M.; Ito, T. M.; Ramsey, J. C.; Wei, W.; Blatnik, M. A.; Filippone, B. W.; Seidel, G. M.

2018-05-01

A classic electrostatic induction machine, Cavallo's multiplier, is suggested for in situ production of very high voltage in cryogenic environments. The device is suitable for generating a large electrostatic field under conditions of very small load current. Operation of the Cavallo multiplier is analyzed, with quantitative description in terms of mutual capacitances between electrodes in the system. A demonstration apparatus was constructed, and measured voltages are compared to predictions based on measured capacitances in the system. The simplicity of the Cavallo multiplier makes it amenable to electrostatic analysis using finite element software, and electrode shapes can be optimized to take advantage of a high dielectric strength medium such as liquid helium. A design study is presented for a Cavallo multiplier in a large-scale, cryogenic experiment to measure the neutron electric dipole moment.

Electricity and Magnetism

NASA Astrophysics Data System (ADS)

Glazebrook, R. T.

2016-10-01

1. Electrostatics: fundamental facts; 2. Electricity as a measurable quantity; 3. Measurement of electric force and potential; 4. Condensers; 5. Electrical machines; 6. Measurement of potential and electric force; 7. Magnetic attraction and repulsion; 8. Laws of magnetic force; 9. Experiments with magnets; 10. Magnetic calculations; 11. Magnetic measurements; 12. Terrestrial magnetism; 13. The electric current; 14. Relation between electromagnetic force and current; 15. Measurement of current; 16. Measurement of resistance and electromotive force; 17. Measurement of quantity of electricity, condensers; 18. Thermal activity of a current; 19. The voltaic cell (theory); 20. Electromagnetism; 21. Magnetisation of iron; 22. Electromagnetic instruments; 23. Electromagnetic induction; 24. Applications of electromagnetic induction; 25. Telegraphy and telephony; 26. Electric waves; 27. Transference of electricity through gases: corpuscles and electrons; Answers to examples; Index.

INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS

NASA Technical Reports Server (NTRS)

Iverson, David L.

2005-01-01

Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.

Inductive sensor performance in partial discharges and noise separation by means of spectral power ratios.

PubMed

Ardila-Rey, Jorge Alfredo; Rojas-Moreno, Mónica Victoria; Martínez-Tarifa, Juan Manuel; Robles, Guillermo

2014-02-19

Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among them, spectral power ratio representation shows great flexibility in the separation of the sources of PD. Mapping spectral power ratios in two-dimensional plots leads to clusters of points which group pulses with similar characteristics. The position in the map depends on the nature of the partial discharge, the setup and the frequency response of the sensors. If these clusters are clearly separated, the subsequent task of identifying the source of the discharge is straightforward so the distance between clusters can be a figure of merit to suggest the best option for PD recognition. In this paper, two inductive sensors with different frequency responses to pulsed signals, a high frequency current transformer and an inductive loop sensor, are analyzed to test their performance in detecting and separating the sources of partial discharges.

Welding of Aluminum Alloys to Steels: An Overview

DTIC Science & Technology

2013-08-01

and deformations are a few examples of the unwanted consequences which somehow would lead to brittle fracture, fatigue fracture, shape instability...was made under the copper tips of the spot welding machine. The fatigue results showed higher fatigue strength of the joints with transition layer...kHz ultrasonic butt welding system with a vibration source applying eight bolt-clamped Langevin type PZT transducers and a 50 kW static induction

Optimized Generator Designs for the DTU 10-MW Offshore Wind Turbine using GeneratorSE

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

Sethuraman, Latha; Maness, Michael; Dykes, Katherine

Compared to land-based applications, offshore wind imposes challenges for the development of next generation wind turbine generator technology. Direct-drive generators are believed to offer high availability, efficiency, and reduced operation and maintenance requirements; however, previous research suggests difficulties in scaling to several megawatts or more in size. The resulting designs are excessively large and/or massive, which are major impediments to transportation logistics, especially for offshore applications. At the same time, geared wind turbines continue to sustain offshore market growth through relatively cheaper and lightweight generators. However, reliability issues associated with mechanical components in a geared system create significant operation andmore » maintenance costs, and these costs make up a large portion of overall system costs offshore. Thus, direct-drive turbines are likely to outnumber their gear-driven counterparts for this market, and there is a need to review the costs or opportunities of building machines with different types of generators and examining their competitiveness at the sizes necessary for the next generation of offshore wind turbines. In this paper, we use GeneratorSE, the National Renewable Energy Laboratory's newly developed systems engineering generator sizing tool to estimate mass, efficiency, and the costs of different generator technologies satisfying the electromagnetic, structural, and basic thermal design requirements for application in a very large-scale offshore wind turbine such as the Technical University of Denmark's (DTU) 10-MW reference wind turbine. For the DTU reference wind turbine, we use the previously mentioned criteria to optimize a direct-drive, radial flux, permanent-magnet synchronous generator; a direct-drive electrically excited synchronous generator; a medium-speed permanent-magnet generator; and a high-speed, doubly-fed induction generator. Preliminary analysis of leveled costs of energy indicate that for large turbines, the cost of permanent magnets and reliability issues associated with brushes in electrically excited machines are the biggest deterrents for building direct-drive systems. The advantage of medium-speed permanent-magnet machines over doubly-fed induction generators is evident, yet, variability in magnet prices and solutions to address reliability issues associated with gearing and brushes can change this outlook. This suggests the need to potentially pursue fundamentally new innovations in generator designs that help avoid high capital costs but still have significant reliability related to performance.« less

Optimized Generator Designs for the DTU 10-MW Offshore Wind Turbine using GeneratorSE: Preprint

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

Sethuraman, Latha; Maness, Michael; Dykes, Katherine

Compared to land-based applications, offshore wind imposes challenges for the development of next generation wind turbine generator technology. Direct-drive generators are believed to offer high availability, efficiency, and reduced operation and maintenance requirements; however, previous research suggests difficulties in scaling to several megawatts or more in size. The resulting designs are excessively large and/or massive, which are major impediments to transportation logistics, especially for offshore applications. At the same time, geared wind turbines continue to sustain offshore market growth through relatively cheaper and lightweight generators. However, reliability issues associated with mechanical components in a geared system create significant operation andmore » maintenance costs, and these costs make up a large portion of overall system costs offshore. Thus, direct-drive turbines are likely to outnumber their gear-driven counterparts for this market, and there is a need to review the costs or opportunities of building machines with different types of generators and examining their competitiveness at the sizes necessary for the next generation of offshore wind turbines. In this paper, we use GeneratorSE, the National Renewable Energy Laboratory's newly developed systems engineering generator sizing tool to estimate mass, efficiency, and the costs of different generator technologies satisfying the electromagnetic, structural, and basic thermal design requirements for application in a very large-scale offshore wind turbine such as the Technical University of Denmark's (DTU) 10-MW reference wind turbine. For the DTU reference wind turbine, we use the previously mentioned criteria to optimize a direct-drive, radial flux, permanent-magnet synchronous generator; a direct-drive electrically excited synchronous generator; a medium-speed permanent-magnet generator; and a high-speed, doubly-fed induction generator. Preliminary analysis of leveled costs of energy indicate that for large turbines, the cost of permanent magnets and reliability issues associated with brushes in electrically excited machines are the biggest deterrents for building direct-drive systems. The advantage of medium-speed permanent-magnet machines over doubly-fed induction generators is evident, yet, variability in magnet prices and solutions to address reliability issues associated with gearing and brushes can change this outlook. This suggests the need to potentially pursue fundamentally new innovations in generator designs that help avoid high capital costs but still have significant reliability related to performance.« less

Effect of Post-Braze Heat Treatment on the Microstructure and Shear Strength of Cemented Carbide and Steel Using Ag-Based Alloy

NASA Astrophysics Data System (ADS)

Winardi, Y.; Triyono; Muhayat, N.

2018-03-01

The aim of the present study was to investigate the effect temperature of heat treatment process on the interfacial microstructure and mechanical properties of cemented carbide/carbon steel single lap joint brazed using Ag based alloy filler metal. The brazing process was carried out using torch brazing. Heat treatment process was carried out in induction furnace on the temperature of 700, 725, and 750°C, for 30 minutes. Microstructural examinations and phase analysis were performed using scanning electron microscopy (SEM) equipped with energy dispersion spectrometry (EDS). Shear strength of the joints was measured by the universal testing machine. The results of the microstructural analyses of the brazed area indicate that the increase temperature of treatment lead to the increase of solid solution phase of enrichted Cu. Based on EDS test, the carbon elements spread to all brazed area, which is disseminated by base metals. Shear strength joint is increased with temperature treatment. The highest shear strength of the brazed joint was 214,14 MPa when the heated up at 725°C.

Development of High Temperature Electro-Magnetic Actuators (HTEMA) for Aircraft Propulsion Systems (Preprint)

DTIC Science & Technology

2013-05-01

an 18 inch gap diameter has roughly a 2 foot outer diameter                                                              2 “ Brushless  Permanent...require PMs include wound rotor DC (brush and brushless ), Variable or Switched reluctance (VR or SR) machines and squirrel cage induction motors...Trades have identified Brushless DC PM and SR machines are of primary interest. Both motors can use sensorless commutation methods. A VR resolver can

Target-controlled inhalation anaesthesia: A cost-benefit analysis based on the cost per minute of anaesthesia by inhalation.

PubMed

Ponsonnard, Sébastien; Galy, Antoine; Cros, Jérôme; Daragon, Armelle Marie; Nathan, Nathalie

2017-02-01

End-tidal target-controlled inhalational anaesthesia (TCIA) with halogenated agents (HA) provides a faster and more accurately titrated anaesthesia as compared to manually-controlled anaesthesia. This study aimed to measure the macro-economic cost-benefit ratio of TCIA as compared to manually-controlled anaesthesia. This retrospective and descriptive study compared direct drug spending between two hospitals before 2011 and then after the replacement of three of six anaesthesia machines with TCIA mode machines in 2012 (Aisys carestation ® , GE). The direct costs were obtained from the pharmacy department and the number and duration of the anaesthesia procedures from the computerized files of the hospital. The cost of halogenated agents was reduced in the hospital equipped with an Aisys carestation ® by 13% as was the cost of one minute of anaesthesia by inhalation (€0.138 and €0.121/min between 2011 and 2012). The extra cost of the implementation of the 3 anaesthesia machines could be paid off with the resulting savings over 6 years. TCIA appears to have a favourable cost-benefit ratio. Despite a number of factors, which would tend to minimise the saving and increase costs, we still managed to observe a 13% savings. Shorter duration of surgery, type of induction as well as the way HA concentration is targeted may influence the savings results obtained. Copyright © 2016 Société française d’anesthésie et de réanimation (Sfar). Published by Elsevier Masson SAS. All rights reserved.

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...

Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach

PubMed Central

Sternberg, Michael J.E.; Tamaddoni-Nezhad, Alireza; Lesk, Victor I.; Kay, Emily; Hitchen, Paul G.; Cootes, Adrian; van Alphen, Lieke B.; Lamoureux, Marc P.; Jarrell, Harold C.; Rawlings, Christopher J.; Soo, Evelyn C.; Szymanski, Christine M.; Dell, Anne; Wren, Brendan W.; Muggleton, Stephen H.

2013-01-01

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. PMID:23103756

Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach.

PubMed

Sternberg, Michael J E; Tamaddoni-Nezhad, Alireza; Lesk, Victor I; Kay, Emily; Hitchen, Paul G; Cootes, Adrian; van Alphen, Lieke B; Lamoureux, Marc P; Jarrell, Harold C; Rawlings, Christopher J; Soo, Evelyn C; Szymanski, Christine M; Dell, Anne; Wren, Brendan W; Muggleton, Stephen H

2013-01-09

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning-the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. Copyright © 2012 Elsevier Ltd. All rights reserved.

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

NASA Astrophysics Data System (ADS)

Bereau, Tristan; DiStasio, Robert A.; Tkatchenko, Alexandre; von Lilienfeld, O. Anatole

2018-06-01

Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions—electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters—optimized once and for all across compounds. We validate IPML on various gas-phase dimers at and away from equilibrium separation, where we obtain mean absolute errors between 0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of non-covalent interactions in biologically relevant molecules. We further focus on hydrogen-bonded complexes—essential but challenging due to their directional nature—where datasets of DNA base pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look, we consider IPML for denser systems: water clusters, supramolecular host-guest complexes, and the benzene crystal.

Feature-based versus category-based induction with uncertain categories.

PubMed

Griffiths, Oren; Hayes, Brett K; Newell, Ben R

2012-05-01

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is known. The present experiments examined the conditions that drive feature-based and category-based strategies in induction under category uncertainty. Specifically, 2 experiments investigated whether reliance on feature-based inductive strategies is a product of the lack of coherence in the categories used in previous research or is due to the use of a decision-only induction procedure. Experiment 1 found that feature-based reasoning remained the preferred strategy even when categories with relatively high internal coherence were used. Experiment 2 found a shift toward category-based reasoning when participants were trained to classify category members prior to feature induction. Together, these results suggest that an appropriate conceptual representation must be formed through experience with a category before it is likely to be used as a basis for feature induction. (c) 2012 APA, all rights reserved.

Detection of Buried Targets via Active Selection of Labeled Data: Application to Sensing Subsurface UXO

DTIC Science & Technology

2007-06-01

images,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 13, no. 2, pp. 99–113, 1991. [15] C. Bouman and M. Shapiro, “A multiscale random...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing...this project was on developing new statistical algorithms for analysis of electromagnetic induction (EMI) and magnetometer data measured at actual

Camera-based micro interferometer for distance sensing

NASA Astrophysics Data System (ADS)

Will, Matthias; Schädel, Martin; Ortlepp, Thomas

2017-12-01

Interference of light provides a high precision, non-contact and fast method for measurement method for distances. Therefore this technology dominates in high precision systems. However, in the field of compact sensors capacitive, resistive or inductive methods dominates. The reason is, that the interferometric system has to be precise adjusted and needs a high mechanical stability. As a result, we have usual high-priced complex systems not suitable in the field of compact sensors. To overcome these we developed a new concept for a very small interferometric sensing setup. We combine a miniaturized laser unit, a low cost pixel detector and machine vision routines to realize a demonstrator for a Michelson type micro interferometer. We demonstrate a low cost sensor smaller 1cm3 including all electronics and demonstrate distance sensing up to 30 cm and resolution in nm range.

In-duct identification of a rotating sound source with high spatial resolution

NASA Astrophysics Data System (ADS)

Heo, Yong-Ho; Ih, Jeong-Guon; Bodén, Hans

2015-11-01

To understand and reduce the flow noise generation from in-duct fluid machines, it is necessary to identify the acoustic source characteristics precisely. In this work, a source identification technique, which can identify the strengths and positions of the major sound radiators in the source plane, is studied for an in-duct rotating source. A linear acoustic theory including the effects of evanescent modes and source rotation is formulated based on the modal summation method, which is the underlying theory for the inverse source reconstruction. A validation experiment is conducted on a duct system excited by a loudspeaker in static and rotating conditions, with two different speeds, in the absence of flow. Due to the source rotation, the measured pressure spectra reveal the Doppler effect, and the amount of frequency shift corresponds to the multiplication of the circumferential mode order and the rotation speed. Amplitudes of participating modes are estimated at the shifted frequencies in the stationary reference frame, and the modal amplitude set including the effect of source rotation is collected to investigate the source behavior in the rotating reference frame. By using the estimated modal amplitudes, the near-field pressure is re-calculated and compared with the measured pressure. The obtained maximum relative error is about -25 and -10 dB for rotation speeds at 300 and 600 rev/min, respectively. The spatial distribution of acoustic source parameters is restored from the estimated modal amplitude set. The result clearly shows that the position and magnitude of the main sound source can be identified with high spatial resolution in the rotating reference frame.

Method and system for fault accommodation of machines

NASA Technical Reports Server (NTRS)

Goebel, Kai Frank (Inventor); Subbu, Rajesh Venkat (Inventor); Rausch, Randal Thomas (Inventor); Frederick, Dean Kimball (Inventor)

2011-01-01

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Expert system and process optimization techniques for real-time monitoring and control of plasma processes

NASA Astrophysics Data System (ADS)

Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.

1991-03-01

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).

Dynamics of a Flywheel Energy Storage System Supporting a Wind Turbine Generator in a Microgrid

NASA Astrophysics Data System (ADS)

Nair S, Gayathri; Senroy, Nilanjan

2016-02-01

Integration of an induction machine based flywheel energy storage system with a wind energy conversion system is implemented in this paper. The nonlinear and linearized models of the flywheel are studied, compared and a reduced order model of the same simulated to analyze the influence of the flywheel inertia and control in system response during a wind power change. A quantification of the relation between the inertia of the flywheel and the controller gain is obtained which allows the system to be considered as a reduced order model that is more controllable in nature. A microgrid setup comprising of the flywheel energy storage system, a two mass model of a DFIG based wind turbine generator and a reduced order model of a diesel generator is utilized to analyse the microgrid dynamics accurately in the event of frequency variations arising due to wind power change. The response of the microgrid with and without the flywheel is studied.

Machine Learning Based Malware Detection

DTIC Science & Technology

2015-05-18

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

Magnetization of ternary alloys based on Fe0.65Ni0.35 invar with 3d transition metal additions: An ab initio study

NASA Astrophysics Data System (ADS)

Onoue, Masatoshi; Trimarchi, Giancarlo; Freeman, Arthur J.; Popescu, Voicu; Matsen, Marc R.

2015-01-01

Smart susceptors are being developed for use as tooling surfaces in molding machines that use apply electro-magnetic induction heating to mold and form plastics or metal powders into structural parts, e.g., on aerospace and automotive manufacturing lines. The optimal magnetic materials for the induction heating process should have large magnetization, high magnetic permeability, but also small thermal expansion coefficient. The Fe0.65Ni0.35 invar alloy with its negligible thermal expansion coefficient is thus a natural choice for this application. Here, we use density functional theory as implemented through the Korringa-Kohn-Rostoker method within the coherent-potential approximation, to design new alloys with the large magnetization desired for smart susceptor applications. We consider the Fe0.65-xNi0.35-yMx+y alloys derived from Fe0.65Ni0.35 invar adding a third element M = Sc, Ti, V, Cr, Mn, or Co with concentration (x + y) reaching up to 5 at. %. We find that the total magnetization depends linearly on the concentration of M. Specifically, the early 3d transition metals from Sc to Cr decrease the magnetization with respect to that of the invar alloy whereas Mn and Co increase it.

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

PubMed

Moore, Jason H; Gilbert, Joshua C; Tsai, Chia-Ti; Chiang, Fu-Tien; Holden, Todd; Barney, Nate; White, Bill C

2006-07-21

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.

Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes

NASA Astrophysics Data System (ADS)

Amezquita-Sanchez, Juan P.; Valtierra-Rodriguez, Martin; Perez-Ramirez, Carlos A.; Camarena-Martinez, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

2017-07-01

Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz’ FD, Higuchi’s FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness.

