WorldWideScience

Sample records for neural electric machine

  1. Optimization of Wire Electrical Discharge Machining Process Using Taguchi Method and Back Propagation Neural Network

    OpenAIRE

    SAĞBAŞ, Aysun; KAHRAMAN, Funda; Esme, Uğur

    2017-01-01

    In this study, it isattempted to model and optimize the wire electrical discharge machining (WEDM)process using Taguchi design of experiment and artificial neural network. Aneural network with back propagation algorithm was developed to predict theperformance characteristic, namely surface roughness. An approach to determineoptimal machining parameters setting was proposed based on the Taguchi designmethod. In addition, analysis of variance (ANOVA) was performed to identify thesignificant par...

  2. Electric machine

    Science.gov (United States)

    El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

    2012-07-17

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

  3. Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-01-01

    Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

  4. A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Sanjeev; Batish, Ajay [Thapar University, Patiala (India); Singh, Rupinder [GNDEC, Ludhiana (India); Singh, T. P. [Symbiosis Institute of Technology, Pune (India)

    2014-07-15

    In the present study, electric discharge machining process was used for machining of titanium alloys. Eight process parameters were varied during the process. Experimental results showed that current and pulse-on-time significantly affected the performance characteristics. Artificial neural network coupled with Taguchi approach was applied for optimization and prediction of surface roughness. The experimental results and the predicted results showed good agreement. SEM was used to investigate the surface integrity. Analysis for migration of different chemical elements and formation of compounds on the surface was performed using EDS and XRD pattern. The results showed that high discharge energy caused surface defects such as cracks, craters, thick recast layer, micro pores, pin holes, residual stresses and debris. Also, migration of chemical elements both from electrode and dielectric media were observed during EDS analysis. Presence of carbon was seen on the machined surface. XRD results showed formation of titanium carbide compound which precipitated on the machined surface.

  5. Electric machines

    CERN Document Server

    Gross, Charles A

    2006-01-01

    BASIC ELECTROMAGNETIC CONCEPTSBasic Magnetic ConceptsMagnetically Linear Systems: Magnetic CircuitsVoltage, Current, and Magnetic Field InteractionsMagnetic Properties of MaterialsNonlinear Magnetic Circuit AnalysisPermanent MagnetsSuperconducting MagnetsThe Fundamental Translational EM MachineThe Fundamental Rotational EM MachineMultiwinding EM SystemsLeakage FluxThe Concept of Ratings in EM SystemsSummaryProblemsTRANSFORMERSThe Ideal n-Winding TransformerTransformer Ratings and Per-Unit ScalingThe Nonideal Three-Winding TransformerThe Nonideal Two-Winding TransformerTransformer Efficiency and Voltage RegulationPractical ConsiderationsThe AutotransformerOperation of Transformers in Three-Phase EnvironmentsSequence Circuit Models for Three-Phase Transformer AnalysisHarmonics in TransformersSummaryProblemsBASIC MECHANICAL CONSIDERATIONSSome General PerspectivesEfficiencyLoad Torque-Speed CharacteristicsMass Polar Moment of InertiaGearingOperating ModesTranslational SystemsA Comprehensive Example: The ElevatorP...

  6. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a

  7. Electrical machines diagnosis

    CERN Document Server

    Trigeassou, Jean-Claude

    2013-01-01

    Monitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives.This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit.Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is perf

  8. Electrical machines & drives

    CERN Document Server

    Hammond, P

    1985-01-01

    Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

  9. Rotating electrical machines

    CERN Document Server

    Le Doeuff, René

    2013-01-01

    In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

  10. Neuro-vector-based electrical machine driver combining a neural plant identifier and a conventional vector controller

    Science.gov (United States)

    Madani, Kurosh; Mercier, Gilles; Dinarvand, Mohammad; Depecker, Jean-Charles

    1999-03-01

    One of the most important problems, for a machine control process is the system identification. To identify varying parameters which are dependent from other system's parameters (speed, voltage and currents, etc.), one must have an adaptive control system. Synchronous machines conventional vector control's implementation using PID controllers have been recently proposed presenting the best actual solution. It supposes an appropriated model of the plant. But real plant's parameters vary and the P.I.D. controller is not suitable because of the parameters variation and non-linearity introduced by the machine's physical structure. In this paper, we present an on-line dynamic adaptive neural based vector control system identifying the motor's parameters of a synchronous machine. We present and discuss a DSP based real- time implementation of our adaptive neuro-controller. Simulation and experimental results validating our approach have been reported.

  11. Electrical Discharge Machining.

    Science.gov (United States)

    Montgomery, C. M.

    The manual is for use by students learning electrical discharge machining (EDM). It consists of eight units divided into several lessons, each designed to meet one of the stated objectives for the unit. The units deal with: introduction to and advantages of EDM, the EDM process, basic components of EDM, reaction between forming tool and workpiece,…

  12. Electrical machines & their applications

    CERN Document Server

    Hindmarsh, J

    1984-01-01

    A self-contained, comprehensive and unified treatment of electrical machines, including consideration of their control characteristics in both conventional and semiconductor switched circuits. This new edition has been expanded and updated to include material which reflects current thinking and practice. All references have been updated to conform to the latest national (BS) and international (IEC) recommendations and a new appendix has been added which deals more fully with the theory of permanent-magnets, recognising the growing importance of permanent-magnet machines. The text is so arra

  13. Design of rotating electrical machines

    CERN Document Server

    Pyrhonen , Juha; Hrabovcova , Valeria

    2013-01-01

    In one complete volume, this essential reference presents an in-depth overview of the theoretical principles and techniques of electrical machine design. This timely new edition offers up-to-date theory and guidelines for the design of electrical machines, taking into account recent advances in permanent magnet machines as well as synchronous reluctance machines. New coverage includes: Brand new material on the ecological impact of the motors, covering the eco-design principles of rotating electrical machinesAn expanded section on the design of permanent magnet synchronous machines, now repo

  14. Electrical machines with Matlab

    CERN Document Server

    Gonen, Turan

    2011-01-01

    Basic ConceptsDistribution SystemImpact of Dispersed Storage and GenerationBrief Overview of Basic Electrical MachinesReal and Reactive Powers in Single-Phase AC CircuitsThree-Phase CircuitsThree-Phase SystemsUnbalanced Three-Phase LoadsMeasurement of Average Power in Three-Phase CircuitsPower Factor CorrectionMagnetic CircuitsMagnetic Field of Current-Carrying ConductorsAmpère's Magnetic Circuital LawMagnetic CircuitsMagnetic Circuit with Air GapBrief Review of FerromagnetismMagnetic Core LossesHow to Determine Flux for a Given MMFPermanent MagnetsTransformersTransformer ConstructionBrief Rev

  15. Non-conventional electrical machines

    CERN Document Server

    Rezzoug, Abderrezak

    2013-01-01

    The developments of electrical machines are due to the convergence of material progress, improved calculation tools, and new feeding sources. Among the many recent machines, the authors have chosen, in this first book, to relate the progress in slow speed machines, high speed machines, and superconducting machines. The first part of the book is dedicated to materials and an overview of magnetism, mechanic, and heat transfer.

  16. Neural Approaches to Machine Consciousness

    Science.gov (United States)

    Aleksander, Igor; Eng., F. R.

    2008-10-01

    `Machine Consciousness', which some years ago might have been suppressed as an inappropriate pursuit, has come out of the closet and is now a legitimate area of research concern. This paper briefly surveys the last few years of worldwide research in this area which divides into rule-based and neural approaches and then reviews the work of the author's laboratory during the last ten years. The paper develops a fresh perspective on this work: it is argued that neural approaches, in this case, digital neural systems, can address phenomenological consciousness. Important clarifications of phenomenology and virtuality which enter this modelling are explained in the early parts of the paper. In neural models, phenomenology is a form of depictive inner representation that has five specific axiomatic features: a sense of self-presence in an external world; a sense of imagination of past experience and fiction; a sense of attention; a capacity for planning; a sense of emotion-based volition that influences planning. It is shown that these five features have separate but integrated support in dynamic neural systems.

  17. CNC electrical discharge machining centers

    Energy Technology Data Exchange (ETDEWEB)

    Jaggars, S.R.

    1991-10-01

    Computer numerical control (CNC) electrical discharge machining (EDM) centers were investigated to evaluate the application and cost effectiveness of establishing this capability at Allied-Signal Inc., Kansas City Division (KCD). In line with this investigation, metal samples were designed, prepared, and machined on an existing 15-year-old EDM machine and on two current technology CNC EDM machining centers at outside vendors. The results were recorded and evaluated. The study revealed that CNC EDM centers are a capability that should be established at KCD. From the information gained, a machine specification was written and a shop was purchased and installed in the Engineering Shop. The older machine was exchanged for a new model. Additional machines were installed in the Tool Design and Fabrication and Precision Microfinishing departments. The Engineering Shop machine will be principally used for the following purposes: producing deep cavities in small corner radii, machining simulated casting models, machining difficult-to-machine materials, and polishing difficult-to-hand polish mold cavities. 2 refs., 18 figs., 3 tabs.

  18. Permutation parity machines for neural cryptography.

    Science.gov (United States)

    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.

  19. Can neural machine translation do simultaneous translation?

    OpenAIRE

    Cho, Kyunghyun; Esipova, Masha

    2016-01-01

    We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segmen...

  20. Electrical machines and drives

    CERN Document Server

    Hindmarsh, John

    2002-01-01

    Recent years have brought substantial developments in electrical drive technology, with the appearance of highly rated, very-high-speed power-electronic switches, combined with microcomputer control systems.This popular textbook has been thoroughly revised and updated in the light of these changes. It retains its successful formula of teaching through worked examples, which are put in context with concise explanations of theory, revision of equations and discussion of the engineering implications. Numerous problems are also provided, with answers supplied.The third edition in

  1. Magnet management in electric machines

    Science.gov (United States)

    Reddy, Patel Bhageerath; El-Refaie, Ayman Mohamed Fawzi; Huh, Kum Kang

    2017-03-21

    A magnet management method of controlling a ferrite-type permanent magnet electrical machine includes receiving and/or estimating the temperature permanent magnets; determining if that temperature is below a predetermined temperature; and if so, then: selectively heating the magnets in order to prevent demagnetization and/or derating the machine. A similar method provides for controlling magnetization level by analyzing flux or magnetization level. Controllers that employ various methods are disclosed. The present invention has been described in terms of specific embodiment(s), and it is recognized that equivalents, alternatives, and modifications, aside from those expressly stated, are possible and within the scope of the appending claims.

  2. Continual Learning through Evolvable Neural Turing Machines

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Risi, Sebastian

    2016-01-01

    Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENT......) approach is able to perform one-shot learning in a reinforcement learning task without catastrophic forgetting of previously stored associations.......Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM...

  3. Rotating electrical machines: Poynting flow

    Science.gov (United States)

    Donaghy-Spargo, C.

    2017-09-01

    This paper presents a complementary approach to the traditional Lorentz and Faraday approaches that are typically adopted in the classroom when teaching the fundamentals of electrical machines—motors and generators. The approach adopted is based upon the Poynting vector, which illustrates the ‘flow’ of electromagnetic energy. It is shown through simple vector analysis that the energy-flux density flow approach can provide insight into the operation of electrical machines and it is also shown that the results are in agreement with conventional Maxwell stress-based theory. The advantage of this approach is its complementary completion of the physical picture regarding the electromechanical energy conversion process—it is also a means of maintaining student interest in this subject and as an unconventional application of the Poynting vector during normal study of electromagnetism.

  4. Electric machine for hybrid motor vehicle

    Science.gov (United States)

    Hsu, John Sheungchun

    2007-09-18

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

  5. Multi-winding homopolar electric machine

    Science.gov (United States)

    Van Neste, Charles W

    2012-10-16

    A multi-winding homopolar electric machine and method for converting between mechanical energy and electrical energy. The electric machine includes a shaft defining an axis of rotation, first and second magnets, a shielding portion, and a conductor. First and second magnets are coaxial with the shaft and include a charged pole surface and an oppositely charged pole surface, the charged pole surfaces facing one another to form a repulsive field therebetween. The shield portion extends between the magnets to confine at least a portion of the repulsive field to between the first and second magnets. The conductor extends between first and second end contacts and is toroidally coiled about the first and second magnets and the shield portion to develop a voltage across the first and second end contacts in response to rotation of the electric machine about the axis of rotation.

  6. Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

    Directory of Open Access Journals (Sweden)

    Ming Cheng

    2015-09-01

    Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

  7. Torque ripple reduction in electric machines

    Science.gov (United States)

    Reddy, Patel Bhageerath; Huh, Kum-Kang; El-Refaie, Ayman Mohamed Fawzi; Galioto, Steven Joseph

    2017-08-22

    An electric machine, such as an Internal Permanent magnet or Synchronous Reluctance machine, having X phases, that includes a stator assembly, having M slots, with a stator core and stator teeth, that is further configured with stator windings to generate a stator magnetic field when excited with alternating currents and extends along a longitudinal axis with an inner surface that defines a cavity; and a rotor assembly, having N poles, disposed within the cavity which is configured to rotate about the longitudinal axis, wherein the rotor assembly includes a shaft, a rotor core located circumferentially around the shaft. The machine is configured such that a value k=M/(X*N) wherein k is a non-integer greater than about 1.3. The electric machine may alternatively, or additionally, include a non-uniformed gap between the exterior surface of the rotor spokes and the interior stator surface of the stator.

  8. High slot utilization systems for electric machines

    Science.gov (United States)

    Hsu, John S

    2009-06-23

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

  9. Apparatus for cooling an electric machine

    Science.gov (United States)

    Palafox, Pepe; Gerstler, William Dwight; Shen, Xiaochun; El-Refaie, Ayman Mohamed Fawzi; Lokhandwalla, Murtuza; Salasoo, Lembit

    2013-07-16

    Provided is an apparatus, for example, for use with a rotating electric machine, that includes a housing. The housing can include a housing main portion and a housing end portion. The housing main portion can be configured to be disposed proximal to a body portion of a stator section of an electric machine. The housing main portion can define a main fluid channel that is configured to conduct fluid therethrough. The housing end portion can receive fluid from said main fluid channel and direct fluid into contact with a winding end portion of a conductive winding of the stator section.

  10. Insulation assembly for electric machine

    Science.gov (United States)

    Rhoads, Frederick W.; Titmuss, David F.; Parish, Harold; Campbell, John D.

    2013-10-15

    An insulation assembly is provided that includes a generally annularly-shaped main body and at least two spaced-apart fingers extending radially inwards from the main body. The spaced-apart fingers define a gap between the fingers. A slot liner may be inserted within the gap. The main body may include a plurality of circumferentially distributed segments. Each one of the plurality of segments may be operatively connected to another of the plurality of segments to form the continuous main body. The slot liner may be formed as a single extruded piece defining a plurality of cavities. A plurality of conductors (extendable from the stator assembly) may be axially inserted within a respective one of the plurality of cavities. The insulation assembly electrically isolates the conductors in the electric motor from the stator stack and from other conductors.

  11. Soft Magnetic Composites in Novel Designs of Electrical Traction Machines

    OpenAIRE

    Zhang, Bo

    2017-01-01

    Nowadays, the manufacturing of electrical machines based on electrical steel laminations has been well established worldwide. Compared with the electrical steel, the soft magnetic composites (SMC) shows magnetic isotropy and lower eddy current losses. Thus, it becomes an important impulse promoting the development of new topologies of electrical machine. The application of SMC in the electrical traction machine for hybrid electrical vehicle or electrical vehicle has been researched in the work.

  12. Developments in electrical machines using permanent magnets

    Science.gov (United States)

    Chalmers, B. J.

    1996-05-01

    The availability of high-field permanent-magnet materials has created opportunities for the development of electrical machines with advantageous properties including high efficiency, compact size, low weight and brushless operation. The paper reports the design and performance of a number of motors and generators which have recently been developed and demonstrated.

  13. SIMULATION TOOLS FOR ELECTRICAL MACHINES MODELLING ...

    African Journals Online (AJOL)

    Dr Obe

    [10]D.W. Marquardt, "An Algorithm for least-square estimation of non-linear parameters" J Soc. Ind. Appl. Math, voI.1l, No.2, June 1963,pp.431-441. [11] Peter Vas, Electrical machines and drives-A space vector theory approach, Clarendon Press,. Oxford, 1992. [12] MATLAB User's Guide. The Mathworks, Inc,. Natick, 199l.

  14. Tampa Electric Neural Network Sootblowing

    Energy Technology Data Exchange (ETDEWEB)

    Mark A. Rhode

    2003-12-31

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NO{sub x} formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent soot-blowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat

  15. Tampa Electric Neural Network Sootblowing

    Energy Technology Data Exchange (ETDEWEB)

    Mark A. Rhode

    2004-09-30

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate

  16. Tampa Electric Neural Network Sootblowing

    Energy Technology Data Exchange (ETDEWEB)

    Mark A. Rhode

    2004-03-31

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing co-funding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate

  17. Support vector machine for day ahead electricity price forecasting

    Science.gov (United States)

    Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

    2015-05-01

    Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

  18. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  19. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  20. Analysis Of Electrical – Thermal Coupling Of Induction Machine ...

    African Journals Online (AJOL)

    The interaction of the Electrical and mechanical parts of Electrical machines gives rise to the heating of the machine's constituent parts. This consequently leads to an increase in temperature which if not properly monitored may lead to the breakdown of the machine. This paper therefore presents the Electrical and thermal ...

  1. Neural networks for perception human and machine perception

    CERN Document Server

    Wechsler, Harry

    1991-01-01

    Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model ofobject recognition in human vision, the self-organization of functional architecture in t

  2. Influence of electrical resistivity and machining parameters on electrical discharge machining performance of engineering ceramics.

    Directory of Open Access Journals (Sweden)

    Renjie Ji

    Full Text Available Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR, electrode wear ratio (EWR, and surface roughness (SR. The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical

  3. Influence of electrical resistivity and machining parameters on electrical discharge machining performance of engineering ceramics.

    Science.gov (United States)

    Ji, Renjie; Liu, Yonghong; Diao, Ruiqiang; Xu, Chenchen; Li, Xiaopeng; Cai, Baoping; Zhang, Yanzhen

    2014-01-01

    Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge

  4. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    . The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....... and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right...

  5. A Novel Configuration of Feedback's Electric Machine Tutor (EMT ...

    African Journals Online (AJOL)

    This paper reports a successful adaptation of a laboratory teaching machine - Electrical Machine Tutor (EMT) model 180 as an asynchronous composite polyphase electric motor without rotor conductors. The device comprises two such identical machines without rotor conductors, all the conductors being on the stator side, ...

  6. Linear electric machines, drives, and MAGLEVs handbook

    CERN Document Server

    Boldea, Ion

    2013-01-01

    Based on author Ion Boldea's 40 years of experience and the latest research, Linear Electric Machines, Drives, and Maglevs Handbook provides a practical and comprehensive resource on the steady improvement in this field. The book presents in-depth reviews of basic concepts and detailed explorations of complex subjects, including classifications and practical topologies, with sample results based on an up-to-date survey of the field. Packed with case studies, this state-of-the-art handbook covers topics such as modeling, steady state, and transients as well as control, design, and testing of li

  7. NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems

    Directory of Open Access Journals (Sweden)

    Caglayan Ozan

    2017-10-01

    Full Text Available In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.

  8. Empirical Investigation of Optimization Algorithms in Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Bahar Parnia

    2017-06-01

    Full Text Available Training neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.

  9. Electric machine and current source inverter drive system

    Science.gov (United States)

    Hsu, John S

    2014-06-24

    A drive system includes an electric machine and a current source inverter (CSI). This integration of an electric machine and an inverter uses the machine's field excitation coil for not only flux generation in the machine but also for the CSI inductor. This integration of the two technologies, namely the U machine motor and the CSI, opens a new chapter for the component function integration instead of the traditional integration by simply placing separate machine and inverter components in the same housing. Elimination of the CSI inductor adds to the CSI volumetric reduction of the capacitors and the elimination of PMs for the motor further improve the drive system cost, weight, and volume.

  10. Electric vehicle machines and drives design, analysis and application

    CERN Document Server

    Chau, K

    2015-01-01

    A timely comprehensive reference consolidates the research and development of electric vehicle machines and drives for electric and hybrid propulsions • Focuses on electric vehicle machines and drives • Covers the major technologies in the area including fundamental concepts and applications • Emphasis the design criteria, performance analyses and application examples or potentials of various motor drives and machine systems • Accompanying website includes the simulation models and outcomes as supplementary material

  11. Convolutional over Recurrent Encoder for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Dakwale Praveen

    2017-06-01

    Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

  12. Ensemble Learning for Multi-Source Neural Machine Translation

    NARCIS (Netherlands)

    Garmash, E.; Monz, C.

    2016-01-01

    In this paper we describe and evaluate methods to perform ensemble prediction in neural machine translation (NMT). We compare two methods of ensemble set induction: sampling parameter initializations for an NMT system, which is a relatively established method in NMT (Sutskever et al., 2014), and NMT

  13. Passivity-Based Control of Electric Machines

    Energy Technology Data Exchange (ETDEWEB)

    Nicklasson, P.J.

    1996-12-31

    This doctoral thesis presents new results on the design and analysis of controllers for a class of electric machines. Nonlinear controllers are derived from a Lagrangian model representation using passivity techniques, and previous results on induction motors are improved and extended to Blondel-Park transformable machines. The relation to conventional techniques is discussed, and it is shown that the formalism introduced in this work facilitates analysis of conventional methods, so that open questions concerning these methods may be resolved. In addition, the thesis contains the following improvements of previously published results on the control of induction motors: (1) Improvement of a passivity-based speed/position controller, (2) Extension of passivity-based (observer-less and observer-based) controllers from regulation to tracking of rotor flux norm, (3) An extension of the classical indirect FOC (Field-Oriented Control) scheme to also include global rotor flux norm tracking, instead of only torque tracking and rotor flux norm regulation. The design is illustrated experimentally by applying the proposed control schemes to a squirrel-cage induction motor. The results show that the proposed methods have advantages over previous designs with respect to controller tuning, performance and robustness. 145 refs., 21 figs.

  14. Quantum neural network based machine translator for Hindi to English.

    Science.gov (United States)

    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.

  15. Open-Source Neural Machine Translation API Server

    Directory of Open Access Journals (Sweden)

    Tars Sander

    2017-10-01

    Full Text Available We introduce an open-source implementation of a machine translation API server. The aim of this software package is to enable anyone to run their own multi-engine translation server with neural machine translation engines, supporting an open API for client applications. Besides the hub with the implementation of the client API and the translation service providers running in the background we also describe an open-source demo web application that uses our software package and implements an online translation tool that supports collecting translation quality comparisons from users.

  16. Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines

    Directory of Open Access Journals (Sweden)

    Hubert Roth

    2008-11-01

    Full Text Available The specialized hairs and slit sensillae of spiders (Cupiennius salei can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.

  17. Performance evaluation of coherent Ising machines against classical neural networks

    Science.gov (United States)

    Haribara, Yoshitaka; Ishikawa, Hitoshi; Utsunomiya, Shoko; Aihara, Kazuyuki; Yamamoto, Yoshihisa

    2017-12-01

    The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.

  18. Neural Network Machine Learning and Dimension Reduction for Data Visualization

    Science.gov (United States)

    Liles, Charles A.

    2014-01-01

    Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

  19. Electric machines steady state, transients, and design with Matlab

    CERN Document Server

    Boldea, Ion

    2009-01-01

    Part I: Steady StateIntroductionElectric Energy and Electric MachinesBasic Types of Transformers and Electric MachinesLosses and EfficiencyPhysical Limitations and RatingsNameplate RatingsMethods of AnalysisState of the Art and Perspective Electric TransformersAC Coil with Magnetic Core and Transformer Principles Magnetic Materials in EMs and Their LossesElectric Conductors and Their Skin EffectsComponents of Single- and 3-Phase TransformersFlux Linkages and Inductances of Single-Phase TransformersCircuit Equations of Single-Phase Transformers With Core LossesSteady State and Equivalent Circui

  20. Electric machines with axial magnetic flux

    Science.gov (United States)

    Nuca, I.; Ambros, T.; Burduniuc, M.; Deaconu, S. I.; Turcanu, A.

    2018-01-01

    The paper contains information on the performance of axial machines compared to cylindrical ones. At the same time, various constructive schemes of synchronous electromechanical converters with permanent magnets and asynchronous with short-circuited rotor are presented. In the developed constructions, the aim is to maximize the usage of the material of the stator windings. The design elements of the axial machine magnetic system are presented. The FEMM application depicted the array of the magnetic field of an axial machine.

  1. Unraveling the Contribution of Image Captioning and Neural Machine Translation for Multimodal Machine Translation

    Directory of Open Access Journals (Sweden)

    Lala Chiraag

    2017-06-01

    Full Text Available Recent work on multimodal machine translation has attempted to address the problem of producing target language image descriptions based on both the source language description and the corresponding image. However, existing work has not been conclusive on the contribution of visual information. This paper presents an in-depth study of the problem by examining the differences and complementarities of two related but distinct approaches to this task: textonly neural machine translation and image captioning. We analyse the scope for improvement and the effect of different data and settings to build models for these tasks. We also propose ways of combining these two approaches for improved translation quality.

  2. Effect of machining fluid on the process performance of wire electrical discharge machining of nanocomposite ceramic

    Directory of Open Access Journals (Sweden)

    Zhang Chengmao

    2015-01-01

    Full Text Available Wire electric discharge machining (WEDM promise to be effective and economical techniques for the production of tools and parts from conducting ceramic blanks. However, the manufacturing of nanocomposite ceramics blanks with these processes is a long and costly process. This paper presents a new process of machining nanocomposite ceramics using WEDM. WEDM uses water based emulsion, polyvinyl alcohol and distilled water as the machining fluid. Machining fluid is a primary factor that affects the material removal rate and surface quality of WEDM. The effects of emulsion concentration, polyvinyl alcohol concentration and distilled water of the machining fluid on the process performance have been investigated.

  3. On the Optimal Selection of Electrical Machines Fans

    Directory of Open Access Journals (Sweden)

    Mădălin Costin

    2014-09-01

    Full Text Available In this paper an analytic relationship for electrical machine fan design has been developed. In the particularly case of salient poles synchronous machine (with salient poles – for electromagnetic field excitation or surface mounded permanent magnet, this approach allowed to express the fan power as a function of machine middle axe air gap. This analytic foundation developed may leads to different optimization criteria as specific active materials or costs. Numerical simulations confirm our approach.

  4. Experimental Investigation of process parameters influence on machining Inconel 800 in the Electrical Spark Eroding Machine

    Science.gov (United States)

    Karunakaran, K.; Chandrasekaran, M.

    2016-11-01

    The Electrical Spark Eroding Machining is an entrenched sophisticated machining process for producing complex geometry with close tolerances in hard materials like super alloy which are extremely difficult-to-machine by using conventional machining processes. It is sometimes offered as a better alternative or sometimes as an only alternative for generating accurate 3D complex shapes of macro, micro and nano-features in such difficult-to-machine materials among other advanced machining processes. The accomplishment of such challenging task by use of Electrical Spark Eroding Machining or Electrical Discharge Machining (EDM) is depending upon selection of apt process parameters. This paper is about analyzing the influencing of parameter in electrical eroding machining for Inconel 800 with electrolytic copper as a tool. The experimental runs were performed with various input conditions to process Inconel 800 nickel based super alloy for analyzing the response of material removal rate, surface roughness and tool wear rate. These are the measures of performance of individual experimental value of parameters such as pulse on time, Pulse off time, peak current. Taguchi full factorial Design by using Minitab release 14 software was employed to meet the manufacture requirements of preparing process parameter selection card for Inconel 800 jobs. The individual parameter's contribution towards surface roughness was observed from 13.68% to 64.66%.

  5. a novel configuration of feedback's electric machine tutor (emt)

    African Journals Online (AJOL)

    NIJOTECH

    They have the advantage of reliable operation at high speeds and potential for long life in a hostile environment. Permanent magnet machines with rotating magnets can be used to fulfill this objective. What is described in this paper is how to achieve this with Feedback Electrical. Machines Tutor Model 180 with all windings.

  6. Electrical discharge machining studies on reactive sintered FeAl

    Indian Academy of Sciences (India)

    Electrical discharge machining (EDM) studies on reactive sintered FeAl were carried out with different process parameters. The metal removal rate and tool removal rate were found to increase with the applied pulse on-time. The surface roughness of machined surface also changed with the applied pulse on-time.

  7. A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces.

    Science.gov (United States)

    Chen, Yi; Yao, Enyi; Basu, Arindam

    2016-06-01

    Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X.

  8. Outsmarting neural networks: an alternative paradigm for machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Protopopescu, V.; Rao, N.S.V.

    1996-10-01

    We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

  9. Neural cell image segmentation method based on support vector machine

    Science.gov (United States)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  10. Is Neural Machine Translation the New State of the Art?

    Directory of Open Access Journals (Sweden)

    Castilho Sheila

    2017-06-01

    Full Text Available This paper discusses neural machine translation (NMT, a new paradigm in the MT field, comparing the quality of NMT systems with statistical MT by describing three studies using automatic and human evaluation methods. Automatic evaluation results presented for NMT are very promising, however human evaluations show mixed results. We report increases in fluency but inconsistent results for adequacy and post-editing effort. NMT undoubtedly represents a step forward for the MT field, but one that the community should be careful not to oversell.

  11. Converter applications and their influence on large electrical machines

    CERN Document Server

    Drubel, Oliver

    2013-01-01

    Converter driven applications are applied in more and more processes. Almost any installed wind-farm, ship drives, steel mills, several boiler feed water pumps, extruder and many other applications operate much more efficient and economic in case of variable speed solutions. The boundary conditions for a motor or generator will change, if it is supplied by a converter. An electrical machine, which is operated by a converter, can no longer be regarded as an independent component, but is embedded in a system consisting of converter and machine. This book gives an overview of existing converter designs for large electrical machines. Methods for the appropriate calculation of machine phenomena, which are implied by converters are derived in the power range above 500kVA. It is shown how due to the converter inherent higher voltage harmonics and pulse frequencies special phenomena are caused inside the machine which can be the reason for malfunction. It is demonstrated that additional losses create additional tempe...

  12. A Review of Design Optimization Methods for Electrical Machines

    Directory of Open Access Journals (Sweden)

    Gang Lei

    2017-11-01

    Full Text Available Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

  13. Standard of Electrical Washing Machine for Household and Similar Purposes

    Institute of Scientific and Technical Information of China (English)

    Lu Jianguo

    2011-01-01

    Background With further improvement of people's living,the household washing machine industry has entered a new stage of development.However,some indicators of GB/T 4288-2003 have become no longer suitable for the development of household washing machine products at present.Particularly,with an increasing number of basic functions and auxiliary functions,many aspects are not covered by the existing standard.In order to further improve the overall quality of China's household washing machines and enhance their competitiveness in the international market,guide manufacturers to produce household washing machines in line with the demands of consumers and instruct consumers to properly purchase and use household washing machines,it is imperative to revise the GB/T 4288-2003 Household Electric Washing Machine.

  14. A COMPUTERIZED DIAGNOSTIC COMPLEX FOR RELIABILITY TESTING OF ELECTRIC MACHINES

    Directory of Open Access Journals (Sweden)

    O.О. Somka

    2015-06-01

    Full Text Available Purpose. To develop a diagnostic complex meeting the criteria and requirements for carrying out accelerated reliability test and realizing the basic modes of electric machines operation and performance of the posed problems necessary in the process of such test. Methodology. To determine and forecast the indices of electric machines reliability in accordance with the statistic data of repair plants we have conditionally divided them into structural parts that are most likely to fail. We have preliminarily assessed the state of each of these parts, which includes revelation of faults and deviations of technical and geometric parameters. We have determined the analyzed electric machine controlled parameters used for assessment of quantitative characteristics of reliability of these parts and electric machines on the whole. Results. As a result of the research, we have substantiated the structure of a computerized complex for electric machines reliability test. It allows us to change thermal and vibration actions without violation of the physics of the processes of aging and wearing of the basic structural parts and elements material. The above mentioned makes it possible to considerably reduce time spent on carrying out electric machines reliability tests and improve trustworthiness of the data obtained as a result of their performance. Originality. A special feature of determination of the controlled parameters consists in removal of vibration components in the idle mode and after disconnection of the analyzed electric machine from the power supply with the aim of singling out the vibration electromagnetic component, fixing the degree of sparking and bend of the shaft by means of phototechnique and local determination of structural parts temperature provided by corresponding location of thermal sensors. Practical value. We have offered a scheme of location of thermal and vibration sensors, which allows improvement of parameters measuring accuracy

  15. A Review on the Faults of Electric Machines Used in Electric Ships

    OpenAIRE

    Dionysios V. Spyropoulos; Epaminondas D. Mitronikas

    2013-01-01

    Electric propulsion systems are today widely applied in modern ships, including transport ships and warships. The ship of the future will be fully electric, and not only its propulsion system but also all the other services will depend on electric power. The robust and reliable operation of the ship’s power system is essential. In this work, a review on the mechanical and electrical faults of electric machines that are used in electric ships is presented.

  16. A Review on the Faults of Electric Machines Used in Electric Ships

    Directory of Open Access Journals (Sweden)

    Dionysios V. Spyropoulos

    2013-01-01

    Full Text Available Electric propulsion systems are today widely applied in modern ships, including transport ships and warships. The ship of the future will be fully electric, and not only its propulsion system but also all the other services will depend on electric power. The robust and reliable operation of the ship’s power system is essential. In this work, a review on the mechanical and electrical faults of electric machines that are used in electric ships is presented.

  17. Power converters and AC electrical drives with linear neural networks

    CERN Document Server

    Cirrincione, Maurizio

    2012-01-01

    The first book of its kind, Power Converters and AC Electrical Drives with Linear Neural Networks systematically explores the application of neural networks in the field of power electronics, with particular emphasis on the sensorless control of AC drives. It presents the classical theory based on space-vectors in identification, discusses control of electrical drives and power converters, and examines improvements that can be attained when using linear neural networks. The book integrates power electronics and electrical drives with artificial neural networks (ANN). Organized into four parts,

  18. SIMULATION TOOLS FOR ELECTRICAL MACHINES MODELLING ...

    African Journals Online (AJOL)

    Dr Obe

    This paper illustrates the way MATLAB is used to model non-linearites in synchronous ... Keywords: Asynchronous machine; MATLAB scripts; engineering education; skin-effect; saturation effect; dynamic behavour. 1.0 Introduction .... algorithm of Marquardt [10] is employed. In figure 1, the estimated function becomes,.

  19. Artificial neural networks in predicting current in electric arc furnaces

    Science.gov (United States)

    Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.

    2014-03-01

    The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.

  20. Support Vector Machines for decision support in electricity markets׳ strategic bidding

    DEFF Research Database (Denmark)

    Pinto, Tiago; Sousa, Tiago M.; Praça, Isabel

    2015-01-01

    by being included in ALBidS and then compared with the application of an Artificial Neural Network (ANN), originating promising results: an effective electricity market price forecast in a fast execution time. The proposed approach is tested and validated using real electricity markets data from MIBEL......׳ research group has developed a multi-agent system: Multi-Agent System for Competitive Electricity Markets (MASCEM), which simulates the electricity markets environment. MASCEM is integrated with Adaptive Learning Strategic Bidding System (ALBidS) that works as a decision support system for market players....... The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated...

  1. Iron Losses in Electrical Machines - Influence of Material Properties, Manufacturing Processes, and Inverter Operation

    OpenAIRE

    Krings, Andreas

    2014-01-01

    As the major electricity consumer, electrical machines play a key role for global energy savings. Machine manufacturers put considerable efforts into the development of more efficient electrical machines for loss reduction and higher power density achievements. A consolidated knowledge of the occurring losses in electrical machines is a basic requirement for efficiency improvements. This thesis deals with iron losses in electrical machines. The major focus is on the influences of the stator c...

  2. Electricity price prediction: a comparison of machine learning algorithms

    OpenAIRE

    Wormstrand, Øystein

    2011-01-01

    In this master thesis we have worked with seven different machine learning methods to discover which algorithm is best suited for predicting the next-day electricity price for the Norwegian price area NO1 on Nord Pool Spot. Based on historical price, consumption, weather and reservoir data, we have created our own data sets. Data from 2001 through 2009 was gathered, where the last one third of the period was used for testing. We have tested our selected machine learning methods ...