A Life Study of Ausforged, Standard Forged and Standard Machined AISI M-50 Spur Gears

NASA Technical Reports Server (NTRS)

Townsend, D. P.; Bamberger, E. N.; Zaretsky, E. V.

1975-01-01

Tests were conducted at 350 K (170 F) with three groups of 8.9 cm (3.5 in.) pitch diameter spur gears made of vacuum induction melted (VIM) consumable-electrode vacuum-arc melted (VAR), AISI M-50 steel and one group of vacuum-arc remelted (VAR) AISI 9310 steel. The pitting fatigue life of the standard forged and ausforged gears was approximately five times that of the VAR AISI 9310 gears and ten times that of the bending fatigue life of the standard machined VIM-VAR AISI M-50 gears run under identical conditions. There was a slight decrease in the 10-percent life of the ausforged gears from that for the standard forged gears, but the difference is not statistically significant. The standard machined gears failed primarily by gear tooth fracture while the forged and ausforged VIM-VAR AISI M-50 and the VAR AISI 9310 gears failed primarily by surface pitting fatigue. The ausforged gears had a slightly greater tendency to fail by tooth fracture than the standard forged gears.

A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring

NASA Astrophysics Data System (ADS)

Zhu, Xiaoliang; Zhong, Chong; Zhe, Jiang

2017-07-01

Detecting wear debris and measuring the increasing number of wear debris in lubrication oil can indicate abnormal machine wear well ahead of machine failure, and thus are indispensable for online machine health monitoring. A portable wear debris sensor with ferrite cores for online monitoring is presented. The sensor detects wear debris by measuring the inductance change of two planar coils wound around a pair of ferrite cores that make the magnetic flux denser and more uniform in the sensing channel, thereby improving the sensitivity of the sensor. Static testing results showed this wear debris sensor is capable of detecting 11 µm and 50 µm ferrous debris in 1 mm and 7 mm diameter fluidic pipes, respectively; such a high sensitivity has not been achieved before. Furthermore, a synchronized sampling method was also applied to reduce the data size and realize real-time data processing. Dynamic testing results demonstrated that the sensor is capable of detecting wear debris in real time with a high throughput of 750 ml min-1 the measured debris concentration is in good agreement with the actual concentration.

Design and analysis of a 3D-flux flux-switching permanent magnet machine with SMC cores and ferrite magnets

NASA Astrophysics Data System (ADS)

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

2017-05-01

Since permanent magnets (PM) are stacked between the adjacent stator teeth and there are no windings or PMs on the rotor, flux-switching permanent magnet machine (FSPMM) owns the merits of good flux concentrating and robust rotor structure. Compared with the traditional PM machines, FSPMM can provide higher torque density and better thermal dissipation ability. Combined with the soft magnetic composite (SMC) material and ferrite magnets, this paper proposes a new 3D-flux FSPMM (3DFFSPMM). The topology and operation principle are introduced. It can be found that the designed new 3DFFSPMM has many merits over than the traditional FSPMM for it can utilize the advantages of SMC material. Moreover, the PM flux of this new motor can be regulated by using the mechanical method. 3D finite element method (FEM) is used to calculate the magnetic field and parameters of the motor, such as flux density, inductance, PM flux linkage and efficiency map. The demagnetization analysis of the ferrite magnet is also addressed to ensure the safety operation of the proposed motor.

Inductive reasoning.

PubMed

Hayes, Brett K; Heit, Evan; Swendsen, Haruka

2010-03-01

Inductive reasoning entails using existing knowledge or observations to make predictions about novel cases. We review recent findings in research on category-based induction as well as theoretical models of these results, including similarity-based models, connectionist networks, an account based on relevance theory, Bayesian models, and other mathematical models. A number of touchstone empirical phenomena that involve taxonomic similarity are described. We also examine phenomena involving more complex background knowledge about premises and conclusions of inductive arguments and the properties referenced. Earlier models are shown to give a good account of similarity-based phenomena but not knowledge-based phenomena. Recent models that aim to account for both similarity-based and knowledge-based phenomena are reviewed and evaluated. Among the most important new directions in induction research are a focus on induction with uncertain premise categories, the modeling of the relationship between inductive and deductive reasoning, and examination of the neural substrates of induction. A common theme in both the well-established and emerging lines of induction research is the need to develop well-articulated and empirically testable formal models of induction. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website. Copyright © 2010 John Wiley & Sons, Ltd.

Feature-Based versus Category-Based Induction with Uncertain Categories

ERIC Educational Resources Information Center

Griffiths, Oren; Hayes, Brett K.; Newell, Ben R.

2012-01-01

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is…

Hardware-in-the-Loop emulator for a hydrokinetic turbine

NASA Astrophysics Data System (ADS)

Rat, C. L.; Prostean, O.; Filip, I.

2018-01-01

Hydroelectric power has proven to be an efficient and reliable form of renewable energy, but its impact on the environment has long been a source of concern. Hydrokinetic turbines are an emerging class of renewable energy technology designed for deployment in small rivers and streams with minimal environmental impact on the local ecosystem. Hydrokinetic technology represents a truly clean source of energy, having the potential to become a highly efficient method of harvesting renewable energy. However, in order to achieve this goal, extensive research is necessary. This paper presents a Hardware-in-the-Loop emulator for a run-of-the-river type hydrokinetic turbine. The HIL system uses an ABB ACS800 drive to control an induction machine as a significant means of replicating the behavior of the real turbine. The induction machine is coupled to a permanent magnet synchronous generator and the corresponding load. The ACS800 drive is controlled through the software system, which comprises of the hydrokinetic turbine real-time simulation through mathematical modeling in the LabVIEW programming environment running on a NI CompactRIO (cRIO) platform. The advantages of this method are that it can provide a means for testing many control configurations without requiring the presence of the real turbine. This paper contains the basic principles of a hydrokinetic turbine, particularly the run-of-the-river configurations along with the experimental results obtained from the HIL system.

Electromechanical systems with transient high power response operating from a resonant AC link

NASA Technical Reports Server (NTRS)

Burrows, Linda M.; Hansen, Irving G.

1992-01-01

The combination of an inherently robust asynchronous (induction) electrical machine with the rapid control of energy provided by a high frequency resonant AC link enables the efficient management of higher power levels with greater versatility. This could have a variety of applications from launch vehicles to all-electric automobiles. These types of systems utilize a machine which is operated by independent control of both the voltage and frequency. This is made possible by using an indirect field-oriented control method which allows instantaneous torque control in all four operating quadrants. Incorporating the AC link allows the converter in these systems to switch at the zero crossing of every half cycle of the AC waveform. This zero loss switching of the link allows rapid energy variations to be achieved without the usual frequency proportional switching loss. Several field-oriented control systems were developed by LeRC and General Dynamics Space Systems Division under contract to NASA. A description of a single motor, electromechanical actuation system is presented. Then, focus is on a conceptual design for an AC electric vehicle. This design incorporates an induction motor/generator together with a flywheel for peak energy storage. System operation and implications along with the associated circuitry are addressed. Such a system would greatly improve all-electric vehicle ranges over the Federal Urban Driving Cycle (FUD).

Nanomodified composite magnetic materials and their molding technologies

NASA Astrophysics Data System (ADS)

Timoshkov, I.; Gao, Q.; Govor, G.; Sakova, A.; Timoshkov, V.; Vetcher, A.

2018-05-01

Advanced electro-magnetic machines and systems require new materials with improved properties. Heterogeneous 3D nanomodified soft magnetic materials could be efficiently applied. Multistage technology of iron particle surface nanomodification by sequential oxidation and Si-organic coatings will be reported. The thickness of layers is 0.5-5 nm. Compaction and annealing are the final steps of magnetic parts and components shaping. The soft magnetic composite material shows the features: resistivity is controlled by insulating coating thickness and equals up to ρ =10-4 Ωṡm for metallic state and ρ =104 Ωṡm for insulator state, maximum magnetic permeability is μm = 2500 and μm = 300 respectively, induction is up to Bm=2.1 T. These properties of composite soft magnetic material allow applying for transformers, throttles, stator-rotor of high-efficient and powerful electric machines in 10 kHz-1MGz frequency range. For microsystems and microcomponents application, good opportunity to improve their reliability is the use of nanocomposite materials. Electroplating technology of nanocomposite magnetic materials into the ultra-thick micromolds will be presented. Co-deposition of the soft magnetic alloys with inert hard nanoparticles allows obtaining materials with magnetic permeability up to μm=104, magnetic induction of Bs=(0.62-1.3) T. Such LIGA-like technology will be applied in MEMS to produce high reliable devices with advanced physical properties.

Inductive Sensor Performance in Partial Discharges and Noise Separation by Means of Spectral Power Ratios

PubMed Central

Ardila-Rey, Jorge Alfredo; Rojas-Moreno, Mónica Victoria; Martínez-Tarifa, Juan Manuel; Robles, Guillermo

2014-01-01

Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among them, spectral power ratio representation shows great flexibility in the separation of the sources of PD. Mapping spectral power ratios in two-dimensional plots leads to clusters of points which group pulses with similar characteristics. The position in the map depends on the nature of the partial discharge, the setup and the frequency response of the sensors. If these clusters are clearly separated, the subsequent task of identifying the source of the discharge is straightforward so the distance between clusters can be a figure of merit to suggest the best option for PD recognition. In this paper, two inductive sensors with different frequency responses to pulsed signals, a high frequency current transformer and an inductive loop sensor, are analyzed to test their performance in detecting and separating the sources of partial discharges. PMID:24556674

Conceptual Design of the ITER Plasma Control System

NASA Astrophysics Data System (ADS)

Snipes, J. A.

2013-10-01

The conceptual design of the ITER Plasma Control System (PCS) has been approved and the preliminary design has begun for the 1st plasma PCS. This is a collaboration of many plasma control experts from existing devices to design and test plasma control techniques applicable to ITER on existing machines. The conceptual design considered all phases of plasma operation, ranging from non-active H/He plasmas through high fusion gain inductive DT plasmas to fully non-inductive steady-state operation, to ensure that the PCS control functionality and architecture can satisfy the demands of the ITER Research Plan. The PCS will control plasma equilibrium and density, plasma heat exhaust, a range of MHD instabilities (including disruption mitigation), and the non-inductive current profile required to maintain stable steady-state scenarios. The PCS architecture requires sophisticated shared actuator management and event handling systems to prioritize control goals, algorithms, and actuators according to dynamic control needs and monitor plasma and plant system events to trigger automatic changes in the control algorithms or operational scenario, depending on real-time operating limits and conditions.

Three-dimensional analysis of tubular permanent magnet machines

NASA Astrophysics Data System (ADS)

Chai, J.; Wang, J.; Howe, D.

2006-04-01

This paper presents results from a three-dimensional finite element analysis of a tubular permanent magnet machine, and quantifies the influence of the laminated modules from which the stator core is assembled on the flux linkage and thrust force capability as well as on the self- and mutual inductances. The three-dimensional finite element (FE) model accounts for the nonlinear, anisotropic magnetization characteristic of the laminated stator structure, and for the voids which exist between the laminated modules. Predicted results are compared with those deduced from an axisymmetric FE model. It is shown that the emf and thrust force deduced from the three-dimensional model are significantly lower than those which are predicted from an axisymmetric field analysis, primarily as a consequence of the teeth and yoke being more highly saturated due to the presence of the voids in the laminated stator core.

Introducing the Filtered Park's and Filtered Extended Park's Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number

NASA Astrophysics Data System (ADS)

Gyftakis, Konstantinos N.; Marques Cardoso, Antonio J.; Antonino-Daviu, Jose A.

2017-09-01

The Park's Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park's Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park's/Extended Park's Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable.

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

ERIC Educational Resources Information Center

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

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

Experiments on PIM in Support of the Development of IVA Technology for Radiography at AWE

NASA Astrophysics Data System (ADS)

Clough, Stephen G.; Thomas, Kenneth J.; Williamson, Mark C.; Phillips, Martin J.; Smith, Ian D.; Bailey, Vernon L.; Kishi, Hiroshi J.; Maenchen, John E.; Johnson, David L.

2002-12-01

The PIM machine has been designed and constructed at AWE as part of a program to investigate IVA technology for radiographic applications. PIM, as originally constructed, was a prospective single module of a 14 MV, 100 kA, ten module machine. The design of such a machine is a primary goal of the program as several are required to provide multi-axis radiography in a new Hydrodynamics Research Facility (HRF). Another goal is to design lower voltage machines (ranging from 1 to 5 MV) utilizing PIM style components. The original PIM machine consisted of a single inductive cavity pulsed by a 10 ohm water dielectric Blumlein pulse forming line (PFL) charged by a Marx generator. These components successfully achieved their design voltages and data on the prepulse was obtained showing it to be worse than expected. This information provided a basis for design work on the 14 MV HRF IVA, carried out by Titan-PSD, resulting in a proposal for a prepulse switch, a prototype of which should be installed on PIM by the end of this year. The original single, coaxial switch used to initiate the Blumlein has been replaced by a prototype laser triggered switching arrangement, also designed by Titan-PSD, which it was desired to test prior to its eventual use in the HRF. Despite problems with the laser, which will necessitate further experiments, it was determined that laser triggering with low jitter was occurring. A split oil co-ax feed has now been used to install a second cavity, in parallel with the first, on the PIM Blumlein. This two cavity configuration provides a prototype for future radiographic machines operating at up to 3 MV and a test facility for diode research.

The research on construction and application of machining process knowledge base

NASA Astrophysics Data System (ADS)

Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

2018-03-01

In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

A simple ion implanter for material modifications in agriculture and gemmology

NASA Astrophysics Data System (ADS)

Singkarat, S.; Wijaikhum, A.; Suwannakachorn, D.; Tippawan, U.; Intarasiri, S.; Bootkul, D.; Phanchaisri, B.; Techarung, J.; Rhodes, M. W.; Suwankosum, R.; Rattanarin, S.; Yu, L. D.

2015-12-01

In our efforts in developing ion beam technology for novel applications in biology and gemmology, an economic simple compact ion implanter especially for the purpose was constructed. The designing of the machine was aimed at providing our users with a simple, economic, user friendly, convenient and easily operateable ion implanter for ion implantation of biological living materials and gemstones for biotechnological applications and modification of gemstones, which would eventually contribute to the national agriculture, biomedicine and gem-industry developments. The machine was in a vertical setup so that the samples could be placed horizontally and even without fixing; in a non-mass-analyzing ion implanter style using mixed molecular and atomic nitrogen (N) ions so that material modifications could be more effective; equipped with a focusing/defocusing lens and an X-Y beam scanner so that a broad beam could be possible; and also equipped with a relatively small target chamber so that living biological samples could survive from the vacuum period during ion implantation. To save equipment materials and costs, most of the components of the machine were taken from decommissioned ion beam facilities. The maximum accelerating voltage of the accelerator was 100 kV, ideally necessary for crop mutation induction and gem modification by ion beams from our experience. N-ion implantation of local rice seeds and cut gemstones was carried out. Various phenotype changes of grown rice from the ion-implanted seeds and improvements in gemmological quality of the ion-bombarded gemstones were observed. The success in development of such a low-cost and simple-structured ion implanter provides developing countries with a model of utilizing our limited resources to develop novel accelerator-based technologies and applications.

Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

A Collaborative Knowledge Plane for Autonomic Networks

NASA Astrophysics Data System (ADS)

Mbaye, Maïssa; Krief, Francine

Autonomic networking aims to give network components self-managing capabilities. Several autonomic architectures have been proposed. Each of these architectures includes sort of a knowledge plane which is very important to mimic an autonomic behavior. Knowledge plane has a central role for self-functions by providing suitable knowledge to equipment and needs to learn new strategies for more accuracy.However, defining knowledge plane's architecture is still a challenge for researchers. Specially, defining the way cognitive supports interact each other in knowledge plane and implementing them. Decision making process depends on these interactions between reasoning and learning parts of knowledge plane. In this paper we propose a knowledge plane's architecture based on machine learning (inductive logic programming) paradigm and situated view to deal with distributed environment. This architecture is focused on two self-functions that include all other self-functions: self-adaptation and self-organization. Study cases are given and implemented.

Optimal model of PDIG based microgrid and design of complementary stabilizer using ICA.

PubMed

Amini, R Mohammad; Safari, A; Ravadanegh, S Najafi

2016-09-01

The generalized Heffron-Phillips model (GHPM) for a microgrid containing a photovoltaic (PV)-diesel machine (DM)-induction motor (IM)-governor (GV) (PDIG) has been developed at the low voltage level. A GHPM is calculated by linearization method about a loading condition. An effective Maximum Power Point Tracking (MPPT) approach for PV network has been done using sliding mode control (SMC) to maximize output power. Additionally, to improve stability of microgrid for more penetration of renewable energy resources with nonlinear load, a complementary stabilizer has been presented. Imperialist competitive algorithm (ICA) is utilized to design of gains for the complementary stabilizer with the multiobjective function. The stability analysis of the PDIG system has been completed with eigenvalues analysis and nonlinear simulations. Robustness and validity of the proposed controllers on damping of electromechanical modes examine through time domain simulation under input mechanical torque disturbances. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Induced sensorimotor brain plasticity controls pain in phantom limb patients

PubMed Central

Yanagisawa, Takufumi; Fukuma, Ryohei; Seymour, Ben; Hosomi, Koichi; Kishima, Haruhiko; Shimizu, Takeshi; Yokoi, Hiroshi; Hirata, Masayuki; Yoshimine, Toshiki; Kamitani, Yukiyasu; Saitoh, Youichi

2016-01-01

The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback. PMID:27807349

Life-assessment technique for nuclear power plant cables

NASA Astrophysics Data System (ADS)

Bartoníček, B.; Hnát, V.; Plaček, V.

1998-06-01

The condition of polymer-based cable material can be best characterized by measuring elongation at break of its insulating materials. However, it is not often possible to take sufficiently large samples for measurement with the tensile testing machine. The problem has been conveniently solved by utilizing differential scanning calorimetry technique. From the tested cable, several microsamples are taken and the oxidation induction time (OIT) is determined. For each cable which is subject to the assessment of the lifetime, the correlation of OIT with elongation at break and the correlation of elongation at break with the cable service time has to be performed. A reliable assessment of the cable lifetime depends on accuracy of these correlations. Consequently, synergistic effects well known at this time - dose rate effects and effects resulting from the different sequence of applying radiation and elevated temperature must be taken into account.

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.

PubMed

Nassif, Houssam; Wu, Yirong; Page, David; Burnside, Elizabeth

2012-01-01

Overdiagnosis is a phenomenon in which screening identities cancer which may not go on to cause symptoms or death. Women over 65 who develop breast cancer bear the heaviest burden of overdiagnosis. This work introduces novel machine learning algorithms to improve diagnostic accuracy of breast cancer in aging populations. At the same time, we aim at minimizing unnecessary invasive procedures (thus decreasing false positives) and concomitantly addressing overdiagnosis. We develop a novel algorithm. Logical Differential Prediction Bayes Net (LDP-BN), that calculates the risk of breast disease based on mammography findings. LDP-BN uses Inductive Logic Programming (ILP) to learn relational rules, selects older-specific differentially predictive rules, and incorporates them into a Bayes Net, significantly improving its performance. In addition, LDP-BN offers valuable insight into the classification process, revealing novel older-specific rules that link mass presence to invasive, and calcification presence and lack of detectable mass to DCIS.