  3. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  4. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Directory of Open Access Journals (Sweden)

    Eric A Pohlmeyer

    Full Text Available Brain-machine interface (BMI systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings. These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  6. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Science.gov (United States)

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  7. Design of electric control system for automatic vegetable bundling machine

    Science.gov (United States)

    Bao, Yan

    2017-06-01

    A design can meet the requirements of automatic bale food structure and has the advantages of simple circuit, and the volume is easy to enhance the electric control system of machine carrying bunch of dishes and low cost. The bundle of vegetable machine should meet the sensor to detect and control, in order to meet the control requirements; binding force can be adjusted by the button to achieve; strapping speed also can be adjusted, by the keys to set; sensors and mechanical line connection, convenient operation; can be directly connected with the plug, the 220V power supply can be connected to a power source; if, can work, by the transmission signal sensor, MCU to control the motor, drive and control procedures for small motor. The working principle of LED control circuit and temperature control circuit is described. The design of electric control system of automatic dish machine.

  8. Electrical Machines Laminations Magnetic Properties: A Virtual Instrument Laboratory

    Science.gov (United States)

    Martinez-Roman, Javier; Perez-Cruz, Juan; Pineda-Sanchez, Manuel; Puche-Panadero, Ruben; Roger-Folch, Jose; Riera-Guasp, Martin; Sapena-Baño, Angel

    2015-01-01

    Undergraduate courses in electrical machines often include an introduction to their magnetic circuits and to the various magnetic materials used in their construction and their properties. The students must learn to be able to recognize and compare the permeability, saturation, and losses of these magnetic materials, relate each material to its…

  9. Analytical calculation of vibrations of electromagnetic origin in electrical machines

    Science.gov (United States)

    McCloskey, Alex; Arrasate, Xabier; Hernández, Xabier; Gómez, Iratxo; Almandoz, Gaizka

    2018-01-01

    Electrical motors are widely used and are often required to satisfy comfort specifications. Thus, vibration response estimations are necessary to reach optimum machine designs. This work presents an improved analytical model to calculate vibration response of an electrical machine. The stator and windings are modelled as a double circular cylindrical shell. As the stator is a laminated structure, orthotropic properties are applied to it. The values of those material properties are calculated according to the characteristics of the motor and the known material properties taken from previous works. Therefore, the model proposed takes into account the axial direction, so that length is considered, and also the contribution of windings, which differs from one machine to another. These aspects make the model valuable for a wide range of electrical motor types. In order to validate the analytical calculation, natural frequencies are calculated and compared to those obtained by Finite Element Method (FEM), giving relative errors below 10% for several circumferential and axial mode order combinations. It is also validated the analytical vibration calculation with acceleration measurements in a real machine. The comparison shows good agreement for the proposed model, being the most important frequency components in the same magnitude order. A simplified two dimensional model is also applied and the results obtained are not so satisfactory.

  10. Electric machines modeling, condition monitoring, and fault diagnosis

    CERN Document Server

    Toliyat, Hamid A; Choi, Seungdeog; Meshgin-Kelk, Homayoun

    2012-01-01

    With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condi

  11. Identification of neural connectivity signatures of autism using machine learning

    Directory of Open Access Journals (Sweden)

    Gopikrishna eDeshpande

    2013-10-01

    Full Text Available Alterations in neural connectivity have been suggested as a signature of the pathobiology of autism. Although disrupted correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the directional causal influence between brain regions is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind in 15 high-functioning adolescents and adults with autism (ASD and 15 typically developing (TD controls. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. Causal brain connectivity obtained from a multivariate autoregressive model, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant’s group membership (ASD or TD. We found a maximum classification accuracy of 95.9 % with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between ASD and TD groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point towards the fact that alterations in causal brain connectivity in individuals with ASD could serve as a potential non-invasive neuroimaging signature for autism

  12. COMPARISON OF CRYO TREATMENT EFFECT ON MACHINING CHARACTERISTICS OF TITANIUM IN ELECTRIC DISCHARGE MACHINING

    Directory of Open Access Journals (Sweden)

    Bhupinder Singh

    2011-06-01

    Full Text Available Earlier studies on cryogenic treatment highlighted that certain metals, after being cryogenically treated, show a significant increase in tool life when used in manufacturing, cutting and shaping processes. The present work deals with experimental investigation of the role of cryogenic treatment on the machining characteristics of titanium in electric discharge machining (EDM. EDM is a potential process to commercially machine tough materials like titanium alloys, due to the properties of non-mechanical contact between the tool and workpiece and the capability to machine intricate shapes. In this research work an effort has been made to compare the machining characteristics of titanium with EDM, before and after cryogenic treatment of the tool and workpiece using a Taguchi design approach. The output parameters for study are material removal rate (MRR, tool wear rate (TWR, surface roughness (SR and dimensional accuracy (Δd. The results of the study suggest that with cryogenic treatment MRR, TWR, SR and Δd show an improvement of 60.39%, 58.77%, 7.99% and 80.00% respectively.

  13. Importance of polarity change in the electrical discharge machining

    Science.gov (United States)

    Schulze, H.-P.

    2017-10-01

    The polarity change in the electrical discharge machining is still a problem and is often performed completely unmotivated or randomly. The polarity must be designated primarily, i.e. the anodic part must be clearly assigned to the tool or the workpiece. Normally, the polarity of the workpiece electrode is named. In paper, will be shown which determine fundamental causes the structural behavior of the cathode and anode, and when it makes sense to change the polarity. The polarity change is primarily dependent on the materials that are used as cathode and anode. This distinction must be made if there are pure metals or complex materials. Secondary of the polarity change is also affected by the process energy source (PES) and the supply line. The polarity change is mostly influenced by the fact that the removal is to be maximized on the workpiece while the tool is minimal removal (wear) occur. A second factor that makes a polarity change needed is the use of electrical discharge in combination with other machining methods, such as electrochemical machining (ECM).

  14. Application brushless machines with combine excitation for a hybrid car and an electric car

    OpenAIRE

    Gandzha S.A.; Kiessh I.E.

    2015-01-01

    This article shows advantages of application the brushless machines with combined excitation (excitation from permanent magnets and excitation winding) for the hybrid car and the electric car. This type of electric machine is compared with a typical brushless motor and an induction motor. The main advantage is the decrease of the dimensions of electric machine and the reduction of the price for an electronic control system. It is shown the design and the principle of operation of the electric...

  15. Electrical test prediction using hybrid metrology and machine learning

    Science.gov (United States)

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

    2017-03-01

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

  16. Application brushless machines with combine excitation for a hybrid car and an electric car

    Directory of Open Access Journals (Sweden)

    Gandzha S.A.

    2015-08-01

    Full Text Available This article shows advantages of application the brushless machines with combined excitation (excitation from permanent magnets and excitation winding for the hybrid car and the electric car. This type of electric machine is compared with a typical brushless motor and an induction motor. The main advantage is the decrease of the dimensions of electric machine and the reduction of the price for an electronic control system. It is shown the design and the principle of operation of the electric machine. The machine was modeled using Solidworks program for creating design and Maxwell program for the magnetic field analysis. The result of tests is shown as well.

  17. Static Measurements on HTS Coils of Fully Superconducting AC Electric Machines for Aircraft Electric Propulsion System

    Science.gov (United States)

    Choi, Benjamin B.; Hunker, Keith R.; Hartwig, Jason; Brown, Gerald V.

    2017-01-01

    The NASA Glenn Research Center (GRC) has been developing the high efficiency and high-power density superconducting (SC) electric machines in full support of electrified aircraft propulsion (EAP) systems for a future electric aircraft. A SC coil test rig has been designed and built to perform static and AC measurements on BSCCO, (RE)BCO, and YBCO high temperature superconducting (HTS) wire and coils at liquid nitrogen (LN2) temperature. In this paper, DC measurements on five SC coil configurations of various geometry in zero external magnetic field are measured to develop good measurement technique and to determine the critical current (Ic) and the sharpness (n value) of the super-to-normal transition. Also, standard procedures for coil design, fabrication, coil mounting, micro-volt measurement, cryogenic testing, current control, and data acquisition technique were established. Experimentally measured critical currents are compared with theoretical predicted values based on an electric-field criterion (Ec). Data here are essential to quantify the SC electric machine operation limits where the SC begins to exhibit non-zero resistance. All test data will be utilized to assess the feasibility of using HTS coils for the fully superconducting AC electric machine development for an aircraft electric propulsion system.

  18. 49 CFR 236.340 - Electromechanical interlocking machine; locking between electrical and mechanical levers.

    Science.gov (United States)

    2010-10-01

    ... Electromechanical interlocking machine; locking between electrical and mechanical levers. In electro-mechanical interlocking machine, locking between electric and mechanical levers shall be maintained so that mechanical... 49 Transportation 4 2010-10-01 2010-10-01 false Electromechanical interlocking machine; locking...

  19. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  20. Identification of neural connectivity signatures of autism using machine learning.

    Science.gov (United States)

    Deshpande, Gopikrishna; Libero, Lauren E; Sreenivasan, Karthik R; Deshpande, Hrishikesh D; Kana, Rajesh K

    2013-01-01

    Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of

  1. Weak electric fields detectability in a noisy neural network.

    Science.gov (United States)

    Zhao, Jia; Deng, Bin; Qin, Yingmei; Men, Cong; Wang, Jiang; Wei, Xile; Sun, Jianbing

    2017-02-01

    We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.

  2. Engineering electrodynamics electric machine, transformer, and power equipment design

    CERN Document Server

    Turowski, Janusz

    2013-01-01

    Due to a huge concentration of electromagnetic fields and eddy currents, large power equipment and systems are prone to crushing forces, overheating, and overloading. Luckily, power failures due to disturbances like these can be predicted and/or prevented.Based on the success of internationally acclaimed computer programs, such as the authors' own RNM-3D, Engineering Electrodynamics: Electric Machine, Transformer, and Power Equipment Design explains how to implement industry-proven modeling and design techniques to solve complex electromagnetic phenomena. Considering recent progress in magneti

  3. Power quality in power systems and electrical machines

    CERN Document Server

    Fuchs, Ewald

    2015-01-01

    The second edition of this must-have reference covers power quality issues in four parts, including new discussions related to renewable energy systems. The first part of the book provides background on causes, effects, standards, and measurements of power quality and harmonics. Once the basics are established the authors move on to harmonic modeling of power systems, including components and apparatus (electric machines). The final part of the book is devoted to power quality mitigation approaches and devices, and the fourth part extends the analysis to power quality solutions for renewable

  4. Brains--Computers--Machines: Neural Engineering in Science Classrooms

    Science.gov (United States)

    Chudler, Eric H.; Bergsman, Kristen Clapper

    2016-01-01

    Neural engineering is an emerging field of high relevance to students, teachers, and the general public. This feature presents online resources that educators and scientists can use to introduce students to neural engineering and to integrate core ideas from the life sciences, physical sciences, social sciences, computer science, and engineering…

  5. Development of the Cylindrical Wire Electrical Discharge Machining Process.

    Energy Technology Data Exchange (ETDEWEB)

    McSpadden, SB

    2002-01-22

    Results of applying the wire Electrical Discharge Machining (EDM) process to generate precise cylindrical forms on hard, difficult-to-machine materials are presented. A precise, flexible, and corrosion-resistant underwater rotary spindle was designed and added to a conventional two-axis wire EDM machine to enable the generation of free-form cylindrical geometries. A detailed spindle error analysis identifies the major source of error at different frequency. The mathematical model for the material removal of cylindrical wire EDM process is derived. Experiments were conducted to explore the maximum material removal rate for cylindrical and 2D wire EDM of carbide and brass work-materials. Compared to the 2D wire EDM, higher maximum material removal rates may be achieved in the cylindrical wire EDM. This study also investigates the surface integrity and roundness of parts created by the cylindrical wire EDM process. For carbide parts, an arithmetic average surface roughness and roundness as low as 0.68 and 1.7 {micro}m, respectively, can be achieved. Surfaces of the cylindrical EDM parts were examined using Scanning Electron Microscopy (SEM) to identify the craters, sub-surface recast layers and heat-affected zones under various process parameters. This study has demonstrated that the cylindrical wire EDM process parameters can be adjusted to achieve either high material removal rate or good surface integrity.

  6. Prediction and control of neural responses to pulsatile electrical stimulation

    Science.gov (United States)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  7. artificial neural network (ann) approach to electrical load

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... UNIVERSITY POWER HOUSE. A.A.AKINTOLA", G.A. ADEROUNMU and O.E. ... The model was tested using two of the seven feeders of the Obafemi. Awolowo University electric network. The results of .... The architecture of a neural network is the specific arrangement and connections of the neurons that.

  8. Convolutional over Recurrent Encoder for Neural Machine Translation

    National Research Council Canada - National Science Library

    Praveen Dakwale; Christof Monz

    2017-01-01

    ...) called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN...

  9. Electrical machines and assemblies including a yokeless stator with modular lamination stacks

    Science.gov (United States)

    Qu, Ronghai; Jansen, Patrick Lee; Bagepalli, Bharat Sampathkumar; Carl, Jr., Ralph James; Gadre, Aniruddha Dattatraya; Lopez, Fulton Jose

    2010-04-06

    An electrical machine includes a rotor with an inner rotor portion and an outer rotor portion, and a double-sided yokeless stator. The yokeless stator includes modular lamination stacks and is configured for radial magnetic flux flow. The double-sided yokeless stator is concentrically disposed between the inner rotor portion and the outer rotor portion of the electrical machine. Examples of particularly useful embodiments for the electrical machine include wind turbine generators, ship propulsion motors, switch reluctance machines and double-sided synchronous machines.

  10. Electrical machines monitoring using partial discharges; Monitorizacion de maquinas electricas mediante descargas parciales

    Energy Technology Data Exchange (ETDEWEB)

    Cano, J. C.; Rodriguez Ruiz, S.

    2006-07-01

    Electrical Machines Monitoring is a philosophy that is being more and more accepted in maintenance, the application of these techniques has a lot of advantages as the life evaluation non-intrusively and the detection and evolution evaluation of defects. this paper presents the monitoring of electrical machines using the Partial Discharges technique, which allow the evaluation of insulation of Electrical Machines. Real Cases are included in the paper as samples in which this techniques has been useful to detecting defects. (Author)

  11. Three-Level Direct Torque Control Based on Artificial Neural Network of Double Star Synchronous Machine

    Directory of Open Access Journals (Sweden)

    Elakhdar BENYOUSSEF

    2014-02-01

    Full Text Available This paper presents a direct torque control strategy for double star synchronous machine fed by two three-level inverters. The analysis of the torque and the stator flux linkage reference frame shows that the concept of direct torque control can be extended easily to double star synchronous machine. The proposed approach consists to replace the switching tables by one artificial neural networks controller. The output switching states vectors of the artificial neural networks controller are used to control the two three-level inverters. Simulations results are given to show the effectiveness and the robustness of the suggested control method.

  12. Domain specialization: a post-training domain adaptation for Neural Machine Translation

    OpenAIRE

    Servan, Christophe; Crego, Josep; Senellart, Jean

    2016-01-01

    Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.

  13. Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Krzysztof Gajowniczek

    2017-10-01

    Full Text Available Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution and deliver accurate forecasts, with mean absolute percentage error (MAPE of 3.10% and resistant mean absolute percentage error (r-MAPE of 2.70% for the 24 h forecasting horizon.

  14. Neural computing architectures: The design of brain-like machines

    Energy Technology Data Exchange (ETDEWEB)

    Aleksander, I.

    1989-01-01

    Theoretical and applications aspects of neural-network (NN) computers are discussed in chapters contributed by European experts. Topics addressed include speech recognition based on topology-preserving neural maps, neural-map applications, backpropagation in nonfeedforward NNs, a parallel-distributed-processing learning approach to natural language, the learning capabilities of Boolean NNs, the logic of connectionist systems, and a probabilistic-logic NN for associative learning. Consideration is given to N-tuple sampling and genetic algorithms for speech recognition; the dynamic behavior of Boolean NNs; statistical mechanics and NNs; digital NNs, matched filters, and optical implementations; heteroassociative NNs using cabling vs link-disabling local modification rules; and the generation of movement trajectories in primates and robots. Also provided is an overview of parallel distributed processing.

  15. Pursuing optimal electric machines transient diagnosis: The adaptive slope transform

    Science.gov (United States)

    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.

  16. Computer Aided Design Of Electrical Machines For Variable Speed Applications.

    Science.gov (United States)

    Krishnan, R.; Aravind, S.; Materu, P.

    1987-10-01

    In recent years, the product life cycle has decreased and demands for new products have emerged due to competition, modern industrial needs and rapidly changing technology. This has necessitated changes in design, development and manufacturing processes so as to improve quality and efficiency as well as reducing costs. Computer Aided Design (CAD) helps to meet this challenge in the design evaluation and final product design stages. This paper presents the development of an interactive software for the optimal design of a motor intended for variable speed applications. The use of finite element analysis methods is proposed as an indispensable part of the CAD system for electrical machine design. An illustration of the method is given for the design of a switched reluctance motor.

  17. Wire electrical discharge machining of E110 zirconium alloy

    Science.gov (United States)

    Bobkov, N. V.; Fedorov, A. A.; Blesman, A. I.; Postnikov, D. V.; Polonyankin, D. A.

    2017-06-01

    The paper deals with the results of experimental research carried out by scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) to define, how the modes of wire electrical discharge machining (WEDM) influence on the elemental and the phase composition of E110 zirconium alloy’s surface layer.Investigation of the phase composition allowed us to determine the main α and δ phase’s distribution through the depth of zirconium surface layer, in common with phases of oxygen, copper, zirconium, and niobium specific compounds. It was also established the maximum depth of the defect level containing amorphous phase for all of WEDM modes, and proposed the grinding and polishing as potential mechanical methods of its removal.

  18. [Design and application of medical electric leg-raising machine].

    Science.gov (United States)

    Liang, Jintang; Chen, Jinyuan; Zhao, Zixian; Lin, Jinfeng; Li, Juanhong; Zhong, Jingliang

    2017-08-01

    Passive leg raising is widely used in clinic, but it lacks of specialized mechanical raise equipment. It requires medical staff to raise leg by hand or requires a multi-functional bed to raise leg, which takes time and effort. Therefore we have developed a new medical electric leg-raising machine. The equipment has the following characteristics: simple structure, stable performance, easy operation, fast and effective, safe and comfortable. The height range of the lifter is 50-120 cm, the range of the angle of raising leg is 10degree angle-80degree angle, the maximum supporting weight is 40 kg. Because of raising the height of the lower limbs and making precise angle, this equipment can completely replace the traditional manner of lifting leg by hand with multi-functional bed to lift patients' leg and can reduce the physical exhaustion and time consumption of medical staff. It can change the settings at any time to meet the needs of the patient; can be applied to the testing of PLR and dynamically assessing the hemodynamics; can prevent deep vein thrombosis and some related complications of staying in bed; and the machine is easy to be cleaned and disinfected, which can effectively avoid hospital acquired infection and cross infection; and can also be applied to emergency rescue of various disasters and emergencies.

  19. Acid-base machines: electrical work from neutralization reactions.

    Science.gov (United States)

    Lima, Gilberto; Morais, William G; Gomes, Wellington J A S; Huguenin, Fritz

    2017-11-29

    We have developed an electrochemical system that performs electrical work due to changes in alkaline ion and proton activities associated with acidic solution neutralization. This system can be used to treat wastewater, contributing to sustainable growth. The system includes an electrochemical machine that operates between an acidic and a basic reservoir to produce work in cycles comprising four stages: two isothermal ionic insertion/de-insertion steps and two steps involving acid and base injection. On the basis of the mixing free energy associated with the reaction free energy, we have developed the thermodynamic formalism by considering reversible electrochemical processes to determine the maximum work performed by this acid-base machine and the efficiency. Electrochemical methods in the time and frequency domains helped in investigating the kinetics of sodium ions and proton insertion in host matrices consisting of copper hexacyanoferrate and phosphomolybdic acid, respectively, to improve our understanding of the factors underlying dissipation as a function of pH and pNa. The full cell composed of these insertion electrodes was used as a proof of concept. It performed a maximum work of 26.4 kJ per mol of electro-inserted ion from HCl solution neutralization with the addition of NaOH, to simulate acidic wastewater treatment in a profitable and sustainable way.

  20. APPROXIMATION OF UNIVERSAL MAGNETIC CHARACTERISTIC FOR MODELLING ELECTRIC TRACTION MACHINES

    Directory of Open Access Journals (Sweden)

    A. Yu. Drubetskyi

    2017-02-01

    Full Text Available Purpose. The scientific work is aimed to obtain an analytic expression describing universal magnetic characteristic and enabling to take into account the demagnetizing effect of the armature. On the basis of the universal magnetic characteristics one need to obtain universal expressions for inductive parameters of electric traction machines of direct and pulsating currents. Methodology. A universal magnetic characteristic (UMC is the dependence of the relative units of the magnetic flux on the magnetomotive force (MMF of the excitation winding. Since MMF was built for machines operating under load, therefore, in fact it is a dependency on the MMF and on the MMF of the armature reaction. For the calculation of electromechanical characteristics at constant excitation one can use one of the well-known expressions approximating the UMC. However, during modeling the electric traction engine operation in wide ranges of excitation change it is necessary the expression, in which there is a second variable in the form of MMF of the anchor reaction. Such an expression is also necessary to determine the inductive parameters of electric traction engine, to a large extent dependent on the current. The expression for the approximation of the UMC with two variables can be obtained by analyzing the magnetic field distribution in the air gap at the calculated pole arc. Findings. The author obtained expression for approximation of the UMC, which depends on two variables: MMF of excitation and MMF of armature reaction. For a particular mode of excitation weakening it is possible to convert the expression into the function of one variable, for example, the anchor current. Also, the MMF of excitation winding can be the argument. Originality. For the UMC approximation it was proposed a methodology that makes it possible to record into approximating expression the second variable in the form of the anchor reaction MMF. Practical value. Due to the presence of speed

  1. Multiphysics simulation by design for electrical machines, power electronics and drives

    CERN Document Server

    Rosu, Marius; Lin, Dingsheng; Ionel, Dan M; Popescu, Mircea; Blaabjerg, Frede; Rallabandi, Vandana; Staton, David

    2018-01-01

    This book combines the knowledge of experts from both academia and the software industry to present theories of multiphysics simulation by design for electrical machines, power electronics, and drives. The comprehensive design approach described within supports new applications required by technologies sustaining high drive efficiency. The highlighted framework considers the electric machine at the heart of the entire electric drive. The book also emphasizes the simulation by design concept--a concept that frames the entire highlighted design methodology, which is described and illustrated by various advanced simulation technologies. Multiphysics Simulation by Design for Electrical Machines, Power Electronics and Drives begins with the basics of electrical machine design and manufacturing tolerances. It also discusses fundamental aspects of the state of the art design process and includes examples from industrial practice. It explains FEM-based analysis techniques for electrical machine design--providing deta...

  2. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    Directory of Open Access Journals (Sweden)

    Poramate eManoonpong

    2013-02-01

    Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  3. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    Science.gov (United States)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  4. Artificial neural networks and support vector machine in banking computer systems

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2013-12-01

    Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.

  5. Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Wörgötter, Florentin

    2007-01-01

    as a sensory fusion unit. It filters sensory noise and shapes sensory data to drive the corresponding reactive behavior. On the other hand, modular neural control based on a central pattern generator is applied for locomotion of walking machines. It coordinates leg movements and can generate omnidirectional...

  6. Iron Losses in Electrical Machines Due to Non Sinusoidal Alternating Fluxes

    DEFF Research Database (Denmark)

    Ritchie, Ewen; Walker, J.A.; Dorrell, D. G.

    2007-01-01

    This paper shows how the flux waveform in the core of an electrical machine can be vary non- sinusoidally which complicates the calculation of the iron loss in a machine. A set of tests are conducted on a steel sample using an Epstein square where harmonics are injected into the flux waveform which...... of a machine....

  7. Rotating electrical machines part 4: methods for determining synchronous machine quantities from tests

    CERN Document Server

    International Electrotechnical Commission. Geneva

    1985-01-01

    Applies to three-phase synchronous machines of 1 kVA rating and larger with rated frequency of not more than 400 Hz and not less than 15 Hz. An appendix gives unconfirmed test methods for determining synchronous machine quantities. Notes: 1 -Tests are not applicable to synchronous machines such as permanent magnet field machines, inductor type machines, etc. 2 -They also apply to brushless machines, but certain variations exist and special precautions should be taken.

  8. Thermal Management and Reliability of Power Electronics and Electric Machines

    Energy Technology Data Exchange (ETDEWEB)

    Narumanchi, Sreekant

    2016-06-13

    Increasing the number of electric-drive vehicles (EDVs) on America's roads has been identified as a strategy with near-term potential for dramatically decreasing the nation's dependence on oil - by the U.S. Department of Energy, the federal cross-agency EV-Everywhere Challenge, and the automotive industry. Mass-market deployment will rely on meeting aggressive technical targets, including improved efficiency and reduced size, weight, and cost. Many of these advances will depend on optimization of thermal management. Effective thermal management is critical to improving the performance and ensuring the reliability of EDVs. Efficient heat removal makes higher power densities and lower operating temperatures possible, and in turn enables cost and size reductions. The National Renewable Energy Laboratory (NREL), along with DOE and industry partners is working to develop cost-effective thermal management solutions to increase device and component power densities. In this presentation, the activities in recent years related to thermal management and reliability of automotive power electronics and electric machines are presented.

  9. Thermal Management and Reliability of Power Electronics and Electric Machines

    Energy Technology Data Exchange (ETDEWEB)

    Narumanchi, Sreekant

    2016-08-03

    Increasing the number of electric-drive vehicles (EDVs) on America's roads has been identified as a strategy with near-term potential for dramatically decreasing the nation's dependence on oil -- by the U.S. Department of Energy, the federal cross-agency EV-Everywhere Challenge, and the automotive industry. Mass-market deployment will rely on meeting aggressive technical targets, including improved efficiency and reduced size, weight, and cost. Many of these advances will depend on optimization of thermal management. Effective thermal management is critical to improving the performance and ensuring the reliability of EDVs. Efficient heat removal makes higher power densities and lower operating temperatures possible, and in turn enables cost and size reductions. The National Renewable Energy Laboratory (NREL), along with DOE and industry partners is working to develop cost-effective thermal management solutions to increase device and component power densities. In this presentation, the activities in recent years related to thermal management and reliability of automotive power electronics and electric machines will be presented.

  10. Successful attack on permutation-parity-machine-based neural cryptography.

    Science.gov (United States)

    Seoane, Luís F; Ruttor, Andreas

    2012-02-01

    An algorithm is presented which implements a probabilistic attack on the key-exchange protocol based on permutation parity machines. Instead of imitating the synchronization of the communicating partners, the strategy consists of a Monte Carlo method to sample the space of possible weights during inner rounds and an analytic approach to convey the extracted information from one outer round to the next one. The results show that the protocol under attack fails to synchronize faster than an eavesdropper using this algorithm.

  11. Characterization of nanoparticles from abrasive waterjet machining and electrical discharge machining processes.

    Science.gov (United States)

    Ling, Tsz Yan; Pui, David Y H

    2013-11-19

    Abrasive Waterjet Machining (AWM) and Electrical Discharge Machining (EDM) processes are found to produce nanoparticles during operation. Impacts of engineered nanoparticles released to the environment and biological system have caused much concern. Similarly, the nanoparticles unintentionally produced by the AWM and EDM can lead to comparable effects. By application of the Nanoparticle Tracking Analysis (NTA) technique, the size distribution and concentration of nanoparticles in the water used in AWM and EDM were measured. The particles generally have a peak size of 100-200 nm. The filtration systems of the AWM and EDM processes were found to remove 70% and 90% the nanoparticles present, respectively. However, the particle concentration of the filtered water from the AWM was still four times higher than that found in regular tap water. These nanoparticles are mostly agglomerated, according to the microscopy analysis. Using the electron dispersive spectroscopy (EDS) technique, the particles are confirmed to come from the debris of the materials cut with the equipment. Since AWM and EDM are widely used, the handling and disposal of used filters collected with nanoparticles, release of nanoparticles to the sewer, and potential use of higher performance filters for these processes will deserve further consideration.

  12. New Balancing Equipment for Mass Production of Small and Medium-Sized Electrical Machines

    DEFF Research Database (Denmark)

    Argeseanu, Alin; Ritchie, Ewen; Leban, Krisztina Monika

    2010-01-01

    The level of vibration and noise is an important feature. It is good practice to explain the significance of the indicators of the quality of electrical machines. The mass production of small and medium-sized electrical machines demands speed (short typical measurement time), reliability...

  13. Machine Comprehension by Text-to-Text Neural Question Generation

    OpenAIRE

    Yuan, Xingdi; Wang, Tong; Gulcehre, Caglar; Sordoni, Alessandro; Bachman, Philip; Subramanian, Sandeep; Zhang, Saizheng; Trischler, Adam

    2017-01-01

    We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question ...

  14. Characterization of Contact and Bulk Thermal Resistance of Laminations for Electric Machines

    Energy Technology Data Exchange (ETDEWEB)

    Cousineau, J. Emily [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bennion, Kevin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); DeVoto, Doug [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Mihalic, Mark [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Narumanchi, Sreekant [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2015-06-30

    The ability to remove heat from an electric machine depends on the passive stack thermal resistances within the machine and the convective cooling performance of the selected cooling technology. This report focuses on the passive thermal design, specifically properties of the stator and rotor lamination stacks. Orthotropic thermal conductivity, specific heat, and density are reported. Four materials commonly used in electric machines were tested, including M19 (29 and 26 gauge), HF10, and Arnon 7 materials.

  15. Can Neural Activity Propagate by Endogenous Electrical Field?

    Science.gov (United States)

    Qiu, Chen; Shivacharan, Rajat S.; Zhang, Mingming

    2015-01-01

    It is widely accepted that synaptic transmissions and gap junctions are the major governing mechanisms for signal traveling in the neural system. Yet, a group of neural waves, either physiological or pathological, share the same speed of ∼0.1 m/s without synaptic transmission or gap junctions, and this speed is not consistent with axonal conduction or ionic diffusion. The only explanation left is an electrical field effect. We tested the hypothesis that endogenous electric fields are sufficient to explain the propagation with in silico and in vitro experiments. Simulation results show that field effects alone can indeed mediate propagation across layers of neurons with speeds of 0.12 ± 0.09 m/s with pathological kinetics, and 0.11 ± 0.03 m/s with physiologic kinetics, both generating weak field amplitudes of ∼2–6 mV/mm. Further, the model predicted that propagation speed values are inversely proportional to the cell-to-cell distances, but do not significantly change with extracellular resistivity, membrane capacitance, or membrane resistance. In vitro recordings in mice hippocampi produced similar speeds (0.10 ± 0.03 m/s) and field amplitudes (2.5–5 mV/mm), and by applying a blocking field, the propagation speed was greatly reduced. Finally, osmolarity experiments confirmed the model's prediction that cell-to-cell distance inversely affects propagation speed. Together, these results show that despite their weak amplitude, electric fields can be solely responsible for spike propagation at ∼0.1 m/s. This phenomenon could be important to explain the slow propagation of epileptic activity and other normal propagations at similar speeds. SIGNIFICANCE STATEMENT Neural activity (waves or spikes) can propagate using well documented mechanisms such as synaptic transmission, gap junctions, or diffusion. However, the purpose of this paper is to provide an explanation for experimental data showing that neural signals can propagate by means other than synaptic

  16. Evolving Neural Turing Machines for Reward-based Learning

    DEFF Research Database (Denmark)

    Greve, Rasmus Boll; Jacobsen, Emil Juul; Risi, Sebastian

    2016-01-01

    and integrating new information without losing previously acquired skills. Here we build on recent work by Graves et al. [5] who extended the capabilities of an ANN by combining it with an external memory bank trained through gradient descent. In this paper, we introduce an evolvable version of their Neural...... version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE....

  17. Neural Operant Conditioning as a Core Mechanism of Brain-Machine Interface Control

    Directory of Open Access Journals (Sweden)

    Yoshio Sakurai

    2016-08-01

    Full Text Available The process of changing the neuronal activity of the brain to acquire rewards in a broad sense is essential for utilizing brain-machine interfaces (BMIs, which is essentially operant conditioning of neuronal activity. Currently, this is also known as neural biofeedback, and it is often referred to as neurofeedback when human brain activity is targeted. In this review, we first illustrate biofeedback and operant conditioning, which are methodological background elements in neural operant conditioning. Then, we introduce research models of neural operant conditioning in animal experiments and demonstrate that it is possible to change the firing frequency and synchronous firing of local neuronal populations in a short time period. We also debate the possibility of the application of neural operant conditioning and its contribution to BMIs.

  18. Machining Performance and Surface Integrity of AISI D2 Die Steel Machined Using Electrical Discharge Surface Grinding Process

    Science.gov (United States)

    Choudhary, Rajesh; Kumar, Harmesh; Singh, Shankar

    2013-12-01

    The aim of this study is to establish optimum machining conditions for EDSG of AISI D2 die steel through an experimental investigation using Taguchi Methodology. To achieve combined grinding and electrical discharge machining, metal matrix composite electrodes (Cu-SiCp) were processed through powder metallurgy route. A rotary spindle attachment was developed to perform the EDSG experimental runs on EDM machine. Relationships were developed between various input parameters such as peak current, speed, pulse-on time, pulse-off time, abrasive particle size, and abrasive particle concentration, and output characteristics such as material removal rate and surface roughness. The optimized parameters were further validated by conducting confirmation experiments.

  19. Surface quality analysis of die steels in powder-mixed electrical discharge machining using titan powder in fine machining

    Directory of Open Access Journals (Sweden)

    Banh Tien Long

    2016-06-01

    Full Text Available Improving the quality of surface molds after electrical discharge machining is still being considered by many researchers. Powder-mixed dielectric in electrical discharge machining showed that it is one of the processing methods with high efficiency. This article reports on the results of surface quality of mold steels after powder-mixed electrical discharge machining using titanium powder in fine machining. The process parameters such as electrode material, workpiece material, electrode polarity, pulse on-time, pulse off-time, current, and titanium powder concentration were considered in the research. These materials are most commonly used with die-sinking electrical discharge machining in the manufacture of molds and has been selected as the subject of research: workpiece materials were SKD61, SKT4, and SKD11 mold steels, and electrode materials were copper and graphite. Taguchi’s method is used to design experiments. The influence of the parameters on surface roughness was evaluated through the average value and ratio (S/N. Results showed that the parameters such as electrical current, electrode material, pulse on-time, electrode polarity, and interaction between the electrode materials with concentration powder mostly influence surface roughness and surface roughness at optimal parameters SRopt = 1.73 ± 0.39 µm. Analysis of the surface layer after powder-mixed electrical discharge machining using titanium powder in optimal conditions has shown that the white layer with more uniform thickness and increased hardness (≈861.0 HV, and amount and size of microscopic cracks, is reduced. This significantly leads to the increase in the quality of the surface layer.

  20. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

    Science.gov (United States)

    Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

    2017-10-17

    With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

  1. Rotor apparatus for high strength undiffused brushless electric machine

    Science.gov (United States)

    Hsu, John S [Oak Ridge, TN

    2006-01-24

    A radial gap brushless electric machine (30) having a stator (31) and a rotor (32) and a main air gap (34) also has at least one stationary excitation coil (35a, 36a) separated from the rotor (32) by a secondary air gap (35e, 35f, 36e, 36f) so as to induce a secondary flux in the rotor (32) which controls a resultant flux in the main air gap (34). Permanent magnetic (PM) material (38) is disposed in spaces between the rotor pole portions (39) to inhibit the second flux from leaking from the pole portions (39) prior to reaching the main air gap (34). By selecting the direction of current in the stationary excitation coil (35a, 36a) both flux enhancement and flux weakening are provided for the main air gap (34). Improvements of a laminated rotor, an end pole structure, and an arrangement of the PM elements for providing an arrangement of the flux paths from the auxiliary field coil assemblies are also disclosed.

  2. Combining decoder design and neural adaptation in brain-machine interfaces.

    Science.gov (United States)

    Shenoy, Krishna V; Carmena, Jose M

    2014-11-19

    Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Current approaches to model extracellular electrical neural microstimulation

    Directory of Open Access Journals (Sweden)

    Sébastien eJoucla

    2014-02-01

    Full Text Available Nowadays, high-density microelectrode arrays provide unprecedented possibilities to precisely activate spatially well-controlled central nervous system (CNS areas. However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues. In this context, modeling approaches provide flexible ways to predict the outcome of electrical stimulation in terms of CNS activation. In this paper, we present state-of-the-art modeling methods with sufficient details to allow the reader to rapidly build numerical models of neuronal extracellular microstimulation. These include 1 the computation of the electrical potential field created by the stimulation in the tissue, and 2 the response of a target neuron to this field. Two main approaches are described: First we describe the classical hybrid approach that combines the finite element modeling of the potential field with the calculation of the neuron’s response in a cable equation framework (compartmentalized neuron models. Then, we present a whole finite element approach allows the simultaneous calculation of the extracellular and intracellular potentials, by representing the neuronal membrane with a thin-film approximation. This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level. Here, we show on an example that the latter modeling scheme can reveal important sub-compartment behavior of the neural membrane that cannot be resolved using the hybrid approach. The goal of this paper is also to describe in detail the practical implementation of these methods to allow the reader to easily build new models using standard software packages. These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.

  4. Electromagnetic Design of a New Electrically Controlled Magnetic Variable-Speed Gearing Machine

    Directory of Open Access Journals (Sweden)

    Chunhua Liu

    2014-03-01

    Full Text Available This paper proposes a new electrically controlled magnetic variable-speed gearing (EC-MVSG machine, which is capable of providing controllable gear ratios for hybrid electric vehicle (HEV applications. The key design feature involves the adoption of a magnetic gearing structure and acceptance of the memory machine flux-mnemonic concept. Hence, the proposed machine can not only offer a gear-shifting mechanism for torque and speed transmission, but also provide variable gear ratios for torque and speed variation. The electromagnetic design is studied and discussed. The finite-element method is developed with the hysteresis model to verify the validity of the machine design.

  5. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate......, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...

  6. Rotating electrical machines - Part 5: Degrees of protection provided by the integral design of rotating electrical machines (IP code) - Classification

    CERN Document Server

    International Electrotechnical Commission. Geneva

    2000-01-01

    Gives definitions for standard degrees of protection provided by enclosures; protection of machines against harmful effects due to the ingress of water; protection of machines against ingress of solid foreign objects; Protection of persons against contact with or approach to live parts and against contact with moving parts. Gives designations for these protective degrees and tests to verify that the machines meet the requirements.

  7. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  8. Some Issues of the Paradigm of Multi-learning Machine - Modular Neural Networks

    DEFF Research Database (Denmark)

    Wang, Pan; Feng, Shuai; Fan, Zhun

    2009-01-01

    This paper addresses some issues on the weighted linear integration of modular neural networks (MNN: a paradigm of hybrid multi-learning machines). First, from the general meaning of variable weights and variable elements synthesis, three basic kinds of integrated models are discussed that are in......This paper addresses some issues on the weighted linear integration of modular neural networks (MNN: a paradigm of hybrid multi-learning machines). First, from the general meaning of variable weights and variable elements synthesis, three basic kinds of integrated models are discussed...... that are intrinsic-factors-determined, extrinsic-factors-determined, and hybrid-factors-determined. The authors point out: integrations dominated by both of the internal and external elements are highly correlative with not only the historical quality of the sub-networks, but also with the environment in which...

  9. RELIABILITY EVALUATION OF THE ACTIVATION MACHINE FOR THE ELECTRIC DETONATING CAPS-EKA 350

    Directory of Open Access Journals (Sweden)

    Ljubinka Radosavljević

    2007-09-01

    Full Text Available The machine - EKA 350 is designed for the activation of the serial or mixed connected electric detonating caps EK - 40 - 69 in explosive fillings at mining and demolition. For the analyzes of reliability it is important that the machine works in the three regimes of function: LOAD, FIRE and EMPTY. Modeling of reliability was executed for each of the mentioned regimes of the EKA 350 machine. In the machine are incorporated the components dedicated to the professional usage and satisfaction of the MIL standards. The machine is treated as it works in a single - stage mission which lasts 20 seconds.

  10. A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control

    OpenAIRE

    Yousefian, Reza; Kamalasadan, Sukumar

    2017-01-01

    This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for power system rotor angle stabilization and control problem is discussed. The main focus of this paper i...

  11. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions

    OpenAIRE

    Nam, Juno; Kim, Jurae

    2016-01-01

    Finding the main product of a chemical reaction is one of the important problems of organic chemistry. This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions. In order to translate 'reactants and reagents' to 'products', a gated recurrent unit based sequence-to-sequence model and a parser to generate input tokens for model from reaction SMILES strings were built. Training sets are composed of reactions from the patent datab...

  12. Electric stimulation with sinusoids and white noise for neural prostheses

    Directory of Open Access Journals (Sweden)

    Daniel K Freeman

    2010-02-01

    Full Text Available We are investigating the use of novel stimulus waveforms in neural prostheses to determine whether they can provide more precise control over the temporal and spatial pattern of elicited activity as compared to conventional pulsatile stimulation. To study this, we measured the response of retinal ganglion cells to both sinusoidal and white noise waveforms. The use of cell-attached and whole cell patch clamp recordings allowed the responses to be observed without significant obstruction from the stimulus artifact. Electric stimulation with sinusoids elicited robust responses. White noise analysis was used to derive the linear kernel for the ganglion cell’s spiking response as well as for the underlying excitatory currents. These results suggest that in response to electric stimulation, presynaptic retinal neurons exhibit bandpass filtering characteristics with peak response that occur 25ms after onset. The experimental approach demonstrated here may be useful for studying the temporal response properties of other neurons in the CNS.

  13. Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using of Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ahmad Aryafar

    2016-06-01

    Full Text Available Nowadays, estimating the ampere consumption and achieve to the optimum condition from the perspective of energy consumption is one of the most important steps to reduce the production costs. In this research it is tried to develop an accurate model for estimating the ampere consumption by using the artificial neural networks (ANN.In the first step, experimental studies were carried out on 7 carbonate rock samples in different conditions at particular feed rates (100, 200, 300and 400 and depth of cuts (15, 22, 30and 35mm using a fully instrumented laboratory rig that is enable to change the machine parameters and measure the ampere consumption. In next step, a back propagation neural network was designed for modelling the sawing process for predicting the ampere consumption. The input network consisting of two parts: machine, work piece characteristics and the output of neural network was ampere consumption. This research evaluated the competencies of neural networks to estimate the ampere consumption in sawing process. The correlation coefficient between measured and predicted data in training and testing data is 0.95 and 0.97 respectively. The root mean square error (RMSE for train and test data is 1.2 and 0.7 respectively. The results of this study showed that the ANNs can be used to estimate the ampere consumption with high ability and low error for industrial applications. Moreover, the cost of sawing machine ampere consumption can be accurately estimated using this neural model from some important physical and mechanical properties of rock.

  14. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies.

    Science.gov (United States)

    Armenta Salas, Michelle; Helms Tillery, Stephen I

    2016-01-01

    The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.

  16. Electric field effects in hyperexcitable neural tissue: A review

    Energy Technology Data Exchange (ETDEWEB)

    Durand, D.M

    2003-07-01

    Uniform electric fields applied to neural tissue can modulate neuronal excitability with a threshold value of about 1mV mm{sup -1} in normal physiological conditions. However, electric fields could have a lower threshold in conditions where field sensitivity is enhanced, such as those simulating epilepsy. Uniform electrical fields were applied to hippocampal brain slices exposed to picrotoxin, high potassium or low calcium solutions. The results in the low calcium medium show that neuronal activity can be completely blocked in 10% of the 30 slices tested with a field amplitude of 1mV mm{sup -1}. These results suggest that the threshold for this effect is clearly smaller than 1mV mm{sup -1}. The hypothesis that the extracellular resistance could affect the sensitivity to the electrical fields was tested by measuring the effect of the osmolarity of the extracellular solution on the efficacy of the field. A 10% decrease on osmolarity resulted in a 56% decrease (n=4) in the minimum field required for full suppression. A 14% in osmolarity produced an 81% increase in the minimum field required for full suppression. These results show that the extracellular volume can modulate the efficacy of the field and could lower the threshold field amplitudes to values lower than {approx}1mmV mm{sup -.} (author)

  17. electrical-thermal coupling of induction machine for improved

    African Journals Online (AJOL)

    user

    The system of non-linear ordinary differential equations which describe the thermal behaviour of the machine in transient state were solved numerically using the fourth-order Runge-Kutta method. MATLAB m-files .... symmetrical induction machine in an arbitrary reference frame could be derived from the d-q equivalent ...

  18. Simulation Tools for Electrical Machines Modelling: Teaching and ...

    African Journals Online (AJOL)

    Simulation tools are used both for research and teaching to allow a good comprehension of the systems under study before practical implementations. This paper illustrates the way MATLAB is used to model non-linearites in synchronous machine. The machine is modeled in rotor reference frame with currents as state ...

  19. Electrical-thermal coupling of induction machine for improved ...

    African Journals Online (AJOL)

    The system of non-linear ordinary differential equations which describe the thermal behaviour of the machine in transient state were solved numerically using the fourth-order Runge-Kutta method. MATLAB m-files were developed and were used to solve the coupled machine model under transient condition. The thermal ...

  20. Sensorless Suitability Analysis of Hybrid PM Machines for Electric Vehicles

    DEFF Research Database (Denmark)

    Matzen, Torben Nørregaard; Rasmussen, Peter Omand

    2009-01-01

    , control seems necessary to implement. For hybrid permanent magnet (PM) machines torque control in an indirect fashion using dq-current control is frequently done. This approach requires knowledge about the machine shaft position which may be obtained sensorless. In this article a method based on accurate...

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

    National Research Council Canada - National Science Library

    Wu, Zhihong; Lu, Ke; Zhu, Yuan

    2015-01-01

    The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary...

  2. A Practical Torque Estimation Method for Interior Permanent Magnet Synchronous Machine in Electric Vehicles: e0130923

    National Research Council Canada - National Science Library

    Zhihong Wu; Ke Lu; Yuan Zhu

    2015-01-01

      The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary...

  3. A new MSc course on diagnostics of electrical machines and power electronics

    DEFF Research Database (Denmark)

    Leban, Krisztina Monika; Ritchie, Ewen

    2011-01-01

    students. Additionally, specific subjects requested by participants, basic diagnosis and testing methods were presented during the lectures and workshops. General engineering knowledge about electric machines, power electronics and the combination of these was presented. The laboratory method, experiments...

  4. Fractional Slot Concentrated Windings: A New Method to Manage the Mutual Inductance between Phases in Three-Phase Electrical Machines and Multi-Star Electrical Machines

    Directory of Open Access Journals (Sweden)

    Olivier Barre

    2015-06-01

    Full Text Available Mutual inductance is a phenomenon caused by the circulation of the magnetic flux in the core of an electrical machine. It is the result of the effect of the current flowing in one phase on the other phases. In conventional three-phase machines, such an effect has no influence on the electrical behaviour of the device. Although these machines are powered by power inverters, no problem should occur. The result is not the same for multi-star machines. If these machines are using a conventional winding structure, namely distributed windings, and are powered by voltage source converters, current ripples appear in the power supply lines. These current ripples are related to magnetic couplings between the stars. Designers should check these current ripples in order to stay within the limits imposed by the specifications. These electric current disturbances also provide torque ripples. With concentrated windings, a new degree of freedom appears; the configuration—number of slots/number of poles—can have a positive impact. The circulation of the magnetic flux is the initial phenomenon that produces the mutual inductance. The main goal of this discussion is to describe a design method that is able to produce not only a machine with low mutual inductance between phases, but also a multi-star machine where the stars and the phases are magnetically decoupled or less coupled. This discussion only takes into account the machines that use permanent magnets mounted on the rotor surface. This article is part of a study aimed at designing a high efficiency generator using fractional-slot concentrated-windings (FSCW.

  5. Hydraulic and electric drivelines for mobile working machines

    OpenAIRE

    Gallmeier, M.;Auernhammer, H.

    2015-01-01

    The field tests identify optimized controllability at inverter controlled electric drives because of easy closed loop control. During full load operation 17% increased efficiency by electric driveline. Further increased advantages for partial load operation. Lower load dependability of the efficiency of the electric driveline. Disadvantageous power-to-weight ratio requires further work for „mobile“ electric drives.

  6. TESTING OF ELECTRIC MACHINES IN INDUSTRIAL ENVIRONMENT USING A DATA ACQUISITION AND PROCESSING SYSTEM

    OpenAIRE

    Toma DORDEA; Marius BIRIESCU; Petru ANDEA; Groza, Voicu; Vladimir CREŢU; Marţian MOŢ; Gheorghe MADESCU; Ciprian ŞORÂNDARU

    2009-01-01

    The paper presents some significant aspects concerning testing electrical machines, including high power ones, using a Data Acquisition and Processing System (DAPS), based on a PC compatible microsystem. There are described the main measurement tasks of DAPS in electrical machines testing, in various functional conditions: constant frequency steady state (used in classical standard tests), variable frequency conditions (used in asynchronous motors testing by mixed frequency method) and finall...

  7. Midterm Electricity Market Clearing Price Forecasting Using Two-Stage Multiple Support Vector Machine

    OpenAIRE

    Yan, Xing; Chowdhury, Nurul A.

    2015-01-01

    Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP), but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM) based midterm forecasting model of the electricity MCP is proposed in t...

  8. Machining and Surface Characteristics of AISI 304L After Electric Discharge Machining for Copper and Graphite Electrodes in Different Dielectric Liquids

    National Research Council Canada - National Science Library

    S. Anjum; M. Shah; N. A. Anjum; S. Mehmood; W. Anwar

    2017-01-01

    In Electric Discharge Machining (EDM), the thermal energy used for material erosion depends on the intensity of electric sparks, the thermal conductivities of electrode material and the dielectric liquid...

  9. Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

    Directory of Open Access Journals (Sweden)

    Guanbin Gao

    2017-01-01

    Full Text Available Articulated arm coordinate measuring machine (AACMM is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM.

  10. ENERGY EFFICIENCY DETERMINATION OF LOADING-BACK SYSTEM OF ELECTRIC TRACTION MACHINES

    Directory of Open Access Journals (Sweden)

    A. M. Afanasov

    2014-03-01

    Full Text Available Purpose.Acceptance post-repair testsof electric traction machinesare conducted onloading-backstandsthat reducethe overall power costsfor the tests.Currentlya numberof possiblecircuit designs of loading-backsystems of electric machines are known, but there is nomethod of determiningtheir energy efficiency. This in turn makes difficult the choiceof rationaloptions. The purpose of the article is the development of the corresponding methodo-logy to make easier this process. Methodology. Expressions for determining theenergy efficiency ofa stand for testingof electric traction machineswere obtained using the generalizedscheme analysisof energy transformationsin the loading-backsystems of universal structure. Findings.Thetechnique wasoffered and the analytical expressions for determining the energy efficiency of loading-backsystemsof electric traction machines wereobtained. Energy efficiency coefficientofloading-backsystemisproposed to consider as the ratio of the total actionenergy of the mechanical and electromotive forces, providing anchors rotation and flowof currents in electric machines, which are being tested,to the total energy, consumed during the test from the external network. Originality. The concept was introduced and the analytical determination method of the energy efficiency of loading-backsystem in electric traction machines was offered. It differs by efficiency availability of power sources and converters, as well as energy efficiency factors of indirect methods of loss compensation. Practical value. The proposed technique of energy efficiency estimation of a loading-backsystemcan be used in solving the problem of rational options choice of schematics stands decisions for electric traction machines acceptance tests of main line and industrial transport.

  11. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2015-05-01

    Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  12. Sustainable Electric Vehicle Management using Coordinated Machine Learning

    NARCIS (Netherlands)

    K. Valogianni (Konstantina)

    2016-01-01

    markdownabstractThe purpose of this dissertation is to investigate how intelligent algorithms can support electricity customers in their complex decisions within the electricity grid. In particular, we focus on how electric vehicle (EV) owners can be supported in their charging and discharging

  13. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Development of new metal matrix composite electrodes for electrical discharge machining through powder metallurgy process

    Directory of Open Access Journals (Sweden)

    C. Mathalai Sundaram

    2014-12-01

    Full Text Available Electrical discharge machining (EDM is one of the widely used nontraditional machining methods to produce die cavities by the erosive effect of electrical discharges. This method is popular due to the fact that a relatively soft electrically conductive tool electrode can machine hard work piece. Copper electrode is normally used for machining process. Electrode wear rate is the major drawback for EDM researchers. This research focus on fabrication of metal matrix composite (MMC electrode by mixing copper powder with titanium carbide (TiC and Tungsten carbide (WC powder through powder metallurgy process, Copper powder is the major amount of mixing proportion with TiC and WC. However, this paper focus on the early stage of the project where powder metallurgy route was used to determine suitable mixing time, compaction pressure and sintering and compacting process in producing EDM electrode. The newly prepared composite electrodes in different composition are tested in EDM for OHNS steel.

  15. Computationally-efficient finite-element-based thermal and electromagnetic models of electric machines

    Science.gov (United States)

    Zhou, Kan

    With the modern trend of transportation electrification, electric machines are a key component of electric/hybrid electric vehicle (EV/HEV) powertrains. It is therefore important that vehicle powertrain-level and system-level designers and control engineers have access to accurate yet computationally-efficient (CE), physics-based modeling tools of the thermal and electromagnetic (EM) behavior of electric machines. In this dissertation, CE yet sufficiently-accurate thermal and EM models for electric machines, which are suitable for use in vehicle powertrain design, optimization, and control, are developed. This includes not only creating fast and accurate thermal and EM models for specific machine designs, but also the ability to quickly generate and determine the performance of new machine designs through the application of scaling techniques to existing designs. With the developed techniques, the thermal and EM performance can be accurately and efficiently estimated. Furthermore, powertrain or system designers can easily and quickly adjust the characteristics and the performance of the machine in ways that are favorable to the overall vehicle performance.

  16. A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-05-01

    Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

  17. Modelling electric trains energy consumption using Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez Fernandez, P.; Garcia Roman, C.; Insa Franco, R.

    2016-07-01

    Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways. (Author)

  18. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    Science.gov (United States)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

  19. The method for controlling electric machine parameters based on the analysis of starting currents

    Directory of Open Access Journals (Sweden)

    Remezovsky V.M.

    2015-03-01

    Full Text Available The theoretical and experimental analysis of the electric machine technical condition by studying activate currents has been carried out. It has been shown that by means of express-methods it is possible to estimate the electric engine parameters with sufficient degree of accuracy

  20. USING OF OBJECT-ORIENTED DESIGN PRINCIPLES IN ELECTRIC MACHINES DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    N.N. Zablodskii

    2016-03-01

    Full Text Available Purpose. To develop the theoretical basis of electrical machines object-oriented design, mathematical models and software to improve their design synthesis, analysis and optimization. Methodology. We have applied object-oriented design theory in electric machines optimal design and mathematical modelling of electromagnetic transients and electromagnetic field distribution. We have correlated the simulated results with the experimental data obtained by means of the double-stator screw dryer with an external solid rotor, brushless turbo-generator exciter and induction motor with squirrel cage rotor. Results. We have developed object-oriented design methodology, transient mathematical modelling and electromagnetic field equations templates for cylindrical electrical machines, improved and remade Cartesian product and genetic optimization algorithms. This allows to develop electrical machines classifications models, included not only structure development but also parallel synthesis of mathematical models and design software, to improve electric machines efficiency and technical performance. Originality. For the first time, we have applied a new way of design and modelling of electrical machines, which is based on the basic concepts of the object-oriented analysis. For the first time is suggested to use a single class template for structural and system organization of electrical machines, invariant to their specific variety. Practical value. We have manufactured screw dryer for coil dust drying and mixing based on the performed object-oriented theory. We have developed object-oriented software for design and optimization of induction motor with squirrel cage rotor of AIR series and brushless turbo-generator exciter. The experimental studies have confirmed the adequacy of the developed object-oriented design methodology.

  1. Continuous Learning from Human Post-Edits for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Turchi Marco

    2017-06-01

    Full Text Available Improving machine translation (MT by learning from human post-edits is a powerful solution that is still unexplored in the neural machine translation (NMT framework. Also in this scenario, effective techniques for the continuous tuning of an existing model to a stream of manual corrections would have several advantages over current batch methods. First, they would make it possible to adapt systems at run time to new users/domains; second, this would happen at a lower computational cost compared to NMT retraining from scratch or in batch mode. To attack the problem, we explore several online learning strategies to stepwise fine-tune an existing model to the incoming post-edits. Our evaluation on data from two language pairs and different target domains shows significant improvements over the use of static models.

  2. Application of neural networks and support vector machine for significant wave height prediction

    Directory of Open Access Journals (Sweden)

    Jadran Berbić

    2017-07-01

    Full Text Available For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN and support vector machine (SVM. The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.

  3. A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran

    Science.gov (United States)

    Hamidi, Omid; Poorolajal, Jalal; Sadeghifar, Majid; Abbasi, Hamed; Maryanaji, Zohreh; Faridi, Hamid Reza; Tapak, Lily

    2015-02-01

    This study compared two machine learning techniques, support vector machines (SVM), and artificial neural network (ANN) in modeling monthly precipitation fluctuations. The SVM and ANN approaches were applied to the monthly precipitation data of two synoptic stations in Hamadan (Airport and Nojeh), the west of Iran. To avoid overfitting, the data were divided into two parts of training (70 %) and test sets (30 %). Then, monthly data from July 1976 to June 2001 and data from April 1961 to November 1996 were considered as training set for the Hamadan and Nojeh stations, respectively, and the remaining were used as test set. The results of the SVM model were compared with those of the ANN based on the root mean square errors, mean absolute errors, determination coefficient, and efficiency coefficient criteria. Based on the comparison, it was found that the SVM model outperformed the ANN, and the estimated precipitation values were in good agreement with the corresponding observed values.

  4. Application of artificial neural network with extreme learning machine for economic growth estimation

    Science.gov (United States)

    Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

    2017-01-01

    The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

  5. Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

    Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

  6. Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex.

    Science.gov (United States)

    Even-Chen, Nir; Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

    2015-01-01

    Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.

  7. Electromechanical Battery EMB Mass Minimization taking into Account its Electrical Machines Rotor Energy

    Directory of Open Access Journals (Sweden)

    Podgornovs Andrejs

    2014-12-01

    Full Text Available In this paper the electromechanical battery (EMB with synchronous machine is described. Theoretically, if electrical machines rotor stored energy is known, it is possible to reduce the flywheel mass of electromechanical battery. For example, the efficiency of energy recovery (kilowatt-hours out versus kilowatthours in in nowadays appliances exceeds 95 % which is considerably better than of any electrochemical battery, such as lead-acid battery. For the rotor stored energy amount calculation, it is necessary to find all geometrical dimensions of the electrical machine. To achieve this goal the iterative calculation method was used. Electromechanical battery mass was analyzed as a discharge process rotation speed function. Taking into account the rotor stored energy, we can increase the minimum rotation speed thus reducing the electrical machine mass and increasing the flywheel mass, which provides EMB cost reduction. Additionally, the possibilities of using numerical approximation calculations of magnetization curves are discussed. Each iteration of numerical application necessary for the method for rapid calculation is essential when calculating the field problems. Nowadays there are a lot of computer added design programs for electromagnetic field calculation in different types of applications, electrical machines and apparatus. For the electromagnetic field calculation process some more commonly used magnetization curve approximation methods are described, and the machine calculation time is tested for different numbers of calculations.

  8. Electrical discharge machining studies on reactive sintered FeAl

    Indian Academy of Sciences (India)

    Unknown

    machine (Cheng et al 1996). Intermetallic alloys such as FeAl are potential materials for high temperature applications. This iron aluminide has a remarkable oxidation and corrosion resistance. Its main applications are in hot gas filters, furnace fixtures, heating elements, automobile components subjected to high tempe-.

  9. Two phase gap cooling of an electrical machine

    Energy Technology Data Exchange (ETDEWEB)

    Shoykhet, Boris A.

    2016-10-04

    An electro-dynamic machine has a rotor and stator with a gap therebetween. The machine has a frame defining a hollow interior with end cavities on axially opposite ends of the frame. A gas circulating system has an inlet that supplies high pressure gas to the frame interior and an outlet to collect gas passing therethrough. A liquid coolant circulating system has an inlet that supplies coolant to the frame interior and an outlet that collects coolant passing therethrough. The coolant inlet and gas inlet are generally located on the frame in a manner to allow coolant from the coolant inlet to flow with gas from the gas inlet to the gap. The coolant outlet and gas outlet are generally located on the frame in a manner to allow the coolant to be separated from the gas with the separated coolant and gas collected for circulation through their respective circulating systems.

  10. On the Carter's Factor Calculation for Slotted Electric Machines

    Directory of Open Access Journals (Sweden)

    VIOREL, I. A.

    2007-11-01

    Full Text Available The air-gap flux density in a single side slotted unsaturated machine is computed via two dimensions finite element method (2D-FEM and via some analytical approximations. The Carter's factor values are calculated using different equations and a comparison between the obtained results is presented, allowing for pertinent conclusions concerning the flux density analytical estimation or the Carter's factor calculation.

  11. CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2010-12-01

    Full Text Available Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic programs.

  12. CAD-CAE in Electrical Machines and Drives Teaching.

    Science.gov (United States)

    Belmans, R.; Geysen, W.

    1988-01-01

    Describes the use of computer-aided design (CAD) techniques in teaching the design of electrical motors. Approaches described include three technical viewpoints, such as electromagnetics, thermal, and mechanical aspects. Provides three diagrams, a table, and conclusions. (YP)

  13. Electromechanical Battery EMB Mass Minimization taking into Account its Electrical Machines Rotor Energy

    OpenAIRE

    Podgornovs Andrejs; Sipovichs Antons

    2014-01-01

    In this paper the electromechanical battery (EMB) with synchronous machine is described. Theoretically, if electrical machines rotor stored energy is known, it is possible to reduce the flywheel mass of electromechanical battery. For example, the efficiency of energy recovery (kilowatt-hours out versus kilowatthours in) in nowadays appliances exceeds 95 % which is considerably better than of any electrochemical battery, such as lead-acid battery. For the rotor stored energy amount calculation...

  14. A Cooperative Control Method for Fully Mechanized Mining Machines Based on Fuzzy Logic Theory and Neural Networks

    Directory of Open Access Journals (Sweden)

    Chao Tan

    2015-01-01

    Full Text Available In a fully mechanized mining face, the coordinated control of coal mining machines has a significant promoting effect to perfect the mining environment and improve the efficiency of coal production and has become a research focus all over the world. In this paper, a cooperative control method based on the integration of fuzzy logic theory and neural networks was proposed. The improved Elman neural network (ENN through a threshold strategy was presented to predict the running parameters of coal mining machines. On the basis of coupling analysis of coal mining machines, the expert knowledge base of scraper conveyor was established based on fuzzy logic theory. Furthermore, the probabilistic neural network (PNN was applied to evaluate the running status of scraper conveyor, and the cooperative control flow was designed and analyzed. Finally, a simulation example was provided and the comparison results illustrated that the proposed method was feasible and superior to the manual control.

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

    Science.gov (United States)

    Bucak, Ihsan Ömür

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    İhsan Ömür Bucak

    2010-03-01

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

  17. Feasibility Study for Electrical Discharge Machining of Large DU-Mo Castings

    Energy Technology Data Exchange (ETDEWEB)

    Hill, Mary Ann [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Dombrowski, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Clarke, Kester Diederik [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Forsyth, Robert Thomas [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Aikin, Robert M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Alexander, David John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Tegtmeier, Eric Lee [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Robison, Jeffrey Curt [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Beard, Timothy Vance [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Edwards, Randall Lynn [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Mauro, Michael Ernest [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Scott, Jeffrey E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division; Strandy, Matthew Thomas [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). SIGMA Division

    2016-10-31

    U-10 wt. % Mo (U-10Mo) alloys are being developed as low enrichment monolithic fuel for the CONVERT program. Optimization of processing for the monolithic fuel is being pursued with the use of electrical discharge machining (EDM) under CONVERT HPRR WBS 1.2.4.5 Optimization of Coupon Preparation. The process is applicable to manufacturing experimental fuel plate specimens for the Mini-Plate-1 (MP-1) irradiation campaign. The benefits of EDM are reduced machining costs, ability to achieve higher tolerances, stress-free, burr-free surfaces eliminating the need for milling, and the ability to machine complex shapes. Kerf losses are much smaller with EDM (tenths of mm) compared to conventional machining (mm). Reliable repeatability is achievable with EDM due to its computer-generated machining programs.

  18. Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Bao Wang

    2012-11-01

    Full Text Available The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA, generalized regression neural network (GRNN and regression model.

  19. Shaping of steel mold surface of lens array by electrical discharge machining with single rod electrode.

    Science.gov (United States)

    Takino, Hideo; Hosaka, Takahiro

    2014-11-20

    We propose a method for fabricating a lens array mold by electrical discharge machining (EDM). In this method, the tips of rods are machined individually to form a specific surface, and then a number of the machined rods are arranged to construct an electrode for EDM. The repetition of the EDM process using the electrode enables a number of lens elements to be produced on the mold surface. The effectiveness of our proposed method is demonstrated by shaping a lens array mold made of stainless steel with 16 spherical elements, in which the EDM process with a single rod electrode is repeatedly conducted.

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

    Science.gov (United States)

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

    2017-04-01

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

  1. Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

    Science.gov (United States)

    Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

    2018-01-01

    In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

  2. Application of neural networks and other machine learning algorithms to DNA sequence analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Barnes, C.; Burks, C.; Farber, R.; Sirotkin, K.

    1988-01-01

    In this article we report initial, quantitative results on application of simple neutral networks, and simple machine learning methods, to two problems in DNA sequence analysis. The two problems we consider are: (1) determination of whether procaryotic and eucaryotic DNA sequences segments are translated to protein. An accuracy of 99.4% is reported for procaryotic DNA (E. coli) and 98.4% for eucaryotic DNA (H. Sapiens genes known to be expressed in liver); (2) determination of whether eucaryotic DNA sequence segments containing the dinucleotides ''AG'' or ''GT'' are transcribed to RNA splice junctions. Accuracy of 91.2% was achieved on intron/exon splice junctions (acceptor sites) and 92.8% on exon/intron splice junctions (donor sites). The solution of these two problems, by use of information processing algorithms operating on unannotated base sequences and without recourse to biological laboratory work, is relevant to the Human Genome Project. A variety of neural network, machine learning, and information theoretic algorithms are used. The accuracies obtained exceed those of previous investigations for which quantitative results are available in the literature. They result from an ongoing program of research that applies machine learning algorithms to the problem of determining biological function of DNA sequences. Some predictions of possible new genes using these methods are listed -- although a complete survey of the H. sapiens and E. coli sections of GenBank will be given elsewhere. 36 refs., 6 figs., 6 tabs.

  3. Specification Requirement for Thermal Stability of Sintered NdFeB Materials for Electrical Machines

    Institute of Scientific and Technical Information of China (English)

    Lin Yan; Jiang Daiwei; Chen Lixiang; Chen Hailing; Bi Haitao; Tang Renyuan

    2004-01-01

    Based on IEC standards and Chinese national standards of sintered NdFeB materials, in the paper the hightemperature, room-temperature properties and thermal stability of about one hundred samples of NdFeB materials for electrical machines were measured and analyzed.These materials are produced by ten representative manufactories in China.Combined with the analysis results, the paper points out that the magnetic properties of sintered NdFeB materials for electrical machines should meet not only the specific values in standards, such as Br, (BH)max ,HcJ ,but also the requirement of temperature coefficients a (Br) , a (HcJ).

  4. Computational Models of Financial Price Prediction: A Survey of Neural Networks, Kernel Machines and Evolutionary Computation Approaches

    Directory of Open Access Journals (Sweden)

    Javier Sandoval

    2011-12-01

    Full Text Available A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted.  The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets.  The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.

  5. PROCESSING OF SOFT MAGNETIC MATERIALS BY POWDER METALLURGY AND ANALYSIS OF THEIR PERFORMANCE IN ELECTRICAL MACHINES

    Directory of Open Access Journals (Sweden)

    W. H. D. Luna

    2017-12-01

    Full Text Available This article presents the use of finite elements to analyze the yield of electric machines based on the use of different soft magnetic materials for the rotor and the stator, in order to verify the performance in electric machine using powder metallurgy. Traditionally, the cores of electric machines are built from rolled steel plates, thus the cores developed in this work are obtained from an alternative process known as powder metallurgy, where powders of soft magnetic materials are compacted and sintered. The properties of interest were analyzed (magnetic, electric and mechanical properties and they were introduced into the software database. The topology of the rotor used was 400 W three-phase synchronous motor manufactured by WEG Motors. The results show the feasibility to replace the metal sheets of the electric machines by solid blocks obtained by powder metallurgy process with only 0.37% yield losses. In addition, the powder metallurgical process reduces the use of raw materials and energy consumption per kg of raw material processed.

  6. INFLUENCE OF FEEDING ELECTRIC ENERGY QUALITY ON HEATING OF THE AUXILIARY MA-CHINES OF AC ELECTRIC ROLLING STOCK

    Directory of Open Access Journals (Sweden)

    O. YU. Baliichuk

    2014-04-01

    Full Text Available Purpose. The article aims to study the problem of increase the reliability of auxiliary machines for AC electric trains during operation in real conditions. Methodology. The peculiarity of system construction of auxiliary machines for AC electric rolling stock is the use of asynchronous motors for general industrial purpose. An engineering method of influence determination on the feeding voltage asymmetry and its deviation from the nominal value on heating of auxiliary machines insulation was proposed. Findings. It is found out that in case when the auxiliary machines of AC electric trains work under asymmetry factor of the voltage 10% or more and feeding voltage deviation from the nominal order 0.6 relative unit then it is possible the overheat of their isolation, even if it has class H. Originality. For the first time the issue of the total insulation heating under such boundary parameters combinations of energy quality, when each of them contributes to the heating insulation increase as compared to the nominal regime of the "rotating phase splitter−auxiliary machinery" system was illuminated. Practical value. Conducted research allow us to establish the boundary parameter values of feeding energy quality (asymmetry factor, feeding voltage deviations from the nominal value, at which additional isolation overheating of this class under the effect of specified factors will not exceed the agreed value.

  7. Wire Electrical Discharge Machining of a Hybrid Composite: Evaluation of Kerf Width and Surface Roughness

    Directory of Open Access Journals (Sweden)

    Abdil KUŞ

    2016-06-01

    Full Text Available In this study, the machinability characteristics of Al/B4C-Gr hybrid composite were investigated using wire electrical discharge machining (WEDM. In the experiments, the machining parameters of wire speed, pulse-on time and pulse-off time were varied in order to explaiın their effects on machining performance, including the width of slit (kerf and surface roughness values (Rz and Rt. According to the Taguchi quality design concept, a L18 (21×32 orthogonal array was used to determine the S/N ratio, and analysis of variance (ANOVA and the F-test were used to indicate the significant machining parameters affecting the machining performance. From the ANOVA and F-test results, the significant factors were determined for each of the machining performance criteria of kerf, Rz and Rt. The variations of kerf, Rz and Rt with the machining parameters were statistically modeled via the regression analysis method. The optimum levels of the control factors for kerf, Rz and Rt were specified as A1B1C1, A1B1C2 and A1B1C2, respectively. The correlation coefficients of the predictive equations developed for kerf, Rz and Rt were calculated as 0.98, 0.828 and 0.855, respectively.

  8. Self-control of chaos in neural circuits with plastic electrical synapses

    Science.gov (United States)

    Zhigulin, V. P.; Rabinovich, M. I.