Iron-carbon compacts and process for making them

DOEpatents

Sheinberg, Haskell

2000-01-01

The present invention includes iron-carbon compacts and a process for making them. The process includes preparing a slurry comprising iron powder, furfuryl alcohol, and a polymerization catalyst for initiating the polymerization of the furfuryl alcohol into a resin, and heating the slurry to convert the alcohol into the resin. The resulting mixture is pressed into a green body and heated to form the iron-carbon compact. The compact can be used as, or machined into, a magnetic flux concentrator for an induction heating apparatus.

Utilization of low temperatures in electrical machines

NASA Astrophysics Data System (ADS)

Kwasniewska-Jankowicz, L.; Mirski, Z.

1983-09-01

The dimensions of conventional and superconducting direct and alternating current generators are compared and the advantages of using superconducting magnets are examined. The critical temperature, critical current, and critical magnetic field intensity of superconductors in an induction winding are discussed as well as the mechanical properties needed for bending connectors at small radii. Investigations of cryogenic cooling, cryostats, thermal insulation and rotary seals are reported as well as results of studies of the mechanical properties of austenitic Cr-Ni steels, welded joints and plastics for insulation.

Learning To Recognize Visual Concepts: Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning

DTIC Science & Technology

1993-01-01

Maria and My Parents, Helena and Andrzej IV ACKNOWLEDGMENTS I would like to first of all thank my advisor. Dr. Ryszard Michalski. who introduced...represent the current state of the art in machine learning methodology. The most popular method. the minimization of Bayes risk [ Duda and Hart. 1973]. is a...34 Pattern Recognition, Vol. 23, no. 3-4, pp. 291-309, 1990. Duda , O. and P. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons. 1973

Magnetic Induction Machines Integrated into Bulk-Micromachined Silicon

DTIC Science & Technology

2006-04-01

Actuator Workshop (Hilton Head 2000), pp. 43–7, Jun. 2000. [5] H. Guckel et al., “A first functional current excited planar rotational magnetic micromotor ...in Proc. IEEE Micro Electro Mechanical Sys- tems (MEMS’93), Feb. 1993, pp. 7–11. [6] , “Planar rotational magnetic micromotors ,” Int. J. Appl... micromotor with fully integrated stator and coils,” J. Micro- electromech. Syst., vol. 2, no. 4, pp. 165–73, Dec. 1993. [8] B. Wagner, M. Kreutzer, and W

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

Dobson, Ian; Hiskens, Ian; Linderoth, Jeffrey

Building on models of electrical power systems, and on powerful mathematical techniques including optimization, model predictive control, and simluation, this project investigated important issues related to the stable operation of power grids. A topic of particular focus was cascading failures of the power grid: simulation, quantification, mitigation, and control. We also analyzed the vulnerability of networks to component failures, and the design of networks that are responsive to and robust to such failures. Numerous other related topics were investigated, including energy hubs and cascading stall of induction machines

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

Zhang, Y.; Bank, J.; Wan, Y. H.

The total inertia stored in all rotating masses that are connected to power systems, such as synchronous generations and induction motors, is an essential force that keeps the system stable after disturbances. To ensure bulk power system stability, there is a need to estimate the equivalent inertia available from a renewable generation plant. An equivalent inertia constant analogous to that of conventional rotating machines can be used to provide a readily understandable metric. This paper explores a method that utilizes synchrophasor measurements to estimate the equivalent inertia that a wind plant provides to the system.

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

PubMed

Bagheri, Hossein; Hooshmand, Tabassom; Aghajani, Farzaneh

2015-09-01

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

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

PubMed Central

Bagheri, Hossein; Aghajani, Farzaneh

2015-01-01

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

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Automated discovery systems and the inductivist controversy

NASA Astrophysics Data System (ADS)

Giza, Piotr

2017-09-01

The paper explores possible influences that some developments in the field of branches of AI, called automated discovery and machine learning systems, might have upon some aspects of the old debate between Francis Bacon's inductivism and Karl Popper's falsificationism. Donald Gillies facetiously calls this controversy 'the duel of two English knights', and claims, after some analysis of historical cases of discovery, that Baconian induction had been used in science very rarely, or not at all, although he argues that the situation has changed with the advent of machine learning systems. (Some clarification of terms machine learning and automated discovery is required here. The key idea of machine learning is that, given data with associated outcomes, software can be trained to make those associations in future cases which typically amounts to inducing some rules from individual cases classified by the experts. Automated discovery (also called machine discovery) deals with uncovering new knowledge that is valuable for human beings, and its key idea is that discovery is like other intellectual tasks and that the general idea of heuristic search in problem spaces applies also to discovery tasks. However, since machine learning systems discover (very low-level) regularities in data, throughout this paper I use the generic term automated discovery for both kinds of systems. I will elaborate on this later on). Gillies's line of argument can be generalised: thanks to automated discovery systems, philosophers of science have at their disposal a new tool for empirically testing their philosophical hypotheses. Accordingly, in the paper, I will address the question, which of the two philosophical conceptions of scientific method is better vindicated in view of the successes and failures of systems developed within three major research programmes in the field: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group and the programme called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett and P. Thagard.

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

PubMed

Nakai, Yasushi; Takiguchi, Tetsuya; Matsui, Gakuyo; Yamaoka, Noriko; Takada, Satoshi

2017-10-01

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.

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

Electromechanical systems with transient high power response operating from a resonant ac link

NASA Technical Reports Server (NTRS)

Burrows, Linda M.; Hansen, Irving G.

1992-01-01

The combination of an inherently robust asynchronous (induction) electrical machine with the rapid control of energy provided by a high frequency resonant ac link enables the efficient management of higher power levels with greater versatility. This could have a variety of applications from launch vehicles to all-electric automobiles. These types of systems utilize a machine which is operated by independent control of both the voltage and frequency. This is made possible by using an indirect field-oriented control method which allows instantaneous torque control all four operating quadrants. Incorporating the ac link allows the converter in these systems to switch at the zero crossing of every half cycle of the ac waveform. This zero loss switching of the link allows rapid energy variations to be achieved without the usual frequency proportional switching loss. Several field-oriented control systems were developed under contract to NASA.

Pursuing optimal electric machines transient diagnosis: The adaptive slope transform

NASA Astrophysics Data System (ADS)

Pons-Llinares, Joan; Riera-Guasp, Martín; Antonino-Daviu, Jose A.; Habetler, Thomas G.

2016-12-01

The aim of this paper is to introduce a new linear time-frequency transform to improve the detection of fault components in electric machines transient currents. Linear transforms are analysed from the perspective of the atoms used. A criterion to select the atoms at every point of the time-frequency plane is proposed, taking into account the characteristics of the searched component at each point. This criterion leads to the definition of the Adaptive Slope Transform, which enables a complete and optimal capture of the different components evolutions in a transient current. A comparison with conventional linear transforms (Short-Time Fourier Transform and Wavelet Transform) is carried out, showing their inherent limitations. The approach is tested with laboratory and field motors, and the Lower Sideband Harmonic is captured for the first time during an induction motor startup and subsequent load oscillations, accurately tracking its evolution.

Learning Machine, Vietnamese Based Human-Computer Interface.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

The sixth session of IT@EDU98 consisted of seven papers on the topic of the learning machine--Vietnamese based human-computer interface, and was chaired by Phan Viet Hoang (Informatics College, Singapore). "Knowledge Based Approach for English Vietnamese Machine Translation" (Hoang Kiem, Dinh Dien) presents the knowledge base approach,…

Machine characterization based on an abstract high-level language machine

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

A Comparison of Inductive Sensors in the Characterization of Partial Discharges and Electrical Noise Using the Chromatic Technique.

PubMed

Ardila-Rey, Jorge Alfredo; Montaña, Johny; de Castro, Bruno Albuquerque; Schurch, Roger; Covolan Ulson, José Alfredo; Muhammad-Sukki, Firdaus; Bani, Nurul Aini

2018-03-29

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals.

High-frequency rotational losses in different soft magnetic composites

NASA Astrophysics Data System (ADS)

de la Barrière, O.; Appino, C.; Ragusa, C.; Fiorillo, F.; Mazaleyrat, F.; LoBue, M.

2014-05-01

The isotropic properties of Soft Magnetic Composites (SMC) favor the design of new machine topologies and their granular structure can induce a potential decrease of the dynamic loss component. This paper is devoted to the characterization of the broadband magnetic losses of different SMC types under alternating and circular induction. The investigated materials differ by their grain size, heat treatment, compaction rate, and binder type. It is shown that, up to peak polarization Jp = 1.25 T, the ratios between the rotational and the alternating loss components (classical, hysteresis, and excess) are quite independent of the material structural details, quite analogous to the known behavior of nonoriented steel laminations. On the contrary, at higher inductions, it is observed that the Jp value at which the rotational hysteresis loss attains its maximum, related to the progressive disappearance of the domain walls under increasing rotational fields, decreases with the material susceptibility.

Rail Brake System Using a Linear Induction Motor for Dynamic Braking

NASA Astrophysics Data System (ADS)

Sakamoto, Yasuaki; Kashiwagi, Takayuki; Tanaka, Minoru; Hasegawa, Hitoshi; Sasakawa, Takashi; Fujii, Nobuo

One type of braking system for railway vehicles is the eddy current brake. Because this type of brake has the problem of rail heating, it has not been used for practical applications in Japan. Therefore, we proposed the use of a linear induction motor (LIM) for dynamic braking in eddy current brake systems. The LIM reduces rail heating and uses an inverter for self excitation. In this paper, we estimated the performance of an LIM from experimental results of a fundamental test machine and confirmed that the LIM generates an approximately constant braking force under constant current excitation. At relatively low frequencies, this braking force remains unaffected by frequency changes. The reduction ratio of rail heating is also approximately proportional to the frequency. We also confirmed that dynamic braking resulting in no electrical output can be used for drive control of the LIM. These characteristics are convenient for the realization of the LIM rail brake system.

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis.

PubMed

Talhaoui, Hicham; Menacer, Arezki; Kessal, Abdelhalim; Kechida, Ridha

2014-09-01

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of rolling contact fatigue of induction heated 13Cr-2Ni-2Mo Stainless steel bar with Si3N4-ball

NASA Astrophysics Data System (ADS)

Yadoiwa, Ariyasu; Mizobe, Koshiro; Kida, Katsuyuki

2018-03-01

13Cr % martensitic stainless steels were used in various industry, because they have excellent corrosion resistance and high hardness among other stainless steels. They are also expected as a bearing material, however, the research on rolling contact fatigue (RCF) is not enough. In this study, 13Cr-2Ni-2Mo stainless steels were quenched by induction heating and their RCF lives were evaluated. A Si3N4-ball was used in order to apply higher stress (Pmax = 5.6 GPa) than our previous tests (Pmax=5.3 GPa), in a single-ball RCF testing machine. It was found that the basic life (L10) was 2.20×106 cycles and Median life (L50) was 6.04×106 cycles. In addition, Weibull modulus became higher than the previous tests.

Volumetric wireless coil based on periodically coupled split-loop resonators for clinical wrist imaging.

PubMed

Shchelokova, Alena V; van den Berg, Cornelis A T; Dobrykh, Dmitry A; Glybovski, Stanislav B; Zubkov, Mikhail A; Brui, Ekaterina A; Dmitriev, Dmitry S; Kozachenko, Alexander V; Efimtcev, Alexander Y; Sokolov, Andrey V; Fokin, Vladimir A; Melchakova, Irina V; Belov, Pavel A

2018-02-09

Design and characterization of a new inductively driven wireless coil (WLC) for wrist imaging at 1.5 T with high homogeneity operating due to focusing the B 1 field of a birdcage body coil. The WLC design has been proposed based on a volumetric self-resonant periodic structure of inductively coupled split-loop resonators with structural capacitance. The WLC was optimized and studied regarding radiofrequency fields and interaction to the birdcage coil (BC) by electromagnetic simulations. The manufactured WLC was characterized by on-bench measurements and in vivo and phantom study in comparison to a standard cable-connected receive-only coil. The WLC placed into BC gave the measured B1+ increase of the latter by 8.6 times for the same accepted power. The phantom and in vivo wrist imaging showed that the BC in receiving with the WLC inside reached equal or higher signal-to-noise ratio than the conventional clinical setup comprising the transmit-only BC and a commercial receive-only flex-coil and created no artifacts. Simulations and on-bench measurements proved safety in terms of specific absorption rate and reflected transmit power. The results showed that the proposed WLC could be an alternative to standard cable-connected receive coils in clinical magnetic resonance imaging. As an example, with no cable connection, the WLC allowed wrist imaging on a 1.5 T clinical machine using a full-body BC for transmitting and receive with the desired signal-to-noise ratio, image quality, and safety. © 2018 International Society for Magnetic Resonance in Medicine.

Influence of inductive heating on microstructure and material properties in roll forming processes

NASA Astrophysics Data System (ADS)

Guk, Anna; Kunke, Andreas; Kräusel, Verena; Landgrebe, Dirk

2017-10-01

The increasing demand for sheet metal parts and profiles with enhanced mechanical properties by using high and ultra-high-strength (UHS) steels for the automotive industry must be covered by increasing flexibility of tools and machines. This can be achieved by applying innovative technologies such as roll forming with integrated inductive heating. This process is similar to indirect press hardening and can be used for the production of hardened profiles and profiles with graded properties in longitudinal and traverse direction. The advantage is that the production of hardened components takes place in a continuous process and the integration of heating and quenching units in the profiling system increases flexibility, accompanied by shortening of the entire process chain and minimizing the springback risk. The features of the mentioned process consists of the combination of inhomogeneous strain distribution over the stripe width by roll forming and inhomogeneity of microstructure by accelerated inductive heating to austenitizing temperature. Therefore, these two features have a direct influence on the mechanical properties of the material during forming and hardening. The aim of this work is the investigation of the influence of heating rates on microstructure evolution and mechanical properties to determine the process window. The results showed that heating rate should be set at 110 K/s for economic integration of inductive heating into the roll forming process.

Closed Brayton Cycle (CBC) Power Generation from an Electric Systems Perspective

NASA Astrophysics Data System (ADS)

Halsey, David G.; Fox, David A.

2006-01-01

Several forms of closed cycle heat engines exist to produce electrical energy suitable for space exploration or planetary surface applications. These engines include Stirling and Closed Brayton Cycle (CBC). Of these two, CBC has often been cited as providing the best balance of mass and efficiency for deep space or planetary power systems. Combined with an alternator on the same shaft, the hermetically sealed system provides the potential for long life and reliable operation. There is also a list of choices for the type of alternator. Choices include wound rotor machines, induction machines, switched reluctance machines, and permanent magnet generators (PMGs). In trades involving size, mass and efficiency the PMG is a favorable solution. This paper will discuss the consequences of using a CBC-PMG source for an electrical power system, and the system parameters that must be defined and controlled to provide a stable, useful power source. Considerations of voltage, frequency (including DC), and power quality will be discussed. Load interactions and constraints for various power types will also be addressed. Control of the CBC-PMG system during steady state operation and startup is also a factor.s

Summary Report of H- Injection Session II

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

Weiren Chou

1999-06-28

The H - injection was invented many years ago and has since been successfully applied in many machines over the last decades. The challenge to the high intensity machines is how to reduce the injection loss, which is usually the major part of total beam losses in a machine. Painting, both longitudinal and transverse, is an effective way to reduce the space charge e ects and to minimize losses. RF capture of a chopped beam also gives better e ciency than adiabatic capture. To employ a 2nd harmonic rf system to atten the rf bucket shape is another commonly usedmore » scheme. To compensate the capacitive space charge impedance by an inductive insert could be a new venture, but which is not discussed at the workshop due to time limitation. The foil physics is well understood. Simulations seem to be able to include all the important e ects in it, including the space charge. The general feeling is that we are in a good position concerning H - injection studies. Although there remains a number of design issues, the knowledge, experiences and tools in our hand should be able to address each of them properly.« less

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

PubMed Central

2011-01-01

Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). Results We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. Conclusions The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions. PMID:21429187

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

PubMed

Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

2011-03-23

Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility

PubMed Central

Gui, Jiang; Andrew, Angeline S.; Andrews, Peter; Nelson, Heather M.; Kelsey, Karl T.; Karagas, Margaret R.; Moore, Jason H.

2010-01-01

A central goal of human genetics is to identify and characterize susceptibility genes for common complex human diseases. An important challenge in this endeavor is the modeling of gene-gene interaction or epistasis that can result in non-additivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as machine learning alternative to parametric logistic regression for detecting interactions in absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modeling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher’s Exact Test rather than a predetermined threshold. The advantage of this approach is that only those genotype combinations that are determined to be statistically significant are considered in the MDR analysis. We use two simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire. PMID:21091664

Ion behaviour in pulsed plasma regime by means of Time-resolved energy mass spectroscopy (TREMS) applied to an industrial radiofrequency Plasma Immersion Ion Implanter PULSION registered

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

Carrere, M.; Kaeppelin, V.; Torregrosa, F.

2006-11-13

In order to face the requirements for P+/N junctions requested for < 45 nm ITRS nodes, new doping techniques are studied. Among them Plasma Immersion Ion Implantation (PIII) has been largely studied. IBS has designed and developed its own PIII machine named PULSION registered . This machine is using a pulsed plasma. As other modem technological applications of low pressure plasma, PULSION registered needs a precise control over plasma parameters in order to optimise process characteristics. In order to improve pulsed plasma discharge devoted to PIII, a nitrogen pulsed plasma has been studied in the inductively coupled plasma (ICP) ofmore » PULSION registered and an argon pulsed plasma has been studied in the helicon discharge of the laboratory reactor of LPIIM (PHYSIS). Measurements of the Ion Energy Distribution Function (IEDF) with EQP300 (Hidden) have been performed in both pulsed plasma. This study has been done for different energies which allow to reconstruct the IEDF resolved in time (TREMS). By comparing these results, we found that the beginning of the plasma pulse, named ignition, exhaust at least three phases, or more. All these results allowed us to explain plasma dynamics during the pulse while observing transitions between capacitive and inductive coupling. This study leads in a better understanding of changes in discharge parameters as plasma potential, electron temperature, ion density.« less

Tuning the Magnetic Transport of an Induction LINAC using Emittance

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

Houck, T L; Brown, C G; Ong, M M

2006-08-11

The Lawrence Livermore National Laboratory Flash X-Ray (FXR) machine is a linear induction accelerator used to produce a nominal 18 MeV, 3 kA, 65 ns pulse width electron beam for hydrodynamic radiographs. A common figure of merit for this type of radiographic machine is the x-ray dose divided by the spot area on the bremsstrahlung converter where a higher FOM is desired. Several characteristics of the beam affect the minimum attainable x-ray spot size. The most significant are emittance (chaotic transverse energy), chromatic aberration (energy variation), and beam motion (transverse instabilities and corkscrew motion). FXR is in the midst ofmore » a multi-year optimization project to reduce the spot size. This paper describes the effort to reduce beam emittance by adjusting the fields of the transport solenoids and position of the cathode. If the magnetic transport is not correct, the beam will be mismatched and undergo envelope oscillations increasing the emittance. We measure the divergence and radius of the beam in a drift section after the accelerator by imaging the optical transition radiation (OTR) and beam envelope on a foil. These measurements are used to determine an emittance. Relative changes in the emittance can be quickly estimated from the foil measurements allowing for an efficient, real-time study. Once an optimized transport field is determined, the final focus can be adjusted and the new x-ray spot measured. A description of the diagnostics and analysis is presented.« less

A deviation based assessment methodology for multiple machine health patterns classification and fault detection

NASA Astrophysics Data System (ADS)

Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

2018-01-01

Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

Ontological modelling of knowledge management for human-machine integrated design of ultra-precision grinding machine

NASA Astrophysics Data System (ADS)

Hong, Haibo; Yin, Yuehong; Chen, Xing

2016-11-01

Despite the rapid development of computer science and information technology, an efficient human-machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

A Senior Project-Based Multiphase Motor Drive System Development

ERIC Educational Resources Information Center

Abdel-Khalik, Ayman S.; Massoud, Ahmed M.; Ahmed, Shehab

2016-01-01

Adjustable-speed drives based on multiphase motors are of significant interest for safety-critical applications that necessitate wide fault-tolerant capabilities and high system reliability. Although multiphase machines are based on the same conceptual theory as three-phase machines, most undergraduate electrical machines and electric drives…

Conceptual Influences on Induction: A Case for a Late Onset

PubMed Central

Sloutsky, Vladimir M.; (Sophia) Deng, Wei; Fisher, Anna V.; Kloos, Heidi

2015-01-01

This research examines the mechanism of early induction, the development of induction, and the ways attentional and conceptual factors contribute to induction across development. Different theoretical views offer different answers to these questions. Six experiments with 4- and 5-year-olds, 7-year-olds and adults (N = 208) test these competing theories by teaching categories for which category membership and perceptual similarity are in conflict, and varying orthogonally conceptual and attentional factors that may potentially affect inductive inference. The results suggest that early induction is similarity-based; conceptual information plays a negligible role in early induction, but its role increases gradually, with the 7-year-olds being a transitional group. And finally, there is substantial contribution of attention to the development of induction: only adults, but not children, could perform category-based induction without attentional support. Therefore, category-based induction exhibits protracted development, with attentional factors contributing early in development and conceptual factors contributing later in development. These results are discussed in relation to existing theories of development of inductive inference and broader theoretical views on cognitive development. PMID:26350681

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

The Gamma-Ray Burst ToolSHED is Open for Business

NASA Astrophysics Data System (ADS)

Giblin, Timothy W.; Hakkila, Jon; Haglin, David J.; Roiger, Richard J.

2004-09-01

The GRB ToolSHED, a Gamma-Ray Burst SHell for Expeditions in Data-Mining, is now online and available via a web browser to all in the scientific community. The ToolSHED is an online web utility that contains pre-processed burst attributes of the BATSE catalog and a suite of induction-based machine learning and statistical tools for classification and cluster analysis. Users create their own login account and study burst properties within user-defined multi-dimensional parameter spaces. Although new GRB attributes are periodically added to the database for user selection, the ToolSHED has a feature that allows users to upload their own burst attributes (e.g. spectral parameters, etc.) so that additional parameter spaces can be explored. A data visualization feature using GNUplot and web-based IDL has also been implemented to provide interactive plotting of user-selected session output. In an era in which GRB observations and attributes are becoming increasingly more complex, a utility such as the GRB ToolSHED may play an important role in deciphering GRB classes and understanding intrinsic burst properties.

A system framework of inter-enterprise machining quality control based on fractal theory

NASA Astrophysics Data System (ADS)

Zhao, Liping; Qin, Yongtao; Yao, Yiyong; Yan, Peng

2014-03-01

In order to meet the quality control requirement of dynamic and complicated product machining processes among enterprises, a system framework of inter-enterprise machining quality control based on fractal was proposed. In this system framework, the fractal-specific characteristic of inter-enterprise machining quality control function was analysed, and the model of inter-enterprise machining quality control was constructed by the nature of fractal structures. Furthermore, the goal-driven strategy of inter-enterprise quality control and the dynamic organisation strategy of inter-enterprise quality improvement were constructed by the characteristic analysis on this model. In addition, the architecture of inter-enterprise machining quality control based on fractal was established by means of Web service. Finally, a case study for application was presented. The result showed that the proposed method was available, and could provide guidance for quality control and support for product reliability in inter-enterprise machining processes.

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

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

Li Shouju; Kang Chengang; Sun, Wei

2010-05-21

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

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

NASA Astrophysics Data System (ADS)

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

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

Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

NASA Astrophysics Data System (ADS)

Cheng, Kai; Niu, Zhi-Chao; Wang, Robin C.; Rakowski, Richard; Bateman, Richard

2017-09-01

Smart machining has tremendous potential and is becoming one of new generation high value precision manufacturing technologies in line with the advance of Industry 4.0 concepts. This paper presents some innovative design concepts and, in particular, the development of four types of smart cutting tools, including a force-based smart cutting tool, a temperature-based internally-cooled cutting tool, a fast tool servo (FTS) and smart collets for ultraprecision and micro manufacturing purposes. Implementation and application perspectives of these smart cutting tools are explored and discussed particularly for smart machining against a number of industrial application requirements. They are contamination-free machining, machining of tool-wear-prone Si-based infra-red devices and medical applications, high speed micro milling and micro drilling, etc. Furthermore, implementation techniques are presented focusing on: (a) plug-and-produce design principle and the associated smart control algorithms, (b) piezoelectric film and surface acoustic wave transducers to measure cutting forces in process, (c) critical cutting temperature control in real-time machining, (d) in-process calibration through machining trials, (e) FE-based design and analysis of smart cutting tools, and (f) application exemplars on adaptive smart machining.

Cost of unreliability method to estimate loss of revenue based on unreliability data: Case study of Printing Company

NASA Astrophysics Data System (ADS)

alhilman, Judi

2017-12-01

In the production line process of the printing office, the reliability of the printing machine plays a very important role, if the machine fail it can disrupt production target so that the company will suffer huge financial loss. One method to calculate the financial loss cause by machine failure is use the Cost of Unreliability(COUR) method. COUR method works based on down time machine and costs associated with unreliability data. Based on the calculation of COUR method, so the sum of cost due to unreliability printing machine during active repair time and downtime is 1003,747.00.

Lyapunov exponent for aging process in induction motor

NASA Astrophysics Data System (ADS)

Bayram, Duygu; Ünnü, Sezen Yıdırım; Şeker, Serhat

2012-09-01

Nonlinear systems like electrical circuits and systems, mechanics, optics and even incidents in nature may pass through various bifurcations and steady states like equilibrium point, periodic, quasi-periodic, chaotic states. Although chaotic phenomena are widely observed in physical systems, it can not be predicted because of the nature of the system. On the other hand, it is known that, chaos is strictly dependent on initial conditions of the system [1-3]. There are several methods in order to define the chaos. Phase portraits, Poincaré maps, Lyapunov Exponents are the most common techniques. Lyapunov Exponents are the theoretical indicator of the chaos, named after the Russian mathematician Aleksandr Lyapunov (1857-1918). Lyapunov Exponents stand for the average exponential divergence or convergence of nearby system states, meaning estimating the quantitive measure of the chaotic attractor. Negative numbers of the exponents stand for a stable system whereas zero stands for quasi-periodic systems. On the other hand, at least if one of the exponents is positive, this situation is an indicator of the chaos. For estimating the exponents, the system should be modeled by differential equation but even in that case mathematical calculation of Lyapunov Exponents are not very practical and evaluation of these values requires a long signal duration [4-7]. For experimental data sets, it is not always possible to acquire the differential equations. There are several different methods in literature for determining the Lyapunov Exponents of the system [4, 5]. Induction motors are the most important tools for many industrial processes because they are cheap, robust, efficient and reliable. In order to have healthy processes in industrial applications, the conditions of the machines should be monitored and the different working conditions should be addressed correctly. To the best of our knowledge, researches related to Lyapunov exponents and electrical motors are mostly focused on the controlling the mechanical parameters of the electrical machines. Brushless DC motor (BLDCM) and the other general purpose permanent magnet (PM) motors are the most widely examined motors [1, 8, 9]. But the researches, about Lyapunov Exponent, subjected to the induction motors are mostly focused on the control theory of the motors. Flux estimation of rotor, external load disturbances and speed tracking and vector control position system are the main research areas for induction motors [10, 11, 12-14]. For all the data sets which can be collected from an induction motor, vibration data have the key role for understanding the mechanical behaviours like aging, bearing damage and stator insulation damage [15-18]. In this paper aging of an induction motor is investigated by using the vibration signals. The signals consist of new and aged motor data. These data are examined by their 2 dimensional phase portraits and the geometric interpretation is applied for detecting the Lyapunov Exponents. These values are compared in order to define the character and state estimation of the aging processes.

Electrical Motor Current Signal Analysis using a Modulation Signal Bispectrum for the Fault Diagnosis of a Gearbox Downstream

NASA Astrophysics Data System (ADS)

Haram, M.; Wang, T.; Gu, F.; Ball, A. D.

2012-05-01

Motor current signal analysis has been an effective way for many years of monitoring electrical machines themselves. However, little work has been carried out in using this technique for monitoring their downstream equipment because of difficulties in extracting small fault components in the measured current signals. This paper investigates the characteristics of electrical current signals for monitoring the faults from a downstream gearbox using a modulation signal bispectrum (MSB), including phase effects in extracting small modulating components in a noisy measurement. An analytical study is firstly performed to understand amplitude, frequency and phase characteristics of current signals due to faults. It then explores the performance of MSB analysis in detecting weak modulating components in current signals. Experimental study based on a 10kw two stage gearbox, driven by a three phase induction motor, shows that MSB peaks at different rotational frequencies can be based to quantify the severity of gear tooth breakage and the degrees of shaft misalignment. In addition, the type and location of a fault can be recognized based on the frequency at which the change of MSB peak is the highest among different frequencies.

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

PubMed

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

2017-06-19

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

Complete analytical solution of electromagnetic field problem of high-speed spinning ball

NASA Astrophysics Data System (ADS)

Reichert, T.; Nussbaumer, T.; Kolar, J. W.

2012-11-01

In this article, a small sphere spinning in a rotating magnetic field is analyzed in terms of the resulting magnetic flux density distribution and the current density distribution inside the ball. From these densities, the motor torque and the eddy current losses can be calculated. An analytical model is derived, and its results are compared to a 3D finite element analysis. The model gives insight into the torque and loss characteristics of a solid rotor induction machine setup, which aims at rotating the sphere beyond 25 Mrpm.

Superconducting Electric Machine with Permanent Magnets and Bulk HTS Elements

NASA Astrophysics Data System (ADS)

Levin, A. V.; Vasich, P. S.; Dezhin, D. S.; Kovalev, L. K.; Kovalev, K. L.; Poltavets, V. N.; Penkin, V. T.

Theoretical methods of calculating of two-dimensional magnetic fields, inductive parameters and output characteristics of the new type of high-temperature superconducting (HTS) synchronous motors with a composite rotor are presented. The composite rotor has the structure containing HTS flat elements, permanent magnets and ferromagnetic materials. The developed calculation model takes into account the concentrations and physical properties of these rotor elements. The simulation results of experimental HTS motor with a composite rotor are presented. The application of new type of HTS motor in different constructions of industrial high dynamic drivers is discussed.

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

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.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

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

Simulation and Community-Based Instruction of Vending Machines with Time Delay.

ERIC Educational Resources Information Center

Browder, Diane M.; And Others

1988-01-01

The study evaluated the use of simulated instruction on vending machine use as an adjunct to community-based instruction with two moderately retarded children. Results showed concurrent acquisition of the vending machine skills across trained and untrained sites. (Author/DB)

Alumina additions may improve the damage tolerance of soft machined zirconia-based ceramics.

PubMed

Oilo, Marit; Tvinnereim, Helene M; Gjerdet, Nils Roar

2011-01-01

The aim of this study was to evaluate the damage tolerance of different zirconia-based materials. Bars of one hard machined and one soft machined dental zirconia and an experimental 95% zirconia 5% alumina ceramic were subjected to 100,000 stress cycles (n = 10), indented to provoke cracks on the tensile stress side (n = 10), and left untreated as controls (n = 10). The experimental material demonstrated a higher relative damage tolerance, with a 40% reduction compared to 68% for the hard machined zirconia and 84% for the soft machined zirconia.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Improving the reliability of inverter-based welding machines

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

Schiedermayer, M.

1997-02-01

Although inverter-based welding power sources have been available since the late 1980s, many people hesitated to purchase them because of reliability issues. Unfortunately, their hesitancy had a basis, until now. Recent improvements give some inverters a reliability level that approaches that of traditional, transformer-based industrial welding machines, which have a failure rate of about 1%. Acceptance of inverter-based welding machines is important because, for many welding applications, they provide capabilities that solid-state, transformer-based machines cannot deliver. These advantages include enhanced pulsed gas metal arc welding (GMAW-P), lightweight portability, an ultrastable arc, and energy efficiency--all while producing highly aesthetic weld beadsmore » and delivering multiprocess capabilities.« less

Emerging Areas of Inquiry: Special Education Teacher Induction. Induction Insights. Supporting Special Education Teachers - Teacher Educators [TEII-8

ERIC Educational Resources Information Center

National Center to Inform Policy and Practice in Special Education Professional Development, 2010

2010-01-01

General education induction has received substantial attention from policymakers, researchers, and school district practitioners. Yet, the literature base has been described as fragmented, with methodological problems that often make it difficult to draw clear implications. The special education induction literature base is even less developed.…

Evolving rule-based systems in two medical domains using genetic programming.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

2004-11-01

To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

Development of Category-based Induction and Semantic Knowledge

ERIC Educational Resources Information Center

Fisher, Anna V.; Godwin, Karrie E.; Matlen, Bryan J.; Unger, Layla

2015-01-01

Category-based induction is a hallmark of mature cognition; however, little is known about its origins. This study evaluated the hypothesis that category-based induction is related to semantic development. Computational studies suggest that early on there is little differentiation among concepts, but learning and development lead to increased…

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

PubMed Central

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

2017-01-01

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

Multi-Cultural Competency-Based Vocational Curricula. Machine Trades. Multi-Cultural Competency-Based Vocational/Technical Curricula Series.

ERIC Educational Resources Information Center

Hepburn, Larry; Shin, Masako

This document, one of eight in a multi-cultural competency-based vocational/technical curricula series, is on machine trades. This program is designed to run 36 weeks and cover 6 instructional areas: use of measuring tools; benchwork/tool bit grinding; lathe work; milling work; precision grinding; and combination machine work. A duty-task index…

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

A novel logic-based approach for quantitative toxicology prediction.

PubMed

Amini, Ata; Muggleton, Stephen H; Lodhi, Huma; Sternberg, Michael J E

2007-01-01

There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitative ILP-based SAR to quantitative modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.

Knowledge-based load leveling and task allocation in human-machine systems

NASA Technical Reports Server (NTRS)

Chignell, M. H.; Hancock, P. A.

1986-01-01

Conventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.

Fast mental states decoding in mixed reality.

PubMed

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F M J; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.

Fast mental states decoding in mixed reality

PubMed Central

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F. M. J.; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR. PMID:25505878

Design Considerations of a Transverse Flux Machine for Direct-Drive Wind Turbine Applications

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

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

This paper presents the design considerations of a double-sided transverse flux machine (TFM) for direct-drive wind turbine applications. The proposed TFM has a modular structure with quasi-U stator cores and toroidal ring windings. The rotor is constructed with ferrite magnets in a flux-concentrating setup to achieve high air gap flux density. Pole number selection is critical in the design process of a TFM as it affects both the torque density and power factor under fixed magnetic and changing electrical loading. Several key design ratios are introduced to facilitate the initial design procedure. The effect of pole shaping on back-EMF andmore » inductance is also analyzed. These investigations provide guidance toward the required design of a TFM for direct-drive applications. The analyses are carried out using analytical and three-dimensional finite element analysis (FEA). A proof-of-concept prototype was developed to experimentally validate the FEA results.« less

Design Considerations of a Transverse Flux Machine for Direct-Drive Wind Turbine Applications

DOE PAGES

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz; ...

2018-03-12

This paper presents the design considerations of a double-sided transverse flux machine (TFM) for direct-drive wind turbine applications. The proposed TFM has a modular structure with quasi-U stator cores and toroidal ring windings. The rotor is constructed with ferrite magnets in a flux-concentrating setup to achieve high air gap flux density. Pole number selection is critical in the design process of a TFM as it affects both the torque density and power factor under fixed magnetic and changing electrical loading. Several key design ratios are introduced to facilitate the initial design procedure. The effect of pole shaping on back-EMF andmore » inductance is also analyzed. These investigations provide guidance toward the required design of a TFM for direct-drive applications. The analyses are carried out using analytical and three-dimensional finite element analysis (FEA). A proof-of-concept prototype was developed to experimentally validate the FEA results.« less

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

NASA Astrophysics Data System (ADS)

Pinar, Anthony; Masarik, Matthew; Havens, Timothy C.; Burns, Joseph; Thelen, Brian; Becker, John

2015-05-01

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

Collaborative filtering on a family of biological targets.

PubMed

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

Emotional and cognitive influences in air traffic controller tasks: An investigation using a virtual environment?

PubMed

Truschzinski, Martina; Betella, Alberto; Brunnett, Guido; Verschure, Paul F M J

2018-05-01

Air traffic controllers are required to perform complex tasks which require attention and high precision. This study investigates how the difficulty of such tasks influences emotional states, cognitive workload and task performance. We use quantitative and qualitative measurements, including the recording of pupil dilation and changes in affect using questionnaires. Participants were required to perform a number of air traffic control tasks using the immersive human accessible Virtual Reality space in the "eXperience Induction Machine". Based on the data collected, we developed and validated a model which integrates personality, workload and affective theories. Our results indicate that the difficulty of an air traffic control task has a direct influence on cognitive workload as well as on the self-reported mood; whereas both mood and workload seem to change independently. In addition, we show that personality, in particular neuroticism, affects both mood and performance of the participants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Active relearning for robust supervised classification of pulmonary emphysema

NASA Astrophysics Data System (ADS)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

Automatic analysis of medical dialogue in the home hemodialysis domain: structure induction and summarization.

PubMed

Lacson, Ronilda C; Barzilay, Regina; Long, William J

2006-10-01

Spoken medical dialogue is a valuable source of information for patients and caregivers. This work presents a first step towards automatic analysis and summarization of spoken medical dialogue. We first abstract a dialogue into a sequence of semantic categories using linguistic and contextual features integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). We then describe and implement a summarizer that utilizes this automatically induced structure. Our evaluation results indicate that automatically generated summaries exhibit high resemblance to summaries written by humans. In addition, task-based evaluation shows that physicians can reasonably answer questions related to patient care by looking at the automatically generated summaries alone, in contrast to the physicians' performance when they were given summaries from a naïve summarizer (p<0.05). This work demonstrates the feasibility of automatically structuring and summarizing spoken medical dialogue.