    2004-10-01

    Two kinds of connections are known to exist in neural circuits: electrical (also called gap junctions) and chemical. Whereas chemical synapses are known to be plastic (i. e., modifiable), but slow, electrical transmission through gap junctions is not modifiable, but is very fast. We suggest the new artificial synapse that combines the best properties of both: the fast reaction of a gap junction and the plasticity of a chemical synapse. Such a plastic electrical synapse can be used in hybrid neural circuits and for the development of neural prosthetics, i.e., implanted devices that can interact with the real nervous system. Based on the computer modelling we show that such a plastic electrical synapse regularizes chaos in the minimal neural circuit consisting of two chaotic bursting neurons.

  9. Evidence-Based Systematic Review: Effects of Neuromuscular Electrical Stimulation on Swallowing and Neural Activation

    Science.gov (United States)

    Clark, Heather; Lazarus, Cathy; Arvedson, Joan; Schooling, Tracy; Frymark, Tobi

    2009-01-01

    Purpose: To systematically review the literature examining the effects of neuromuscular electrical stimulation (NMES) on swallowing and neural activation. The review was conducted as part of a series examining the effects of oral motor exercises (OMEs) on speech, swallowing, and neural activation. Method: A systematic search was conducted to…

  10. Electromagnetic Analysis and Design of Switched Reluctance Double-Rotor Machine for Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Shouliang Han

    2014-10-01

    Full Text Available The double-rotor machine is a kind of multiple input and output electromechanical energy transducer with two electrical ports and two mechanical ports, which is an ideal transmission system for hybrid electric vehicles and has a series of advantages such as integration of power and energy, high efficiency and compaction. In this paper, a switched reluctance double-rotor machine (SRDRM is proposed for hybrid electric vehicles, while no conductor or PM in the middle rotor. This machine not only inherits the merits of switched reluctance machine, such as simple salient rotor structure, high reliability and wide speed range, but also can avoid the outer rotor’s cooling problem effectively. By using an equivalent magnetic circuit model, the function of middle rotor yoke is analyzed. Electromagnetic analyses of the SRDRM are performed with analytical calculations and 2-D finite element methods, including the effects of main parameters on performance. Finally, a 4.4 kW prototype machine is designed and manufactured, and the tests are performed, which validate the proposed design method.

  11. Impacts of Interior Permanent Magnet Machine Technology for Electric Vehicles

    Science.gov (United States)

    2012-01-01

    corrosion constraints of magnets  Minimum gear and more direct drive  Regenerative braking and short charging cycle of batteries  Impulse...be found in limited applications such as, antilock braking system (ABS) of the vehicles. Considering the performance enhancement and reliability of... system forms the backbone of modern society. Electricity and its accessibility is one of the major engineering achievements. In order to maintain and

  12. Passivity-Based Control of a Class of Blondel-Park Transformable Electric Machines

    Directory of Open Access Journals (Sweden)

    Per J. Nicklasson

    1997-10-01

    Full Text Available In this paper we study the viability of extending, to the general rotating electric machine's model, the passivity-based controller method that we have developed for induction motors. In this approach the passivity (energy dissipation properties of the motor are taken advantage of at two different levels. First, we prove that the motor model can be decomposed as the feedback interconnection of two passive subsystems, which can essentially be identified with the electrical and mechanical dynamics. Then, we design a torque tracking controller that preserves passivity for the electrical subsystem, and leave the mechanical part as a "passive disturbance". In position or speed control applications this procedure naturally leads to the well known cascaded controller structure which is typically analyzed invoking time-scale separation assumptions. A key feature of the new cascaded control paradigm is that the latter arguments are obviated in the stability analysis. Our objective in this paper is to characterize a class of machines for which such a passivity-based controller solves the output feedback torque tracking problem. Roughly speaking, the class consists of machines whose nonactuated dynamics are well damped and whose electrical and mechanical dynamics can be suitably decoupled via a coordinate transformation. The first condition translates into the requirement of approximate knowledge of the rotor resistances to avoid the need of injecting high gain into the loop. The latter condition is known in the electric machines literature as Blondel-Park transformability, and in practical terms it requires that the air-gap magnetomotive force must be suitably approximated by the first harmonic in its Fourier expansion. These conditions, stemming from the construction of the machine, have a clear physical interpretation in terms of the couplings between its electrical, magnetic and mechanical dynamics, and are satisfied by a large number of practical

  13. Possibilities for Automatic Control of Hydro-Mechanical Transmission and Birotating Electric Machine

    Directory of Open Access Journals (Sweden)

    V. V. Mikhailov

    2014-01-01

    Full Text Available The paper presents mathematical models and results of virtual investigations pertaining to the selected motion parameters of a mobile machine equipped with hydro mechanical and modernized transmissions. The machine has been tested in similar technological cycles and it has been equipped with a universal automatic control system. Changes in structure and type of power transmission have been obtained with the help of a control algorithm including an extra reversible electric machine which is switched in at some operational modes.Implementation of the proposed  concept makes it possible to obtain and check the improved C-code of the control system and enhance operational parameters of the transmission and machine efficiency, reduce slippage and tire wear while using braking energy for its later beneficial use which is usually considered as a consumable element.

  14. Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks

    Directory of Open Access Journals (Sweden)

    Kang Xie

    2014-01-01

    Full Text Available In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM migration detection algorithm based on the cellular neural networks (CNNs, is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation allowing the VM migration detection to be performed better.

  15. Generating Alignments Using Target Foresight in Attention-Based Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Peter Jan-Thorsten

    2017-06-01

    Full Text Available Neural machine translation (NMT has shown large improvements in recent years. The currently most successful approach in this area relies on the attention mechanism, which is often interpreted as an alignment, even though it is computed without explicit knowledge of the target word. This limitation is the most likely reason that the quality of attention-based alignments is inferior to the quality of traditional alignment methods. Guided alignment training has shown that alignments are still capable of improving translation quality. In this work, we propose an extension of the attention-based NMT model that introduces target information into the attention mechanism to produce high-quality alignments. In comparison to the conventional attention-based alignments, our model halves the Aer with an absolute improvement of 19.1% Aer. Compared to GIZA++ it shows an absolute improvement of 2.0% Aer.

  16. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  17. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  18. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines

    Science.gov (United States)

    Yang, Bo-Suk; Hwang, Won-Woo; Kim, Dong-Jo; Chit Tan, Andy

    2005-03-01

    The need to increase machine reliability and decrease production loss due to faulty products in highly automated line requires accurate and reliable fault classification technique. Wavelet transform and statistical method are used to extract salient features from raw noise and vibration signals. The wavelet transform decomposes the raw time-waveform signals into two respective parts in the time space and frequency domain. With wavelet transform prominent features can be obtained easily than from time-waveform analysis. This paper focuses on the development of an advanced signal classifier for small reciprocating refrigerator compressors using noise and vibration signals. Three classifiers, self-organising feature map, learning vector quantisation and support vector machine (SVM) are applied in training and testing for feature extraction and the classification accuracies of the techniques are compared to determine the optimum fault classifier. The classification technique selected for detecting faulty reciprocating refrigerator compressors involves artificial neural networks and SVMs. The results confirm that the classification technique can differentiate faulty compressors from healthy ones and with high flexibility and reliability.

  19. Broiler weight estimation based on machine vision and artificial neural network.

    Science.gov (United States)

    Amraei, S; Abdanan Mehdizadeh, S; Salari, S

    2017-04-01

    1. Machine vision and artificial neural network (ANN) procedures were used to estimate live body weight of broiler chickens in 30 1-d-old broiler chickens reared for 42 d. 2. Imaging was performed two times daily. To localise chickens within the pen, an ellipse fitting algorithm was used and the chickens' head and tail removed using the Chan-Vese method. 3. The correlations between the body weight and 6 physical extracted features indicated that there were strong correlations between body weight and the 5 features including area, perimeter, convex area, major and minor axis length. 5. According to statistical analysis there was no significant difference between morning and afternoon data over 42 d. 6. In an attempt to improve the accuracy of live weight approximation different ANN techniques, including Bayesian regulation, Levenberg-Marquardt, Scaled conjugate gradient and gradient descent were used. Bayesian regulation with R 2 value of 0.98 was the best network for prediction of broiler weight. 7. The accuracy of the machine vision technique was examined and most errors were less than 50 g.

  20. Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Rakan Kh. Antar

    2017-12-01

    Full Text Available In this paper, Artificial Neural Network (ANN technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm and mechanical torque (±7 N.m in steady state and dynamic conditions. The simulation results illustratethe effectiveness of the proposed controller without speed or torque sensors.

  1. SKYNET: an efficient and robust neural network training tool for machine learning in astronomy

    Science.gov (United States)

    Graff, Philip; Feroz, Farhan; Hobson, Michael P.; Lasenby, Anthony

    2014-06-01

    We present the first public release of our generic neural network training algorithm, called SKYNET. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. SKYNET uses a `pre-training' method to obtain a set of network parameters that has empirically been shown to be close to a good solution, followed by further optimization using a regularized variant of Newton's method, where the level of regularization is determined and adjusted automatically; the latter uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimize using standard backpropagation techniques. SKYNET employs convergence criteria that naturally prevent overfitting, and also includes a fast algorithm for estimating the accuracy of network outputs. The utility and flexibility of SKYNET are demonstrated by application to a number of toy problems, and to astronomical problems focusing on the recovery of structure from blurred and noisy images, the identification of gamma-ray bursters, and the compression and denoising of galaxy images. The SKYNET software, which is implemented in standard ANSI C and fully parallelized using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.

  2. Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

    Science.gov (United States)

    Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru

    Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

  3. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  4. System and method for smoothing a salient rotor in electrical machines

    Science.gov (United States)

    Raminosoa, Tsarafidy; Alexander, James Pellegrino; El-Refaie, Ayman Mohamed Fawzi; Torrey, David A.

    2016-12-13

    An electrical machine exhibiting reduced friction and windage losses is disclosed. The electrical machine includes a stator and a rotor assembly configured to rotate relative to the stator, wherein the rotor assembly comprises a rotor core including a plurality of salient rotor poles that are spaced apart from one another around an inner hub such that an interpolar gap is formed between each adjacent pair of salient rotor poles, with an opening being defined by the rotor core in each interpolar gap. Electrically non-conductive and non-magnetic inserts are positioned in the gaps formed between the salient rotor poles, with each of the inserts including a mating feature formed an axially inner edge thereof that is configured to mate with a respective opening being defined by the rotor core, so as to secure the insert to the rotor core against centrifugal force experienced during rotation of the rotor assembly.

  5. Neural control of finger movement via intracortical brain–machine interface

    Science.gov (United States)

    Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

    2017-12-01

    Objective. Intracortical brain–machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s‑1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We

  6. Neural control of finger movement via intracortical brain-machine interface.

    Science.gov (United States)

    Irwin, Z T; Schroeder, K E; Vu, P P; Bullard, A J; Tat, D M; Nu, C S; Vaskov, A; Nason, S R; Thompson, D E; Bentley, J N; Patil, P G; Chestek, C A

    2017-12-01

    Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys' ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s -1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step

  7. Development of an Electric Motor Powered Low Cost Coconut Deshelling Machine

    Science.gov (United States)

    Mondal, Imdadul Hoque; Prasanna Kumar, G. V.

    2016-06-01

    An electric motor powered coconut deshelling machine was developed in line with the commercially available unit, but with slight modifications. The machine worked on the principle that the coconut shell can be caused to fail in shear and compressive forces. It consisted of a toothed wheel, a deshelling rod, an electric motor, and a compound chain drive. A bevelled 16 teeth sprocket with 18 mm pitch was used as the toothed wheel. Mild steel round bar of 18 mm diameter was used as the deshelling rod. The sharp edge tip of the deshelling rod was inserted below the shell to apply shear force on the shell, and the fruit was tilted toward the rotary toothed wheel to apply the compressive force on the shell. The speed of rotation of the toothed wheel was set at 34 ± 2 rpm. The output capacity of the machine was found to be 24 coconuts/h with 95 % of the total time effectively used for deshelling. The labour requirement was found to be 43 man-h/1000 nuts. About 13 % of the kernels got scraped and about 7 % got sliced during the operation. The developed coconut deshelling machine was recommended for the minimum annual use of 200 h or deshelling of 4700 coconuts per year. The cost of operation for 200 h of annual use was found to be about ` 47/h. The developed machine was found to be simple, easy to operate, energy efficient, safe and reduce drudgery involved in deshelling by conventional methods.

  8. Design of an Electric Commutated Frog-Leg Winding Permanent-Magnet DC Machine

    Directory of Open Access Journals (Sweden)

    Hang Zhang

    2014-03-01

    Full Text Available An electric commutated frog-leg winding permanent-magnet (PM DC machine is proposed in this paper. It has a semi-closed slotted stator with a classical type of mesh winding introduced from the conventional brushed DC machine and a polyphase electric commutation besides a PM excitation rotor and a circular arrayed Hall position sensor. Under the cooperation between the position sensor and the electric commutation, the proposed machine is basically operated on the same principle of the brushed one. Because of its simplex frog-leg winding, the combination between poles and slots is designed as 4/22, and the number of phases is set as 11. By applying an exact analytical method, which is verified comparable with the finite element analyses (FEA, to the prediction of its instantaneous magnetic field, electromotive force (EMF, cogging torque and output torque, it is well designed with a series of parameters in dimension aiming at the lowest cogging torque. A 230 W, 4-pole, and 22-slot new machine is prototyped and tested to verify the analysis.

  9. Midterm Electricity Market Clearing Price Forecasting Using Two-Stage Multiple Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xing Yan

    2015-01-01

    Full Text Available Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP, but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM based midterm forecasting model of the electricity MCP is proposed in this paper. The first stage is utilized to separate the input data into corresponding price zones by using a single SVM. Then, the second stage is applied utilizing four parallel designed SVMs to forecast the electricity price in four different price zones. Compared to the forecasting model using a single SVM, the proposed model showed improved forecasting accuracy in both peak prices and overall system. PJM interconnection data are used to test the proposed model.

  10. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  11. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  12. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  13. Model study of combined electrical and near-infrared neural stimulation on the bullfrog sciatic nerve.

    Science.gov (United States)

    You, Mengxian; Mou, Zongxia

    2017-07-01

    This paper implemented a model study of combined electrical and near-infrared (808 nm) neural stimulation (NINS) on the bullfrog sciatic nerve. The model includes a COMSOL model to calculate the electric-field distribution of the surrounding area of the nerve, a Monte Carlo model to simulate light transport and absorption in the bullfrog sciatic nerve during NINS, and a NEURON model to simulate the neural electrophysiology changes under electrical stimulus and laser irradiation. The optical thermal effect is considered the main mechanism during NINS. Therefore, thermal change during laser irradiation was calculated by the Monte Carlo method, and the temperature distribution was then transferred to the NEURON model to stimulate the sciatic nerve. The effects on thermal response by adjusting the laser spot size, energy of the beam, and the absorption coefficient of the nerve are analyzed. The effect of the ambient temperature on the electrical stimulation or laser stimulation and the interaction between laser irradiation and electrical stimulation are also studied. The results indicate that the needed stimulus threshold for neural activation or inhibition is reduced by laser irradiation. Additionally, the needed laser energy for blocking the action potential is reduced by electrical stimulus. Both electrical and laser stimulation are affected by the ambient temperature. These results provide references for subsequent animal experiments and could be of great help to future basic and applied studies of infrared neural stimulation (INS).

  14. Effect of Carbon in the Dielectric Fluid and Workpieces on the Characteristics of Recast Layers Machined by Electrical Discharge Machining

    Science.gov (United States)

    Muttamara, Apiwat; Kanchanomai, Chaosuan

    2016-06-01

    Electrical discharge machining (EDM) is a popular non-traditional machining technique that is usually performed in kerosene. Carbon from the kerosene is mixed into the recast layer during EDM, increasing its hardness. EDM can be performed in deionized water, which causes decarburization. We studied the effects of carbon in the dielectric fluid and workpiece on the characteristics of recast layers. Experiments were conducted using gray cast iron and mild steel workpieces in deionized water or kerosene under identical operating conditions. Scanning electron microscopy revealed that the recast layer formed on gray iron was rougher than that produced on mild steel. Moreover, the dispersion of graphite flakes in the gray iron seemed to cause subsurface cracks, even when EDM was performed in deionized water. Dendritic structures and iron carbides were found in the recast layer of gray iron treated in deionized water. Kerosene caused more microcracks to form and increased surface roughness compared with deionized water. The microcrack length per unit area of mild steel treated in deionized water was greater than that treated in kerosene, but the cracks formed in kerosene were wider. The effect of the diffusion of carbon during cooling on the characteristics of the recast layer was discussed.

  15. Influence of Wire Electrical Discharge Machining (WEDM) process parameters on surface roughness

    Science.gov (United States)

    Yeakub Ali, Mohammad; Banu, Asfana; Abu Bakar, Mazilah

    2018-01-01

    In obtaining the best quality of engineering components, the quality of machined parts surface plays an important role. It improves the fatigue strength, wear resistance, and corrosion of workpiece. This paper investigates the effects of wire electrical discharge machining (WEDM) process parameters on surface roughness of stainless steel using distilled water as dielectric fluid and brass wire as tool electrode. The parameters selected are voltage open, wire speed, wire tension, voltage gap, and off time. Empirical model was developed for the estimation of surface roughness. The analysis revealed that off time has a major influence on surface roughness. The optimum machining parameters for minimum surface roughness were found to be at a 10 V open voltage, 2.84 μs off time, 12 m/min wire speed, 6.3 N wire tension, and 54.91 V voltage gap.

  16. Scope for electric field assisted removal of ablated debris from laser machined features in silicon

    Science.gov (United States)

    Coyne, Edward; Mannion, Paul; O'Connor, Gerard M.; Favre, Sebastian; Glynn, Thomas J.

    2005-04-01

    The problem created by the re-deposition of ablated material when laser machining structures in silicon wafers is investigated. The study focuses on the specific case of machining wafer grade silicon with femtosecond pulses centered at a wavelength of 775 nm. Based on the evidence that a highly ionised plasma state exists immediately after laser ablation, this work explores the potential of using electric fields to channel the debris out of the laser machined feature before it becomes deposited. To this extent the work discusses the step-by-step development of different experimental arrangements, by first evaluating its effects, then identifying its limitations and finally by proposing and investigating potential solutions. It is found that a reduction in the amount of re-deposited debris is observed when a carrier-depleted region is generated in silicon materials.

  17. RSM and ANN Modeling of Micro Wire Electrical Discharge Machining of AL 2024 T351

    Directory of Open Access Journals (Sweden)

    Sivaprakasam Palani

    2015-01-01

    Full Text Available This paper presents modeling and analysis of machining characteristics of Micro Wire Electro Discharge Machining (Micro-WEDM process on Aluminium alloy (AL 2024 T351 using the Response Surface Methodology (RSM and Artificial Neural Network (ANN. The input variables of Micro-WEDM process were voltage, capacitance and feed rate. The surface roughness and material removal rate are considered as a response variables. Experiments were carried out on Aluminium alloy using Central Composite Design (CCD. The RSM and ANN models have been developed based on experimental designs. Analysis of variance (ANOVA has been employed to test the significance of RSM model. It has been found out that all the three process parameters are significant and their interaction effects are also significant on the surface roughness and material removal rate. Finally predicted values were compared with ANN.

  18. Method for providing slip energy control in permanent magnet electrical machines

    Science.gov (United States)

    Hsu, John S.

    2006-11-14

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

  19. Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

    Science.gov (United States)

    Parlos, Alexander G.; Toliyat, Hamid A.

    2005-01-01

    An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

  20. The characteristics of chromized 1020 steel with electrical discharge machining and Ni electroplating pretreatments

    Science.gov (United States)

    Bai, Ching-Yuan; Lee, Jeou-Long; Wen, Tse-Min; Hou, Kung-Hsu; Wu, Min-Sheng; Ger, Ming-Der

    2011-02-01

    A uniform and continuous chromized coating on AISI 1020 steel is produced by low-temperature pack chromization (LTPC) with electrical discharge machining and Ni electroplating pretreatments. The anticorrosive performance of the chromized steels is investigated in a 0.5 M H2SO4 solution at room temperature. The testing results indicate that the chromized specimen with electrical discharge machining and Ni electroplating pretreatments exhibits the lowest corrosion current density, 2.16 × 10-8 A cm-2, among the tested specimens. The corrosion resistance of all tested specimens are in the order of bare 1020 1020-Cr(700-2) 1020-Ni-Cr(700-2) 1020-EDM-Ni-Cr(700-2). Moreover, the 1020-Ni-Cr(700-2) specimen have the best conductivity as a result of the less amount of oxides in the superficial coating.

  1. Effect of electric discharge machining on the fatigue life of Inconel 718

    Science.gov (United States)

    Jeelani, S.; Collins, M. R.

    1988-01-01

    The effect of electric discharge machining on the fatigue life of Inconel 718 alloy at room temperature was investigated. Data were generated in the uniaxial tension fatigue mode at ambient temperature using flat 3.175 mm thick specimens. The specimens were machined on a wire-cut electric discharge machine at cutting speeds ranging from 0.5 to 2 mm per minute. The specimens were fatigued at a selected stress, and the resulting fatigue lives compared with that of the virgin material. The surfaces of the fatigued specimens were examined under optical and scanning electron microscopes, and the roughness of the surfaces was measured using a standard profilometer. From the results of the investigation, it was concluded that the fatigue life of the specimens machined using EDM decreased slightly as compared with that of the virgin material, but remained unchanged as the cutting speed was changed. The results are explained using data produced employing microhardness measurements, profilometry, and optical and scanning microscopy.

  2. Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

    Science.gov (United States)

    Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

    2016-12-01

    Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

  3. A control system for and a method of controlling a superconductive rotating electrical machine

    DEFF Research Database (Denmark)

    2014-01-01

    This invention relates to a method of controlling and a control system (100) for a superconductive rotating electric machine (200) comprising at least one superconductive winding (102; 103), where the control system (100) is adapted to control a power unit (101) supplying during use the at least...... or more actual values (110, 111)of one or more parameters for a given superconductive winding (102; 103), each parameter representing a physical condition of the given superconductive winding (102; 103), and to dynamically derive one or more electrical current values to be maintained in the given...... superconductive winding (102; 103) by the power unit (101) where the one or more electrical current values is/are derived taking into account the received one or more actual values (110, 111). In this way,greater flexibility and more precise control of the performance of the superconducting rotating electrical...

  4. Monthly evaporation forecasting using artificial neural networks and support vector machines

    Science.gov (United States)

    Tezel, Gulay; Buyukyildiz, Meral

    2016-04-01

    Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

  5. QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

    Directory of Open Access Journals (Sweden)

    Rachid Darnag

    2017-02-01

    Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

  6. Research on Modeling and Control of Regenerative Braking for Brushless DC Machines Driven Electric Vehicles

    OpenAIRE

    Jian-ping Wen; Chuan-wei Zhang

    2015-01-01

    In order to improve energy utilization rate of battery-powered electric vehicle (EV) using brushless DC machine (BLDCM), the model of braking current generated by regenerative braking and control method are discussed. On the basis of the equivalent circuit of BLDCM during the generative braking period, the mathematic model of braking current is established. By using an extended state observer (ESO) to observe actual braking current and the unknown disturbances of regenerative braking system, ...

  7. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  8. Heat production in the windings of the stators of electric machines under stationary condition

    Science.gov (United States)

    Alebouyeh Samami, Behzad; Pieper, Martin; Breitbach, Gerd; Hodapp, Josef

    2014-12-01

    In electric machines due to high currents and resistive losses (joule heating) heat is produced. To avoid damages by overheating the design of effective cooling systems is required. Therefore the knowledge of heat sources and heat transfer processes is necessary. The purpose of this paper is to illustrate a good and effective calculation method for the temperature analysis based on homogenization techniques. These methods have been applied for the stator windings in a slot of an electric machine consisting of copper wires and resin. The key quantity here is an effective thermal conductivity, which characterizes the heterogeneous wire resin-arrangement inside the stator slot. To illustrate the applicability of the method, the analysis of a simplified, homogenized model is compared with the detailed analysis of temperature behavior inside a slot of an electric machine according to the heat generation. We considered here only the stationary situation. The achieved numerical results are accurate and show that the applied homogenization technique works in practice. Finally the results of simulations for the two cases, the original model of the slot and the homogenized model chosen for the slot (unit cell), are compared to experimental results.

  9. EXPERIMENTAL INVESTIGATION ON ELECTRICAL DISCHARGE MACHINING OF TITANIUM ALLOY USING COPPER, BRASS AND ALUMINUM ELECTRODES

    Directory of Open Access Journals (Sweden)

    S. DHANABALAN

    2015-01-01

    Full Text Available In the present study, an evaluation has been done on Material Removal Rate (MRR, Surface Roughness (SR and Electrode Wear Rate (EWR during Electrical Discharge Machining (EDM of titanium alloy using copper, brass and aluminum electrodes. Analyzing previous work in this field, it is found that electrode wear and material removal rate increases with an increase current. It is also found that the electrode wear ratio increases with an increase in current. The higher wear ratio is found during machining of titanium alloy using a brass electrode. An attempt has been made to correlate the thermal conductivity and melting point of electrode with the MRR and electrode wear. The MRR is found to be high while machining titanium alloy using brass electrode. During machining of titanium alloy using copper electrodes, a comparatively smaller quantity of heat is absorbed by the work material due to low thermal conductivity. Due to the above reason, the MRR becomes very low. Duringmachining of titanium alloy using aluminium electrodes, the material removal rate and electrode wear rate are only average value while machining of titanium alloy using brass and copper electrodes.

  10. Experimental study of surface roughness in Electric Discharge Machining (EDM based on Grey Relational Analysis

    Directory of Open Access Journals (Sweden)

    Mat Deris Ashanira

    2016-01-01

    Full Text Available Electric Discharge Machining (EDM is one of the modern machining which is capable in handling hard and difficult-to-machine material. The successful of EDM basically depends on its performances such as surface roughness (Ra, material removal rate (MRR, electrode wear rate (EWR and dimensional accuracy (DA. Ra is considered as the most important performance due to it role as a technological quality measurement for a product and also a factor that significantly affects the manufacturing process. This paper presents the experimental study of surface roughness in die sinking EDM using stainless steel SS316L with copper impregnated graphite electrode. The machining experimental is conducted based on the two levels full factorial design of design of experiment (DOE with five machining parameters which are peak current, servo voltage, servo speed, pulse on time and pulse off time. The results were analyzed using grey relational analysis (GRA and it was found that pulse on time and servo voltage give the most influence to the Ra value.

  11. Electricity market price forecasting by grid computing optimizing artificial neural networks

    OpenAIRE

    Niimura, T.; Ozawa, K.; Sakamoto, N.

    2007-01-01

    This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using re...

  12. Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

    Science.gov (United States)

    Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

    2017-11-01

    Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

  13. DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

    Science.gov (United States)

    Kim, Lok-Won

    2017-03-08

    Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating 1024 x 1024 network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

  14. A comparative study of support vector machine, artificial neural network and bayesian classifier for mutagenicity prediction.

    Science.gov (United States)

    Sharma, Anju; Kumar, Rajnish; Varadwaj, Pritish Kumar; Ahmad, Ausaf; Ashraf, Ghulam Md

    2011-09-01

    Mutagenicity is the capability of a chemical to carry out mutations in genetic material of an organism. In order to curtail expensive drug failures due to mutagenicity found in late development or even in clinical trials, it is crucial to determine potential mutagenicity problems as early as possible. In this work we have proposed three different classifiers, i.e. Support Vector Machine (SVM), Artificial Neural Network (ANN) and bayesian classifiers, for the prediction of mutagenicity of compounds based on seventeen descriptors. Among the three classifiers Radial Basis Function (RBF) kernel based SVM classifier appeared to be more accurate for classifying the compounds under study on mutagens and non-mutagens. The overall prediction accuracy of SVM model was found to be 71.73% which was appreciably higher than the accuracy of ANN based classifier (59.72%) and bayesian classifier (66.61%). It suggests that SVM based prediction model can be used for predicting mutagenicity more accurately compared to ANN and bayesian classifier for data under consideration.

  15. Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English

    Directory of Open Access Journals (Sweden)

    Ataman Duygu

    2017-06-01

    Full Text Available The necessity of using a fixed-size word vocabulary in order to control the model complexity in state-of-the-art neural machine translation (NMT systems is an important bottleneck on performance, especially for morphologically rich languages. Conventional methods that aim to overcome this problem by using sub-word or character-level representations solely rely on statistics and disregard the linguistic properties of words, which leads to interruptions in the word structure and causes semantic and syntactic losses. In this paper, we propose a new vocabulary reduction method for NMT, which can reduce the vocabulary of a given input corpus at any rate while also considering the morphological properties of the language. Our method is based on unsupervised morphology learning and can be, in principle, used for pre-processing any language pair. We also present an alternative word segmentation method based on supervised morphological analysis, which aids us in measuring the accuracy of our model. We evaluate our method in Turkish-to-English NMT task where the input language is morphologically rich and agglutinative. We analyze different representation methods in terms of translation accuracy as well as the semantic and syntactic properties of the generated output. Our method obtains a significant improvement of 2.3 BLEU points over the conventional vocabulary reduction technique, showing that it can provide better accuracy in open vocabulary translation of morphologically rich languages.

  16. Response surface and artificial neural network prediction model and optimization for surface roughness in machining

    Directory of Open Access Journals (Sweden)

    Ashok Kumar Sahoo

    2015-04-01

    Full Text Available The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity. It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

  17. An adaptive recurrent-neural-network motion controller for X-Y table in CNC machine.

    Science.gov (United States)

    Lin, Faa-Jeng; Shieh, Hsin-Jang; Shieh, Po-Huang; Shen, Po-Hung

    2006-04-01

    In this paper, an adaptive recurrent-neural-network (ARNN) motion control system for a biaxial motion mechanism driven by two field-oriented control permanent magnet synchronous motors (PMSMs) in the computer numerical control (CNC) machine is proposed. In the proposed ARNN control system, a RNN with accurate approximation capability is employed to approximate an unknown dynamic function, and the adaptive learning algorithms that can learn the parameters of the RNN on line are derived using Lyapunov stability theorem. Moreover, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series, external disturbances, cross-coupled interference and friction torque of the system. To relax the requirement for the value of lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is investigated. Using the proposed control, the position tracking performance is substantially improved and the robustness to uncertainties including cross-coupled interference and friction torque can be obtained as well. Finally, some experimental results of the tracking of various reference contours demonstrate the validity of the proposed design for practical applications.

  18. Application of neural network for real-time measurement of electrical resistivity in cold crucible

    Science.gov (United States)

    Votava, Pavel; Poznyak, Igor

    2017-08-01

    The article describes use of an Induction furnace with cold crucible as a tool for real-time measurement of a melted material electrical resistivity. The measurement is based on an inverse problem solution of a 2D mathematical model, possibly implementable in a microcontroller or a FPGA in a form of a neural network. The 2D mathematical model results has been provided as a training set for the neural network. At the end, the implementation results are discussed together with uncertainty of measurement, which is done by the neural network implementation itself.

  19. ELECTRIC MOTOR DIAGNOSTICS OF SWITCHES BASED ON THE NEURAL NETWORK DATA MODELING THE SPECTRAL DECOMPOSITION OF THE CURRENTS

    Directory of Open Access Journals (Sweden)

    O. M. Shvets

    2009-07-01

    Full Text Available The method of automated diagnostics of electric motors is offered. It uses a neural network revealing the electric motor faults on the basis of analysis of frequency spectrum of current flowing through the motor.

  20. Machining and Surface Characteristics of AISI 304L After Electric Discharge Machining for Copper and Graphite Electrodes in Different Dielectric Liquids

    Directory of Open Access Journals (Sweden)

    S. Anjum

    2017-08-01

    Full Text Available In Electric Discharge Machining (EDM, the thermal energy used for material erosion depends on the intensity of electric sparks, the thermal conductivities of electrode material and the dielectric liquid. In this paper, the effect of EDM on AISI 304L steel is studied using copper and graphite electrodes and distilled water and kerosene oil as dielectric liquids. Material Removal Rates (MRR, Tool Wear Rates (TWR and surface conditions are calculated for four different combinations with the two electrode materials and the two dielectric liquids. These investigations are carried out at different pulse currents. Machined surfaces are evaluated by morphological studies, energy dispersive spectrographs (EDS and white layer thickness using Scanning Electron Microscopy (SEM. It is found that a transfer of carbon takes place from the kerosene oil and the graphite electrodes into the machined surface which alters the metallurgical characteristics, depending on the electrical and thermal conductivities of the electrode material and the dielectric liquid.

  1. Dual-Electrical-Port Control of Cascaded Doubly-Fed Induction Machine for EV/HEV Applications

    DEFF Research Database (Denmark)

    Han, Peng; Cheng, Ming; Chen, Zhe

    2017-01-01

    This paper presents a dual-electrical-port control scheme for four-quadrant operation of cascaded doubly-fed induction machine (CDFIM), which has conventionally been used as a variable-speed drive or variable-speed constant-frequency generator for limited-speed-range applications. The proposed......-electrical-port control scheme. It is for the first time revealed that the CDFIM drive that indirectly couples PW and CW through induction behavior can be readily controlled like a conventional induction motor to achieve the highest torque density. The torque density-speed region of the CDFIM falls within...... that of the power machine in singly-fed operation mode, and only a half of that of the power machine in doubly-fed operation mode, which shows the urgent need for torque density enhancement of brushless doubly-fed machines for electric vehicle/hybrid electric vehicle applications. Computer simulations...

  2. DOE FreedomCAR and vehicle technologies program advanced power electronic and electrical machines annual review report

    Energy Technology Data Exchange (ETDEWEB)

    Olszewski, Mitch [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2006-10-11

    This report is a summary of the Review Panel at the FY06 DOE FreedomCAR and Vehicle Technologies (FCVT) Annual Review of Advanced Power Electronics and Electric Machine (APEEM) research activities held on August 15-17, 2006.

  3. The assessment of energy efficiency of electric machines for domestic appliances drive

    Directory of Open Access Journals (Sweden)

    Bogusz Piotr

    2017-01-01

    Full Text Available In the paper, the division of domestic appliances into categories connected with input power was presented. The authors discussed issues connected with energy efficiency of these devices. An example of such a device power consumption of which affects considerably on overall power consumption from the mains is a vacuum cleaner. Vacuum cleaners are used in almost all households and they were covered by the EU regulations which introduced limitations in power consumption from the mains. In the paper, the assessment of energy efficiency of classic electric motors for vacuum cleaners drive was presented. Results of practical tests of chosen vacuum cleaners were presented. The digital power meter was used to measure electric parameters of tested vacuum cleaners and the PC was used to collect measuring data. The assessment of input power influence on energy consumption and energy efficiency was conducted based on tests results. It was shown in conclusions that the one of development directions of domestic appliances, which can cause improvement of energy efficiency, are alternative technologies of electric machines with much higher efficiency i.e. energy-saving electric machines with electronic commutation.

  4. Regulation of unbalanced electromagnetic moment in mutual loading systems of electric machines of traction rolling stock and multiple unit of mainline and industrial transport

    Directory of Open Access Journals (Sweden)

    A. M. Afanasov

    2014-12-01

    Full Text Available Purpose. The research data are aimed to identify the regulatory principles of unbalanced electromagnetic moment of mutually loaded electric machines of traction rolling stock and multiple unit of main and industrial transport. The purpose of this study is energy efficiency increase of the testing of traction electric machines of direct and pulse current using the improvement methods of their mutual loading, including the principles of automatic regulation of mutual loading system. Methodology. The general theoretical provisions and principles of system approach to the theoretical electric engineering, the theory of electric machines and theoretical mechanics are the methodological basis of this research. The known methods of analysis of electromagnetic and electromechanical processes in electrical machines of direct and pulse current are used in the study. Methods analysis of loading modes regulation of traction electric machines was conducted using the generalized scheme of mutual loading. It is universal for all known methods to cover the losses of idling using the electric power. Findings. The general management principles of mutual loading modes of the traction electric machines of direct and pulse current by regulating their unbalanced electric magnetic moment were developed. Regulatory options of unbalanced electromagnetic moment are examined by changing the difference of the magnetic fluxes of mutually loaded electric machines, the current difference of electric machines anchors, the difference of the angular velocities of electric machines shafts. Originality. It was obtained the scientific basis development to improve the energy efficiency test methods of traction electric machines of direct and pulse current. The management principles of mutual loading modes of traction electric machines were formulated. For the first time it is introduced the concept and developed the principles of regulation of unbalanced electromagnetic moment in

  5. Mathematic model of three-phase induction machine connected to advanced inverter for traction system for electric trolley

    OpenAIRE

    BOCII,LIVIU S.; MULLER Valentin

    2013-01-01

    This paper establishes a mathematical model of induction machine connected to a frequency inverter necessary to adjust the electric motor drive. The mathematical model based on the Park's theory allows the analysis of the whole spectrum (electric car – frequency inverter) to drive the electric trolley bus made on ASTRA Bus Arad (Romania). To remove higher order harmonics, the PWM waveform of supply voltage is used, set in the general case. Operating characteristics of electric motor dri...