Grain-based versus alfalfa-based subacute ruminal acidosis induction experiments: Similarities and differences between changes in milk fatty acids.

PubMed

Colman, E; Khafipour, E; Vlaeminck, B; De Baets, B; Plaizier, J C; Fievez, V

2013-07-01

Subacute ruminal acidosis (SARA) is one of the most important metabolic disorders, traditionally characterized by low rumen pH, which might be induced by an increase in the dietary proportion of grains as well as by a reduction of structural fiber. Both approaches were used in earlier published experiments in which SARA was induced by replacing part of the ration by a grain mixture or alfalfa hay by alfalfa pellets. The main differences between both experiments were the presence of blood lipopolysaccharide and Escherichia coli and associated effects on the rumen microbial population in the rumen of grain-based induced SARA animals as well as a great amount of quickly fermentable carbohydrates in the grain-based SARA induction experiment. Both induction approaches changed rumen pH although the pH decrease was more substantial in the alfalfa-based SARA induction protocol. The goal of the current analysis was to assess whether both acidosis induction approaches provoked similar shifts in the milk fatty acid (FA) profile. Similar changes of the odd- and branched-chain FA and the C18 biohydrogenation intermediates were observed in the alfalfa-based SARA induction experiment and the grain-based SARA induction experiment, although they were more pronounced in the former. The proportion of trans-10 C18:1 in the last week of the alfalfa-based induction experiment was 6 times higher than the proportion measured during the control week. The main difference between both induction experiments under similar rumen pH changes was the decreasing sum of iso FA during the grain-based SARA induction experiment whereas the sum of iso FA remained stable during the alfalfa-based SARA induction experiment. The cellulolytic bacterial community seemed to be negatively affected by either the presence of E. coli and the associated lipopolysaccharide accumulation in the rumen or by the amount of starch and quickly fermentable carbohydrates in the diet. In general, changes in the milk FA profile were related to changes in rumen pH. Nevertheless, feed characteristics (low in structural fiber vs. high in starch) also affected the milk FA profile and, as such, both effects should be taken into account when subacute acidosis occurs. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

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

Challenges in Soft Computing: Case Study with Louisville MSD CSO Modeling

NASA Astrophysics Data System (ADS)

Ormsbee, L.; Tufail, M.

2005-12-01

The principal constituents of soft computing include fuzzy logic, neural computing, evolutionary computation, machine learning, and probabilistic reasoning. There are numerous applications of these constituents (both individually and combination of two or more) in the area of water resources and environmental systems. These range from development of data driven models to optimal control strategies to assist in more informed and intelligent decision making process. Availability of data is critical to such applications and having scarce data may lead to models that do not represent the response function over the entire domain. At the same time, too much data has a tendency to lead to over-constraining of the problem. This paper will describe the application of a subset of these soft computing techniques (neural computing and genetic algorithms) to the Beargrass Creek watershed in Louisville, Kentucky. The application include development of inductive models as substitutes for more complex process-based models to predict water quality of key constituents (such as dissolved oxygen) and use them in an optimization framework for optimal load reductions. Such a process will facilitate the development of total maximum daily loads for the impaired water bodies in the watershed. Some of the challenges faced in this application include 1) uncertainty in data sets, 2) model application, and 3) development of cause-and-effect relationships between water quality constituents and watershed parameters through use of inductive models. The paper will discuss these challenges and how they affect the desired goals of the project.

Investigations on the direct introduction of cigarette smoke for trace elements analysis by inductively coupled plasma mass spectrometry

NASA Astrophysics Data System (ADS)

Chang, Michael J.; Naworal, John D.; Walker, Kathleen; Connell, Chris T.

2003-11-01

Direct introduction of mainstream cigarette smoke into an inductively coupled plasma mass spectrometry (ICP-MS) has been investigated with respect to its feasibility for on-line analysis of trace elements. An automated apparatus was designed and built interfacing a smoking machine with an ICP-MS for smoke generation, collection, injection and analysis. Major and minor elements present in the particulate phase and the gas phase of mainstream cigarette smoke of 2R4F reference cigarettes have been qualitatively identified by examination of their full mass spectra. This method provides a rapid-screening analysis of the transfer of trace elements into mainstream smoke during cigarette combustion. A full suite of elements present in the whole cigarette smoke has been identified, including As, B, Ba, Br, Cd, Cl, Cs, Cu, Hg, I, K, Li, Mn, Na, Pb, Rb, Sb, Sn, Tl and Zn. Of these elements, the major portions of B, Ba, Cs, Cu, K, Li, Mn, Na, Pb, Rb, Sn, Tl and Zn are present in the particulate phase, whereas the major portion of Hg is present in the gas phase. As, Br, Cd, Cl, I and Sb exist in a distribution between the gas phase and the particulate phase. Depending on the element, the precision of measurement ranges from 5 to 25% in terms of relative standard deviation of peak height and peak area, based on the fourth puff of 2R4F mainstream cigarette smoke analyzed in five smoking replicates.

A survey of machine readable data bases

NASA Technical Reports Server (NTRS)

Matlock, P.

1981-01-01

Forty-two of the machine readable data bases available to the technologist and researcher in the natural sciences and engineering are described and compared with the data bases and date base services offered by NASA.

The dynamic analysis of drum roll lathe for machining of rollers

NASA Astrophysics Data System (ADS)

Qiao, Zheng; Wu, Dongxu; Wang, Bo; Li, Guo; Wang, Huiming; Ding, Fei

2014-08-01

An ultra-precision machine tool for machining of the roller has been designed and assembled, and due to the obvious impact which dynamic characteristic of machine tool has on the quality of microstructures on the roller surface, the dynamic characteristic of the existing machine tool is analyzed in this paper, so is the influence of circumstance that a large scale and slender roller is fixed in the machine on dynamic characteristic of the machine tool. At first, finite element model of the machine tool is built and simplified, and based on that, the paper carries on with the finite element mode analysis and gets the natural frequency and shaking type of four steps of the machine tool. According to the above model analysis results, the weak stiffness systems of machine tool can be further improved and the reasonable bandwidth of control system of the machine tool can be designed. In the end, considering the shock which is caused by Z axis as a result of fast positioning frequently to feeding system and cutting tool, transient analysis is conducted by means of ANSYS analysis in this paper. Based on the results of transient analysis, the vibration regularity of key components of machine tool and its impact on cutting process are explored respectively.

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study.

PubMed

A Santos, Jose C; Nassif, Houssam; Page, David; Muggleton, Stephen H; E Sternberg, Michael J

2012-07-11

There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

Prediction and Optimization of Phase Transformation Region After Spot Continual Induction Hardening Process Using Response Surface Method

NASA Astrophysics Data System (ADS)

Qin, Xunpeng; Gao, Kai; Zhu, Zhenhua; Chen, Xuliang; Wang, Zhou

2017-09-01

The spot continual induction hardening (SCIH) process, which is a modified induction hardening, can be assembled to a five-axis cooperating computer numerical control machine tool to strengthen more than one small area or relatively large area on complicated component surface. In this study, a response surface method was presented to optimize phase transformation region after the SCIH process. The effects of five process parameters including feed velocity, input power, gap, curvature and flow rate on temperature, microstructure, microhardness and phase transformation geometry were investigated. Central composition design, a second-order response surface design, was employed to systematically estimate the empirical models of temperature and phase transformation geometry. The analysis results indicated that feed velocity has a dominant effect on the uniformity of microstructure and microhardness, domain size, oxidized track width, phase transformation width and height in the SCIH process while curvature has the largest effect on center temperature in the design space. The optimum operating conditions with 0.817, 0.845 and 0.773 of desirability values are expected to be able to minimize ratio (tempering region) and maximize phase transformation width for concave, flat and convex surface workpieces, respectively. The verification result indicated that the process parameters obtained by the model were reliable.

Geophysical investigation of the June 6, 1944 D-Day invasion site at Pointe du Hoc, Normandy, France

NASA Astrophysics Data System (ADS)

Everett, M. E.; Pierce, C. J.; Warden, R. R.; Burt, R. A.

2005-05-01

A near-surface geophysical survey at the D-Day invasion site atop the cliffs at Pointe du Hoc, Normandy, France was carried out using ground-penetrating radar, electromagnetic induction, and magnetic gradiometry equipment. The subsurface targets of investigation are predominantly buried concrete and steel structures and earthworks associated with the German coastal fortifications at this stronpoint of Hitler's Atlantic Wall. The targets are readily detectable embedded within the vadose zone of a weakly magnetic, electrically resistive loess soil cover. The radar and electromagnetic induction responses lend themselves to plan-view imaging of the subsurface, while the magnetics data reveal the presence of buried magnetic bodies in a more subtle fashion. Several intriguing geophysical signatures were discovered, including what may be the buried remains of a railway turntable, ordnance fragments in the bomb craters, a buried steel-reinforced concrete trench, and a linear chain of machine gun firing positins. Geophysical prospecting is shown to be a very powerful tool for historical battlefield characterization.

A Comparison of Inductive Sensors in the Characterization of Partial Discharges and Electrical Noise Using the Chromatic Technique

PubMed Central

Ardila-Rey, Jorge Alfredo; Montaña, Johny; Schurch, Roger; Covolan Ulson, José Alfredo; Bani, Nurul Aini

2018-01-01

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals. PMID:29596337

Wind-energy recovery by a static Scherbius induction generator

NASA Astrophysics Data System (ADS)

Smith, G. A.; Nigim, K. A.

1981-11-01

The paper describes a technique for controlling a doubly fed induction generator driven by a windmill, or other form of variable-speed prime mover, to provide power generation into the national grid system. The secondary circuit of the generator is supplied at a variable frequency from a current source inverter which for test purposes is rated to allow energy recovery, from a simulated windmill, from maximum speed to standstill. To overcome the stability problems normally associated with doubly fed machines a novel signal generator, which is locked in phase with the rotor EMF, controls the secondary power to provide operation over a wide range of subsynchronous and supersynchronous speeds. Consideration of power flow enables the VA rating of the secondary power source to be determined as a function of the gear ratio and online operating range of the system. A simple current source model is used to predict performance which is compared with experimental results. The results indicate a viable system, and suggestions for further work are proposed.

Method and system for controlling a synchronous machine over full operating range

DOEpatents

Walters, James E.; Gunawan, Fani S.; Xue, Yanhong

2002-01-01

System and method for controlling a synchronous machine are provided. The method allows for calculating a stator voltage index. The method further allows for relating the magnitude of the stator voltage index against a threshold voltage value. An offset signal is generated based on the results of the relating step. A respective state of operation of the machine is determined. The offset signal is processed based on the respective state of the machine.

A Typology of UK Slot Machine Gamblers: A Longitudinal Observational and Interview Study

ERIC Educational Resources Information Center

Griffiths, Mark D.

2011-01-01

Slot machine gambling is a popular leisure activity worldwide yet there has been very little research into different types of slot machine gamblers. Earlier typologies of slot machine gamblers have only concentrated on adolescents in arcade environments. This study presents a new typology of slot machine players based on over 1000 h of participant…

Relative Kerf and Sawing Variation Values for Some Hardwood Sawing Machines

Treesearch

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

1992-01-01

Information on the conversion efficiency of sawing machines is important to those involved in the management, maintenance, and design of sawmills. Little information on the conversion characteristics of hardwood sawing machines has been available. This study, based on 266 studies of 6 machine types, provides an analysis of the machine characteristics of kerf width,...

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.

Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.

PubMed

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L; Hemmatzadeh, Farhid; Ebrahimie, Esmaeil

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

PubMed Central

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L.

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics. PMID:24809455

A comparative evaluation of bond strength of feldspathic porcelain to nickel-chromium alloy, when subjected to various surface treatments: An in vitro study

PubMed Central

Patel, Kavan A.; Mathur, Somil; Upadhyay, Snehal

2015-01-01

Purpose of the Study: The purpose was to evaluate the effect of various surface treatments and sandblasting with different particle size on the bond strength of feldspathic porcelain with predominantly base metal alloys, using a universal testing machine. Materials and Methods: Totally, 40 specimen of nickel-chromium alloy were prepared in an induction casting machine. The groups divided were as follows: Group I-sandblasted with 50 μ Al2O3, Group II-sandblasted with 110 μ Al2O3, Group III-sandblasted with 250 μ Al2O3 and Group IV-sandblasted with 250 μ Al2O3, followed by oxidation and again sandblasted with 250 μ Al2O3. The dimensions of each specimen were adjusted so as to maintain the thickness of ceramic at 1 mm. The specimen were loaded on the assembly of the universal testing machine, and a cross head speed of 0.5 mm/min was used to apply a compressive force at the junction of metal and feldspathic porcelain. The force application continued until adhesive fracture occurred, and the readings of the load applied to that particular specimen were recorded. Results: The means for shear bond strength for Group I, II, III and IV were found to be (226.92 ± 1.67), (233.16 ± 3.85), (337.81 ± 16.97) and (237.08 ± 4.33), respectively. Means of shear bond strength among the groups were compared using one-way analysis of variance test. Comparison between individual groups were made with Tukey's Honestly Significant Difference post-hoc test. Conclusion: Different particle size and surface treatment have an important role on the bond strength of ceramic-metal interface. Greater particle size demonstrated higher bond strength. PMID:26929487

LHCb experience with running jobs in virtual machines

NASA Astrophysics Data System (ADS)

McNab, A.; Stagni, F.; Luzzi, C.

2015-12-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites managed using Vcycle/OpenStack, Vac, and HTCondor Vacuum. Finally we show how our use of these resources is monitored using Ganglia and DIRAC.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Improving Energy Efficiency in CNC Machining

NASA Astrophysics Data System (ADS)

Pavanaskar, Sushrut S.

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

Competency-Based Education Curriculum for Machine Shop. Teacher's Guide.

ERIC Educational Resources Information Center

Associated Educational Consultants, Inc., Pittsburgh, PA.

This teacher's guide is designed to accompany the machine shop competency-based education curriculum for secondary students in West Virginia. It has been developed to facilitate use of the curriculum by instructors of machine shop programs. The teacher's guide contains the following material: an explanation of the curriculum and suggested usage; a…

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Nano Mechanical Machining Using AFM Probe

NASA Astrophysics Data System (ADS)

Mostofa, Md. Golam

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

View north of west gallery of inside machine shop 36; ...

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

View north of west gallery of inside machine shop 36; the gallery housed turret, engine and toolroom lathes, small milling machines and drill presses used for machining small parts. - Naval Base Philadelphia-Philadelphia Naval Shipyard, Structure Shop, League Island, Philadelphia, Philadelphia County, PA

Development of a low energy micro sheet forming machine

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

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

On the theory of 3-phase squirrel-cage induction motors including space harmonics and mutual slotting

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

Papp, G.C.

1991-03-01

In this paper general equations for the asynchronous squirrel-cage motor which contain the influence of space harmonics and the mutual slotting are derived by using among others the power-invariant symmetrical component transformation and a time-dependent transformation with which, under certain circumstances, the rotor-position angle can be removed from the coefficient matrix. The developed models implemented in a machine-independent computer program form powerful tools, with which the influence of space harmonics in relation to the geometric data of specific motors can be analyzed for steady-state and transient performances.

Simulation of switching overvoltages in the mine electric power supply system

NASA Astrophysics Data System (ADS)

Ivanchenko, D. I.; Novozhilov, N. G.

2017-02-01

Overvoltages occur in mine power supply systems during switching off consumers with high inductive load, such as transformers, reactors and electrical machines. Overvoltages lead to an increase of insulation degradation rate and may cause electric faults, power outage, fire and explosion of methane and coal dust. This paper is dedicated to simulation of vacuum circuit breaker switching overvoltages in a mine power supply system by means of Simulink MATLAB. The model of the vacuum circuit breaker implements simulation of transient recovery voltage, current chopping and an electric arc. Obtained results were compared to available experimental data.

Articulated, Performance-Based Instruction Objectives Guide for Machine Shop Technology.

ERIC Educational Resources Information Center

Henderson, William Edward, Jr., Ed.

This articulation guide contains 21 units of instruction for two years of machine shop. The objectives of the program are to provide the student with the basic terminology and fundamental knowledge and skills in machining (year 1) and to teach him/her to set up and operate machine tools and make or repair metal parts, tools, and machines (year 2).…

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.

Entanglement-Based Machine Learning on a Quantum Computer

NASA Astrophysics Data System (ADS)

Cai, X.-D.; Wu, D.; Su, Z.-E.; Chen, M.-C.; Wang, X.-L.; Li, Li; Liu, N.-L.; Lu, C.-Y.; Pan, J.-W.

2015-03-01

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Relational machine learning for electronic health record-driven phenotyping.

PubMed

Peissig, Peggy L; Santos Costa, Vitor; Caldwell, Michael D; Rottscheit, Carla; Berg, Richard L; Mendonca, Eneida A; Page, David

2014-12-01

Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping. Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance. We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003). ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts. Relational learning using ILP offers a viable approach to EHR-driven phenotyping. Copyright © 2014 Elsevier Inc. All rights reserved.

Machine learning-based methods for prediction of linear B-cell epitopes.

PubMed

Wang, Hsin-Wei; Pai, Tun-Wen

2014-01-01

B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

Comparative adoption of cone beam computed tomography and panoramic radiography machines across Australia.

PubMed

Zhang, A; Critchley, S; Monsour, P A

2016-12-01

The aim of the present study was to assess the current adoption of cone beam computed tomography (CBCT) and panoramic radiography (PR) machines across Australia. Information regarding registered CBCT and PR machines was obtained from radiation regulators across Australia. The number of X-ray machines was correlated with the population size, the number of dentists, and the gross state product (GSP) per capita, to determine the best fitting regression model(s). In 2014, there were 232 CBCT and 1681 PR machines registered in Australia. Based on absolute counts, Queensland had the largest number of CBCT and PR machines whereas the Northern Territory had the smallest number. However, when based on accessibility in terms of the population size and the number of dentists, the Australian Capital Territory had the most CBCT machines and Western Australia had the most PR machines. The number of X-ray machines correlated strongly with both the population size and the number of dentists, but not with the GSP per capita. In 2014, the ratio of PR to CBCT machines was approximately 7:1. Projected increases in either the population size or the number of dentists could positively impact on the adoption of PR and CBCT machines in Australia. © 2016 Australian Dental Association.

Numerical investigation of melting and solidification processes in modified surface layers of metal at induction heating

NASA Astrophysics Data System (ADS)

Shchukin, V. G.; Popov, V. N.