  6. Three-phase electrical signals analysis for mechanical faults monitoring in rotating machine systems

    Science.gov (United States)

    Cablea, Georgia; Granjon, Pierre; Bérenguer, Christophe

    2017-08-01

    The current paper proposes a method to detect mechanical faults in rotating machines using three-phase electrical currents analysis. The proposed fault indicator relies on the use of instantaneous symmetrical components (ISCs), followed by a demodulation step enhancing the small modulations generated in electrical signals by mechanical faults. The limitations due to the multi-component nature of electrical signals, as well as to the noise naturally present in the measured signals are studied and taken into account in order to elaborate a proper and efficient algorithm to compute a mechanical fault indicator. It is theoretically shown that the ISCs based approach results in an increase of the signal-to-noise ratio compared to a single-phase approach, finally leading to an improvement of early fault detection capabilities. This result is validated using both synthetic and experimental signals where the proposed method is used to detect bearing faults and the obtained results are compared to single-phase results.

  7. Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)

    2006-04-15

    This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)

  8. Transcutaneous parasacral electrical neural stimulation in children with primary monosymptomatic enuresis: a prospective randomized clinical trial.

    Science.gov (United States)

    de Oliveira, Liliana Fajardo; de Oliveira, Dayana Maria; da Silva de Paula, Lidyanne Ilídia; de Figueiredo, André Avarese; de Bessa, José; de Sá, Cacilda Andrade; Bastos Netto, José Murillo

    2013-10-01

    Parasacral transcutaneous electrical neural stimulation is widely used to treat hyperactive bladder in children and adults. Its use in nonmonosymptomatic enuresis has demonstrated improvement in number of dry nights. We assessed the effectiveness of parasacral transcutaneous electrical neural stimulation in the treatment of monosymptomatic primary enuresis. This prospective randomized clinical trial included 29 girls and 16 boys older than 6 years with primary monosymptomatic enuresis. Children were randomly divided into 2 groups consisting of controls, who were treated with behavioral therapy, and an experimental group, who were treated with behavioral therapy plus 10 sessions of parasacral transcutaneous electrical neural stimulation. Neural stimulation was performed with the electrodes placed in the sacral region (S2/S3). Sessions always followed the same pattern, with duration of 20 minutes, frequency of 10 Hz, a generated pulse of 700 μs and intensity determined by the sensitivity threshold of the child. Sessions were done 3 times weekly on alternate days. Patients in both groups were followed at 2-week intervals for the first month and then monthly for 6 consecutive months. Rate of wet nights was 77% in controls and 78.3% in the experimental group at onset of treatment (p = 0.82), and 49.5% and 31.2%, respectively, at the end of treatment (p = 0.02). Analyzing the average rate of improvement, there was a significantly greater increase in dry nights in the group undergoing neural stimulation (61.8%) compared to controls (37.3%, p = 0.0038). At the end of treatment percent improvement in children undergoing electrical stimulation had no relation to gender (p = 0.391) or age (p = 0.911). Treatment of primary monosymptomatic enuresis with 10 sessions of parasacral transcutaneous electrical neural stimulation plus behavioral therapy proved to be effective. However, no patient had complete resolution of symptoms. Copyright © 2013 American Urological Association

  9. Research of the possibility of using an electrical discharge machining metal powder in selective laser melting

    Science.gov (United States)

    Golubeva, A. A.; Sotov, A. V.; Agapovichev, A. V.; Smelov, V. G.; Dmitriev, V. N.

    2017-02-01

    In this paper the research of a Ni-20Cr-10Fe-3Ti (heat-resistant) alloy metal powder conducted for use in a selective laser melting technology. This metal powder is the slime after electric discharge machining. The technology of cleaning and melting the powder discussed in this article. As a control input of the powder, immediately before 3D printing, dimensional analysis, surface morphology and the internal structure of the powder particles after the treatment were examined using optical and electron microscopes. The powder granules are round, oval, of different diameters with non-metallic inclusions. The internal structure of the particles is solid with no apparent defects. The content of the required diameter of the total volume of test powder granules was 15%. X-ray fluorescence analysis of the powder materials carried out. The possibility of powder melting was investigated in the selective laser melting machine ‘SLM 280HL’. A selection of the melting modes based on the physical properties of the Ni-20Cr-10Fe-3Ti alloy, data obtained from similar studies and a mathematical model of the process. Conclusions on the further investigation of the possibility of using electric discharge machining slime were made.

  10. KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2017-09-01

    Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

  11. The birth of the electric machines: a commentary on Faraday (1832) 'Experimental researches in electricity'.

    Science.gov (United States)

    Al-Khalili, Jim

    2015-04-13

    The history of science is filled with examples of key discoveries and breakthroughs that have been published as landmark texts or journal papers, and to which one can trace the origins of whole disciplines. Such paradigm-shifting publications include Copernicus' De revolutionibus orbium coelestium (1543), Isaac Newton's Philosophiæ Naturalis Principia Mathematica (1687) and Albert Einstein's papers on relativity (1905 and 1915). Michael Faraday's 1832 paper on electromagnetic induction sits proudly among these works and in a sense can be regarded as having an almost immediate effect in transforming our world in a very real sense more than any of the others listed. Here we review the status of the subject-the relationship between magnetism and electricity both before and after Faraday's paper and delve into the details of the key experiments he carried out at the Royal Institution outlining clearly how he discovered the process of electromagnetic induction, whereby an electric current could be induced to flow through a conductor that experiences a changing magnetic field. His ideas would not only enable Maxwell's later development of his theory of classical electromagnetism, but would directly lead to the development of the electric dynamo and electric motor, two technological advances that are the very foundations of the modern world. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.

  12. Modeling and optimization of Electrical Discharge Machining (EDM using statistical design

    Directory of Open Access Journals (Sweden)

    Hegab Husein A.

    2015-01-01

    Full Text Available Modeling and optimization of nontraditional machining is still an ongoing area of research. The objective of this work is to optimize Electrical Discharge Machining process parameters of Aluminum-multiwall carbon Nanotube composites (AL-CNT model. Material Removal Rate (MRR, Wear Electrode Ratio (EWR and Average Surface Roughness (Ra are primary objectives. The Machining parameters are machining-on time (sec, discharge current (A, voltage (V, total depth of cut (mm, and %wt. CNT added. Mathematical models for all responses as function of significant process parameters are developed using Response Surface Methodology (RSM. Experimental results show optimum levels for material removal rate are %wt. CNT (0%, high level of discharge current (6A and low level of voltage (50 V while optimum levels for Electrode wear ratio are %wt. CNT (5%, high level of discharge current (6A and optimum levels for average surface roughness are %wt. CNT (0%, low level of discharge current (2A and high level of depth of cut (1 mm. Single-objective optimization is formulated and solved via Genetic Algorithm. Multi-objective optimization model is then formulated for the three responses of interest. This methodology gathers experimental results, builds mathematical models in the domain of interest and optimizes the process models. As such, process analysis, modeling, design and optimization are achieved.

  13. Delivering key signals to the machine: seeking the electric signal that muscles emanate

    Science.gov (United States)

    Bani Hashim, A. Y.; Maslan, M. N.; Izamshah, R.; Mohamad, I. S.

    2014-11-01

    Due to the limitation of electric power generation in the human body, present human-machine interfaces have not been successful because of the nature of standard electronics circuit designs, which do not consider the specifications of signals that resulted from the skin. In general, the outcomes and applications of human-machine interfaces are limited to custom-designed subsystems, such as neuroprosthesis. We seek to model the bio dynamical of sub skin into equivalent mathematical definitions, descriptions, and theorems. Within the human skin, there are networks of nerves that permit the skin to function as a multi dimension transducer. We investigate the nature of structural skin. Apart from multiple networks of nerves, there are other segments within the skin such as minute muscles. We identify the segments that are active when there is an electromyography activity. When the nervous system is firing signals, the muscle is being stimulated. We evaluate the phenomena of biodynamic of the muscles that is concerned with the electromyography activity of the nervous system. In effect, we design a relationship between the human somatosensory and synthetic systems sensory as the union of a complete set of the new domain of the functional system. This classifies electromyogram waveforms linked to intent thought of an operator. The system will become the basis for delivering key signals to machine such that the machine is under operator's intent, hence slavery.

  14. Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor

    Science.gov (United States)

    Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Kim, Hee-Young; Park, Daihee; Yoon, Sukhan

    2017-01-01

    Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification. PMID:28146057

  15. Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor.

    Science.gov (United States)

    Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Kim, Hee-Young; Park, Daihee; Yoon, Sukhan

    2017-01-29

    Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect-using electric current shape analysis-for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between "does-not-need-to-be-replaced" and "needs-to-be-replaced" shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.

  16. Drilling of Hybrid Titanium Composite Laminate (HTCL) with Electrical Discharge Machining.

    Science.gov (United States)

    Ramulu, M; Spaulding, Mathew

    2016-09-01

    An experimental investigation was conducted to determine the application of die sinker electrical discharge machining (EDM) as it applies to a hybrid titanium thermoplastic composite laminate material. Holes were drilled using a die sinker EDM. The effects of peak current, pulse time, and percent on-time on machinability of hybrid titanium composite material were evaluated in terms of material removal rate (MRR), tool wear rate, and cut quality. Experimental models relating each process response to the input parameters were developed and optimum operating conditions with a short cutting time, achieving the highest workpiece MRR, with very little tool wear were determined to occur at a peak current value of 8.60 A, a percent on-time of 36.12%, and a pulse time of 258 microseconds. After observing data acquired from experimentation, it was determined that while use of EDM is possible, for desirable quality it is not fast enough for industrial application.

  17. Drilling of Hybrid Titanium Composite Laminate (HTCL with Electrical Discharge Machining

    Directory of Open Access Journals (Sweden)

    M. Ramulu

    2016-09-01

    Full Text Available An experimental investigation was conducted to determine the application of die sinker electrical discharge machining (EDM as it applies to a hybrid titanium thermoplastic composite laminate material. Holes were drilled using a die sinker EDM. The effects of peak current, pulse time, and percent on-time on machinability of hybrid titanium composite material were evaluated in terms of material removal rate (MRR, tool wear rate, and cut quality. Experimental models relating each process response to the input parameters were developed and optimum operating conditions with a short cutting time, achieving the highest workpiece MRR, with very little tool wear were determined to occur at a peak current value of 8.60 A, a percent on-time of 36.12%, and a pulse time of 258 microseconds. After observing data acquired from experimentation, it was determined that while use of EDM is possible, for desirable quality it is not fast enough for industrial application.

  18. Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Ali A. Alani

    2017-11-01

    Full Text Available Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted by the user via digital devices. Numerous studies have been proposed in the past and in recent years to improve handwritten digit recognition in various languages. Research on handwritten digit recognition in Arabic is limited. At present, deep learning algorithms are extremely popular in computer vision and are used to solve and address important problems, such as image classification, natural language processing, and speech recognition, to provide computers with sensory capabilities that reach the ability of humans. In this study, we propose a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM and convolutional neural network (CNN deep learning algorithms. In particular, we propose an Arabic handwritten digit recognition approach that works in two phases. First, we use the RBM, which is a deep learning technique that can extract highly useful features from raw data, and which has been utilized in several classification problems as a feature extraction technique in the feature extraction phase. Then, the extracted features are fed to an efficient CNN architecture with a deep supervised learning architecture for the training and testing process. In the experiment, we used the CMATERDB 3.3.1 Arabic handwritten digit dataset for training and testing the proposed method. Experimental results show that the proposed method significantly improves the accuracy rate, with accuracy reaching 98.59%. Finally, comparison of our results with those of other studies on the CMATERDB 3.3.1 Arabic handwritten digit dataset shows that our approach achieves the highest accuracy rate.

  19. Augmenting intracortical brain-machine interface with neurally driven error detectors

    Science.gov (United States)

    Even-Chen, Nir; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2017-12-01

    Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain–machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

  20. Electrical measurement system in milling balance machine based on embedded optimization

    Science.gov (United States)

    Wang, Yijun; Mei, Yushan

    2015-12-01

    Electrical measurement system in milling balance machine currently consists of micro-controller and peripheral devices. The structure has the problems which include low integration, single signal processing algorithms and great measurement error. Therefore, electrical measurement system in milling balance machine based on embedded optimization is presented in the paper. Firstly, the device control electrical measuring system by ARM subsystem of OMAP dual-core architecture and DSP subsystem realizes digital signal processing and unbalance computing. Also, the low-pass filtering circuit is designed for solving frequency interference. Secondly, the system implement digital band-pass tracking filter based on harmonic wavelet packet. Thirdly, the system extracts any period of weak signal characteristics using the unlimited segmentation features harmonic for wavelet packet signal in the frequency domain. Simulation results show that the system effectively inhibits nearly frequency signal interference, improves signal to noise ratio, and reduces the initial imbalance signal characteristics. And test results improve that precision indexes and technical specifications could meet the design goals.

  1. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  2. Matlab for Forecasting of Electric Power Load Based on BP Neural Network

    Science.gov (United States)

    Wang, Xi-Ping; Shi, Ming-Xi

    Modeling and predicting electricity consumption play a vital role both in developed and developing countries for policy makers and related organizations. Improve load forecasting technology level is not only beneficial to plan power management and make reasonable construction plan, but also good for saving energy and reducing power cost, and then, it can improve the economic benefits and social benefit for power system. BP neural network is one of the most widely used neural networks and it has many advantages in the power load forecasting. Matlab has become the best technology application software which has been internationally recognized, the software has many characteristics, such as data visualization function and neural network toolbox, for these, it is the essential software when we do some research on neural network.

  3. Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

    Science.gov (United States)

    Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

    2015-05-01

    Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

  4. Effect of Micro Electrical Discharge Machining Process Conditions on Tool Wear Characteristics: Results of an Analytic Study

    DEFF Research Database (Denmark)

    Puthumana, Govindan; P., Rajeev

    2016-01-01

    Micro electrical discharge machining is one of the established techniques to manufacture high aspect ratio features on electrically conductive materials. This paper presents the results and inferences of an analytical study for estimating theeffect of process conditions on tool electrode wear...

  5. Thermal Management and Reliability of Automotive Power Electronics and Electric Machines

    Energy Technology Data Exchange (ETDEWEB)

    Narumanchi, Sreekant V [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bennion, Kevin S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cousineau, Justine E [National Renewable Energy Laboratory (NREL), Golden, CO (United States); DeVoto, Douglas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Feng, Xuhui [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kekelia, Bidzina [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kozak, Joseph P [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Major, Joshua [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Moreno, Gilberto [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Paret, Paul P [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Tomerlin, Jeff J [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2018-02-09

    Low-cost, high-performance thermal management technologies are helping meet aggressive power density, specific power, cost, and reliability targets for power electronics and electric machines. The National Renewable Energy Laboratory is working closely with numerous industry and research partners to help influence development of components that meet aggressive performance and cost targets through development and characterization of cooling technologies, and thermal characterization and improvements of passive stack materials and interfaces. Thermomechanical reliability and lifetime estimation models are important enablers for industry in cost-and time-effective design.

  6. Bidding strategy with forecast technology based on support vector machine in the electricity market

    Science.gov (United States)

    Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Wan, Qiulan; Zhou, Jian

    2008-06-01

    The participants in the electricity market are concerned very much with the market price evolution. Various technologies have been developed for price forecasting. The SVM (Support Vector Machine) has shown its good performance in market price forecasting. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecasting accuracy, with which the rejection risk is defined. The other takes into account the impact of the producer’s own bid. The risks associated with the bidding are controlled by the parameter settings. The proposed approaches have been tested on a numerical example.

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

    Science.gov (United States)

    Wu, Zhihong; Lu, Ke; Zhu, Yuan

    2015-01-01

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

  8. Research on Modeling and Control of Regenerative Braking for Brushless DC Machines Driven Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Jian-ping Wen

    2015-01-01

    Full Text Available In order to improve energy utilization rate of battery-powered electric vehicle (EV using brushless DC machine (BLDCM, the model of braking current generated by regenerative braking and control method are discussed. On the basis of the equivalent circuit of BLDCM during the generative braking period, the mathematic model of braking current is established. By using an extended state observer (ESO to observe actual braking current and the unknown disturbances of regenerative braking system, the autodisturbances rejection controller (ADRC for controlling the braking current is developed. Experimental results show that the proposed method gives better recovery efficiency and is robust to disturbances.

  9. Fiber optic vibration sensor for high-power electric machines realized using 3D printing technology

    Science.gov (United States)

    Igrec, Bojan; Bosiljevac, Marko; Sipus, Zvonimir; Babic, Dubravko; Rudan, Smiljko

    2016-03-01

    The objective of this work was to demonstrate a lightweight and inexpensive fiber-optic vibration sensor, built using 3D printing technology, for high-power electric machines and similar applications. The working principle is based on modulating the light intensity using a blade attached to a bendable membrane. The sensor prototype was manufactured using PolyJet Matrix technology with DM 8515 Grey 35 Polymer. The sensor shows linear response, expected bandwidth (< 150 Hz), and from our measurements we estimated the damping ratio for used polymer to be ζ ≍ 0.019. The developed prototype is simple to assemble, adjust, calibrate and repair.

  10. Machine & electrical double control air dryer for vehicle air braking system

    Science.gov (United States)

    Zhang, Xuan; Yang, Liu; Wang, Xian Yan; Tan, Xiao Yan; Wang, Wei

    2017-09-01

    As is known to all, a vehicle air brake system, in which usually contains moisture. To solve the problem, it is common to use air dryer to dry compressed air effectively and completely remove the moisture and oil of braking system. However, the existing air dryer is not suitable for all commercial vehicles. According to the operational status of the new energy vehicles in the initial operating period, the structure design principle of the machine & electric control air dryer is expounded from the aspects of the structure and operating principle, research & development process.

  11. Parameters for Fabricating Nano-Au Colloids through the Electric Spark Discharge Method with Micro-Electrical Discharge Machining.

    Science.gov (United States)

    Tseng, Kuo-Hsiung; Chung, Meng-Yun; Chang, Chaur-Yang

    2017-06-02

    In this study, the Electric Spark Discharge Method (ESDM) was employed with micro-electrical discharge machining (m-EDM) to create an electric arc that melted two electrodes in deionized water (DW) and fabricated nano-Au colloids through pulse discharges with a controlled on-off duration (T ON -T OFF ) and a total fabrication time of 1 min. A total of six on-off settings were tested under normal experimental conditions and without the addition of any chemical substances. Ultraviolet-visible spectroscopy (UV-Vis), Zetasizer Nano measurements, and scanning electron microscopy-energy dispersive X-ray (SEM-EDX) analyses suggested that the nano-Au colloid fabricated at 10-10 µs (10 µs on, 10 µs off) had higher concentration and suspension stability than products made at other T ON -T OFF settings. The surface plasmon resonance (SPR) of the colloid was 549 nm on the first day of fabrication and stabilized at 532 nm on the third day. As the T ON -T OFF period increased, the absorbance (i.e., concentration) of all nano-Au colloids decreased. Absorbance was highest at 10-10 µs. The SPR peaks stabilized at 532 nm across all T ON -T OFF periods. The Zeta potential at 10-10 µs was -36.6 mV, indicating that no nano-Au agglomeration occurred and that the particles had high suspension stability.

  12. Gross domestic product estimation based on electricity utilization by artificial neural network

    Science.gov (United States)

    Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

    2018-01-01

    The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

  13. Application of neural network and Levenberg–Marquardt algorithm for electrical grid planning

    Science.gov (United States)

    Boopathi, M.; Senthil Kumar, S.; Kalaiarassan, G.

    2017-11-01

    In the present scenario of industrial growth, technological advancement and population growth, the single most inseparable commodity is electrical energy. This paper presents a novel way to use Neural Networks to forecast long-term electricity load and use the result for proper grid planning in terms of expansion and maintenance. Uninterrupted, reliable and cheap electricity can only be available if there is a proper planned and stable grid, which can be achieved only through proper futuristic grid planning models. In this paper, focus is centered to form the cluster of areas based on the forecasted energy needs in India. Priority ranking also allocated to decide the level of expansion for forthcoming decades.

  14. Renewal-process approximation of a stochastic threshold model for electrical neural stimulation.

    Science.gov (United States)

    Bruce, I C; Irlicht, L S; White, M W; O'Leary, S J; Clark, G M

    2000-01-01

    In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.

  15. Methodology for testing a system for remote monitoring and control on auxiliary machines in electric vehicles

    Directory of Open Access Journals (Sweden)

    Dimitrov Vasil

    2017-01-01

    Full Text Available A laboratory system for remote monitoring and control of an asynchronous motor controlled by a soft starter and contemporary measuring and control devices has been developed and built. This laboratory system is used for research and in teaching. A study of the principles of operation, setting up and examination of intelligent energy meters, soft starters and PLC has been made as knowledge of the relevant software products is necessary. This is of great importance because systems for remote monitoring and control of energy consumption, efficiency and proper operation of the controlled objects are very often used in different spheres of industry, in building automation, transport, electricity distribution network, etc. Their implementation in electric vehicles for remote monitoring and control on auxiliary machines is also possible and very useful. In this paper, a methodology of tests is developed and some experiments are presented. Thus, an experimental verification of the developed methodology is made.

  16. A Closed Loop Brain-machine Interface for Epilepsy Control Using Dorsal Column Electrical Stimulation.

    Science.gov (United States)

    Pais-Vieira, Miguel; Yadav, Amol P; Moreira, Derek; Guggenmos, David; Santos, Amílcar; Lebedev, Mikhail; Nicolelis, Miguel A L

    2016-09-08

    Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44% and seizure duration by 38%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.

  17. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  18. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

  1. Design and Optimization of Permanent Magnet Brushless Machines for Electric Vehicle Applications

    Directory of Open Access Journals (Sweden)

    Weiwei Gu

    2015-12-01

    Full Text Available In this paper, by considering and establishing the relationship between the maximum operating speed and d-axis inductance, a new design and optimization method is proposed. Thus, a more extended constant power speed range, as well as reduced losses and increased efficiency, especially in the high-speed region, can be obtained, which is essential for electric vehicles (EVs. In the first step, the initial permanent magnet (PM brushless machine is designed based on the consideration of the maximum speed and performance specifications in the entire operation region. Then, on the basis of increasing d-axis inductance, and meanwhile maintaining constant permanent magnet flux linkage, the PM brushless machine is optimized. The corresponding performance of the initial and optimal PM brushless machines are analyzed and compared by the finite-element method (FEM. Several tests are carried out in an EV simulation model based on the urban dynamometer driving schedule (UDDS for evaluation. Both theoretical analysis and simulation results verify the validity of the proposed design and optimization method.

  2. Combination of power electronic models with the two-dimensional finite element analysis of electrical machines

    Science.gov (United States)

    Vaeaenaenen, J.

    1994-04-01

    An analysis method for power electronic drives of electrical machines is presented. The machine is modeled by a two dimensional finite element method which allows the presence of magnetically nonlinear materials and the motion of the rotor. The power electronic device connected to the machine is modeled by a nonlinear circuit model. The field and the circuit equations are coupled together as a system of equations. The power electronic circuit can have a general topology given by a net-list type input file. Specific attention is paid to the numerical stability and efficiency of the combined field-circuit formulation. The computational efficiency and the numerical reliability of the method is investigated with the aid of theoretical cases. According to results, the inclusion of the nonlinear circuit model does not increase the computational costs significantly, provided that the sparsity of the system equations is preserved. The method is tested with three practical examples. The results obtained by the method are compared with the measured ones. The first example is a permanent magnet generator feeding a diode-rectifier. In the second example, a filter circuit is added in parallel with the rectifier. The third example is a cage-induction motor fed by a static frequency converter. The computed results agree well with the measured ones.

  3. Hybrid Swarm Algorithms for Parameter Identification of an Actuator Model in an Electrical Machine

    Directory of Open Access Journals (Sweden)

    Ying Wu

    2011-01-01

    Full Text Available Efficient identification and control algorithms are needed, when active vibration suppression techniques are developed for industrial machines. In the paper a new actuator for reducing rotor vibrations in electrical machines is investigated. Model-based control is needed in designing the algorithm for voltage input, and therefore proper models for the actuator must be available. In addition to the traditional prediction error method a new knowledge-based Artificial Fish-Swarm optimization algorithm (AFA with crossover, CAFAC, is proposed to identify the parameters in the new model. Then, in order to obtain a fast convergence of the algorithm in the case of a 30 kW two-pole squirrel cage induction motor, we combine the CAFAC and Particle Swarm Optimization (PSO to identify parameters of the machine to construct a linear time-invariant(LTI state-space model. Besides that, the prediction error method (PEM is also employed to identify the induction motor to produce a black box model with correspondence to input-output measurements.

  4. Mechanical characteristics of a double-fed machine in asynchronous mode and prospects of its application in the electric drive of mining machines

    Science.gov (United States)

    Ostrovlyanchik, V. Yu; Popolzin, I. Yu; Kubarev, V. A.; Marshev, D. A.

    2017-09-01

    The concept of a double-fed machine as an asynchronous motor with a phase rotor and a source of additional voltage is defined. Based on the analysis of a circuit replacing the double-fed machine, an expression is derived relating the moment, slip, amplitude and phase of additional voltage across the rotor. The conditions maximizing the moment with respect to amplitude and phase of additional voltage in the rotor circuit are also obtained, the phase surface of function of machine electromagnetic moment is constructed. The analysis of basic equation of electric drive motion in relation to electric drive of mine hoisting installations and the conclusion about the necessity of work in all four quadrants of coordinate plane “moment-slip” are made. Family of mechanical characteristics is constructed for a double-fed machine and its achievable speed control range in asynchronous mode is determined. Based on the type of mechanical characteristics and the calculated range of speed control, the conclusion is made about the suitability of using a dual-fed asynchronous machine for driving mine mechanisms with a small required speed control range and the need for organizing a combined operating mode for driving mine hoisting installations and other mechanisms with a large speed control range.

  5. Experimental Investigation of Surface Layer Properties of High Thermal Conductivity Tool Steel after Electrical Discharge Machining

    Directory of Open Access Journals (Sweden)

    Rafał Świercz

    2017-12-01

    Full Text Available New materials require the use of advanced technology in manufacturing complex shape parts. One of the modern materials widely used in the tool industry for injection molds or hot stamping dies is high conductivity tool steel (HTCS 150. Due to its hardness (55 HRC and thermal conductivity at 66 W/mK, this material is difficult to machine by conventional treatment and is being increasingly manufactured by nonconventional technology such as electrical discharge machining (EDM. In the EDM process, material is removed from the workpiece by a series of electrical discharges that cause changes to the surface layers properties. The final state of the surface layer directly influences the durability of the produced elements. This paper presents the influence of EDM process parameters: discharge current Ic and the pulse time ton on surface layer properties. The experimental investigation was carried out with an experimental methodology design. Surface layers properties including roughness 3D parameters, the thickness of the white layer, heat affected zone, tempered layer and occurring micro cracks were investigated and described. The influence of the response surface methodology (RSM of discharge current Ic and the pulse time ton on the thickness of the white layer and roughness parameters Sa, Sds and Ssc were described and established.

  6. Numerical investigation of refrigeration machine compressor operation considering single-phase electric motor dynamic characteristics

    Science.gov (United States)

    Baidak, Y.; Smyk, V.

    2017-08-01

    Using as the base the differential equations system which was presented in relative units for generalized electric motor of hermetic refrigeration compressor, mathematical model of the software for dynamic performance calculation of refrigeration machine compressors drive low-power asynchronous motors was developed. Performed on its ground calculations of the basic model of two-phase electric motor drive of hermetic compressor and the proposed newly developed model of the motor with single-phase stator winding, which is an alternative to the industrial motor winding, have confirmed the benefits of the motor with innovative stator winding over the base engine. Given calculations of the dynamic characteristics of compressor drive motor have permitted to determine the value of electromagnetic torque swinging for coordinating compressor and motor mechanical characteristics, and for taking them into consideration in choosing compressor elements construction materials. Developed and used in the process of investigation of refrigeration compressor drive asynchronous single-phase motor mathematical and software can be considered as an element of computer-aided design system for design of the aggregate of refrigeration compression unit refrigerating machine.

  7. A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks.

    Science.gov (United States)

    Martin, Sébastien; Choi, Charles T M

    2017-01-01

    Electrical Impedance Tomography (EIT) is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort. In this paper, a post-processing technique based on an artificial neural network (ANN) is proposed to obtain a nonlinear solution to the inverse problem, starting from a linear solution. While common reconstruction methods based on ANNs estimate the solution directly from the measured data, the method proposed here enhances the solution obtained from a linear solver. Applying a linear reconstruction algorithm before applying an ANN reduces the effects of noise and modeling errors. Hence, this approach significantly reduces the error associated with solving 2D inverse problems using machine-learning-based algorithms. This work presents radical enhancements in the stability of nonlinear methods for biomedical EIT applications.

  8. Convective Heat Transfer Coefficients of Automatic Transmission Fluid Jets with Implications for Electric Machine Thermal Management: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Bennion, Kevin; Moreno, Gilberto

    2015-09-29

    Thermal management for electric machines (motors/ generators) is important as the automotive industry continues to transition to more electrically dominant vehicle propulsion systems. Cooling of the electric machine(s) in some electric vehicle traction drive applications is accomplished by impinging automatic transmission fluid (ATF) jets onto the machine's copper windings. In this study, we provide the results of experiments characterizing the thermal performance of ATF jets on surfaces representative of windings, using Ford's Mercon LV ATF. Experiments were carried out at various ATF temperatures and jet velocities to quantify the influence of these parameters on heat transfer coefficients. Fluid temperatures were varied from 50 degrees C to 90 degrees C to encompass potential operating temperatures within an automotive transaxle environment. The jet nozzle velocities were varied from 0.5 to 10 m/s. The experimental ATF heat transfer coefficient results provided in this report are a useful resource for understanding factors that influence the performance of ATF-based cooling systems for electric machines.

  9. The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

    Science.gov (United States)

    Casey, M

    1996-08-15

    Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

  10. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  11. Investigation of a Co-Axial Dual-Mechanical Ports Flux-Switching Permanent Magnet Machine for Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Wei Hua

    2015-12-01

    Full Text Available In this paper, a co-axial dual-mechanical ports flux-switching permanent magnet (CADMP-FSPM machine for hybrid electric vehicles (HEVs is proposed and investigated, which is comprised of two conventional co-axial FSPM machines, namely one high-speed inner rotor machine and one low-speed outer rotor machine and a non-magnetic ring sandwiched in between. Firstly, the topology and operation principle of the CADMP-FSPM machine are introduced; secondly, the control system of the proposed electronically-controlled continuously-variable transmission (E-CVT system is given; thirdly, the key design specifications of the CADMP-FSPM machine are determined based on a conventional dual-mechanical ports (DMP machine with a wound inner rotor. Fourthly, the performances of the CADMP-FSPM machine and the normal DMP machine under the same overall volume are compared, and the results indicate that the CADMP-FSPM machine has advantages over the conventional DMP machine in the elimination of brushes and slip rings, improved thermal dissipation conditions for the inner rotor, direct-driven operation, more flexible modes, lower cogging torque and torque ripple, lower total harmonic distortion (THD values of phase PM flux linkage and phase electro-motive force (EMF, higher torque output capability and is suitable for the E-CVT systems. Finally, the pros and cons of the CADMP-FSPM machine are highlighted. This paper lays a theoretical foundation for further research on CADMP-FSPM machines used for HEVs.

  12. Auditory Responses to Electric and Infrared Neural Stimulation of the Rat Cochlear Nucleus

    Science.gov (United States)

    Verma, Rohit; Guex, Amelie A.; Hancock, Kenneth E.; Durakovic, Nedim; McKay, Colette M.; Slama, Michaël C. C.; Brown, M. Christian; Lee, Daniel J.

    2014-01-01

    In an effort to improve the auditory brainstem implant, a prosthesis in which user outcomes are modest, we applied electric and infrared neural stimulation (INS) to the cochlear nucleus in a rat animal model. Electric stimulation evoked regions of neural activation in the inferior colliculus and short-latency, multipeaked auditory brainstem responses (ABRs). Pulsed INS, delivered to the surface of the cochlear nucleus via an optical fiber, evoked broad neural activation in the inferior colliculus. Strongest responses were recorded when the fiber was placed at lateral positions on the cochlear nucleus, close to the temporal bone. INS-evoked ABRs were multipeaked but longer in latency than those for electric stimulation; they resembled the responses to acoustic stimulation. After deafening, responses to electric stimulation persisted, whereas those to INS disappeared, consistent with a reported “optophonic” effect, a laser-induced acoustic artifact. Thus, for deaf individuals who use the auditory brainstem implant, INS alone did not appear promising as a new approach. PMID:24508368

  13. Auditory responses to electric and infrared neural stimulation of the rat cochlear nucleus.

    Science.gov (United States)

    Verma, Rohit U; Guex, Amélie A; Hancock, Kenneth E; Durakovic, Nedim; McKay, Colette M; Slama, Michaël C C; Brown, M Christian; Lee, Daniel J

    2014-04-01

    In an effort to improve the auditory brainstem implant, a prosthesis in which user outcomes are modest, we applied electric and infrared neural stimulation (INS) to the cochlear nucleus in a rat animal model. Electric stimulation evoked regions of neural activation in the inferior colliculus and short-latency, multipeaked auditory brainstem responses (ABRs). Pulsed INS, delivered to the surface of the cochlear nucleus via an optical fiber, evoked broad neural activation in the inferior colliculus. Strongest responses were recorded when the fiber was placed at lateral positions on the cochlear nucleus, close to the temporal bone. INS-evoked ABRs were multipeaked but longer in latency than those for electric stimulation; they resembled the responses to acoustic stimulation. After deafening, responses to electric stimulation persisted, whereas those to INS disappeared, consistent with a reported "optophonic" effect, a laser-induced acoustic artifact. Thus, for deaf individuals who use the auditory brainstem implant, INS alone did not appear promising as a new approach. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN and Supporting Vector Machine (SVR

    Directory of Open Access Journals (Sweden)

    Seyedehelham Sadatiseyedmahalleh

    2016-02-01

    Full Text Available This research examines and proves this effectiveness connected with artificial neural networks (ANNs as an alternative approach to the use of Support Vector Machine (SVR in the tourism research. This method can be used for the tourism industry to define the turism’s demands in Iran. The outcome reveals the use of ANNs in tourism research might result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand evaluation is needed to establish and validate the effects.

  15. Selection of Wire Electrical Discharge Machining Process Parameters on Stainless Steel AISI Grade-304 using Design of Experiments Approach

    Science.gov (United States)

    Lingadurai, K.; Nagasivamuni, B.; Muthu Kamatchi, M.; Palavesam, J.

    2012-06-01

    Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts of hard materials with complex shapes. Parts having sharp edges that pose difficulties to be machined by the main stream machining processes can be easily machined by WEDM process. Design of Experiments approach (DOE) has been reported in this work for stainless steel AISI grade-304 which is used in cryogenic vessels, evaporators, hospital surgical equipment, marine equipment, fasteners, nuclear vessels, feed water tubing, valves, refrigeration equipment, etc., is machined by WEDM with brass wire electrode. The DOE method is used to formulate the experimental layout, to analyze the effect of each parameter on the machining characteristics, and to predict the optimal choice for each WEDM parameter such as voltage, pulse ON, pulse OFF and wire feed. It is found that these parameters have a significant influence on machining characteristic such as metal removal rate (MRR), kerf width and surface roughness (SR). The analysis of the DOE reveals that, in general the pulse ON time significantly affects the kerf width and the wire feed rate affects SR, while, the input voltage mainly affects the MRR.

  16. Classification of BMI control commands from rat's neural signals using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Shin Hyung-Cheul

    2009-10-01

    Full Text Available Abstract A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34 of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.

  17. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  18. Directed Migration of Embryonic Stem Cell-derived Neural Cells In An Applied Electric Field

    OpenAIRE

    Li, Yongchao; Weiss, Mark; Yao, Li

    2014-01-01

    Spinal cord injury or diseases, such as amyotrophic lateral sclerosis, can cause the loss of motor neurons and therefore results in the paralysis of muscles. Stem cells may improve functional recovery by promoting endogenous regeneration, or by directly replacing neurons. Effective directional migration of grafted neural cells to reconstruct functional connections is crucial in the process. Steady direct current electric fields (EFs) play an important role in the development of the central ne...

  19. Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

    Science.gov (United States)

    Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

    2016-06-01

    Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

  20. An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data

    Directory of Open Access Journals (Sweden)

    Khursiah Zainal-Mokhtar

    2013-08-01

    Full Text Available This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical Capacitance Tomography (ECT data. An artificial Neural Network (ANN has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.

  1. Effect of electrical discharge machining on dental Y-TZP ceramic-resin bonding.

    Science.gov (United States)

    Rona, Nergiz; Yenisey, Murat; Kucukturk, Gokhan; Gurun, Hakan; Cogun, Can; Esen, Ziya

    2017-04-01

    The study determined (i) the effects of electrical discharge machining (EDM) on the shear-bond strength (SBS) of the bond between luting resin and zirconia ceramic and (ii) zirconia ceramic's flexural strength with the three-point bending (TPB) test. Sixty 4.8mm×4.8mm×3.2mm zirconia specimens were fabricated and divided into four groups (n=15): SBG: sandblasted+silane, TSCG: tribochemical silica coated+silane, LTG: Er:YAG laser treated+silane, EDMG: EDM+silane. The specimens were then bonded to a composite block with a dual-cure resin cement and thermal cycled (6000 times) prior to SBS testing. The SBS tests were performed in a universal testing machine. The SBS values were statistically analyzed using ANOVA and Tukey's test. To determine flexural strength, sixty zirconia specimens were prepared and assigned to the same groups (n=15) mentioned earlier. After surface treatment TPB tests were performed in a universal testing machine (ISO 6872). The flexural strength values were statistically analyzed using ANOVA and Tukey's test (α=0.05). The bond strengths for the four test groups (mean±SD; MPa) were as follows: SBG (Control), 12.73±3.41, TSCG, 14.99±3.14, LTG, 7.93±2.07, EDMG, 17.05±2.71. The bond strength of the EDMG was significantly higher than those of the SBG and LTG (p0.05). The EDM process improved the SBS. In addition, there was no significant adverse effect of EDM on the flexural strength of zirconia. Copyright © 2016 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

  2. Computational modelling for type-II superconductivity and the investigation of high temperature superconducting electrical machines

    CERN Document Server

    Barnes, G J

    2000-01-01

    are clearly revealed. Once this has been achieved, further studies indicate the most desirable parameters which are expected to optimise the performance. In recent years, the possibility of incorporating type-ll superconducting materials into engineering power applications such as motors, generators, bearings and levitation systems has attracted much attention. However, in order to fully develop the potential of using these relatively new materials in such applications, suitable computational modelling is required. The aim of the research presented in this thesis was to further the development of electrical machines incorporating high temperature superconductors (HTSs) by formulating and then implementing mathematical models. After identifying and justifying necessary assumptions, two such models are developed: the first deriving from the ideas of fluxon motion leading to a finite difference scheme, and the second deriving from more fundamental macroscopic ideas of induced currents leading to a finite element...

  3. Modeling and measurements of circular and trapezoidal shape HTS coils for electrical machines applications

    Science.gov (United States)

    Messina, G.; Morici, L.; Besi Vetrella, U.; Celentano, G.; Marchetti, M.; Viola, R.; Sabatino, P.

    2014-05-01

    Axial Flux Electrical Machines (AFEM) with good power-to-weight and diameter-to-length ratio and high efficiency are very attractive for most industrial and power applications. Investigations with both theoretical and experimental methods of ac losses are important for a reliable prediction of dissipation mechanisms in AFEM. In this paper, simulated and measured results for both critical current (Ic) and transport current losses (Ploss), obtained on HTS coils, are reported. To investigate shape effects, double pancake coils with variable turns and shapes have been manufacted. Commercial grade ReBa2Cu3O7-x (Re = Y or rare earths, ReBCO) tape and epoxy resin has been used for coil winding. A magneto-static 2D finite element model (FEM) for the coils cross section, and a lumped model for AC losses estimations, have been implemented. The agreement among measured and simulated results are satisfactory.

  4. Electrical performance of a string of magnets representing a half-cell of the LHC machine

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez-Mateos, F.; Coull, L.; Dahlerup-Petersen, K.; Hagedorn, D.; Krainz, G.; Rijllart, A. [European Organization for Nuclear Research, Geneva (Switzerland); McInturff, A. [Lawrence Berkeley Lab., CA (United States)

    1995-06-21

    Tests have been carried out on a string prototype superconducting magnets, consisting of one double-quadrupole and two double-dipoles forming the major part of a half-cell of the LHC machine. The magnets are protected individually by ``cold diodes`` and quench heaters. The electrical aspects of these tests are described here. The performance during quench of the protection diodes and the associated interconnections was studied. Tests determined the magnet quench performance in training and at different ramp-rates, and investigated the inter-magnet propagation of quenches. Current lead and inter-magnet contact resistances were controlled and the performance of the power converter and the dump switches assessed.

  5. Electrical performance of a string of magnets representing a half-cell of the LHC machine

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez-Mateos, F.; Coull, L.; Dahlerup-Petersen, K.; Hagedorn, D.; Krainz, G.; Rijllart, A. [CERN, Geneva (Switzerland); McInturff, A. [Lawrence Berkeley Lab., CA (United States)

    1996-07-01

    Tests have been carried out on a string of prototype superconducting magnets, consisting of one double-quadrupole and two double-dipoles forming the major part of a half-cell of the LHC machine. The magnets are protected individually by cold diodes and quench heaters. The electrical aspects of these tests are described here. The performance during quench of the protection diodes and the associated interconnections was studied. Tests determined the magnet quench performance in training and at different ramp-rates, and investigated the inter-magnet propagation of quenches. Current lead and inter-magnet contact resistances were controlled and the performance of the power converter and the dump switches assessed.

  6. Performance Optimization of Electrical Discharge Machining (Die Sinker for Al-6061 via Taguchi Approach

    Directory of Open Access Journals (Sweden)

    Muhammad Qaiser Saleem

    2015-04-01

    Full Text Available This paper parametrically optimizes the EDM (Electrical Discharge Machining process in die sinking mode for material removal rate, surface roughness and edge quality of aluminum alloy Al-6061. The effect of eight parameters namely discharge current, pulse on-time, pulse off-time, auxiliary current, working time, jump time distance, servo speed and work piece hardness are investigated. Taguchi's orthogonal array L18 is employed herein for experimentation. ANOVA (Analysis of Variance with F-ratio criterion at 95% confidence level is used for identification of significant parameters whereas SNR (Signal to Noise Ratio is used for determination of optimum levels. Optimization obtained for Al-6061 with parametric combination investigated herein is validated by the confirmation run.

  7. Detection of needle to nerve contact based on electric bioimpedance and machine learning methods.

    Science.gov (United States)

    Kalvoy, Havard; Tronstad, Christian; Ullensvang, Kyrre; Steinfeldt, Thorsten; Sauter, Axel R

    2017-07-01

    In an ongoing project for electrical impedance-based needle guidance we have previously showed in an animal model that intraneural needle positions can be detected with bioimpedance measurement. To enhance the power of this method we in this study have investigated whether an early detection of the needle only touching the nerve also is feasible. Measurement of complex impedance during needle to nerve contact was compared with needle positions in surrounding tissues in a volunteer study on 32 subjects. Classification analysis using Support-Vector Machines demonstrated that discrimination is possible, but that the sensitivity and specificity for the nerve touch algorithm not is at the same level of performance as for intra-neuralintraneural detection.

  8. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2015-01-01

    Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  9. OPTIMIZATION OF ELECTRICAL DISCHARGE MACHINING PARAMETERS OF ALUMINIUM HYBRID COMPOSITES USING TAGUCHI METHOD

    Directory of Open Access Journals (Sweden)

    N. RADHIKA

    2014-08-01

    Full Text Available Metal matrix composites utilises the combined properties of the constituent material that finds applications in various fields. The present study investigates the influence of peak current, flushing pressure and pulse-on time on Electrical Discharge Machining of AlSi10Mg alloy reinforced with 3 wt% graphite and 9 wt% alumina hybrid metal matrix composites. Taguchi’s Design of Experiment was used to analyse the machining characteristics of hybrid composites. Analysis of Variance and Signal-to-Noise ratio were used to determine the influence of input process parameters on the surface roughness, material removal rate and tool wear rate. Signal to Noise ratio and Analysis of Variance revealed that peak current was the most influential parameter on surface roughness followed by pulse on time and flushing pressure. For material removal rate, the major parameter was flushing pressure followed by peak current and pulse on time. The most significant parameter of tool wear rate was pulse on time followed by peak current and flushing pressure. Interaction terms also have significant effect on their output responses.

  10. Deposition and micro electrical discharge machining of CVD-diamond layers incorporated with silicon

    Science.gov (United States)

    Kühn, R.; Berger, T.; Prieske, M.; Börner, R.; Hackert-Oschätzchen, M.; Zeidler, H.; Schubert, A.

    2017-10-01

    In metal forming, lubricants have to be used to prevent corrosion or to reduce friction and tool wear. From an economical and ecological point of view, the aim is to avoid the usage of lubricants. For dry deep drawing of aluminum sheets it is intended to apply locally micro-structured wear-resistant carbon based coatings onto steel tools. One type of these coatings are diamond layers prepared by chemical vapor deposition (CVD). Due to the high strength of diamond, milling processes are unsuitable for micro-structuring of these layers. In contrast to this, micro electrical discharge machining (micro EDM) is a suitable process for micro-structuring CVD-diamond layers. Due to its non-contact nature and its process principle of ablating material by melting and evaporating, it is independent of the hardness, brittleness or toughness of the workpiece material. In this study the deposition and micro electrical discharge machining of silicon incorporated CVD-diamond (Si-CVD-diamond) layers were presented. For this, 10 µm thick layers were deposited on molybdenum plates by a laser-induced plasma CVD process (LaPlas-CVD). For the characterization of the coatings RAMAN- and EDX-analyses were conducted. Experiments in EDM were carried out with a tungsten carbide tool electrode with a diameter of 90 µm to investigate the micro-structuring of Si-CVD-diamond. The impact of voltage, discharge energy and tool polarity on process speed and resulting erosion geometry were analyzed. The results show that micro EDM is a suitable technology for micro-structuring of silicon incorporated CVD-diamond layers.

  11. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    Science.gov (United States)

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  12. A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.

    Science.gov (United States)

    Yang, Yuning; Boling, Sam; Mason, Andrew J

    2017-08-01

    Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm2 area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm2 electrode array.

  13. Impact of equalizing currents on losses and torque ripples in electrical machines with fractional slot concentrated windings

    Science.gov (United States)

    Toporkov, D. M.; Vialcev, G. B.

    2017-10-01

    The implementation of parallel branches is a commonly used manufacturing method of the realizing of fractional slot concentrated windings in electrical machines. If the rotor eccentricity is enabled in a machine with parallel branches, the equalizing currents can arise. The simulation approach of the equalizing currents in parallel branches of an electrical machine winding based on magnetic field calculation by using Finite Elements Method is discussed in the paper. The high accuracy of the model is provided by the dynamic improvement of the inductances in the differential equation system describing a machine. The pre-computed table flux linkage functions are used for that. The functions are the dependences of the flux linkage of parallel branches on the branches currents and rotor position angle. The functions permit to calculate self-inductances and mutual inductances by partial derivative. The calculated results obtained for the electric machine specimen are presented. The results received show that the adverse combination of design solutions and the rotor eccentricity leads to a high value of the equalizing currents and windings heating. Additional torque ripples also arise. The additional ripples harmonic content is not similar to the cogging torque or ripples caused by the rotor eccentricity.

  14. International Conference on Small and Special Electrical Machines, 2nd, London, England, September 22-24, 1981, Proceedings

    Science.gov (United States)

    Papers are presented on recent research concerning small and special electrical machines, including machine selection and environmental aspects; induction motors; stepping motors and drives; actuators, torque motors, and couplers; hysteresis and reluctance motors; synchronous motors and generators (including permanent magnet); control schemes and servo machines; and dc motors (including permanent magnet and brushless). Topics examined include the reliability of small ironless rotor dc motors, a new form of induction motor for fan drives, a study of the components of interbar voltage and magnetic field at the surface of small skewed diecast aluminum rotors, the microprocessor control of a step motor with various inertia loads, the synchronization of reluctance motor without pole-slipping, and the normal force in linear stepping motors. Also discussed are a direct simulation method using magnetic equivalent circuits for converter-fed reluctance machines, the synchronous performance of a single-phase machine with induced excitation, the application of design and analysis in small machines for aircraft, the microprocessor control of an inverter-driven reluctance motor, an electric main propulsion drive for a remotely piloted vehicle, and small dc motors with controllable electronic commutators. No individual items are abstracted in this volume

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

    Science.gov (United States)

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

    2017-08-01

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

  16. Methodology for Diagnostics of Transformers and D.C. Electric Machines

    Directory of Open Access Journals (Sweden)

    I. I. Branovitsky

    2009-01-01

    Full Text Available The paper considers methods for testing electric power equipment with reference to power transformers and electric direct current machines by measuring complex of their parameters. The above-mentioned methods have been realized in the devices DST-1M and IPEM, respectively.An influence of inductive and capacitive elements included as components of low frequency filters in a measuring device on a value of an extra phase displacement between measured input currents and voltages being caused by them has been analyzed in the paper. The paper reveals that the extra phase displacement is initiated by oscillations of actual inductive and capacitive element values relative to their nominal values. Dependence of root-mean-square deviation of power measurement error due to phase displacement angle under load conditions at various tolerance values of the indicated elements and distribution of actual values of their nominal ones within these tolerances according to a normal low   has been calculated in the paper

  17. Effect of Electric Discharge Machining on Material Removal Rate and White Layer Composition

    Directory of Open Access Journals (Sweden)

    SHAHID MEHMOOD

    2017-01-01

    Full Text Available In this study the MRR (Material Removal Rate of the aerospace grade (2024 T6 aluminum alloy 2024 T6 has been determined with copper electrode and kerosene oil is used as dielectric liquid. Discharge energy is controlled by electric current while keeping Pulse-ON time and Pulse-OFF time as constant. The characteristics of the EDMed (Electric Discharge Machined surface are discussed. The sub-surface defect due to arcing has been explained. As the surface material of tool electrode and workpiece melts simultaneously and there are chances of the contamination of both surfaces by the contents of each other. Therefore, the EDS (Energy Dispersive Spectroscopy of the white layer and base material of the workpiece was performed by SEM (Scanning Electron Microscope at the discharge currents of 3, 6 and 12 amperes. It was conformed that the contamination of the surface of the workpiece material occurred by carbon, copper and oxygen contents. The quantitative analysis of these contents with respect to the discharge current has been presented in this paper.

  18. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Directory of Open Access Journals (Sweden)

    Mark D McDonnell

    Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  19. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Science.gov (United States)

    McDonnell, Mark D; Tissera, Migel D; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

    2015-01-01

    Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  20. Investigation of the influence of air gap thickness and eccentricity on the noise of the rotating electrical machine

    Directory of Open Access Journals (Sweden)

    Donát M.

    2013-12-01

    Full Text Available This article deals with the numerical modelling of the dynamic response of the rotating electrical machine on the application of the magnetic forces. The special attention is paid to the modelling of the magnetic forces that act on the stator winding of the machine and the computational model of the modal properties of the stator winding. The created computational model was used to investigation of the influence of the nominal air gap thickness and the air gap eccentricity on the sound power radiated by outer surface of the stator of the machine. The obtained results show that the nominal air gap thickness has slightly greater influence on the sound power of the machine than eccentricity of the air gap.

  1. The study on the atomic force microscopy base nanoscale electrical discharge machining.

    Science.gov (United States)

    Huang, Jen-Ching; Chen, Chung-Ming

    2012-01-01

    This study proposes an innovative atomic force microscopy (AFM) based nanoscale electrical discharge machining (AFM-based nanoEDM) system which combines an AFM with a self-produced metallic probe and a high-voltage generator to create an atmospheric environment AFM-based nanoEDM system and a deionized water (DI water) environment AFM-based nanoEDM system. This study combines wire-cut processing and electrochemical tip sharpening techniques on a 40-µm thick stainless steel sheet to produce a high conductive AFM probes, the production can withstand high voltage and large current. The tip radius of these probes is approximately 40 nm. A probe test was executed on the AFM using probes to obtain nanoscales morphology of Si wafer surface. The silicon wafer was as a specimen to carry out AFM-base nanoEDM process in atmospheric and DI water environments by AFM-based nanoEDM system. After experiments, the results show that the atmospheric and DI water environment AFM-based nanoEDM systems operate smoothly. From experimental results, it can be found that the electric discharge depth of the silicon wafer at atmospheric environments is a mere 14.54 nm. In a DI water environment, the depth of electric discharge of the silicon wafer can reach 25.4 nm. This indicates that the EDM ability of DI water environment AFM-based nanoEDM system is higher than that of atmospheric environment AFM-based nanoEDM system. After multiple nanoEDM process, the tips become blunt. After applying electrochemical tip sharpening techniques, the tip radius can return to approximately 40 nm. Therefore, AFM probes produced in this study can be reused. © Wiley Periodicals, Inc.

  2. Dreaming Machines : On multimodal fusion and information retrieval using neural-symbolic cognitive agents

    NARCIS (Netherlands)

    Penning, H.L.H. de; Avila Garcez, A. d; Meyer, J.J.C.

    2013-01-01

    Deep Boltzmann Machines (DBM) have been used as a computational cognitive model in various AI-related research and applications, notably in computational vision and multimodal fusion. Being regarded as a biological plausible model of the human brain, the DBM is also becoming a popular instrument to

  3. Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS

    NARCIS (Netherlands)

    De Geeter, N.; Crevecoeur, G.; Leemans, A.; Dupré, L.

    2015-01-01

    In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically,

  4. The monitoring of transient regimes on machine tools based on speed, acceleration and active electric power absorbed by motors

    Science.gov (United States)

    Horodinca, M.

    2016-08-01

    This paper intend to propose some new results related with computer aided monitoring of transient regimes on machine-tools based on the evolution of active electrical power absorbed by the electric motor used to drive the main kinematic chains and the evolution of rotational speed and acceleration of the main shaft. The active power is calculated in numerical format using the evolution of instantaneous voltage and current delivered by electrical power system to the electric motor. The rotational speed and acceleration of the main shaft are calculated based on the signal delivered by a sensor. Three real-time analogic signals are acquired with a very simple computer assisted setup which contains a voltage transformer, a current transformer, an AC generator as rotational speed sensor, a data acquisition system and a personal computer. The data processing and analysis was done using Matlab software. Some different transient regimes were investigated; several important conclusions related with the advantages of this monitoring technique were formulated. Many others features of the experimental setup are also available: to supervise the mechanical loading of machine-tools during cutting processes or for diagnosis of machine-tools condition by active electrical power signal analysis in frequency domain.

  5. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  6. Electric load forecasting for northern Vietnam, using an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Bhattacharyya, S.C. [Asian Institute of Technology, Pathum Thani (Thailand); Thanh, L.T. [Power Company No. 1 (Viet Nam)

    2003-06-01

    This paper employs a feed-forward neural network with a back-propagation algorithm for the short-term electric load forecasting of daily peak (valley) loads and hourly loads in the northern areas of Vietnam. A large set of data on peak loads, valley loads, hourly loads and temperatures was used to train and calibrate the artificial neural network (ANN). The calibrated network was used for load forecasting. The mean percentage errors for the peak load, valley load, one-hour-ahead hourly load and 24-hour-ahead hourly load were -1.47%, -3.29%, -2.64% and -4.39%, respectively. These results compare well with similar studies. (author)

  7. Neural network based forward prediction of bladder pressure using pudendal nerve electrical activity.

    Science.gov (United States)

    Geramipour, A; Makki, S; Erfanian, A

    2015-01-01

    Individuals with spinal cord injury or neurological disorders have problems in urinary bladder storage and in voiding function. In these people, the detrusor of bladder contracts at low volume and this causes incontinence. The goal of bladder control is to increase the bladder capacity by electrical stimulation of relative nerves such as pelvic nerves, sacral nerve roots or pudendal nerves. For this purpose, the bladder pressure has to be monitored continuously. In this paper, we propose a method for real-time estimating the bladder pressure using artificial neural network. The method is based upon measurements of electroneurogram (ENG) signal of pudendal nerve. This approach yields synthetic bladder pressure estimates during bladder contraction. The experiments were conducted on three rats. The results show that neural predictor can provide accurate estimation and prediction of bladder pressure with good generalization ability. The average error of 1-second and 5-second ahead prediction of bladder pressure are 9.62% and 10.54%, respectively.

  8. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  9. Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks

    Directory of Open Access Journals (Sweden)

    Stuart Parsons

    2009-07-01

    Full Text Available Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA, support vector machines (SVM and ensembles of neural networks (ENN. Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97% consistently outperformed SVMs (mean identification rate – 87%. Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

  10. Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

    Directory of Open Access Journals (Sweden)

    Raul Garcia-Segura

    2017-09-01

    Full Text Available Electric arc furnaces (EAFs contribute to almost one third of the global steel production. Arc furnaces use a large amount of electrical energy to process scrap or reduced iron and are relevant to study because small improvements in their efficiency account for significant energy savings. Optimal controllers need to be designed and proposed to enhance both process performance and energy consumption. Due to the random and chaotic nature of the electric arcs, neural networks and other soft computing techniques have been used for modeling EAFs. This study proposes a methodology for modeling EAFs that considers the time varying arc length as a relevant input parameter to the arc furnace model. Based on actual voltages and current measurements taken from an arc furnace, it was possible to estimate an arc length suitable for modeling the arc furnace using neural networks. The obtained results show that the model reproduces not only the stable arc conditions but also the unstable arc conditions, which are difficult to identify in a real heat process. The presented model can be applied for the development and testing of control systems to improve furnace energy efficiency and productivity.

  11. Alternating current multi-circuit electric machines a new approach to the steady-state parameter determination

    CERN Document Server

    Asanbayev, Valentin

    2015-01-01

    This book details an approach for realization of the field decomposition concept. The book presents the  methods as well as techniques and procedures for establishing electric machine circuit-loops and determining their parameters. The methods developed have been realized using the models of machines with laminated and solid rotor having classical structure. The use of such models are well recognized and simplifies practical implementation of the obtained results. This book also: ·         Includes methods for a construction of electric machine equivalent circuits that allows the replacement of the field models of the machine with simple circuit models ·         Demonstrates the practical implementation of the proposed techniques and procedures ·         Presents parameters of the circuit-loops in the form most convenient for practical implementation ·         Uses methods based on machine models widely used in practice

  12. Power distribution of a co-axial dual-mechanical-port flux-switching permanent magnet machine for fuel-based extended range electric vehicles

    Science.gov (United States)

    Zhou, Lingkang; Hua, Wei; Zhang, Gan

    2017-05-01

    In this paper, power distribution between the inner and outer machines of a co-axial dual-mechanical-port flux-switching permanent magnet (CADMP-FSPM) machine is investigated for fuel-based extended range electric vehicle (ER-EV). Firstly, the topology and operation principle of the CADMP-FSPM machine are introduced, which consist of an inner FSPM machine used for high-speed, an outer FSPM machine for low-speed, and a magnetic isolation ring between them. Then, the magnetic field coupling of the inner and outer FSPM machines is analyzed with more attention paid to the optimization of the isolation ring thickness. Thirdly, the power-dimension (PD) equations of the inner and outer FSPM machines are derived, respectively, and thereafter, the PD equation of the whole CADMP-FSPM machine can be given. Finally, the PD equations are validated by finite element analysis, which supplies the guidance on the design of this type of machines.

  13. The identification of the relationship between chemical and electrical parameters of honeys using artificial neural networks.

    Science.gov (United States)

    Pentoś, Katarzyna; Luczycka, Deta; Wróbel, Radosław

    2014-10-01

    A number of significant scientific studies have confirmed the health benefits of honey. Due to the high price of natural honey, it is a common target for adulteration which reduces its medicinal value. Adulteration detection methods require specific laboratory equipment and are very expensive. The development of measurement techniques enables the measurement of electrical characteristics of strained honey. Honey electrical parameters can possibly be used for its quality assessment. The identification of the relationship between chemical and electrical parameters of honeys and analysis to determine if there are frequency-dependent changes, can help in developing of that group of methods. The aim of this research was to determine how the chemical parameters of certain honeys influence the dielectric loss factor and the permittivity of strained honey measured in various frequencies. Another aim was to determine whether the percentage influence of certain chemical parameters of honeys on electrical characteristics significantly depends on frequency value. The research was based on neural network models and sensitivity analysis. The percentage influence of certain chemical parameters on electrical characteristics significantly depends on frequency value. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Non-metallic coating thickness prediction using artificial neural network and support vector machine with time resolved thermography

    Science.gov (United States)

    Wang, Hongjin; Hsieh, Sheng-Jen; Peng, Bo; Zhou, Xunfei

    2016-07-01

    A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It's theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model.

  15. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine

    Directory of Open Access Journals (Sweden)

    Ali Hashemi

    2014-06-01

    Full Text Available Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.

  17. How social is error observation? The neural mechanisms underlying the observation of human and machine errors.

    Science.gov (United States)

    Desmet, Charlotte; Deschrijver, Eliane; Brass, Marcel

    2014-04-01

    Recently, it has been shown that the medial prefrontal cortex (MPFC) is involved in error execution as well as error observation. Based on this finding, it has been argued that recognizing each other's mistakes might rely on motor simulation. In the current functional magnetic resonance imaging (fMRI) study, we directly tested this hypothesis by investigating whether medial prefrontal activity in error observation is restricted to situations that enable simulation. To this aim, we compared brain activity related to the observation of errors that can be simulated (human errors) with brain activity related to errors that cannot be simulated (machine errors). We show that medial prefrontal activity is not only restricted to the observation of human errors but also occurs when observing errors of a machine. In addition, our data indicate that the MPFC reflects a domain general mechanism of monitoring violations of expectancies.

  18. Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

    OpenAIRE

    Chen, Mingzhe; Challita, Ursula; Saad, Walid; Yin, Changchuan; Debbah, Mérouane

    2017-01-01

    Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user d...

  19. Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models

    Science.gov (United States)

    Porto, C. D. N.; Costa Filho, C. F. F.; Macedo, M. M. G.; Gutierrez, M. A.; Costa, M. G. F.

    2017-03-01

    Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.

  20. RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

    Science.gov (United States)

    Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

    2008-10-01

    A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

  1. Analysis of aerosol emission and hazard evaluation of electrical discharge machining (EDM) process.

    Science.gov (United States)

    Jose, Mathew; Sivapirakasam, S P; Surianarayanan, M

    2010-01-01

    The safety and environmental aspects of a manufacturing process are important due to increased environmental regulations and life quality. In this paper, the concentration of aerosols in the breathing zone of the operator of Electrical Discharge Machining (EDM), a commonly used non traditional manufacturing process is presented. The pattern of aerosol emissions from this process with varying process parameters such as peak current, pulse duration, dielectric flushing pressure and the level of dielectric was evaluated. Further, the HAZOP technique was employed to identify the inherent safety aspects and fire risk of the EDM process under different working conditions. The analysis of aerosol exposure showed that the concentration of aerosol was increased with increase in the peak current, pulse duration and dielectric level and was decreased with increase in the flushing pressure. It was also found that at higher values of peak current (7A) and pulse duration (520 micros), the concentration of aerosols at breathing zone of the operator was above the permissible exposure limit value for respirable particulates (5 mg/m(3)). HAZOP study of the EDM process showed that this process is vulnerable to fire and explosion hazards. A detailed discussion on preventing the fire and explosion hazard is presented in this paper. The emission and risk of fire of the EDM process can be minimized by selecting proper process parameters and employing appropriate control strategy.

  2. NOTE: Effects of powder additives suspended in dielectric on crater characteristics for micro electrical discharge machining

    Science.gov (United States)

    Yeo, S. H.; Tan, P. C.; Kurnia, W.

    2007-11-01

    The effects of using powder additives suspended in dielectric on crater characteristics for micro electrical discharge machining (PSD micro-EDM) are investigated through the conduct of single RC discharge experiments at low discharge energies of 2.5 µJ, 5 µJ and 25 µJ. Through the introduction of additive particles into the dielectric, results of the single discharge experiments show the formation of craters with smaller diameters and depths, and having more consistent circular shapes than those produced in dielectric without additive. These craters also possess a noticeable morphological difference compared to those generated in dielectric without additive. In addition, discharge current measurements show a smaller amount of charges flowing between the tool electrode and workpiece, and at a slower flow rate when additives are present in the dielectric. Furthermore, based on the experimental results and findings from studies done in nanofluids, a hypothesis is made on the effects of powder suspended dielectric on the crater formation mechanism. The increased viscosity and enhanced thermal conductivity of a powder suspended dielectric lower the plasma heat flux into the electrode and raise the rate of heat dissipation away from the molten cavity. As a result, a smaller-sized crater having a larger amount of resolidified material within the crater cavity is formed.

  3. Mathematical modeling and multi-criteria optimization of rotary electrical discharge machining process

    Science.gov (United States)

    Shrinivas Balraj, U.

    2015-12-01

    In this paper, mathematical modeling of three performance characteristics namely material removal rate, surface roughness and electrode wear rate in rotary electrical discharge machining RENE80 nickel super alloy is done using regression approach. The parameters considered are peak current, pulse on time, pulse off time and electrode rotational speed. The regression approach is very much effective in mathematical modeling when the performance characteristic is influenced by many variables. The modeling of these characteristics is helpful in predicting the performance under a given set of combination of input process parameters. The adequacy of developed models is tested by correlation coefficient and Analysis of Variance. It is observed that the developed models are adequate in establishing the relationship between input parameters and performance characteristics. Further, multi-criteria optimization of process parameter levels is carried using grey based Taguchi method. The experiments are planned based on Taguchi's L9 orthogonal array. The proposed method employs single grey relational grade as a performance index to obtain optimum levels of parameters. It is found that peak current and electrode rotational speed are influential on these characteristics. Confirmation experiments are conducted to validate optimal parameters and it reveals the improvements in material removal rate, surface roughness and electrode wear rate as 13.84%, 12.91% and 19.42% respectively.

  4. A novel thermo-hydraulic coupling model to investigate the crater formation in electrical discharge machining

    Science.gov (United States)

    Tang, Jiajing; Yang, Xiaodong

    2017-09-01

    A novel thermo-hydraulic coupling model was proposed in this study to investigate the crater formation in electrical discharge machining (EDM). The temperature distribution of workpiece materials was included, and the crater formation process was explained from the perspective of hydrodynamic characteristics of the molten region. To better track the morphology of the crater and the movement of debris, the level-set method was introduced in this study. Simulation results showed that the crater appears shortly after the ignition of the discharge, and the molten material is removed by vaporizing in the initial stage, then by splashing at the following time. The driving force for the detachment of debris in the splashing removal stage comes from the extremely large pressure difference in the upper part of the molten region, and the morphology of the crater is also influenced by the shearing flow of molten material. It was found that the removal ratio of molten material is only about 7.63% under the studied conditions, leaving most to form the re-solidification layer on the surface of the crater. The size of the crater reaches the maximum at the end of discharge duration then experiences a slight reduction because of the reflux of molten material after the discharge. The results of single pulse discharge experiments showed that the morphologies and sizes between the simulation crater and actual crater are good at agreement, verifying the feasibility of the proposed thermo-hydraulic coupling model in explaining the mechanisms of crater formation in EDM.

  5. Preliminary Numerical Investigations of Entropy Generation in Electric Machines Based on a Canonical Configuration

    Directory of Open Access Journals (Sweden)

    Toni Eger

    2015-12-01

    Full Text Available The present paper analyzes numerically the entropy generation induced by forced convection in a canonical configuration. The configuration itself includes two well known fluid dynamic problems: (1 an external flow (flow around a cylinder, Kármán flow; and (2 an internal flow (flow between two concentric rotating cylinders, Couette flow. In many daily engineering issues (e.g., cooling of electric machines, a combination of these problems occurs and has to be investigated. Using the canonical configuration, the fields of entropy generation are analyzed in this work for a constant wall heat flux but varying two key parameters (Reynolds numbers Re∞ and Re0. The entropy generation due to conduction shows an absolute minimum around Re0 = 10,000. The same minima can be found by a detailed analysis of the temperature profile. Thus, entropy generation seems to be a suitable indicator for optimizing heat exchange processes and delivers a large amount of information concerning fluid and heat transport.

  6. Measurement of Heat Losses on The Milking Machine Electric Motor at Various Regulations of Vacuum Using Methods of Thermal Imagery

    Directory of Open Access Journals (Sweden)

    Jan Kudělka

    2014-01-01

    Full Text Available To ensure the desirable vacuum in the milking machines, use is currently made predominantly of rotary vacuum pumps. These vacuum pumps are driven by a squirrel-cage induction motor. Until recently, the vacuum in the system to achieve the required value was controlled by a main control valve sucking in ambient air into the system. During the milking process itself and during other activities (flushing, sanitation, this control method consumed a large amount of electricity. The technical solution to electricity demand reduction was introduced with the emergence and development of frequency converters. The frequency converters control the operation of the asynchronous electric motor so that the actual delivery of the vacuum pumps equals the volume of air sucked into the vacuum pipe. The motor supply by the frequency converter brings about a host of adverse phenomena. This paper is dedicated to motor heating and heat losses on the surface of the electric motor at different regulations of vacuum in milking machines. The objective of the paper is to determine the immediate specific heat flows along the surface of the electric motor of the milking machine during milking using a control valve regulation and a control using the frequency converter, and compare the resulting value. The specific heat flows were determined by means of a non-traditional method of temperature field measurement using a system of thermal imagery. The calculated and measured data obtained from both these systems were statistically evaluated and compared. Use was made of a milking machine located in the cooperative Hospodářské obchodní družstvo (HOD Jabloňov.

  7. 15 CFR 700.31 - Metalworking machines.

    Science.gov (United States)

    2010-01-01

    ... Drilling and tapping machines Electrical discharge, ultrasonic and chemical erosion machines Forging machinery and hammers Gear cutting and finishing machines Grinding machines Hydraulic and pneumatic presses...

  8. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Karin Kandananond

    2011-08-01

    Full Text Available Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA, artificial neural network (ANN and multiple linear regression (MLR—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance

  9. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market.

    Science.gov (United States)

    Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan

    2017-01-01

    Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

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

    Science.gov (United States)

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

    2013-06-01

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

  11. An experimental study on the effect of parameters on the depth of crater machined by electrostatic field–induced electrolyte jet micro electrical discharge machining

    Directory of Open Access Journals (Sweden)

    Yaou Zhang

    2016-04-01

    Full Text Available Electrostatic field–induced electrolyte jet micro electrical discharge machining depends on heat generated by the periodic pulsed discharge between the workpiece and the electrolyte fine jet from the tip of Taylor cone, induced by the intense electric field, to erode the material from the workpiece. To further investigate the characteristics of this discharge process, with the NaCl solution as the electrostatic field–induced electrolyte jet electrolyte and the silicon wafer as the workpiece, the governing factors of machining polarity, nozzle-to-workpiece distance, voltage applied between positive and negative polarities, and the effect of concentration of the electrolyte on the depth of crater after a single electrostatic field–induced electrolyte jet discharge have been studied. The experimental results show that the average depth of crater increases with the increase in the voltage applied between the nozzle and the workpiece, and increases with the increase in the concentration of the electrolyte, but decreases with the increase in the distance between the nozzle and the workpiece. The results have also demonstrated that the polarity has no clear influence on the average depth of crater after a single discharge.

  12. In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression.

    Science.gov (United States)

    Chen, Hai-Feng

    2009-08-01

    Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.