2017-10-01

One of the perspective ways to improve the operational properties of parts of machines during induction treatment of their surfaces is the modification of the melt by specially prepared nanoscale particles of refractory compounds (carbides, nitrides, carbonitrides, etc.). This approach allows us to increase the number of crystallization centers and to refine the structural components of the solidified metal. The resulting high dispersity and homogeneity of crystalline grains favorably affect the quality of the treated surfaces. 3D numerical simulation of thermophysical processes in the modification of the surface layer of metal in a moving substrate was carried out. It is assumed that the surface of the substrate is covered with a layer of specially prepared nanoscale particles of a refractory compound, which, upon penetration into the melt, are uniformly distributed in it. The possibility of applying a high-frequency electromagnetic field of high power for heating and melting of a metal (iron) for the purpose of its subsequent modification is investigated. The distribution of electromagnetic energy in the metal is described by empirical formulas. Melting of the metal is considered in the Stefan approximation, and upon solidification it is assumed that all nanoparticles serve as centers for volume-sequential crystallization. Calculations were carried out with the following parameters: specific power p0 = 35 and 40 kW/cm2 at frequency f = 440 and 1200 kHz, the substrate velocity V = 0.5-2.5 cm/s, the nanoparticles' size is 50 nm and concentration Np = 2.0 . 109 cm-3. Based on the results obtained in a quasi-stationary formulation, the distribution of the temperature field, the dimensions of the melting and crystallization zones, the change in the solid fraction in the two-phase zone, the area of the treated substrate surface, depending on the speed of its movement and induction heating characteristics were estimated.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

PubMed

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

PubMed Central

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method. PMID:26884745

Metalworking and machining fluids

DOEpatents

Erdemir, Ali; Sykora, Frank; Dorbeck, Mark

2010-10-12

Improved boron-based metal working and machining fluids. Boric acid and boron-based additives that, when mixed with certain carrier fluids, such as water, cellulose and/or cellulose derivatives, polyhydric alcohol, polyalkylene glycol, polyvinyl alcohol, starch, dextrin, in solid and/or solvated forms result in improved metalworking and machining of metallic work pieces. Fluids manufactured with boric acid or boron-based additives effectively reduce friction, prevent galling and severe wear problems on cutting and forming tools.

Do Development and Learning Really Decrease Memory? On Similarity and Category-Based Induction in Adults and Children

ERIC Educational Resources Information Center

Wilburn, Catherine; Feeney, Aidan

2008-01-01

In a recently published study, Sloutsky and Fisher [Sloutsky, V. M., & Fisher, A.V. (2004a). When development and learning decrease memory: Evidence against category-based induction in children. "Psychological Science", 15, 553-558; Sloutsky, V. M., & Fisher, A. V. (2004b). Induction and categorization in young children: A similarity-based model.…

FUZZY-LOGIC-BASED CONTROLLERS FOR EFFICIENCY OPTIMIZATION OF INVERTER-FED INDUCTION MOTOR DRIVES

EPA Science Inventory

This paper describes a fuzzy-logic-based energy optimizing controller to improve the efficiency of induction motor/drives operating at various load (torque) and speed conditions. Improvement of induction motor efficiency is important not only from the considerations of energy sav...

Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

NASA Astrophysics Data System (ADS)

Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

2017-09-01

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

Quantum neural network based machine translator for Hindi to English.

PubMed

Narayan, Ravi; Singh, V P; Chakraverty, S

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

National Aspects of Creating and Using MARC/RECON Records.

ERIC Educational Resources Information Center

Rather, John C., Ed.; Avram, Henriette D., Ed.

The Retrospective Conversion (RECON) Working Task Force investigated the problems of converting retrospective catalog records to machine readable form. The major conclusions and recommendations of the Task Force cover five areas: the level of machine-readable records, conversion of other machine-readable data bases, a machine-readable National…

Permutation parity machines for neural cryptography.

PubMed

Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

2010-06-01

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

Permutation parity machines for neural cryptography

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

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

2010-06-15

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

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results.

PubMed

Otero, Fernando E B; Freitas, Alex A

2016-01-01

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

Differentiation of nonferrous metal particles in lubrication oil using an electrical conductivity measurement-based inductive sensor

NASA Astrophysics Data System (ADS)

Wu, Yu; Zhang, Hongpeng; Wang, Man; Chen, Haiquan

2018-02-01

A method that measures the electrical conductivity of metal based on monitoring the inductance changes of coils via an inductive sensor is introduced in this work to differentiate metal particles in lubrication oil. Theoretical analysis coupled with experimentation is employed to differentiate varieties of nonferrous metal particles, including copper and aluminum particles, ranging from 860 μm to 880 μm in diameter. The results show that the inductive sensor is capable of the identification and differentiation of nonferrous metal particles in lubrication oil based on the electrical conductivity measurement. The concept demonstrated in this paper can be extended to inductive sensors in metal particle detection and other scientific and industrial applications.

Eye motion triggered self-powered mechnosensational communication system using triboelectric nanogenerator.

PubMed

Pu, Xianjie; Guo, Hengyu; Chen, Jie; Wang, Xue; Xi, Yi; Hu, Chenguo; Wang, Zhong Lin

2017-07-01

Mechnosensational human-machine interfaces (HMIs) can greatly extend communication channels between human and external devices in a natural way. The mechnosensational HMIs based on biopotential signals have been developing slowly owing to the low signal-to-noise ratio and poor stability. In eye motions, the corneal-retinal potential caused by hyperpolarization and depolarization is very weak. However, the mechanical micromotion of the skin around the corners of eyes has never been considered as a good trigger signal source. We report a novel triboelectric nanogenerator (TENG)-based micromotion sensor enabled by the coupling of triboelectricity and electrostatic induction. By using an indium tin oxide electrode and two opposite tribomaterials, the proposed flexible and transparent sensor is capable of effectively capturing eye blink motion with a super-high signal level (~750 mV) compared with the traditional electrooculogram approach (~1 mV). The sensor is fixed on a pair of glasses and applied in two real-time mechnosensational HMIs-the smart home control system and the wireless hands-free typing system with advantages of super-high sensitivity, stability, easy operation, and low cost. This TENG-based micromotion sensor is distinct and unique in its fundamental mechanism, which provides a novel design concept for intelligent sensor technique and shows great potential application in mechnosensational HMIs.

Eye motion triggered self-powered mechnosensational communication system using triboelectric nanogenerator

PubMed Central

Pu, Xianjie; Guo, Hengyu; Chen, Jie; Wang, Xue; Xi, Yi; Hu, Chenguo; Wang, Zhong Lin

2017-01-01

Mechnosensational human-machine interfaces (HMIs) can greatly extend communication channels between human and external devices in a natural way. The mechnosensational HMIs based on biopotential signals have been developing slowly owing to the low signal-to-noise ratio and poor stability. In eye motions, the corneal-retinal potential caused by hyperpolarization and depolarization is very weak. However, the mechanical micromotion of the skin around the corners of eyes has never been considered as a good trigger signal source. We report a novel triboelectric nanogenerator (TENG)–based micromotion sensor enabled by the coupling of triboelectricity and electrostatic induction. By using an indium tin oxide electrode and two opposite tribomaterials, the proposed flexible and transparent sensor is capable of effectively capturing eye blink motion with a super-high signal level (~750 mV) compared with the traditional electrooculogram approach (~1 mV). The sensor is fixed on a pair of glasses and applied in two real-time mechnosensational HMIs—the smart home control system and the wireless hands-free typing system with advantages of super-high sensitivity, stability, easy operation, and low cost. This TENG-based micromotion sensor is distinct and unique in its fundamental mechanism, which provides a novel design concept for intelligent sensor technique and shows great potential application in mechnosensational HMIs. PMID:28782029

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.

PubMed

Saidi, Lotfi; Ali, Jaouher Ben; Fnaiech, Farhat

2014-09-01

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Exploring Teacher Induction: Collaborative Self-Studies across Institutions

ERIC Educational Resources Information Center

Smith, Déirdre; Engemann, Joe

2015-01-01

Educators from eight institutions engaged in collaborative self-studies of their own practices to gain deeper insight into the significance of narrative-based writing supporting the process of teacher induction. A series of teacher induction institutes based on narrative writing processes provided the context for critical exploration of the lived…

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

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

Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

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

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

AC Loss Analysis of MgB2-Based Fully Superconducting Machines

NASA Astrophysics Data System (ADS)

Feddersen, M.; Haran, K. S.; Berg, F.

2017-12-01

Superconducting electric machines have shown potential for significant increase in power density, making them attractive for size and weight sensitive applications such as offshore wind generation, marine propulsion, and hybrid-electric aircraft propulsion. Superconductors exhibit no loss under dc conditions, though ac current and field produce considerable losses due to hysteresis, eddy currents, and coupling mechanisms. For this reason, many present machines are designed to be partially superconducting, meaning that the dc field components are superconducting while the ac armature coils are conventional conductors. Fully superconducting designs can provide increases in power density with significantly higher armature current; however, a good estimate of ac losses is required to determine the feasibility under the machines intended operating conditions. This paper aims to characterize the expected losses in a fully superconducting machine targeted towards aircraft, based on an actively-shielded, partially superconducting machine from prior work. Various factors are examined such as magnet strength, operating frequency, and machine load to produce a model for the loss in the superconducting components of the machine. This model is then used to optimize the design of the machine for minimal ac loss while maximizing power density. Important observations from the study are discussed.

Optimal quantum cloning based on the maximin principle by using a priori information

NASA Astrophysics Data System (ADS)

Kang, Peng; Dai, Hong-Yi; Wei, Jia-Hua; Zhang, Ming

2016-10-01

We propose an optimal 1 →2 quantum cloning method based on the maximin principle by making full use of a priori information of amplitude and phase about the general cloned qubit input set, which is a simply connected region enclosed by a "longitude-latitude grid" on the Bloch sphere. Theoretically, the fidelity of the optimal quantum cloning machine derived from this method is the largest in terms of the maximin principle compared with that of any other machine. The problem solving is an optimization process that involves six unknown complex variables, six vectors in an uncertain-dimensional complex vector space, and four equality constraints. Moreover, by restricting the structure of the quantum cloning machine, the optimization problem is simplified as a three-real-parameter suboptimization problem with only one equality constraint. We obtain the explicit formula for a suboptimal quantum cloning machine. Additionally, the fidelity of our suboptimal quantum cloning machine is higher than or at least equal to that of universal quantum cloning machines and phase-covariant quantum cloning machines. It is also underlined that the suboptimal cloning machine outperforms the "belt quantum cloning machine" for some cases.

Machine Learning

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

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

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Discovering rules for protein-ligand specificity using support vector inductive logic programming.

PubMed

Kelley, Lawrence A; Shrimpton, Paul J; Muggleton, Stephen H; Sternberg, Michael J E

2009-09-01

Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/.

Visual management of large scale data mining projects.

PubMed

Shah, I; Hunter, L

2000-01-01

This paper describes a unified framework for visualizing the preparations for, and results of, hundreds of machine learning experiments. These experiments were designed to improve the accuracy of enzyme functional predictions from sequence, and in many cases were successful. Our system provides graphical user interfaces for defining and exploring training datasets and various representational alternatives, for inspecting the hypotheses induced by various types of learning algorithms, for visualizing the global results, and for inspecting in detail results for specific training sets (functions) and examples (proteins). The visualization tools serve as a navigational aid through a large amount of sequence data and induced knowledge. They provided significant help in understanding both the significance and the underlying biological explanations of our successes and failures. Using these visualizations it was possible to efficiently identify weaknesses of the modular sequence representations and induction algorithms which suggest better learning strategies. The context in which our data mining visualization toolkit was developed was the problem of accurately predicting enzyme function from protein sequence data. Previous work demonstrated that approximately 6% of enzyme protein sequences are likely to be assigned incorrect functions on the basis of sequence similarity alone. In order to test the hypothesis that more detailed sequence analysis using machine learning techniques and modular domain representations could address many of these failures, we designed a series of more than 250 experiments using information-theoretic decision tree induction and naive Bayesian learning on local sequence domain representations of problematic enzyme function classes. In more than half of these cases, our methods were able to perfectly discriminate among various possible functions of similar sequences. We developed and tested our visualization techniques on this application.

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Dictionary Based Machine Translation from Kannada to Telugu

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2008-03-01

Inductive bias is the set of assumptions that a person or procedure makes in making a prediction based on data. Different methods for ligand-based predictive modeling have different inductive biases, with a particularly sharp contrast between 2D and 3D similarity methods. A unique aspect of ligand design is that the data that exist to test methodology have been largely man-made, and that this process of design involves prediction. By analyzing the molecular similarities of known drugs, we show that the inductive bias of the historic drug discovery process has a very strong 2D bias. In studying the performance of ligand-based modeling methods, it is critical to account for this issue in dataset preparation, use of computational controls, and in the interpretation of results. We propose specific strategies to explicitly address the problems posed by inductive bias considerations.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

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

High performance cutting of aircraft and turbine components

NASA Astrophysics Data System (ADS)

Krämer, A.; Lung, D.; Klocke, F.

2012-04-01

Titanium and nickel-based alloys belong to the group of difficult-to-cut materials. The machining of these high-temperature alloys is characterized by low productivity and low process stability as a result of their physical and mechanical properties. Major problems during the machining of these materials are low applicable cutting speeds due to excessive tool wear, long machining times, and thus high manufacturing costs, as well as the formation of ribbon and snarled chips. Under these conditions automation of the production process is limited. This paper deals with strategies to improve machinability of titanium and nickel-based alloys. Using the example of the nickel-based alloy Inconel 718 high performance cutting with advanced cutting materials, such as PCBN and cutting ceramics, is presented. Afterwards the influence of different cooling strategies, like high-pressure lubricoolant supply and cryogenic cooling, during machining of TiAl6V4 is shown.

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

Ken L. Stratton

The objective of this project is to investigate the applicability of a combined Global Positioning System and Inertial Measurement Unit (GPS/IMU) for information based displays on earthmoving machines and for automated earthmoving machines in the future. This technology has the potential of allowing an information-based product like Caterpillar's Computer Aided Earthmoving System (CAES) to operate in areas with satellite shading. Satellite shading is an issue in open pit mining because machines are routinely required to operate close to high walls, which reduces significantly the amount of the visible sky to the GPS antenna mounted on the machine. An inertial measurementmore » unit is a product, which provides data for the calculation of position based on sensing accelerations and rotation rates of the machine's rigid body. When this information is coupled with GPS it results in a positioning system that can maintain positioning capability during time periods of shading.« less

Quantum Neural Network Based Machine Translator for Hindi to English

PubMed Central

Singh, V. P.; Chakraverty, S.

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Fifteen-hour day shifts have little effect on the performance of taskwork by anaesthesia trainees during uncomplicated clinical simulation.

PubMed

Garden, A L; Robinson, B J; Kappus, L J; Macleod, I; Gander, P H

2012-11-01

Shiftwork and work-hour limits for junior doctors are now well established in hospital work patterns. In order to ensure that trainees have adequate exposure to daytime elective surgical procedures, there is a tendency to have long shifts that include an after-hours component. However, long shifts can cause performance decrement due to time-on-task fatigue. In addition, shifts that encroach upon sleep time result in sleep loss. Using a high-fidelity patient simulation environment, we undertook a randomised, controlled trial to examine fatigue effects. A within-subjects comparison was used to evaluate the effect of 15-hour day shifts on the performance of 12 anaesthesia registrars. Preoperative assessment, machine check and taskwork using 42 task categories were evaluated. In both conditions, there was failure to meet current guidelines for preoperative evaluation or machine check, and when fatigued there was a 'trend' (P=0.06) to a reduction in the number of items in the machine check. With increase in time awake, there was an increase in time taken for explanation to the patient, an increase in mean duration of explanation to the patient, more time looking at the intravenous line or fluids when multi-tasking but less time adjusting the intravenous fluid. These effects are minor during routine uncomplicated induction of anaesthesia, but further investigation is needed to examine fatigue effects during non-routine circumstances.

Characterization of Ni-Cr alloys using different casting techniques and molds.

PubMed

Chen, Wen-Cheng; Teng, Fu-Yuan; Hung, Chun-Cheng

2014-02-01

This study differentiated the mechanical properties of nickel-chromium (Ni-Cr) alloys under various casting techniques (different casting molds and casting atmospheres). These techniques were sampled by a sand mold using a centrifugal machine in ambient air (group I) and electromagnetic induction in an automatic argon castimatic casting machine (group II). The specimen casting used a graphite mold by a castimatic casting machine (group III). The characteristics of the Ni-Cr alloys, yield and ultimate tensile strength, bending modulus, microhardness, diffraction phase, grindability, ability to spring back, as well as ground microstructure and pattern under different casting conditions were evaluated. The group III specimens exhibited the highest values in terms of strength, modulus, hardness, and grindability at a grind rate of 500 rpm. Moreover, group III alloys exhibited smaller grain sizes, higher ability to spring back, and greater ductility than those casted by sand investment (groups I and II). The main factor, "casting mold," significantly influenced all mechanical properties. The graphite mold casting of the Ni-Cr dental alloys in a controlled atmosphere argon casting system provided an excellent combination of high mechanical properties and good ability to spring back, and preserved the ductile properties for application in Ni-Cr porcelain-fused system. The results can offer recommendations to assist a prosthetic technician in selecting the appropriate casting techniques to obtain the desired alloy properties. Copyright © 2013 Elsevier B.V. All rights reserved.

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics

NASA Astrophysics Data System (ADS)

Digalwar, Abhijeet K.

2018-04-01

Sustainable processes and techniques are getting increased attention over the last few decades due to rising concerns over the environment, improved focus on productivity and stringency in environmental as well as occupational health and safety norms. The present work analyzes the research on sustainable machining techniques and identifies techniques and parameters on which sustainability of a process is evaluated. Based on the analysis these parameters are then adopted as criteria’s to evaluate different sustainable machining techniques such as Cryogenic Machining, Dry Machining, Minimum Quantity Lubrication (MQL) and High Pressure Jet Assisted Machining (HPJAM) using a fuzzy TOPSIS framework. In order to facilitate easy arithmetic, the linguistic variables represented by fuzzy numbers are transformed into crisp numbers based on graded mean representation. Cryogenic machining was found to be the best alternative sustainable technique as per the fuzzy TOPSIS framework adopted. The paper provides a method to deal with multi criteria decision making problems in a complex and linguistic environment.

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

PubMed

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

2014-01-01

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

Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.

PubMed

Grouin, Cyril; Zweigenbaum, Pierre

2013-01-01

In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.

View of west elevation of building 18 section of machine ...

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

View of west elevation of building 18 section of machine shops. Jet Lowe, Haer staff photographer, summer 1995. - Naval Base Philadelphia-Philadelphia Naval Shipyard, Machine Shops, League Island, Philadelphia, Philadelphia County, PA

The Role of Guided Induction in Paper-Based Data-Driven Learning

ERIC Educational Resources Information Center

Smart, Jonathan

2014-01-01

This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven…

Machine Learning

NASA Astrophysics Data System (ADS)

Hoffmann, Achim; Mahidadia, Ashesh

The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules - a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future.

A dynamic model of reasoning and memory.