  13. MRR and TWR evaluation on electrical discharge machining of Ti-6Al-4V using tungsten : copper composite electrode

    Science.gov (United States)

    Prasanna, J.; Rajamanickam, S.; Amith Kumar, O.; Karthick Raj, G.; Sathya Narayanan, P. V. V.

    2017-05-01

    In this paper Ti-6Al-4V used as workpiece material and it is keenly seen in variety of field including medical, chemical, marine, automotive, aerospace, aviation, electronic industries, nuclear reactor, consumer products etc., The conventional machining of Ti-6Al-4V is very difficult due to its distinctive properties. The Electrical Discharge Machining (EDM) is right choice of machining this material. The tungsten copper composite material is employed as tool material. The gap voltage, peak current, pulse on time and duty factor is considered as the machining parameter to analyze the machining characteristics Material Removal Rate (MRR) and Tool Wear Rate (TWR). The Taguchi method is provided to work for finding the significant parameter of EDM. It is found that for MRR significant parameters rated in the following order Gap Voltage, Pulse On-Time, Peak Current and Duty Factor. On the other hand for TWR significant parameters are listed in line of Gap Voltage, Duty Factor, Peak Current and Pulse On-Time.

  14. Processing and Characterization of Novel Biomimetic Nanoporous Bioceramic Surface on β-Ti Implant by Powder Mixed Electric Discharge Machining

    Science.gov (United States)

    Prakash, Chander; Kansal, H. K.; Pabla, B. S.; Puri, Sanjeev

    2015-09-01

    Herein, a β-Ti-based implant was subjected to powder mixed electric discharge machining (PMEDM) for surface modification to produce a novel biomimetic nanoporous bioceramic surface. The microstructure, surface topography, and phase composition of the non-machined and machined (PMEDMed) surfaces were investigated using field-emission scanning electron microscopy, energy-dispersive x-ray spectroscopy, and x-ray diffraction. The microhardness of the surfaces was measured on a Vickers hardness tester. The corrosion resistance of the surfaces was evaluated via potentiodynamic polarization measurements in simulated body fluid. The application of PMEDM not only altered the surface chemistry, but also imparted the surface with a nanoporous topography or a natural bone-like surface structure. The characterization results confirmed that the alloyed layer mainly comprised bioceramic oxides and carbide phases (TiO2, Nb2O5, ZrO2, SiO2, TiC, NbC, SiC). The microhardness of PMEDMed surface was twofold higher than that of the base material (β-Ti alloy), primarily because of the formation of the hard carbide phases on the machined layer. Electrochemical analysis revealed that PMEDMed surface featured insulative and protective properties and thus displayed higher corrosion resistance ability when compared with the non-machined surface. This result was attributed to the formation of the bioceramic oxides on the machined surface. Additionally, the in vitro biocompatibility of the surfaces was evaluated using human osteoblastic cell line MG-63. PMEDMed surface with a micro-, sub-micro-, and nano-structured topography exhibited bioactivity and improved biocompatibility relative to β-Ti surface. Furthermore, PMEDMed surface enabled better adhesion and growth of MG-63 when compared with the non-machined substrate.

  15. Angular Velocity's Neural Network Observer of the Electric Drive of TVR - IM Type Implemented in Software Environment LabVIEW

    Science.gov (United States)

    Kozlova, L.; Bolovin, E.; Payuk, L.

    2016-06-01

    One of the common ways to manage a smooth starting and stopping of asynchronous motors are soft-start system. For this provision is necessary to use a closed speed asynchronous electric drive of tiristor voltage regulator - induction motor (TVR-IM) type. Using real sensors significantly increases the cost of installation and also introduces a number of inconveniences in the operation of the actuator. Observer has clear advantages that are created on artificial neural network. Creating a neural network observer in program graphic programming LabVIEW will allow to evaluate the speed of rotation of the asynchronous electric.

  16. FINGERPRINT CLASSIFICATION BASED ON RECURSIVE NEURAL NETWORK WITH SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    T. Chakravarthy

    2011-01-01

    Full Text Available Fingerprint classification based on statistical and structural (RNN and SVM approach. RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in this support vector machine. SVMs are combined with a new error correcting codes scheme. This approach has two main advantages. (a It can tolerate the presence of ambiguous fingerprint images in the training set and (b It can effectively identify the most difficult fingerprint images in the test set. In this experiment on the fingerprint database NIST-4 (National Institute of Science and Technology, our best classification accuracy of 94.7% is obtained by training SVM on both fingerCode and RNN –extracted futures of segmentation algorithm which has used very sophisticated “region growing process”.

  17. Misfit of suprastructures on implants processed by electrical discharge machining or the Cresco method.

    Science.gov (United States)

    Fischer, Jens; Thoma, Andrea; Suter, Ana; Lüthy, Heinz; Luder, Hans-Ulrich; Hämmerle, Christoph Hans-Franz

    2009-06-01

    To assess the accuracy of fit of frameworks on implants processed with electrical discharge machining (EDM) or the Cresco technique (Astra Tech). On 12 identical master casts with implants at positions 9(21), 11(23), and 13(25), high-gold alloy frameworks were produced by standard casting procedure. Six frameworks were used for the Cresco technique (group CRE) by employing specific fixed partial denture supports. The remaining 6 frameworks were cast with prefabricated gold copings and served as control. The finished frameworks were screwed onto implant 25 of the corresponding master cast. Dimensions of the marginal gaps were measured at 4 locations on each implant under the scanning electron microscope, applying the replica technique. Subsequently, the control group was processed by EDM (SAE EDM 2000) (group EDM) and analyzed alike. Statistical analysis of the results was performed with Kruskal-Wallis and Mann-Whitney U tests. The mean marginal gaps were measured as follows (CRE/EDM/control): position 25: 0.0 microm/1.0 +/- 1.6 microm/1.5 +/- 2.1 microm; position 23: 5.2 +/- 5.6 microm/18.7 +/- 29.3 microm/23.6 +/- 30.7 microm; and position 21: 36.0 +/- 21.6 microm/40.7 +/- 31.0 microm/46.0 +/- 41.1 microm. The only statistically significant difference was found at location 23 between group CRE on one side and both group EDM and control on the other side. The strong increase of misfit for group CRE from location 23 to location 21 indicates that laser welding is the crucial parameter in this technique. The Cresco technique has a potential to reduce the marginal gap between implants and suprastructures.

  18. Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

    Science.gov (United States)

    Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P.

    2015-01-01

    The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network’s modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves

  19. Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

    Science.gov (United States)

    Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P

    2015-01-01

    The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves

  20. Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

    Science.gov (United States)

    Ahmadi, Hamed; Rodehutscord, Markus

    2017-01-01

    In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [R2 = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM (R2 = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR (R2 = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

  1. Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

    Science.gov (United States)

    Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

    2014-03-15

    The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Laser machining of advanced materials

    CERN Document Server

    Dahotre, Narendra B

    2011-01-01

    Advanced materialsIntroductionApplicationsStructural ceramicsBiomaterials CompositesIntermetallicsMachining of advanced materials IntroductionFabrication techniquesMechanical machiningChemical Machining (CM)Electrical machiningRadiation machining Hybrid machiningLaser machiningIntroductionAbsorption of laser energy and multiple reflectionsThermal effectsLaser machining of structural ceramicsIntrodu

  3. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    Science.gov (United States)

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  4. A RBF neural network model with GARCH errors: Application to electricity price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Coelho, Leandro dos Santos [Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos, Andre A.P. [Department of Statistics, Universidad Carlos III de Madrid, C/ Madrid, 126, 28903 Getafe, Madrid (Spain)

    2011-01-15

    In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. (author)

  5. Safety Assessment for Electrical Motor Drive System Based on SOM Neural Network

    Directory of Open Access Journals (Sweden)

    Linghui Meng

    2016-01-01

    Full Text Available With the development of the urban rail train, safety and reliability have become more and more important. In this paper, the fault degree and health degree of the system are put forward based on the analysis of electric motor drive system’s control principle. With the self-organizing neural network’s advantage of competitive learning and unsupervised clustering, the system’s health clustering and safety identification are worked out. With the switch devices’ faults data obtained from the dSPACE simulation platform, the health assessment algorithm is verified. And the results show that the algorithm can achieve the system’s fault diagnosis and health assessment, which has a point in the health assessment and maintenance for the train.

  6. The neural network as a part of decision support system for quality management for production objects in machining process

    Directory of Open Access Journals (Sweden)

    Cherepanska I.Yu.

    2017-04-01

    Full Text Available The research discusses the use of artificial neural networks (ANN as components of a decision support system (DSS to automate quality control manufacturing facilities machining business at the production, which should be focused on the analysis of large amounts of heterogeneous information. The necessity to use ANN as a part of DSS is justified by the fact that quality control during production is multistage and time-consuming process that is formalized difficult, and moreover requires considerable information and material costs for the efficiency of manufacturing operations performed. Taking into account the existing experience of successful use of ANN to solve difficult formal problems associated with handling large volumes of diverse and rapidly changing information, the authors synthesized ANN for automated determination of the causes deterioration of the quality of production objects (PO in the performance of manufacturing operations application. Particular attention is paid to the definition of the dimension of the hidden layer ANN synthesized due to the fact that today still there is no analytical expression to determine the dimension of the hidden layer ANN and size of the latter is determined only by the experimental results of ANN several different structures by comparison the results, in particular the value of mean square error.

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

    Science.gov (United States)

    Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

    2015-06-01

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

  8. A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

    Science.gov (United States)

    Lee, Yu-Hao; Hsieh, Ya-Ju; Shiah, Yung-Jong; Lin, Yu-Huei; Chen, Chiao-Yun; Tyan, Yu-Chang; GengQiu, JiaCheng; Hsu, Chung-Yao; Chen, Sharon Chia-Ju

    2017-04-01

    To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

  9. Directed Migration of Embryonic Stem Cell-derived Neural Cells In An Applied Electric Field

    Science.gov (United States)

    Weiss, Mark; Yao, Li

    2014-01-01

    Spinal cord injury or diseases, such as amyotrophic lateral sclerosis, can cause the loss of motor neurons and therefore results in the paralysis of muscles. Stem cells may improve functional recovery by promoting endogenous regeneration, or by directly replacing neurons. Effective directional migration of grafted neural cells to reconstruct functional connections is crucial in the process. Steady direct current electric fields (EFs) play an important role in the development of the central nervous system. A strong biological effect of EFs is the induction of directional cell migration. In this study, we investigated the guided migration of embryonic stem cell (ESC) derived presumptive motor neurons in an applied EF. The dissociated mouse ESC derived presumptive motor neurons or embryoid bodies were subjected to EFs stimulation and the cell migration was studied. We found that the migration of neural precursors from embryoid bodies was toward cathode pole of applied EFs. Single motor neurons migrated to the cathode of the EFs and reversal of EFs poles reversed their migration direction. The directedness and displacement of cathodal migration became more significant when the field strength was increased from 50 mV/mm to 100 mV/mm. EFs stimulation did not influence the cell migration velocity. Our work suggests that EFs may serve as a guidance cue to direct grafted cell migration in vivo. PMID:24804615

  10. Use of artificial neural networks for electrical conductivity modeling in Asi River

    Science.gov (United States)

    Ghorbani, Mohammad Ali; Aalami, Mohammad Taghi; Naghipour, Leila

    2017-07-01

    This study aims to model monthly electrical conductivity (EC) values in the Asi River using artificial neural networks (ANNs) to evaluate water quality conditions using pH, temperature, water discharge, sodium, sum of calcium and magnesium concentrations. The results are compared using multiple linear regression (MLR). Recorded data are available at a gauging site in Antakya, Turkey, for the period from 1984 to 2008. Comparing the modeled values by ANNs with the experimental data indicates that neural network model with seven neurons in hidden layer provides accurate results ( R 2 = 0.968, RMSE = 46.927 µS/cm, MAE = 32.462 µS/cm and MRSE = 0.0029 for the training data and R 2 = 0.965, RMSE = 50.810 µS/cm, MAE = 37.495 µS/cm and MRSE = 0.0024 for the testing data). The Garson method of the connection weights of the network was used to study the relative % contribution of each of the input variables. It was found that the sum of calcium and magnesium concentration and temperature had the most effect on the predicted EC. The results indicate that two proposed models were able to approximate the EC parameter reasonably well; however, the ANN was found to perform better than the MLR model.

  11. Directed migration of embryonic stem cell-derived neural cells in an applied electric field.

    Science.gov (United States)

    Li, Yongchao; Weiss, Mark; Yao, Li

    2014-10-01

    Spinal cord injury or diseases, such as amyotrophic lateral sclerosis, can cause the loss of motor neurons and therefore results in the paralysis of muscles. Stem cells may improve functional recovery by promoting endogenous regeneration, or by directly replacing neurons. Effective directional migration of grafted neural cells to reconstruct functional connections is crucial in the process. Steady direct current electric fields (EFs) play an important role in the development of the central nervous system. A strong biological effect of EFs is the induction of directional cell migration. In this study, we investigated the guided migration of embryonic stem cell (ESC) derived presumptive motor neurons in an applied EF. The dissociated mouse ESC derived presumptive motor neurons or embryoid bodies were subjected to EFs stimulation and the cell migration was studied. We found that the migration of neural precursors from embryoid bodies was toward cathode pole of applied EFs. Single motor neurons migrated to the cathode of the EFs and reversal of EFs poles reversed their migration direction. The directedness and displacement of cathodal migration became more significant when the field strength was increased from 50 mV/mm to 100 mV/mm. EFs stimulation did not influence the cell migration velocity. Our work suggests that EFs may serve as a guidance cue to direct grafted cell migration in vivo.

  12. Laser micro-machinability of borosilicate glass surface-modified by electric field-assisted ion-exchange method

    Science.gov (United States)

    Matsusaka, S.; Kobayakawa, T.; Hidai, H.; Morita, N.

    2012-08-01

    In order to improve the laser micro-machinability of borosilicate glass, the glass surface was doped with metal (silver or copper) ions by an electric field-assisted ion-exchange method. Doped ions drifted and diffused into the glass substrate under a DC electric field. The concentration of metal ions within the doped area was approximately constant because the ion penetration was caused by substitution between dopant metal and inherent sodium ions. Nanosecond ultraviolet laser irradiation of metal-containing regions produced flat, smooth and defect-free holes. However, the shapes of holes were degraded when the processed hole bottoms reached ion penetration depths. A numerical analysis of ionic drift-diffusion behaviour in glass material under an electric field was also carried out. The calculated results for penetration depth and ionic flux showed good agreement with the measured values.

  13. Machine Learning Algorithms for Smart Electricity Markets : Essays on autonomous electricity broker design, probabilistic preference modeling, and competitive benchmarking

    NARCIS (Netherlands)

    M. Peters (Markus)

    2015-01-01

    markdownabstract__Abstract__ The shift towards sustainable electricity systems is one of the grand challenges of the twenty-first century. Decentralized production from renewable sources, electric mobility, and related advances are at odds with traditional power systems where central large-scale

  14. Evaluation of head-only electrical stunning for practical application: Assessment of neural and meat quality parameters

    NARCIS (Netherlands)

    Lambooij, E.; Reimert, H.G.M.; Hindle, V.A.

    2010-01-01

    Behavioral and neural responses of 47 broilers to head-only single-bird electrical stunning were evaluated using cone-shaped restrainers in which the broilers were suspended by their feet. Meat quality assessment was performed on 2 groups of 25 broilers stunned using the head-only method or a

  15. On-road magnetic emissions prediction of electric cars in terms of driving dynamics using neural networks

    NARCIS (Netherlands)

    Wefky, Ahmed M.; Espinosa, Felipe; Leferink, Frank Bernardus Johannes; Gardel, Alfredo; Vogt-Ardatjew, R.A.

    2013-01-01

    This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using

  16. INVESTIGATION OF SURFACE PROPERTIES IN MANGANESE POWDER MIXED ELECTRICAL DISCHARGE MACHINING OF OHNS AND D2 DIE STEELS

    Directory of Open Access Journals (Sweden)

    S. Kumar

    2010-12-01

    Full Text Available The electrical discharge machining (EDM process is used for generating accurate internal profiles in hardened materials. An powder additive in the hydrocarbon dielectric affects the energy distribution and sparking efficiency, and consequently the surface finish and micro-hardness. In this paper the Taguchi approach has been used to optimize and compare the surface properties in manganese powder-mixed EDM of oil-hardening non-shrinkable (OHNS and high-carbon high-chromium (D2 die steels. The results of the study show an improvement of 73% and 71.6% in the micro-hardness of OHNS and D2 die steels, respectively. The machining parameters for the best value of micro-hardness are found to be the same for both work materials. A scanning electron microscopy and X-ray diffraction analysis of the machined surfaces show a transfer of manganese and carbon from the plasma channel in the form of manganese carbide. The chemical composition of the machined surface has been further checked on an optical emission spectrometer to verify and quantify the results.

  17. Modeling and Control of a Flux-Modulated Compound-Structure Permanent-Magnet Synchronous Machine for Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Zhiyi Song

    2012-01-01

    Full Text Available The compound-structure permanent-magnet synchronous machine (CS-PMSM, comprising a double rotor machine (DRM and a permanent-magnet (PM motor, is a promising electronic-continuously variable transmission (e-CVT concept for hybrid electric vehicles (HEVs. By CS-PMSM, independent speed and torque control of the vehicle engine is realized without a planetary gear unit. However, the slip rings and brushes of the conventional CS-PMSM are considered a major drawback for vehicle application. In this paper, a brushless flux-modulated CS-PMSM is investigated. The operating principle and basic working modes of the CS-PMSM are discussed. Mathematical models of the CS-PMSM system are given, and joint control of the two integrated machines is proposed. As one rotor of the DRM is mechanically connected with the rotor of the PM motor, special rotor position detection and torque allocation methods are required. Simulation is carried out by Matlab/Simulink, and the feasibility of the control system is proven. Considering the complexity of the controller, a single digital signal processor (DSP is used to perform the interconnected control of dual machines instead of two separate ones, and a typical hardware implementation is proposed.

  18. A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

    Directory of Open Access Journals (Sweden)

    Basabdatta Sen-Bhattacharya

    2017-08-01

    Full Text Available We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG. Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi

  19. Model and Simulation of Permanent Magnets Synchronous Machine (PMSM of the Electric Power Supply System (EPS, in Accordance with the Concept of a More Electric Aircraft (MEA

    Directory of Open Access Journals (Sweden)

    Setlak Lucjan

    2018-01-01

    Full Text Available Based on the mathematical model of synchronous electric machine, basing on permanent magnets, presented in this paper, the key importance of alternator AC power sources in the form of generator (for conventional aircraft and in the form of integrated unit starter/AC synchronous generator S/G AC (with respect to advanced aircraft concept in the field of more/all electric power MEA/AEA was highlighted. In addition, through the analysis and selected simulations of the power supply system of a modern aircrafts, sources of onboard electrical energy (synchronous generator, integrated unit starter/AC generator were located in board autonomic power system ASE (EPS, PES. Key components of this system are the electro-energetic power system EPS and the energo-electronic power system PES. Additionally, the analysis and exemplary simulations of key electricity sources based on mathematical models have contributed to highlighting the main practical applications in line with the trend of a more electric aircraft.

  20. A novel five-phase fault-tolerant modular in-wheel permanent-magnet synchronous machine for electric vehicles

    Science.gov (United States)

    Sui, Yi; Zheng, Ping; Wu, Fan; Wang, Pengfei; Cheng, Luming; Zhu, Jianguo

    2015-05-01

    This paper describes a five-phase fault-tolerant modular in-wheel permanent-magnet synchronous machine (PMSM) for electric vehicles. By adopting both the analytical and finite-element methods, the magnetic isolation abilities of some typical slot/pole combinations are analyzed, and a new fractional-slot concentrated winding topology that features hybrid single/double-layer concentrated windings and modular stator structure is developed. For the proposed hybrid single/double-layer concentrated windings, feasible slot/pole combinations are studied for three-, four-, and five-phase PMSMs. A five-phase in-wheel PMSM that adopts the proposed winding topology is designed and compared with the conventional PMSM, and the proposed machine shows advantages of large output torque, zero mutual inductances, low short-circuit current, and high magnetic isolation ability. Some of the analysis results are verified by experiments.

  1. Single-Electrical-Port Control of Cascaded Doubly-Fed Induction Machine for EV/HEV Applications

    DEFF Research Database (Denmark)

    Han, Peng; Cheng, Ming; Chen, Zhe

    2017-01-01

    A single-electrical-port control scheme, for four-quadrant operation of cascaded doubly-fed induction machine (CDFIM), which has long been conceived as a motor or generator only suitable for limited two-quadrant operation, is proposed and theoretically demonstrated. The drive system is configured...... as a master/slave architecture, that is, the power winding is supplied with a constant-voltage constant-frequency inverter, termed as the master inverter, in an open-loop way, while the control winding is fed by a closed-loop field-oriented-controlled (FOC) variable-voltage variable-frequency inverter, termed...... as slave inverter. With this configuration, the control emphasis is placed on the slave inverter, yielding reduced control complexity and cost, and the inaccuracy of flux estimation in conventional FOC for singly-fed induction machines is avoided at very low or even zero speed. It is found that the doubly...

  2. Design and setting up of a system for remote monitoring and control on auxiliary machines in electric vehicles

    Directory of Open Access Journals (Sweden)

    Dimitrov Vasil

    2017-01-01

    Full Text Available Systems for remote monitoring and control of the proper operation, energy consumption, and efficiency of the controlled objects are very often used in different spheres of industry, in the electricity distribution network, etc. Various types of intelligent energy meters, PLCs and other control devices are involved in such systems. Proper operation of the auxiliary machines in electric vehicles is of great importance and implementation of a system for their remote monitoring and control is useful and ensures reliability and increased efficiency. A system has been designed and built using contemporary devices. An asynchronous motor is controlled by a soft starter and opportunities for remote monitoring (by an intelligent energy meter and control (by a PLC and Touch panel have been provided. Soft starters are widely used in industry for control on asynchronous drives when speed regulation is not a mandatory requirement. They are cheaper than inverters and frequency converters and allow for temporal reduction of the torque and current surge during start-up, as well as smooth deceleration. Therefore they can also be used in electric vehicles to control auxiliary machines (pumps, fans, air coolers, compressors, etc.. The present paper presents a methodology for their design and setting up.

  3. Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

    Directory of Open Access Journals (Sweden)

    Kyle A McQuisten

    Full Text Available BACKGROUND: Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models. PRINCIPAL FINDINGS: Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation. CONCLUSIONS: The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features

  4. Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs

    Directory of Open Access Journals (Sweden)

    Hamed Ahmadi

    2017-06-01

    Full Text Available BackgroundIn the nutrition literature, there are several reports on the use of artificial neural network (ANN and multiple linear regression (MLR approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM method as a new alternative approach to MLR and ANN models is still not fully investigated.MethodsThe MLR, ANN, and SVM models were developed to predict metabolizable energy (ME content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP, ether extract (EE, crude fiber (CF, and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values.ResultsThe results revealed that the developed ANN [R2 = 0.95; root mean square error (RMSE = 0.19 MJ/kg of dry matter] and SVM (R2 = 0.95; RMSE = 0.21 MJ/kg of dry matter models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR (R2 = 0.89; RMSE = 0.27 MJ/kg of dry matter.ConclusionThe developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

  5. Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

    Science.gov (United States)

    Harmon, Frederick G.

    2005-11-01

    Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid-electric

  6. Design of a high-torque machine with two integrated motors axes reducing the electric vehicle consumption

    Directory of Open Access Journals (Sweden)

    M. Chaieb

    2008-03-01

    Full Text Available The motorization of electric vehicle needs to work at a constant power on a wide range of speed. In order to be able to satisfy these requirements, we describe in this paper a solution, which consists in modifying of a simple structure of a permanent magnet motor by a double rotor structure integrating two motor axes into the same machine. This article describes, then, a design methodology of a permanent magnet motor with double rotor, radial flux, and strong starting torque for electric vehicles. This work consists on the analytical dimensioning of the motor by taking into account several operation constraints followed by a modelling by the finite elements method. This study is followed by the comparison between this motor and a motor with one rotor. A global model of the motor- converter is developed for the purpose to answer several optimisation problems

  7. The Study of Permanent Magnets Synchronous Machine (PMSM of the Autonomous Electric Power Supply System (ASE, compatible with the Concept of a More Electric Aircraft (MEA

    Directory of Open Access Journals (Sweden)

    Setlak Lucjan

    2018-01-01

    Full Text Available Based on the analysis and mathematical models of synchronous electric machines (motor/generator, basing on permanent magnets, presented in this paper, the main importance of alternator AC power sources in the form of starter/generator (for conventional aircraft and in the form of integrated unit starter (motor/AC synchronous generator S/G AC (with respect to advanced aircraft concept in terms of more electric aircraft was highlighted. Additionally, through the analysis and selected simulations of the on-board autonomous power supply system of the modern aircrafts, sources of electrical energy (synchronous motor/generator, integrated unit starter/AC generator were located in board autonomic power system ASE (EPS, PES. Main components of this system are the electro-energetic power system EPS and the energo-electronic power system PES. In addition, the analysis and exemplary simulations of main electricity sources based on mathematical models have contributed to highlighting the main practical applications in accordance with the concept of MEA.

  8. Electrical Trees in a Composite Insulating System Consisted of Epoxy Resin and Mica: The Case of Multiple Mica Sheets For Machine Insulation

    Directory of Open Access Journals (Sweden)

    V. A. Kioussis

    2014-08-01

    Full Text Available Epoxy resin and mica sheets consist the essential insulation of rotating machine stator bars. Such an insulation, although very resistant to partial discharges, is subjected to considerable electrical stresses and consequently electrical trees may ensue. In this paper, an effort is made to simulate electrical tree propagation in multiple epoxy resin/mica sheets with the aid of Cellular Automata (CA. An attempt to compare the simulation results with experimental results is also made.

  9. Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS.

    Science.gov (United States)

    De Geeter, N; Crevecoeur, G; Leemans, A; Dupré, L

    2015-01-21

    In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically, it is the component of this field parallel to the neuron's local orientation, the so-called effective electric field, that can initiate neuronal stimulation. Deeper insights on the stimulation mechanisms can be acquired through extensive TMS modelling. Most models study simple representations of neurons with assumed geometries, whereas we embed realistic neural trajectories computed using tractography based on diffusion tensor images. This way of modelling ensures a more accurate spatial distribution of the effective electric field that is in addition patient and case specific. The case study of this paper focuses on the single pulse stimulation of the left primary motor cortex with a standard figure-of-eight coil. Including realistic neural geometry in the model demonstrates the strong and localized variations of the effective electric field between the tracts themselves and along them due to the interplay of factors such as the tract's position and orientation in relation to the TMS coil, the neural trajectory and its course along the white and grey matter interface. Furthermore, the influence of changes in the coil orientation is studied. Investigating the impact of tissue anisotropy confirms that its contribution is not negligible. Moreover, assuming isotropic tissues lead to errors of the same size as rotating or tilting the coil with 10 degrees. In contrast, the model proves to be less sensitive towards the not well-known tissue conductivity values.

  10. A Review of Additive Mixed-Electric Discharge Machining: Current Status and Future Perspectives for Surface Modification of Biomedical Implants

    Directory of Open Access Journals (Sweden)

    Abdul’Azeez Abdu Aliyu

    2017-01-01

    Full Text Available Surface treatment remained a key solution to numerous problems of synthetic hard tissues. The basic methods of implant surface modification include various physical and chemical deposition techniques. However, most of these techniques have several drawbacks such as excessive cost and surface cracks and require very high sintering temperature. Additive mixed-electric discharge machining (AM-EDM is an emerging technology which simultaneously acts as a machining and surface modification technique. Aside from the mere molds, dies, and tool fabrication, AM-EDM is materializing to finishing of automobiles and aerospace, nuclear, and biomedical components, through the concept of material migrations. The mechanism of material transfer by AM-EDM resembles electrophoretic deposition, whereby the additives in the AM-EDM dielectric fluids are melted and migrate to the machined surface, forming a mirror-like finishing characterized by extremely hard, nanostructured, and nanoporous layers. These layers promote the bone in-growth and strengthen the cell adhesion. Implant shaping and surface treatment through AM-EDM are becoming a key research focus in recent years. This paper reports and summarizes the current advancement of AM-EDM as a potential tool for orthopedic and dental implant fabrication. Towards the end of this paper, the current challenges and future research trends are highlighted.

  11. Electrical discharge machining (EDM) of Inconel 718 by using copper electrode at higher peak current and pulse duration

    Science.gov (United States)

    Ahmad, S.; Lajis, M. A.

    2013-12-01

    This experimental work is an attempt to investigate the performance of Copper electrode when EDM of Nickel Based Super Alloy, Inconel 718 is at higher peak current and pulse duration. Peak current, Ip and pulse duration (pulse on-time), ton are selected as the most important electrical pulse parameters. In addition, their influence on material removal rate (MRR), electrode wear rate (EWR), and surface roughness (Ra) are experimentally investigated. The ranges of 10 mm diameter of Copper electrode are used to EDM of Inconel 718. After the experiments, MRR, EWR, and Ra of the machined surfaces need to be measured in order to evaluate the performance of the EDM process. In order to obtain high MRR, higher peak current in range of 20A to 40A and pulse duration in range of 200μs to 400μs were used. Experimental results have shown that machining at a highest peak current used of 40A and the lowest pulse duration of 200μs used for the experiment yields the highest material removal rate (MRR) with value 34.94 mm3/min, whereas machining at a peak current of 20A and pulse duration of 400μs yields the lowest electrode wear rate (EWR) with value -0.0101 mm3/min. The lowest surface roughness (Ra) is 8.53 μm achieved at a lowest peak current used of 20A and pulse duration of 200μs.

  12. Modeling and optimization of process variables of wire-cut electric discharge machining of super alloy Udimet-L605

    Directory of Open Access Journals (Sweden)

    Somvir Singh Nain

    2017-02-01

    Full Text Available This paper presents the behavior of Udimet-L605 after wire electric discharge machining and evaluating the WEDM process using sophisticated machine learning approaches. The experimental work is depicted on the basis of Taguchi orthogonal L27 array, considering six input variables and three interactions. Three models such as support vector machine algorithms based on PUK kernel, non-linear regression and multi-linear regression have been proposed to examine the variance between experimental and predicted outcome and preferred the preeminent model based on its evaluation parameters performance and graph analysis. The grey relational analysis is the relevant approach to obtain the best grouping of input variables for maximum material removal rate and minimum surface roughness. Based on statistical analysis, it has been concluded that pulse-on time, interaction between pulse-on time x pulse-off time, spark-gap voltage and wire tension are the momentous variable for surface roughness while the pulse-on time, spark-gap voltage and pulse-off time are the momentous variables for material removal rate. The micro structural and compositional changes on the surface of work material were examined by means of SEM and EDX analysis. The thickness of the white layer and the recast layer formation increases with increases in the pulse-on time duration.

  13. The release of nickel and other trace elements from electric kettles and coffee machines

    DEFF Research Database (Denmark)

    Berg, T.; Petersen, Annette; Pedersen, Gitte Alsing

    2000-01-01

    was improved. Two of these ten kettles still released more than 50 mu g/l nickel to water under the test conditions. These two kettles, however, were subsequently withdrawn from the market. Coffee machines tested similarly did not release aluminium, lead, chromium or nickel in quantities of any significance....

  14. BINARY CLASSIFICATION OF DAY-AHEAD DEREGULATED ELECTRICITY MARKET PRICES USING NEURAL NETWORK INPUT FEATURED BY DCT

    Directory of Open Access Journals (Sweden)

    S. Anbazhagan

    2012-07-01

    Full Text Available There is a general consensus that the movement of electricity price is crucial for electricity market. The binary electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a binary classification of day-ahead electricity prices that could be realized using discrete cosine transforms (DCT based neural network (NN approach (DCT-NN. These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using NN. The binary classification NN and the proposed DCT-NN were developed and compared to check the performance. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity market of mainland Spain.

  15. Modeling and design of cooperative braking in electric and hybrid vehicles using induction machine and hydraulic brake

    Directory of Open Access Journals (Sweden)

    Zaini Dalimus

    2016-07-01

    Full Text Available In mixed-mode braking applications, the electric motor / generator (M/G and hydraulic pressure valve are controlled to meet the driver’s braking demand. Controlling these braking elements is achieved by modulating the current generated by the M/G and adjusting the fluid pressure to the wheel brake cylinders. This paper aims to model and design combined regenerative and hydraulic braking systems which, comprise an induction electric machine, inverter, NiMH battery, controller, a pressure source, pressure control unit, and brake calipers. A 15 kW 1500 rpm induction machine equipped with a reduction gear having a gear ratio of 4 is used. A hydraulic brake capable to produce fluid pressure up to 40 bar is used. Direct torque control and pressure control are chosen as the control criteria in the M/G and the hydraulic solenoid valve. The braking demands for the system are derived from the Federal Testing Procedure (FTP drive cycle. Two simulation models have been developed in Matlab®/Simulink® to analyze the performance of the control strategy in each braking system. The developed model is validated through experiment. It is concluded that the control system does introduce torque ripple and pressure oscillation in the braking system, but these effects do not affect vehicle braking performance due to the high frequency nature of pressure fluctuation and the damping effect of the vehicle inertia. Moreover, experiment results prove the effectiveness of the developed model.

  16. Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hao Li

    2016-01-01

    Full Text Available 1,1,1,2,3,3,3-Heptafluoropropane (R227ea is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM, and artificial neural networks (ANNs were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs (e.g., the RMSE of the SVM trained with data provided by Fedele et al. [1] is 0.11, while the RMSE of the Song and Mason equation is 196.26. Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly.

  17. Mathematic model of three-phase induction machine connected to advanced inverter for traction system for electric trolley

    Directory of Open Access Journals (Sweden)

    LIVIU S. BOCÎI

    2013-06-01

    Full Text Available This paper establishes a mathematical model of induction machine connected to a frequency inverter necessary to adjust the electric motor drive. The mathematical model based on the Park's theory allows the analysis of the whole spectrum (electric car – frequency inverter to drive the electric trolley bus made on ASTRA Bus Arad (Romania. To remove higher order harmonics, the PWM waveform of supply voltage is used, set in the general case. Operating characteristics of electric motor drive are set to sub-nominal frequency (f Bele 2007.Este documento estabelece um modelo matemático de máquina de indução conectado a um inversor de frequência necessário para ajustar o motor de acionamento elétrico. O modelo matemático baseado na Teoria de Park permite a análise de todo o espectro (carro elétrico com inversor de frequência para dirigir o ônibus elétrico feito em ASTRA Bus Arad (Romênia. Para remover harmônicas de ordem mais alta, a forma de onda da tensão de alimentação PWM é utilizado, definido no caso geral. Características de funcionamento do motor de acionamento elétrico são definidas para frequência sub-nominal (f

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

    Science.gov (United States)

    Su, Gui-Jia [Knoxville, TN

    2005-11-29

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

  19. Monocoil reciprocating permanent magnet electric machine with self-centering force

    Science.gov (United States)

    Bhate, Suresh K. (Inventor); Vitale, Nicholas G. (Inventor)

    1989-01-01

    A linear reciprocating machine has a tubular outer stator housing a coil, a plunger and an inner stator. The plunger has four axially spaced rings of radially magnetized permanent magnets which cooperate two at a time with the stator to complete first or second opposite magnetic paths. The four rings of magnets and the stators are arranged so that the stroke of the plunger is independent of the axial length of the coil.

  20. Angular Velocity's Neural Network Observer of the Electric Drive of TVR - IM Type Implemented in Software Environment LabVIEW

    OpenAIRE

    Kozlova, Liudmila Evgenevna; Bolovin, Evgeny Vladimirovich; Payuk, Lyubov Anatoljevna

    2016-01-01

    One of the common ways to manage a smooth starting and stopping of asynchronous motors are soft-start system. For this provision is necessary to use a closed speed asynchronous electric drive of tiristor voltage regulator - induction motor (TVR-IM) type. Using real sensors significantly increases the cost of installation and also introduces a number of inconveniences in the operation of the actuator. Observer has clear advantages that are created on artificial neural network. Creating a neura...