PubMed

Hawkins, Guy E; Hayes, Brett K; Heit, Evan

2016-02-01

Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop and empirically test the first major quantitative modeling scheme that simultaneously accounts for inductive decisions and their time course. The model assumes that knowledge of similarity relations among novel test probes and items stored in memory drive an accumulation-to-bound sequential sampling process: Test probes with high similarity to studied exemplars are more likely to trigger a generalization response, and more rapidly, than items with low exemplar similarity. We contrast data and model predictions for inductive decisions with a recognition memory task using a common stimulus set. Hierarchical Bayesian analyses across 2 experiments demonstrated that inductive reasoning and recognition memory primarily differ in the threshold to trigger a decision: Observers required less evidence to make a property generalization judgment (induction) than an identity statement about a previously studied item (recognition). Experiment 1 and a condition emphasizing decision speed in Experiment 2 also found evidence that inductive decisions use lower quality similarity-based information than recognition. The findings suggest that induction might represent a less cautious form of recognition. We conclude that sequential sampling models grounded in exemplar-based similarity, combined with hierarchical Bayesian analysis, provide a more fine-grained and informative analysis of the processes involved in inductive reasoning than is possible solely through examination of choice data. PsycINFO Database Record (c) 2016 APA, all rights reserved.

An imperialist competitive algorithm for virtual machine placement in cloud computing

NASA Astrophysics Data System (ADS)

Jamali, Shahram; Malektaji, Sepideh; Analoui, Morteza

2017-05-01

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

Discovering Fine-grained Sentiment in Suicide Notes

PubMed Central

Wang, Wenbo; Chen, Lu; Tan, Ming; Wang, Shaojun; Sheth, Amit P.

2012-01-01

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams. PMID:22879770

NASA's online machine aided indexing system

NASA Technical Reports Server (NTRS)

Silvester, June P.; Genuardi, Michael T.; Klingbiel, Paul H.

1993-01-01

This report describes the NASA Lexical Dictionary, a machine aided indexing system used online at the National Aeronautics and Space Administration's Center for Aerospace Information (CASI). This system is comprised of a text processor that is based on the computational, non-syntactic analysis of input text, and an extensive 'knowledge base' that serves to recognize and translate text-extracted concepts. The structure and function of the various NLD system components are described in detail. Methods used for the development of the knowledge base are discussed. Particular attention is given to a statistically-based text analysis program that provides the knowledge base developer with a list of concept-specific phrases extracted from large textual corpora. Production and quality benefits resulting from the integration of machine aided indexing at CASI are discussed along with a number of secondary applications of NLD-derived systems including on-line spell checking and machine aided lexicography.

A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

PubMed Central

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; de Jesus Romero-Troncoso, Rene

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node. PMID:22163602

A field programmable gate array-based reconfigurable smart-sensor network for wireless monitoring of new generation computer numerically controlled machines.

PubMed

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; Romero-Troncoso, Rene de Jesus

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node.

Predictive Modeling and Optimization of Vibration-assisted AFM Tip-based Nanomachining

NASA Astrophysics Data System (ADS)

Kong, Xiangcheng

The tip-based vibration-assisted nanomachining process offers a low-cost, low-effort technique in fabricating nanometer scale 2D/3D structures in sub-100 nm regime. To understand its mechanism, as well as provide the guidelines for process planning and optimization, we have systematically studied this nanomachining technique in this work. To understand the mechanism of this nanomachining technique, we firstly analyzed the interaction between the AFM tip and the workpiece surface during the machining process. A 3D voxel-based numerical algorithm has been developed to calculate the material removal rate as well as the contact area between the AFM tip and the workpiece surface. As a critical factor to understand the mechanism of this nanomachining process, the cutting force has been analyzed and modeled. A semi-empirical model has been proposed by correlating the cutting force with the material removal rate, which was validated using experimental data from different machining conditions. With the understanding of its mechanism, we have developed guidelines for process planning of this nanomachining technique. To provide the guideline for parameter selection, the effect of machining parameters on the feature dimensions (depth and width) has been analyzed. Based on ANOVA test results, the feature width is only controlled by the XY vibration amplitude, while the feature depth is affected by several machining parameters such as setpoint force and feed rate. A semi-empirical model was first proposed to predict the machined feature depth under given machining condition. Then, to reduce the computation intensity, linear and nonlinear regression models were also proposed and validated using experimental data. Given the desired feature dimensions, feasible machining parameters could be provided using these predictive feature dimension models. As the tip wear is unavoidable during the machining process, the machining precision will gradually decrease. To maintain the machining quality, the guideline for when to change the tip should be provided. In this study, we have developed several metrics to detect tip wear, such as tip radius and the pull-off force. The effect of machining parameters on the tip wear rate has been studied using these metrics, and the machining distance before a tip must be changed has been modeled using these machining parameters. Finally, the optimization functions have been built for unit production time and unit production cost subject to realistic constraints, and the optimal machining parameters can be found by solving these functions.

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD

PubMed Central

Galatzer-Levy, I R; Ma, S; Statnikov, A; Yehuda, R; Shalev, A Y

2017-01-01

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80–0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples. PMID:28323285

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

PubMed

Galatzer-Levy, I R; Ma, S; Statnikov, A; Yehuda, R; Shalev, A Y

2017-03-21

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

Hyperventilation and photic stimulation are useful additions to a placebo-based suggestive seizure induction protocol in patients with psychogenic nonepileptic seizures.

PubMed

Popkirov, Stoyan; Grönheit, Wenke; Wellmer, Jörg

2015-05-01

The early and definitive diagnosis of psychogenic nonepileptic seizures is a common challenge in epileptology practice. Suggestive seizure induction is a valuable tool to aid the differentiation between epileptic and psychogenic nonepileptic seizures, especially when long-term video-EEG monitoring is inconclusive or unavailable. In this retrospective analysis, we compared the diagnostic yield of a classical, placebo-based induction protocol with that of an extended protocol that includes hyperventilation and photic stimulation as means of suggestion while also implementing more open, standardized patient information. We investigated whether the diversification of suggestive seizure induction has an effect on diagnostic yield and whether it preempts the administration of placebo. Data from 52 patients with confirmed psychogenic nonepileptic seizures were analyzed. While suggestive seizure induction using only placebo-based suggestion provoked a typical event in 13 of 20 patients (65%), the extended protocol was positive in 27 of 34 cases (84%); this improvement was not significant (p=0.11). Noninvasive suggestion techniques accounted for 78% of inductions, avoiding placebo administration in a majority of patients. Still, placebo remains an important part of suggestive seizure induction, responsible for 22% (6 out of 27) of successful inductions using our extended protocol. Our study demonstrates that the diversification of suggestive seizure induction is feasible and beneficial for both patients and diagnosticians. Copyright © 2015 Elsevier Inc. All rights reserved.

Machine learning methods applied on dental fear and behavior management problems in children.

PubMed

Klingberg, G; Sillén, R; Norén, J G

1999-08-01

The etiologies of dental fear and dental behavior management problems in children were investigated in a database of information on 2,257 Swedish children 4-6 and 9-11 years old. The analyses were performed using computerized inductive techniques within the field of artificial intelligence. The database held information regarding dental fear levels and behavior management problems, which were defined as outcomes, i.e. dependent variables. The attributes, i.e. independent variables, included data on dental health and dental treatments, information about parental dental fear, general anxiety, socioeconomic variables, etc. The data contained both numerical and discrete variables. The analyses were performed using an inductive analysis program (XpertRule Analyser, Attar Software Ltd, Lancashire, UK) that presents the results in a hierarchic diagram called a knowledge tree. The importance of the different attributes is represented by their position in this diagram. The results show that inductive methods are well suited for analyzing multifactorial and complex relationships in large data sets, and are thus a useful complement to multivariate statistical techniques. The knowledge trees for the two outcomes, dental fear and behavior management problems, were very different from each other, suggesting that the two phenomena are not equivalent. Dental fear was found to be more related to non-dental variables, whereas dental behavior management problems seemed connected to dental variables.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study

PubMed Central

2012-01-01

Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. Results The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. Conclusions In addition to confirming literature results, ProGolem’s model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners. PMID:22783946

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

The effect of piezoelectric ultrasonic instrumentation on titanium discs: a microscopy and trace elemental analysis in vitro study.

PubMed

Tawse-Smith, A; Atieh, M A; Tompkins, G; Duncan, W J; Reid, M R; Stirling, C H

2016-08-01

To evaluate in vitro topographical and composition changes by piezoelectric ultrasonic instrumentation with metallic and plastic tips on machined and moderately roughened titanium surfaces. Twenty machined and moderately roughened laser-marked titanium discs were ultrasonically instrumented with metallic and plastic tips. Surface instrumentation was carried out with controlled pressure for 20 and 30 seconds at two power settings. For each time and power setting, instrumentation was repeated four times with one instrumentation per disc quadrant. Surface topography analysis was performed using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). Surface roughness measurements were compared between instrumented and non-instrumented surfaces. Surface element composition and rinsing solutions were evaluated using energy-dispersive spectroscopy (EDS) and trace elemental analysis using inductively coupled plasma mass spectrometry (ICPMS), respectively. SEM photomicrographs and CLSM 3D surface plot images of instrumented machined and moderately roughened surfaces demonstrated severe surface topographical alterations with metallic tips and mild to moderate changes for plastic tip instrumented sites. ICPMS analysis of the rinsing solutions identified titanium and other metal traces with the use of metallic tips, and mainly titanium and carbon when plastic tips were used. Surface EDS analysis showed elemental traces of the ultrasonic tips. Ultrasonic instrumentation with metallic or plastic tips created surface topographical and compositional changes. Different changes in surface topography were noted between the surfaces, as the roughness of the machined surfaces increased while the extent of roughness of the moderately roughened surfaces decreased. The clinical relevance of these changes is yet to be determined. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

On the decomposition of synchronous state mechines using sequence invariant state machines

NASA Technical Reports Server (NTRS)

Hebbalalu, K.; Whitaker, S.; Cameron, K.

1992-01-01

This paper presents a few techniques for the decomposition of Synchronous State Machines of medium to large sizes into smaller component machines. The methods are based on the nature of the transitions and sequences of states in the machine and on the number and variety of inputs to the machine. The results of the decomposition, and of using the Sequence Invariant State Machine (SISM) Design Technique for generating the component machines, include great ease and quickness in the design and implementation processes. Furthermore, there is increased flexibility in making modifications to the original design leading to negligible re-design time.

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Demonstration of Inductive Flux Saving by Transient CHI on NSTX

NASA Astrophysics Data System (ADS)

Raman, Roger

2010-11-01

Experiments in NSTX have now demonstrated the saving of central solenoid flux equivalent to 200kA of toroidal plasma current after coupling plasmas produced by Transient Coaxial Helicity Injection (CHI) to inductive sustainment and ramp-up of the toroidal plasma current [R. Raman, et al., PRL 104, 095003 (2010)]. This is a record for non-inductive plasma startup, and an important step for developing the spherical torus concept. With an injector current of only 4kA and total power supply energy of only 21 kJ, CHI initiated a toroidal current of 250 kA that when coupled to 0.11 Vs of induction ramped up to 525 kA without using any auxiliary heating, whereas an otherwise identical inductive-only discharge ramped to only 325 kA. This flux saving was realized by reducing the influx of low-Z impurities during the start-up phase through the use of electrode conditioning discharges, followed by lithium evaporative coating of the plasma-facing surfaces and reducing parasitic arcs in the upper divertor region through use of additional shaping-field coils. As a result of these improvements, and for the first time in NSTX, the electron temperature during the CHI phase continually increased with input energy, indicating that the additional injected energy was contributing to heating the plasma instead of being lost through impurity line radiation. Simulations with the Tokamak Simulation Code (TSC) show that the observed scaling of CHI start-up current with toroidal field in NSTX is consistent with theory, suggesting that use of CHI on larger machines is quite attractive. These exciting results from NSTX demonstrate that CHI is a viable solenoid-free plasma startup method for future STs and tokamaks. This work supported by U.S. DOE Contracts DE-AC02-09CH11466 and DE-FG02-99ER54519 AM08.

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

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.

Diamond Turning Of Infra-Red Components

NASA Astrophysics Data System (ADS)

Hodgson, B.; Lettington, A. H.; Stillwell, P. F. T. C.

1986-05-01

Single point diamond machining of infra-red optical components such as aluminium mirrors, germanium lenses and zinc sulphide domes is potentially the most cost effective method for their manufacture since components may be machined from the blanks to a high surface finish, requiring no subsequent polishing, in a few minutes. Machines for the production of flat surfaces are well established. Diamond turning lathes for curved surfaces however require a high capital investment which can be justified only for research purposes or high volume production. The present paper describes the development of a low cost production machine based on a Bryant Symons diamond turning lathe which is able to machine spherical components to the required form and finish. It employs two horizontal spindles one for the workpiece the other for the tool. The machined radius of curvature is set by the alignment of the axes and the radius of the tool motion, as in conventional generation. The diamond tool is always normal to the workpiece and does not need to be accurately profiled. There are two variants of this basic machine. For machining hemispherical domes the axes are at right angles while for lenses with positive or negative curvature these axes are adjustable. An aspherical machine is under development, based on the all mechanical spherical machine, but in which a ± 2 mm aspherecity may be imposed on the best fit sphere by moving the work spindle under numerical control.

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

PubMed

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

2015-09-21

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

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

DTIC Science & Technology

2008-11-01

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

Modelling of internal architecture of kinesin nanomotor as a machine language.

PubMed

Khataee, H R; Ibrahim, M Y

2012-09-01

Kinesin is a protein-based natural nanomotor that transports molecular cargoes within cells by walking along microtubules. Kinesin nanomotor is considered as a bio-nanoagent which is able to sense the cell through its sensors (i.e. its heads and tail), make the decision internally and perform actions on the cell through its actuator (i.e. its motor domain). The study maps the agent-based architectural model of internal decision-making process of kinesin nanomotor to a machine language using an automata algorithm. The applied automata algorithm receives the internal agent-based architectural model of kinesin nanomotor as a deterministic finite automaton (DFA) model and generates a regular machine language. The generated regular machine language was acceptable by the architectural DFA model of the nanomotor and also in good agreement with its natural behaviour. The internal agent-based architectural model of kinesin nanomotor indicates the degree of autonomy and intelligence of the nanomotor interactions with its cell. Thus, our developed regular machine language can model the degree of autonomy and intelligence of kinesin nanomotor interactions with its cell as a language. Modelling of internal architectures of autonomous and intelligent bio-nanosystems as machine languages can lay the foundation towards the concept of bio-nanoswarms and next phases of the bio-nanorobotic systems development.

Osteoporosis risk prediction using machine learning and conventional methods.

PubMed

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

2013-01-01

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

Temperature Measurement and Numerical Prediction in Machining Inconel 718.

PubMed

Díaz-Álvarez, José; Tapetado, Alberto; Vázquez, Carmen; Miguélez, Henar

2017-06-30

Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage such as phase changes and residual stresses, resulting in reduced service life of the component. Measurement of temperature during machining is crucial in order to control the cutting process, avoiding workpiece damage. On the other hand, the development of predictive tools based on numerical models helps in the definition of machining processes and the obtainment of difficult to measure parameters such as the penetration of the heated layer. However, the validation of numerical models strongly depends on the accurate measurement of physical parameters such as temperature, ensuring the calibration of the model. This paper focuses on the measurement and prediction of temperature during the machining of Ni-based superalloys. The temperature sensor was based on a fiber-optic two-color pyrometer developed for localized temperature measurements in turning of Inconel 718. The sensor is capable of measuring temperature in the range of 250 to 1200 °C. Temperature evolution is recorded in a lathe at different feed rates and cutting speeds. Measurements were used to calibrate a simplified numerical model for prediction of temperature fields during turning.

Design Considerations of a Transverse Flux Machine for Direct-Drive Wind Turbine Applications: Preprint

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

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

This paper presents the design considerations of a double-sided transverse flux machine (TFM) for direct-drive wind turbine applications. The TFM has a modular structure with quasi-U stator cores and ring windings. The rotor is constructed with ferrite magnets in a flux-concentrating arrangement to achieve high air gap flux density. The design considerations for this TFM with respect to initial sizing, pole number selection, key design ratios, and pole shaping are presented in this paper. Pole number selection is critical in the design process of a TFM because it affects both the torque density and power factor under fixed magnetic andmore » changing electrical loading. Several key design ratios are introduced to facilitate the design procedure. The effect of pole shaping on back-emf and inductance is also analyzed. These investigations provide guidance toward the required design of a TFM for direct-drive applications. The analyses are carried out using analytical and three-dimensional finite element analysis. A prototype is under construction for experimental verification.« less

Design Considerations of a Transverse Flux Machine for Direct-Drive Wind Turbine Applications

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

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

This paper presents the design considerations of a double-sided transverse flux machine (TFM) for direct-drive wind turbine applications. The TFM has a modular structure with quasi-U stator cores and ring windings. The rotor is constructed with ferrite magnets in a flux-concentrating arrangement to achieve high air gap flux density. The design considerations for this TFM with respect to initial sizing, pole number selection, key design ratios, and pole shaping are presented in this paper. Pole number selection is critical in the design process of a TFM because it affects both the torque density and power factor under fixed magnetic andmore » changing electrical loading. Several key design ratios are introduced to facilitate the design procedure. The effect of pole shaping on back-emf and inductance is also analyzed. These investigations provide guidance toward the required design of a TFM for direct-drive applications. The analyses are carried out using analytical and three-dimensional finite element analysis. A prototype is under construction for experimental verification.« less

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

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

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

PubMed

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

2015-01-01

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

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

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

PubMed Central

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

2017-01-01

A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a ‘sensor box’ to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality. PMID:28691039

Control strategy for a variable-speed wind energy conversion system

NASA Technical Reports Server (NTRS)

Jacob, A.; Veillette, D.; Rajagopalan, V.

1979-01-01

A control concept for a variable-speed wind energy conversion system is proposed, for which a self-exited asynchronous cage generator is used along with a system of thyristor converters. The control loops are the following: (1) regulation of the entrainment speed as function of available mechanical energy by acting on the resistance couple of the asynchronous generator; (2) control of electric power delivered to the asynchronous machine, functioning as a motor, for start-up of the vertical axis wind converter; and (3) limitation of the slip value, and by consequence, of the induction currents in the presence of sudden variations of input parameters.

Magnetic loss and B(H) behaviour of non-oriented electrical sheets under a trapezoidal exciting field

NASA Astrophysics Data System (ADS)

Kedous-Lebouc, A.; Errard, S.; Cornut, B.; Brissonneau, P.

1994-05-01

The excess loss and hysteresis response of electrical steel are measured and discussed in the case of trapezoidal field excitation similar to the current provided by a current commutation supply of a self-synchronous rotating machine. Three industrial non-oriented SiFe samples of different magnetic grades and thicknesses are tested using an automatic Epstein frame equipment. The losses and the unusual observed B( H) loops are analysed in terms of the rate of change of the field, the diffusion of the induction inside the sheet and by the calculation of the theoretical hysteresis cycles due to the eddy currents.

Cleaning of uranium vs machine coolant formulations

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

Cristy, S.S.; Byrd, V.R.; Simandl, R.F.

1984-10-01

This study compares methods for cleaning uranium chips and the residues left on chips from alternate machine coolants based on propylene glycol-water mixtures with either borax, ammonium tetraborate, or triethanolamine tetraborate added as a nuclear poison. Residues left on uranium surfaces machined with perchloroethylene-mineral oil coolant and on surfaces machined with the borax-containing alternate coolant were also compared. In comparing machined surfaces, greater chlorine contamination was found on the surface of the perchloroethylene-mineral oil machined surfaces, but slightly greater oxidation was found on the surfaces machined with the alternate borax-containing coolant. Overall, the differences were small and a change tomore » the alternate coolant does not appear to constitute a significant threat to the integrity of machined uranium parts.« less

Modeling and simulation of five-axis virtual machine based on NX

NASA Astrophysics Data System (ADS)

Li, Xiaoda; Zhan, Xianghui

2018-04-01

Virtual technology in the machinery manufacturing industry has shown the role of growing. In this paper, the Siemens NX software is used to model the virtual CNC machine tool, and the parameters of the virtual machine are defined according to the actual parameters of the machine tool so that the virtual simulation can be carried out without loss of the accuracy of the simulation. How to use the machine builder of the CAM module to define the kinematic chain and machine components of the machine is described. The simulation of virtual machine can provide alarm information of tool collision and over cutting during the process to users, and can evaluate and forecast the rationality of the technological process.

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

PubMed

Li, Yang; Yang, Jianyi

2017-04-24

The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pearson correlation coefficient to about 0.8 on the core set of the PDBbind 2007 database, which is significantly higher than the performance of any conventional scoring function on the same benchmark. A few studies have been made to discuss the performance of machine-learning-based methods, but the reason for this improvement remains unclear. In this study, by systemically controlling the structural and sequence similarity between the training and test proteins of the PDBbind benchmark, we demonstrate that protein structural and sequence similarity makes a significant impact on machine-learning-based methods. After removal of training proteins that are highly similar to the test proteins identified by structure alignment and sequence alignment, machine-learning-based methods trained on the new training sets do not outperform the conventional scoring functions any more. On the contrary, the performance of conventional functions like X-Score is relatively stable no matter what training data are used to fit the weights of its energy terms.

View north of inside machine shop 36; shop floor accommodates ...

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

View north of inside machine shop 36; shop floor accommodates lathes capable of machining a cylinder 60 inches in diameter and 75 feet long; other equipment includes horizontal and vertical jig borders, hydraulic tube straighteners and other equipment for precision machining of large ship components. - Naval Base Philadelphia-Philadelphia Naval Shipyard, Structure Shop, League Island, Philadelphia, Philadelphia County, PA

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

ERIC Educational Resources Information Center

McCurry, Doug

2010-01-01

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

Confabulation Based Sentence Completion for Machine Reading

DTIC Science & Technology

2010-11-01

making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics the...thus making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics...University Press, 1992. [2] H. Motoda and K. Yoshida, “Machine learning techniques to make computers easier to use,” Proceedings of the Fifteenth

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Design of an ultraprecision computerized numerical control chemical mechanical polishing machine and its implementation

NASA Astrophysics Data System (ADS)

Zhang, Chupeng; Zhao, Huiying; Zhu, Xueliang; Zhao, Shijie; Jiang, Chunye

2018-01-01

The chemical mechanical polishing (CMP) is a key process during the machining route of plane optics. To improve the polishing efficiency and accuracy, a CMP model and machine tool were developed. Based on the Preston equation and the axial run-out error measurement results of the m circles on the tin plate, a CMP model that could simulate the material removal at any point on the workpiece was presented. An analysis of the model indicated that lower axial run-out error led to lower material removal but better polishing efficiency and accuracy. Based on this conclusion, the CMP machine was designed, and the ultraprecision gas hydrostatic guideway and rotary table as well as the Siemens 840Dsl numerical control system were incorporated in the CMP machine. To verify the design principles of machine, a series of detection and machining experiments were conducted. The LK-G5000 laser sensor was employed for detecting the straightness error of the gas hydrostatic guideway and the axial run-out error of the gas hydrostatic rotary table. A 300-mm-diameter optic was chosen for the surface profile machining experiments performed to determine the CMP efficiency and accuracy.

[Card-based age control mechanisms at tobacco vending machines. Effect and consequences].

PubMed

Schneider, S; Meyer, C; Löber, S; Röhrig, S; Solle, D

2010-02-01

Until recently, 700,000 tobacco vending machines provided uncontrolled access to cigarettes for children and adolescents in Germany. On January 1, 2007, a card-based electronic locking device was attached to all tobacco vending machines to prevent the purchase of cigarettes by children and adolescents under 16. Starting in 2009, only persons older than 18 are able to buy cigarettes from tobacco vending machines. The aim of the present investigation (SToP Study: "Sources of Tobacco for Pupils" Study) was to assess changes in the number of tobacco vending machines after the introduction of these new technical devices (supplier's reaction). In addition, the ways smoking adolescents make purchases were assessed (consumer's reaction). We registered and mapped the total number of tobacco points of sale (tobacco POS) before and after the introduction of the card-based electronic locking device in two selected districts of the city of Cologne. Furthermore, pupils from local schools (response rate: 83%) were asked about their tobacco consumption and ways of purchase using a questionnaire. Results indicated that in the area investigated the total number of tobacco POSs decreased from 315 in 2005 to 277 in 2007. The rates of decrease were 48% for outdoor vending machines and 8% for indoor vending machines. Adolescents reported circumventing the card-based electronic locking devices (e.g., by using cards from older friends) and using other tobacco POSs (especially newspaper kiosks) or relying on their social network (mainly friends). The decreasing number of tobacco vending machines has not had a significant impact on cigarette acquisition by adolescent smokers as they tend to circumvent the newly introduced security measures.

Issues of Exploitation of Induction Motors in the Course of Underground Mining Operations

NASA Astrophysics Data System (ADS)

Gumula, Stanisław; Hudy, Wiktor; Piaskowska-Silarska, Malgorzata; Pytel, Krzysztof

2017-09-01

Mining industry is one of the most important customers of electric motors. The most commonly used in the contemporary mining industry is alternating current machines used for processing electrical energy into mechanical energy. The operating problems and the influence of qualitative interference acting on the inputs of individual regulators to field-oriented system in the course of underground mining operations has been presented in the publication. The object of controlling the speed is a slip-ring induction motor. Settings of regulators were calculated using an evolutionary algorithm. Examination of system dynamics was performed by a computer with the use of the MATLAB / Simulink software. According to analyzes, large distortion of input signals of regulators adversely affects the rotational speed that pursued by the control system, which may cause a large vibration of the whole system and, consequently, its much faster destruction. Designed system is characterized by a significantly better resistance to interference. The system is stable with the properly selected settings of regulators, which is particularly important during the operation of machinery used in underground mining.

Shock tube and shock wave research; Proceedings of the Eleventh International Symposium, University of Washington, Seattle, Wash., July 11-14, 1977

NASA Technical Reports Server (NTRS)

Ahlborn, B. (Editor); Hertzberg, A.; Russell, D.

1978-01-01

Papers are presented on the applications of shock-wave technology to the study of hydrodynamics, the use of the pressure-wave machine for charging diesel engines, and measurements of the heat-transfer rate in gas-turbine components. Consideration is given to shock propagation along 90-degree bends, the explosive dissemination of liquids, and rotational and vibrational relaxation behind weak shock waves in water vapor. Shock phenomena associated with expansion flows are described and stratospheric-related research using the shock tube is outlined. Attention is given to shock-wave ignition of magnesium powders, Mach reflection and boundary layers, and transition in the shock-induced unsteady boundary layer on a flat plate. Shock-tube measurements of induction and post-induction rates for low-Btu gas mixtures are presented and shock-initiated ignition in COS-N2O-Ar mixtures is described. Cluster growth rates in supersaturated lead vapor are presented and a study of laser-induced plasma motion in a solenoidal magnetic field is reviewed.

Predicting who will drop out of nursing courses: a machine learning exercise.

PubMed

Moseley, Laurence G; Mead, Donna M

2008-05-01

The concepts of causation and prediction are different, and have different implications for practice. This distinction is applied here to studies of the problem of student attrition (although it is more widely applicable). Studies of attrition from nursing courses have tended to concentrate on causation, trying, largely unsuccessfully, to elicit what causes drop out. However, the problem may more fruitfully be cast in terms of predicting who is likely to drop out. One powerful method for attempting to make predictions is rule induction. This paper reports the use of the Answer Tree package from SPSS for that purpose. The main data set consisted of 3978 records on 528 nursing students, split into a training set and a test set. The source was standard university student records. The method obtained 84% sensitivity, 70% specificity, and 94% accuracy on previously unseen cases. The method requires large amounts of high quality data. When such data are available, rule induction offers a way to reduce attrition. It would be desirable to compare its results with those of predictions made by tutors using more informal conventional methods.

A defect-driven diagnostic method for machine tool spindles

PubMed Central

Vogl, Gregory W.; Donmez, M. Alkan

2016-01-01

Simple vibration-based metrics are, in many cases, insufficient to diagnose machine tool spindle condition. These metrics couple defect-based motion with spindle dynamics; diagnostics should be defect-driven. A new method and spindle condition estimation device (SCED) were developed to acquire data and to separate system dynamics from defect geometry. Based on this method, a spindle condition metric relying only on defect geometry is proposed. Application of the SCED on various milling and turning spindles shows that the new approach is robust for diagnosing the machine tool spindle condition. PMID:28065985

A Theoretical Model to Predict Both Horizontal Displacement and Vertical Displacement for Electromagnetic Induction-Based Deep Displacement Sensors

PubMed Central

Shentu, Nanying; Zhang, Hongjian; Li, Qing; Zhou, Hongliang; Tong, Renyuan; Li, Xiong

2012-01-01

Deep displacement observation is one basic means of landslide dynamic study and early warning monitoring and a key part of engineering geological investigation. In our previous work, we proposed a novel electromagnetic induction-based deep displacement sensor (I-type) to predict deep horizontal displacement and a theoretical model called equation-based equivalent loop approach (EELA) to describe its sensing characters. However in many landslide and related geological engineering cases, both horizontal displacement and vertical displacement vary apparently and dynamically so both may require monitoring. In this study, a II-type deep displacement sensor is designed by revising our I-type sensor to simultaneously monitor the deep horizontal displacement and vertical displacement variations at different depths within a sliding mass. Meanwhile, a new theoretical modeling called the numerical integration-based equivalent loop approach (NIELA) has been proposed to quantitatively depict II-type sensors’ mutual inductance properties with respect to predicted horizontal displacements and vertical displacements. After detailed examinations and comparative studies between measured mutual inductance voltage, NIELA-based mutual inductance and EELA-based mutual inductance, NIELA has verified to be an effective and quite accurate analytic model for characterization of II-type sensors. The NIELA model is widely applicable for II-type sensors’ monitoring on all kinds of landslides and other related geohazards with satisfactory estimation accuracy and calculation efficiency. PMID:22368467

A theoretical model to predict both horizontal displacement and vertical displacement for electromagnetic induction-based deep displacement sensors.

PubMed

Shentu, Nanying; Zhang, Hongjian; Li, Qing; Zhou, Hongliang; Tong, Renyuan; Li, Xiong

2012-01-01

Deep displacement observation is one basic means of landslide dynamic study and early warning monitoring and a key part of engineering geological investigation. In our previous work, we proposed a novel electromagnetic induction-based deep displacement sensor (I-type) to predict deep horizontal displacement and a theoretical model called equation-based equivalent loop approach (EELA) to describe its sensing characters. However in many landslide and related geological engineering cases, both horizontal displacement and vertical displacement vary apparently and dynamically so both may require monitoring. In this study, a II-type deep displacement sensor is designed by revising our I-type sensor to simultaneously monitor the deep horizontal displacement and vertical displacement variations at different depths within a sliding mass. Meanwhile, a new theoretical modeling called the numerical integration-based equivalent loop approach (NIELA) has been proposed to quantitatively depict II-type sensors' mutual inductance properties with respect to predicted horizontal displacements and vertical displacements. After detailed examinations and comparative studies between measured mutual inductance voltage, NIELA-based mutual inductance and EELA-based mutual inductance, NIELA has verified to be an effective and quite accurate analytic model for characterization of II-type sensors. The NIELA model is widely applicable for II-type sensors' monitoring on all kinds of landslides and other related geohazards with satisfactory estimation accuracy and calculation efficiency.

High-speed machining of Space Shuttle External Tank (ET) panels

NASA Technical Reports Server (NTRS)

Miller, J. A.

1983-01-01

Potential production rates and project cost savings achieved by converting the conventional machining process in manufacturing shuttle external tank panels to high speed machining (HSM) techniques were studied. Savings were projected from the comparison of current production rates with HSM rates and with rates attainable on new conventional machines. The HSM estimates were also based on rates attainable by retrofitting existing conventional equipment with high speed spindle motors and rates attainable using new state of the art machines designed and built for HSM.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 13: Laser Machining, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 3: Machining, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Kinetics of the melting front movement in process of centrifugal induction surfacing of powder material with nanoscale modificaters

NASA Astrophysics Data System (ADS)

Sasnouski, I.; Kurylionak, A.

2018-03-01

For solving the problem of improving the powder coatings modified by nanostructure components obtained by induction surfacing method tribological characteristics it is necessary to study the kinetics of the powdered layer melting and define the minimum time of melting. For powdered layer predetermined temperature maintenance at sintering mode stage it is required to determine the temperature difference through blank thickness of the for one hundred-day of the define the warm-up swing on of the stocking up by solving the thermal conductivity stationary problem for quill (hollow) cylinder with internal heat source. Herewith, since in practice thickness of the cylinder wall is much less then its diameter and the temperature difference is comparatively small, the thermal conductivity dependence upon the temperature can be treated as negligible. As it was shown by our previous studies, in the induction heating process under powdered material centrifugal surfacing (i.e. before achieving the melting temperature) the temperature distribution in powdered layer thickness may be considered even. Hereinafter, considering the blank part induction heating process quasi-stationarity under Fo big values, it is possible to consider its internal surface heating as developing with constant velocity. As a result of development the melting front movement mathematical model in a powdered material with nanostructure modifiers the minimum surfacing time is defined. It allows to minimize negative impact of thermal influence on formation of applied coating structure, to raise productivity of the process, to lower power inputs and to ensure saving of nonferrous and high alloys by reducing the allowance for machining. The difference of developed mathematical model of melting front movement from previously known is that the surface temperature from which the heat transfer occures is a variable and varies with a time after the linear law.

Evidence of end-effector based gait machines in gait rehabilitation after CNS lesion.

PubMed

Hesse, S; Schattat, N; Mehrholz, J; Werner, C

2013-01-01

A task-specific repetitive approach in gait rehabilitation after CNS lesion is well accepted nowadays. To ease the therapists' and patients' physical effort, the past two decades have seen the introduction of gait machines to intensify the amount of gait practice. Two principles have emerged, an exoskeleton- and an endeffector-based approach. Both systems share the harness and the body weight support. With the end-effector-based devices, the patients' feet are positioned on two foot plates, whose movements simulate stance and swing phase. This article provides an overview on the end-effector based machine's effectiveness regarding the restoration of gait. For the electromechanical gait trainer GT I, a meta analysis identified nine controlled trials (RCT) in stroke subjects (n = 568) and were analyzed to detect differences between end-effector-based locomotion + physiotherapy and physiotherapy alone. Patients practising with the machine effected in a superior gait ability (210 out of 319 patients, 65.8% vs. 96 out of 249 patients, 38.6%, respectively, Z = 2.29, p = 0.020), due to a larger training intensity. Only single RCTs have been reported for other devices and etiologies. The introduction of end-effector based gait machines has opened a new succesful chapter in gait rehabilitation after CNS lesion.

Properties of inductive reasoning.

PubMed

Heit, E

2000-12-01

This paper reviews the main psychological phenomena of inductive reasoning, covering 25 years of experimental and model-based research, in particular addressing four questions. First, what makes a case or event generalizable to other cases? Second, what makes a set of cases generalizable? Third, what makes a property or predicate projectable? Fourth, how do psychological models of induction address these results? The key results in inductive reasoning are outlined, and several recent models, including a new Bayesian account, are evaluated with respect to these results. In addition, future directions for experimental and model-based work are proposed.

Theory-based Bayesian models of inductive learning and reasoning.

PubMed

Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Energy-saving technology of vector controlled induction motor based on the adaptive neuro-controller

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Karandeev, D.

2015-10-01

The ongoing evolution of the power system towards a Smart Grid implies an important role of intelligent technologies, but poses strict requirements on their control schemes to preserve stability and controllability. This paper presents the adaptive neuro-controller for the vector control of induction motor within Smart Gird. The validity and effectiveness of the proposed energy-saving technology of vector controlled induction motor based on adaptive neuro-controller are verified by simulation results at different operating conditions over a wide speed range of induction motor.

Analytical Model-Based Design Optimization of a Transverse Flux Machine

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

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

Clinical and economic outcomes of rabbit antithymocyte globulin induction in adults who received kidney transplants from living unrelated donors and received cyclosporine-based immunosuppression.

PubMed

Miller, James T; Collins, Curtis D; Stuckey, Linda J; Luan, Fu L; Englesbe, Michael J; Magee, John C; Park, Jeong M

2009-10-01

To evaluate the efficacy, safety, and costs of rabbit antithymocyte globulin (TMG) induction in patients who received kidney transplants from living unrelated donors. Retrospective cohort study. Large academic medical center. Eighty-seven patients who received kidney transplants from living unrelated donors: 40 of the recipients underwent transplantation between January 1, 2003, and December 31, 2004, and did not receive TMG induction (no induction group); 47 underwent transplantation between January 1, 2005, and June 30, 2006, and received TMG induction (induction group). All patients received cyclosporine-based immunosuppression. Biopsy-proven acute rejection, posttransplantation complications, and inpatient hospital costs for the first 12 months after transplantation were compared between groups using standard univariate statistical analyses. Induction significantly decreased the occurrence of biopsy-proven acute rejection versus no induction (2% vs 48%, p<0.001). Fifty percent of rejection episodes in the no induction group required hospitalization, and 46% of rejection episodes required TMG treatment. Slightly elevated initial costs associated with TMG induction were offset by lower costs related to rejection treatment. Total inpatient costs for the 12 months after transplantation were comparable between the groups (no induction $66,038 vs induction $74,183, p>0.05). For the no induction versus induction groups, no significant differences in cytomegalovirus disease (5% vs 6%), malignancy (3% vs 2%), graft failures (5% vs 6%), mortality (5% vs 4%), and serum creatinine concentrations (mean +/- SD 1.4 +/- 0.3 vs 1.5 +/- 0.3 mg/dl) were observed at 12 months (p>0.05 for all comparisons). Five-day TMG induction effectively reduced the 1-year acute rejection rate without significantly increasing total inpatient costs or posttransplantation complications among recipients of kidney transplants from living unrelated donors.

TEACHING PHYSICS: A computer-based revitalization of Atwood's machine

NASA Astrophysics Data System (ADS)

Trumper, Ricardo; Gelbman, Moshe

2000-09-01

Atwood's machine is used in a microcomputer-based experiment to demonstrate Newton's second law with considerable precision. The friction force on the masses and the moment of inertia of the pulley can also be estimated.