  1. Power distribution of a co-axial dual-mechanical-port flux-switching permanent magnet machine for fuel-based extended range electric vehicles

    Directory of Open Access Journals (Sweden)

    Lingkang Zhou

    2017-05-01

    Full Text Available In this paper, power distribution between the inner and outer machines of a co-axial dual-mechanical-port flux-switching permanent magnet (CADMP-FSPM machine is investigated for fuel-based extended range electric vehicle (ER-EV. Firstly, the topology and operation principle of the CADMP-FSPM machine are introduced, which consist of an inner FSPM machine used for high-speed, an outer FSPM machine for low-speed, and a magnetic isolation ring between them. Then, the magnetic field coupling of the inner and outer FSPM machines is analyzed with more attention paid to the optimization of the isolation ring thickness. Thirdly, the power-dimension (PD equations of the inner and outer FSPM machines are derived, respectively, and thereafter, the PD equation of the whole CADMP-FSPM machine can be given. Finally, the PD equations are validated by finite element analysis, which supplies the guidance on the design of this type of machines.

  2. Prognostic and Fault Tolerant Reconfiguration Strategies for Aerospace Power Electronic Controllers and Electric Machines Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Impact Technologies proposes to develop a real-time prognostic and fault/failure accommodation system of critical electric power system components including power...

  3. A comparison between the neural correlates of laser and electric pain stimulation and their modulation by expectation.

    Science.gov (United States)

    Hird, E J; Jones, A K P; Talmi, D; El-Deredy, W

    2018-01-01

    Pain is modulated by expectation. Event-related potential (ERP) studies of the influence of expectation on pain typically utilise laser heat stimulation to provide a controllable nociceptive-specific stimulus. Painful electric stimulation has a number of practical advantages, but is less nociceptive-specific. We compared the modulation of electric versus laser-evoked pain by expectation, and their corresponding pain-evoked and anticipatory ERPs. We developed understanding of recognised methods of laser and electric stimulation. We tested whether pain perception and neural activity induced by electric stimulation was modulated by expectation, whether this expectation elicited anticipatory neural correlates, and how these measures compared to those associated with laser stimulation by eliciting cue-evoked expectations of high and low pain in a within-participant design. Despite sensory and affective differences between laser and electric pain, intensity ratings and pain-evoked potentials were modulated equivalently by expectation, though ERPs only correlated with pain ratings in the laser pain condition. Anticipatory correlates differentiated pain intensity expectation to laser but not electric pain. Previous studies show that laser-evoked potentials are modulated by expectation. We extend this by showing electric pain-evoked potentials are equally modulated by expectation, within the same participants. We also show a difference between the pain types in anticipation. Though laser-evoked potentials express a stronger relationship with pain perception, both laser and electric stimulation may be used to study the modulation of pain-evoked potentials by expectation. Anticipatory-evoked potentials are elicited by both pain types, but they may reflect different processes. Copyright © 2017. Published by Elsevier B.V.

  4. Features of torque production of synchronous electric drive with direct torque control of mining machines

    Science.gov (United States)

    Shishkov, A. N.; Sychev, D. A.; Savosteenko, N. V.

    2017-10-01

    In article, the direct torque control method of the synchronous electric drive is considered. This control method is characterized by high performance, robustness and small frequency of switching of keys of the converter. The algorithms and structure of direct torque control of the synchronous electric drive allow creating its operation modes by impact on the form of a triangle with sides: flux linkage of the stator, a rotor, and resultant flux linkage.

  5. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

    Science.gov (United States)

    Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-30

    Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.

  6. Investigation on surface morphology model of Si3N4 ceramics for rotary ultrasonic grinding machining based on the neural network

    Science.gov (United States)

    Jing, Juntao; Feng, Pingfa; Wei, Shiliang; Zhao, Hong

    2017-02-01

    Si3N4 ceramics parts surface morphology is related with surface friction and wear properties directly. Poor surface morphology will result in friction coefficient increases, strength decreases, and even lead to component failures. In order to improve Si3N4 surface morphology, it is necessary to investigate on the relationship model between the surface morphology and process parameters. In the paper, rotary ultrasonic grinding machining (RUGM) was taken as object to establish the model based on back propagation (BP) neural network. However, the nonlinear relationship of the model is complex, and the traditional algorithm cannot realize satisfying results. So an improved BP neural network algorithm based on Powell method has been proposed. The paper gives the theory and calculation flow of the algorithm. It is found the algorithm can accelerate the iteration speed and improve iteration accuracy. The investigation results provide the support for surface morphology optimization.

  7. Driving cycle suitable layout of permanent magnet synchronous machines for hybrid vehicles and electric powered vehicles; Fahrzyklusgerechte Auslegung von permanentmagneterregten Synchronmaschinen fuer Hybrid- und Elektrofahrzeuge

    Energy Technology Data Exchange (ETDEWEB)

    Finken, Thomas

    2011-07-01

    An increasing environmental awareness and the prospect of a shortage of fossil resources will result in a development of efficient vehicles with a lower consumption of fuel. In addition to the hybrid electric vehicle, the electric powered vehicle increasingly is focused in the development of vehicles. A good efficiency is the most important demand on the electrical machine. The author of the book under consideration reports on exemplary operating point distributions for various vehicle concepts and user profiles. After comparing the most common types of machine in terms of the use in electrified powertrains, the permanent magnet synchronous machine is selected and discussed in detail. A table shows the advantages and disadvantages of all considered geometries and variations. Thus, a suitable combination of geometry for a given vehicle concept and its requirements are selected.

  8. Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times

    Directory of Open Access Journals (Sweden)

    Coral Rodrigo

    2015-03-01

    Full Text Available This paper presents a new test method able to infer - in periods of less than 7 seconds - the refrigeration capacity of a compressor used in thermal machines, which represents a time reduction of approximately 99.95% related to the standardized traditional methods. The method was developed aiming at its application on compressor manufacture lines and on 100% of the units produced. Artificial neural networks (ANNs were used to establish a model able to infer the refrigeration capacity based on the data collected directly on the production line. The proposed method does not make use of refrigeration systems and also does not require using the compressor oil.

  9. Analysis of the effect of ultrasonic vibrations on the performance of micro-electrical discharge machining of A2 tool steel

    DEFF Research Database (Denmark)

    Puthumana, Govindan

    2016-01-01

    a systematic analysis of the influence of kinetic effects of the ultrasonic vibrations on the material removal rate (MRR) and tool electrode wear rate (TWR). The tool wear ratio was estimated for the process at all processing conditions. The maximum variation in tool wear ratio is observed to be 82%. Therefore......The application of ultrasonic vibrations to a workpiece or tool is a novel hybrid approach in micro-electrical discharge machining. The advantages of this method include effective flushing out of debris, higher machining efficiency and lesser short-circuits during machining. This paper presents...

  10. A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

    Science.gov (United States)

    Wang, Aimeng; Guo, Jiayu

    2017-12-01

    A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

  11. Key Performance Parameter Driven Technology Goals for Electric Machines and Power Systems

    Science.gov (United States)

    Bowman, Cheryl; Jansen, Ralph; Brown, Gerald; Duffy, Kirsten; Trudell, Jeffrey

    2015-01-01

    Transitioning aviation to low carbon propulsion is one of the crucial strategic research thrust and is a driver in the search for alternative propulsion system for advanced aircraft configurations. This work requires multidisciplinary skills coming from multiple entities. The feasibility of scaling up various electric drive system technologies to meet the requirements of a large commercial transport is discussed in terms of key parameters. Functional requirements are identified that impact the power system design. A breakeven analysis is presented to find the minimum allowable electric drive specific power and efficiency that can preserve the range, initial weight, operating empty weight, and payload weight of the base aircraft.

  12. Optical and electrical recording of neural activity evoked by graded contrast visual stimulus

    Directory of Open Access Journals (Sweden)

    Bulf Luca

    2007-07-01

    Full Text Available Abstract Background Brain activity has been investigated by several methods with different principles, notably optical ones. Each method may offer information on distinct physiological or pathological aspects of brain function. The ideal instrument to measure brain activity should include complementary techniques and integrate the resultant information. As a "low cost" approach towards this objective, we combined the well-grounded electroencephalography technique with the newer near infrared spectroscopy methods to investigate human visual function. Methods The article describes an embedded instrumentation combining a continuous-wave near-infrared spectroscopy system and an electroencephalography system to simultaneously monitor functional hemodynamics and electrical activity. Near infrared spectroscopy (NIRS signal depends on the light absorption spectra of haemoglobin and measures the blood volume and blood oxygenation regulation supporting the neural activity. The NIRS and visual evoked potential (VEP are concurrently acquired during steady state visual stimulation, at 8 Hz, with a b/w "windmill" pattern, in nine human subjects. The pattern contrast is varied (1%, 10%, 100% according to a stimulation protocol. Results In this study, we present the measuring system; the results consist in concurrent recordings of hemodynamic changes and evoked potential responses emerging from different contrast levels of a patterned stimulus. The concentration of [HbO2] increases and [HHb] decreases after the onset of the stimulus. Their variation shows a clear relationship with the contrast value: large contrast produce huge difference in concentration, while low contrast provokes small concentration difference. This behaviour is similar to the already known relationship between VEP response amplitude and contrast. Conclusion The simultaneous recording and analysis of NIRS and VEP signals in humans during visual stimulation with a b/w pattern at variable

  13. Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    Yan Hong Chen

    2016-01-01

    Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

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

    Science.gov (United States)

    Jiang, Ming

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

  15. Characterization of electric discharge machining, subsequent etching and shot-peening as a surface treatment for orthopedic implants

    Science.gov (United States)

    Stráský, Josef; Havlíková, Jana; Bačáková, Lucie; Harcuba, Petr; Mhaede, Mansour; Janeček, Miloš

    2013-09-01

    Presented work aims at multi-method characterization of combined surface treatment of Ti-6Al-4V alloy for biomedical use. Surface treatment consists of consequent use of electric discharge machining (EDM), acid etching and shot peening. Surface layers are analyzed employing scanning electron microscopy and energy dispersive X-ray spectroscopy. Acid etching by strong Kroll's reagent is capable of removing surface layer of transformed material created by EDM. Acid etching also creates partly nanostructured surface and significantly contributes to the enhanced proliferation of the bone cells. The cell growth could be positively affected by the superimposed bone-inspired structure of the surface with the morphological features in macro-, micro- and nano-range. Shot peening significantly improves poor fatigue performance after EDM. Final fatigue performance is comparable to benchmark electropolished material without any adverse surface effect. The proposed three-step surface treatment is a low-cost process capable of producing material that is applicable in orthopedics.

  16. Influence of Different Rotor Teeth Shapes on the Performance of Flux Switching Permanent Magnet Machines Used for Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Jing Zhao

    2014-12-01

    Full Text Available This paper investigated a 12-slot/11-pole flux switching permanent magnet (FSPM machine used for electric vehicles (EVs. Five novel rotor teeth shapes are proposed and researched to reduce the cogging torque and torque ripple of the FSPM machine. These rotor teeth shapes are notched teeth, stepped teeth, eccentric teeth, combination of notched and stepped teeth, and combination of notched and eccentric teeth. They are applied on the rotor and optimized, respectively. The influences of different rotor teeth shapes on cogging torque, torque ripple and electromagnetic torque are analyzed by the 2-D finite-element method (FEM. Then, the performance of FSPMs with different rotor teeth shapes are compared and evaluated comprehensively from the points of view of cogging torque, torque ripple, electromagnetic torque, flux linkage, back electromotive force (EMF, and so on. The results show that the presented rotor teeth shapes, especially the combination of stepped and notched teeth, can greatly reduce the cogging torque and torque ripple with only slight changes in the average electromagnetic torque.

  17. Improvement of MRR and surface roughness during electrical discharge machining (EDM) using aluminum oxide powder mixed dielectric fluid

    Science.gov (United States)

    Khan, A. A.; Mohiuddin, A. K. M.; Latif, M. A. A.

    2018-01-01

    This paper discusses the effect of aluminium oxide (Al203) addition to dielectric fluid during electrical discharge machining (EDM). Aluminium oxide was added to the dielectric used in the EDM process to improve its performance when machining the stainless steel AISI 304, while copper was used as the electrode. Effect of the concentration of Al203 (0.3 mg/L) in dielectric fluid was compared with EDM without any addition of Al203. Surface quality of stainless steel and the material removal rate were investigated. Design of the experiment (DOE) was used for the experimental plan. Statistical analysis was done using ANOVA and then appropriate model was designated. The experimental results show that with dispersing of aluminium oxide in dielectric fluid surface roughness was improved while the material removal rate (MRR) was increased to some extent. These indicate the improvement of EDM performance using aluminium oxide in dielectric fluid. It was also found that with increase in pulse on time both MRR and surface roughness increase sharply.

  18. APPLICATION OF PBL IN THE COURSE FLUID AND ELECTRICAL DRIVE SYSTEMS, CASE STUDY: MANUFACTURING AN AUTOMATED PUNCH MACHINE

    Directory of Open Access Journals (Sweden)

    Ahmad Sedaghat

    2017-01-01

    Full Text Available The PBL unit of fluid and electrical drive systems is taught in final semester of undergraduates in mechanical engineering department of the Australian College of Kuwait (ACK. The recent project on an automated punching machine is discovered more appealing to both students and instructors in triggering new ideas and satisfaction end results. In this case study, the way this PBL unit is coordinated and facilitated is explained. Two examples of student works are presented. The aim is to expose the students to real world engineering problems but in a satisfying manner. Similar to real life problems for engineers, restrictions are applied for the students on costs, availability of ACK facilities, and application of automation tools. Students are directly engaged by using technical standards on punching heads and dies, standard tensile testing of plates, and so on. Arduino microprocessor programming, an open-source hardware and software electronic platform, and electro-pneumatic devices are adopted for developing the automated punching machine. The goal of the PBL course is to acquaint students learning based on the concepts of team working, engineering design, professional manufacturing, and sequential testing of the end product. It is found that students achieved their best and developed new skills in this PBL unit as reflected in their portfolios.

  19. Price forecast in the competitive electricity market by support vector machine

    Science.gov (United States)

    Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Cheng, Haozhong

    2007-08-01

    The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter C i and ε i assigned to each training data, the flexible C i Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin Rm, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).

  20. An Influence of Parameters of Micro-Electrical Discharge Machining On Wear of Tool Electrode

    DEFF Research Database (Denmark)

    Puthumana, Govindan

    2017-01-01

    To achieve better precision of features generated using the micro-electrical dischargemachining (micro-EDM), there is a necessity to minimize the wear of the toolelectrode, because a change in the dimensions of the electrode is reflected directly orindirectly on the feature. This paper presents...

  1. Understanding Power Electronics and Electrical Machines in Multidisciplinary Wind Energy Conversion System Courses

    Science.gov (United States)

    Duran, M. J.; Barrero, F.; Pozo-Ruz, A.; Guzman, F.; Fernandez, J.; Guzman, H.

    2013-01-01

    Wind energy conversion systems (WECS) nowadays offer an extremely wide range of topologies, including various different types of electrical generators and power converters. Wind energy is also an application of great interest to students and with a huge potential for engineering employment. Making WECS the main center of interest when teaching…

  2. Machine Vision System for Characterizing the Electric Field for the 225 Ra EDM Experiment

    Science.gov (United States)

    Sanchez, Andrew

    2017-09-01

    If an atom or fundamental particle possesses an electric dipole moment (EDM), that would imply time-reversal violation. At our current capability, if an EDM is detected in such a particle, that would suggest the discovery of beyond the standard model (BSM) physics. The unique structure of 225 Ra makes its atomic EDM favorable in the BSM search. An upgraded Ra-EDM apparatus will increase experimental sensitivity and the target electric field of 150 kV/cm will more than double the electric field used in previous experiments. To determine the electric field, the potential difference and electrode separation distance must be known. The optical method I have developed is a high-precision, non-invasive technique to measure electrode separation without making contact with the sensitive electrode surfaces. A digital camera utilizes a bi-telecentric lens to reduce parallax error and produce constant magnification throughout the optical system, regardless of object distance. A monochrome LED backlight enhances sharpness of the electrode profile, reducing uncertainty in edge determination and gap width. A program utilizing an edge detection algorithm allows precise, repeatable measurement of the gap width to within 1% and measurement of the relative angle of the electrodes. This work (SAM, Ra EDM) is supported by Michigan State University. This work (REU Program) is supported by U.S. National Science Foundation under Grant Number #1559866.

  3. Neural stem cell differentiation by electrical stimulation using a cross-linked PEDOT substrate: Expanding the use of biocompatible conjugated conductive polymers for neural tissue engineering.

    Science.gov (United States)

    Pires, Filipa; Ferreira, Quirina; Rodrigues, Carlos A V; Morgado, Jorge; Ferreira, Frederico Castelo

    2015-06-01

    The use of conjugated polymers allows versatile interactions between cells and flexible processable materials, while providing a platform for electrical stimulation, which is particularly relevant when targeting differentiation of neural stem cells and further application for therapy or drug screening. Materials were tested for cytotoxicity following the ISO10993-5. PSS was cross-linked. ReNcellVM neural stem cells (NSC) were seeded in laminin coated surfaces, cultured for 4 days in the presence of EGF (20 ng/mL), FGF-2 (20 ng/mL) and B27 (20 μg/mL) and differentiated over eight additional days in the absence of those factors under 100Hz pulsed DC electrical stimulation, 1V with 10 ms pulses. NSC and neuron elongation aspect ratio as well as neurite length were assessed using ImageJ. Cells were immune-stained for Tuj1 and GFAP. F8T2, MEH-PPV, P3HT and cross-linked PSS (x PSS) were assessed as non-cytotoxic. L929 fibroblast population was 1.3 higher for x PSS than for glass control, while F8T2 presents moderate proliferation. The population of neurons (Tuj1) was 1.6 times higher with longer neurites (73 vs 108 μm) for cells cultured under electrical stimulus, with cultured NSC. Such stimulus led also to longer neurons. x PSS was, for the first time, used to elongate human NSC through the application of pulsed current, impacting on their differentiation towards neurons and contributing to longer neurites. The range of conductive conjugated polymers known as non-cytotoxic was expanded. x PSS was introduced as a stable material, easily processed from solution, to interface with biological systems, in particular NSC, without the need of in-situ polymerization. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method.

    Science.gov (United States)

    Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza

    2017-07-05

    In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

    Science.gov (United States)

    Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza

    2017-07-01

    In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300 nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Thierry Jacq

    2010-08-01

    Full Text Available 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.

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

    Science.gov (United States)

    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

  9. A STUDY ON CAPABILITIES OF DIFFERENT ELECTRODE MATERIALS DURING ELECTRICAL DISCHARGE MACHINING (EDM

    Directory of Open Access Journals (Sweden)

    Muataz Hazza Faizi Al Hazza

    2017-12-01

    Full Text Available Electrode material inelectro discharge machining EDM process plays an important role in terms of material removal rate (MRR, electrode wear rate (EWR and surface roughness (Ra. The purpose of this research is to investigate the capability of different electrode materials: copper, aluminum and graphite in EDM of AISI 304 stainless steel as a work piece. The research focuses on three current settings: 2.5A, 4.5A and 6.5A using kerosene as dielectric fluid. The experiment is planned and analyzed using full factorial of the experimental design using response surface methodology (RSM. two outputs have been investigated: MRR and EWR. The results indicated that the responses increased with the increase in current. Finally the desirability function method have been used to determine the optimum values. The resulat show that the maximum MRR and  the minimum EWR were achieved by using graphite electrode at current 6.5A.

  10. Finite-element analysis of eddy currents in the form-wound multi-conductor windings of electrical machines

    Energy Technology Data Exchange (ETDEWEB)

    Islam, M. J.

    2009-07-01

    The aim of this research was to develop comprehensive numerical models for considering eddy currents and circulating currents in the form-wound multi-conductor windings of electrical machines and to study the effects of eddy currents and circulating currents. Time-harmonic and time-discretised finite-element methods were developed. The methods were applied to the stator winding of a 1250-kW cage induction motor and in both the stator and rotor windings of a 1.7-MW doubly-fed induction generator (DFIG). The series and parallel connections of the winding were taken into account. The Newton-Raphson iteration method was used to solve the system of non-linear equations. In time-harmonic FEM, the system of equations was solved iteratively just once for the steady-state solution. In time-discretised FEM, the system of equations was solved iteratively at every time step. The backward Euler method was used for the time discretisation. The radial distance of the stator bars from the air gap has a remarkable effect on losses and was found to be an important design parameter. A significant amount of stator-winding eddy-current loss can be reduced by considering this design parameter. A transposition of the conductors was implemented to reduce the circulating currents between the parallel stator conductors. The eddy-current effects in the form-wound multi-conductor windings of electrical machines were studied for both a sinusoidal and non-sinusoidal supply. A pulse-width-modulated (PWM) voltage supply was achieved by sinus triangle comparison and used as a non-sinusoidal supply for the machine. A PWM supply produced a significant amount of additional eddy-current losses in the form-wound stator winding of the cage induction motor when compared to the sinusoidal supply. The fundamental harmonic voltages of the sinusoidal and PWM supplies were equal for comparing the results. Similar sinusoidal and PWM voltages were used to supply the rotor winding of the DFIG as well. The

  11. Optimal Sizing of Energy Storage System in Solar Energy Electric Vehicle Using Genetic Algorithm and Neural Network

    Science.gov (United States)

    Zhou, Shiqiong; Kang, Longyun; Cheng, Miaomiao; Cao, Binggang

    Owing to sun's rays distributing randomly and discontinuously and load fluctuation, energy storage system is very important in Solar Energy Electric Vehicle (SEEV). The combinatorial optimization by genetic algorithm and neural network was used to optimize the energy storage system (including storage batteries and flywheel).In the optimization design, the operation strategy of the system was fixed and used to instruct the simulation about the system's operation. And the optimal objective was selected as minimizing the total capital cost of the energy storage system, subject to the main constraint of the Loss of Power Supply Probability (LPSP). Studies have proved that the combinatorial optimization by genetic algorithm and neural network converges well, lessen calculation time and it is feasible.

  12. Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

    Directory of Open Access Journals (Sweden)

    Jaime Buitrago

    2017-01-01

    Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

  13. Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

    Directory of Open Access Journals (Sweden)

    Chan-Uk Yeom

    2017-10-01

    Full Text Available This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE as well as mean absolute error (MAE, mean absolute percent error (MAPE, and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU. It possessed superior prediction performance and knowledge information and a small number of rules.

  14. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

    2006-01-01

    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  15. Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Ruijun Liu

    2014-01-01

    Full Text Available Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

  16. The effect of the resistive properties of bone on neural excitation and electric fields in cochlear implant models.

    Science.gov (United States)

    Malherbe, T K; Hanekom, T; Hanekom, J J

    2015-09-01

    The resistivity of bone is the most variable of all the tissues in the human body, ranging from 312 Ω cm to 84,745 Ω cm. Volume conduction models of cochlear implants have generally used a resistivity value of 641 Ω cm for the bone surrounding the cochlea. This study investigated the effect that bone resistivity has on modelled neural thresholds and intracochlear potentials using user-specific volume conduction models of implanted cochleae applying monopolar stimulation. The complexity of the description of the head volume enveloping the cochlea was varied between a simple infinite bone volume and a detailed skull containing a brain volume, scalp and accurate return electrode position. It was found that, depending on the structure of the head model and implementation of the return electrode, different bone resistivity values are necessary to match model predictions to data from literature. Modelled forward-masked spatial tuning curve (fmSTC) widths and slopes and intracochlear electric field profile length constants were obtained for a range of bone resistivity values for the various head models. The predictions were compared to measurements found in literature. It was concluded that, depending on the head model, a bone resistivity value between 3500 Ω cm and 10,500 Ω cm allows prediction of neural and electrical responses that match measured data. A general recommendation is made to use a resistivity value of approximately 10,000 Ω cm for bone volumes in conduction models of the implanted cochlea when neural excitation is predicted and a value of approximately 6500 Ω cm when predicting electric fields inside the cochlear duct. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

    Directory of Open Access Journals (Sweden)

    S. Saravanan

    2012-07-01

    Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

  18. Statistical investigations into the erosion of material from the tool in micro-electrical discharge machining

    DEFF Research Database (Denmark)

    Puthumana, Govindan

    2016-01-01

    This paper presents a statistical study of the erosion of material from the tool electrode in a micro-electrical dischargemachining process. The work involves Analysis of Variance and Analysis of Means approaches on the results of the toolelectrode wear rate obtained based on design of experiments...... ) and discharge frequency (f ) control the erosion of material from the tool electrode. The Material Erosion dfrom the tool electrode increases linearly with the discharge frequency. As the current index increases from 20 to 35,the M decreases linearly, by 29% and then increases by of 36%. The current index of 35...... gives the minimum material eerosion from the tool. It is observed that none of the two-factor interactions are significant in controlling the erosion ofmaterial from the Tool....

  19. Effect of Abrasive Machining on the Electrical Properties Cu86Mn12Ni₂ Alloy Shunts.

    Science.gov (United States)

    Misti, Siti Nabilah; Birkett, Martin; Penlington, Roger; Bell, David

    2017-07-29

    This paper studies the effect of abrasive trimming on the electrical properties of Cu 86 Mn 12 Ni₂ Manganin alloy shunt resistors. A precision abrasive trimming system for fine tuning the resistance tolerance of high current Manganin shunt resistors is proposed. The system is shown to be capable of reducing the resistance tolerance of 100 μΩ shunts from their standard value of ±5% to <±1% by removing controlled amounts of Manganin material using a square cut trim geometry. The temperature coefficient of resistance (TCR), high current, and high temperature performance of the trimmed shunts was compared to that of untrimmed parts to determine if trimming had any detrimental effect on these key electrical performance parameters of the device. It was shown that the TCR value was reduced following trimming with typical results of +106 ppm/°C and +93 ppm/°C for untrimmed and trimmed parts respectively. When subjected to a high current of 200 A the trimmed parts showed a slight increase in temperature rise to 203 °C, as compared to 194 °C for the untrimmed parts, but both had significant temporary increases in resistance of up to 1.3 μΩ. The results for resistance change following high temperature storage at 200 °C for 168 h were also significant for both untrimmed and trimmed parts with shifts of 1.85% and 2.29% respectively and these results were related to surface oxidation of the Manganin alloy which was accelerated for the freshly exposed surfaces of the trimmed part.

  20. Effect of Abrasive Machining on the Electrical Properties Cu86Mn12Ni2 Alloy Shunts

    Directory of Open Access Journals (Sweden)

    Siti Nabilah Misti

    2017-07-01

    Full Text Available This paper studies the effect of abrasive trimming on the electrical properties of Cu86Mn12Ni2 Manganin alloy shunt resistors. A precision abrasive trimming system for fine tuning the resistance tolerance of high current Manganin shunt resistors is proposed. The system is shown to be capable of reducing the resistance tolerance of 100 μΩ shunts from their standard value of ±5% to <±1% by removing controlled amounts of Manganin material using a square cut trim geometry. The temperature coefficient of resistance (TCR, high current, and high temperature performance of the trimmed shunts was compared to that of untrimmed parts to determine if trimming had any detrimental effect on these key electrical performance parameters of the device. It was shown that the TCR value was reduced following trimming with typical results of +106 ppm/°C and +93 ppm/°C for untrimmed and trimmed parts respectively. When subjected to a high current of 200 A the trimmed parts showed a slight increase in temperature rise to 203 °C, as compared to 194 °C for the untrimmed parts, but both had significant temporary increases in resistance of up to 1.3 μΩ. The results for resistance change following high temperature storage at 200 °C for 168 h were also significant for both untrimmed and trimmed parts with shifts of 1.85% and 2.29% respectively and these results were related to surface oxidation of the Manganin alloy which was accelerated for the freshly exposed surfaces of the trimmed part.

  1. Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing

    Directory of Open Access Journals (Sweden)

    Derck Koolen

    2017-11-01

    Full Text Available Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.

  2. Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegie gait with machine learning classification by multilayer perceptron neural network.

    Science.gov (United States)

    LeMoyne, Robert; Mastroianni, Timothy

    2016-08-01

    Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.

  3. Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System

    Directory of Open Access Journals (Sweden)

    Shing-Hong Liu

    2013-01-01

    Full Text Available An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC, premature atrium contraction (PAC, left bundle branch block (LBBB, and right bundle branch block (RBBB. In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.

  4. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

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

    Science.gov (United States)

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

    2008-04-01

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

  6. Electrical stimulation superimposed on voluntary training can limit sensory integration in neural adaptations.

    Science.gov (United States)

    Paillard, Thierry

    2012-01-01

    P. Bezerra, S. Zhou, Z. Crowley, A. Davie, and R. Baglin (2011) suggested that the neural mechanisms responsible for steadiness improvement relate in particular to the discharge behavior of motor units and the practice and learning of skills rather than the strength gain after electromyostimulation superimposed over voluntary training. However, the afferent inputs are determining in control of the force level produced and thus contribute to ensure muscle steadiness. Hence, it is possible that electromyostimulation interferes in neurophysiological afference integration and prevents neural adaptations that enable improvement of the control of force (and then muscle steadiness) to occur. Therefore, the neural adaptations induced by electromyostimulation superimposed onto voluntary training should also be researched in relation to the sensory pathways.

  7. Polypyrrole/Alginate Hybrid Hydrogels: Electrically Conductive and Soft Biomaterials for Human Mesenchymal Stem Cell Culture and Potential Neural Tissue Engineering Applications.

    Science.gov (United States)

    Yang, Sumi; Jang, LindyK; Kim, Semin; Yang, Jongcheol; Yang, Kisuk; Cho, Seung-Woo; Lee, Jae Young

    2016-11-01

    Electrically conductive biomaterials that can efficiently deliver electrical signals to cells or improve electrical communication among cells have received considerable attention for potential tissue engineering applications. Conductive hydrogels are desirable particularly for neural applications, as they can provide electrical signals and soft microenvironments that can mimic native nerve tissues. In this study, conductive and soft polypyrrole/alginate (PPy/Alg) hydrogels are developed by chemically polymerizing PPy within ionically cross-linked alginate hydrogel networks. The synthesized hydrogels exhibit a Young's modulus of 20-200 kPa. Electrical conductance of the PPy/Alg hydrogels could be enhanced by more than one order of magnitude compared to that of pristine alginate hydrogels. In vitro studies with human bone marrow-derived mesenchymal stem cells (hMSCs) reveal that cell adhesion and growth are promoted on the PPy/Alg hydrogels. Additionally, the PPy/Alg hydrogels support and greatly enhance the expression of neural differentiation markers (i.e., Tuj1 and MAP2) of hMSCs compared to tissue culture plate controls. Subcutaneous implantation of the hydrogels for eight weeks induces mild inflammatory reactions. These soft and conductive hydrogels will serve as a useful platform to study the effects of electrical and mechanical signals on stem cells and/or neural cells and to develop multifunctional neural tissue engineering scaffolds. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. Prediction of material removal rate and surface roughness for wire electrical discharge machining of nickel using response surface methodology

    Directory of Open Access Journals (Sweden)

    Thangam Chinnadurai

    2016-12-01

    Full Text Available This study focuses on investigating the effects of process parameters, namely, Peak current (Ip, Pulse on time (Ton, Pulse off time (Toff, Water pressure (Wp, Wire feed rate (Wf, Wire tension (Wt, Servo voltage (Sv and Servo feed setting (Sfs, on the Material Removal Rate (MRR and Surface Roughness (SR for Wire electrical discharge machining (Wire-EDM of nickel using Taguchi method. Response Surface Methodology (RSM is adopted to evolve mathematical relationships between the wire cutting process parameters and the output variables of the weld joint to determine the welding input parameters that lead to the desired optimal wire cutting quality. Besides, using response surface plots, the interaction effects of process parameters on the responses are analyzed and discussed. The statistical software Mini-tab is used to establish the design and to obtain the regression equations. The developed mathematical models are tested by analysis-of-variance (ANOVA method to check their appropriateness and suitability. Finally, a comparison is made between measured and calculated results, which are in good agreement. This indicates that the developed models can predict the responses accurately and precisely within the limits of cutting parameter being used.

  9. Characterization of electric discharge machining, subsequent etching and shot-peening as a surface treatment for orthopedic implants

    Energy Technology Data Exchange (ETDEWEB)

    Stráský, Josef, E-mail: josef.strasky@gmail.com [Charles University, Department of Physics of Materials (Czech Republic); Havlíková, Jana; Bačáková, Lucie [Institute of Physiology, Academy of Sciences of the Czech Republic (Czech Republic); Harcuba, Petr [Charles University, Department of Physics of Materials (Czech Republic); Mhaede, Mansour [Clausthal University of Technology, Institute of Materials Science and Engineering (Germany); Faculty of Engineering, Zagazig University (Egypt); Janeček, Miloš [Charles University, Department of Physics of Materials (Czech Republic)

    2013-09-15

    Presented work aims at multi-method characterization of combined surface treatment of Ti–6Al–4V alloy for biomedical use. Surface treatment consists of consequent use of electric discharge machining (EDM), acid etching and shot peening. Surface layers are analyzed employing scanning electron microscopy and energy dispersive X-ray spectroscopy. Acid etching by strong Kroll's reagent is capable of removing surface layer of transformed material created by EDM. Acid etching also creates partly nanostructured surface and significantly contributes to the enhanced proliferation of the bone cells. The cell growth could be positively affected by the superimposed bone-inspired structure of the surface with the morphological features in macro-, micro- and nano-range. Shot peening significantly improves poor fatigue performance after EDM. Final fatigue performance is comparable to benchmark electropolished material without any adverse surface effect. The proposed three-step surface treatment is a low-cost process capable of producing material that is applicable in orthopedics.

  10. Characterization of particle size distribution of mainstream cigarette smoke generated by smoking machine with an electrical low pressure impactor.

    Science.gov (United States)

    Li, Xiang; Kong, Haohui; Zhang, Xinying; Peng, Bin; Nie, Cong; Shen, Guanglin; Liu, Huimin

    2014-04-01

    Cigarette smoking is a particle-related exposure. Studying the characteristics of the particle size distribution of cigarette smoke can aid in providing knowledge of smoke aerosol attributes. We used an electrical low pressure impactor (ELPI) to measure the particle size distribution of mainstream cigarette smoke generated by a smoking machine and provided a continuum of particle sizes of cigarette smoke from a whole cigarette. The results showed that the aerodynamic diameters (D, geometric mean of a channel) of particles ranged from 0.021 to 1.956 μm, and the number concentrations were on the order of 10(5)-10(9) cm(-3) for different sizes of particles. The particle number of the size category below 0.1 μm approximated that of the category 0.1-2.0 μm, and the particles in the size category of 0.1-2.0 μm contributed extremely heavily to total particulate mass. In addition, the results with small samples indicated that the tar yields normalized per milligram of nicotine showed an approximately linear increase with increasing concentration of total particles. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

  11. Grupos electrógenos y calidad de la energía; Reciprocating Machines and Power Quality

    Directory of Open Access Journals (Sweden)

    Marielys Francisco Fernández

    2011-02-01

    Full Text Available Entre las tecnologías de mayor difusión que hoy día se utilizan dentro de la generación distribuida (GDestán los grupos electrógenos (GE. La presencia de los GE en cualquiera de sus formas de explotación,exige un análisis de los problemas que puedan presentarse por su presencia; uno de estos problemas estárelacionado con la calidad de la energía eléctrica (CEL. El presente trabajo expone los primeros resultadosde un estudio que va dirigido a buscar respuestas sobre este tema ante diferentes tipos de perturbacionesque pueden presentarse en la red: Cortocircuito y variación de la tensión en los terminales del GE y ladesconexión súbita de la carga (rechazo de carga. Reciprocating machine (RM is one of the technology more used on distributed generation (DG. Thepresence of RM not manner its operation form need an analysis about differents problems: One of them isrelated with power quality (PQ. First results obained inside one study directed to obtain answers aboutdifferents perturbations for the RM presence like shortcircuit and voltage variation on RM termianls andrejected charge is presented in this paper. 

  12. Torsional and Cyclic Fatigue Resistance of a New Nickel-Titanium Instrument Manufactured by Electrical Discharge Machining.

    Science.gov (United States)

    Pedullà, Eugenio; Lo Savio, Fabio; Boninelli, Simona; Plotino, Gianluca; Grande, Nicola M; La Rosa, Guido; Rapisarda, Ernesto

    2016-01-01

    The purpose of this study was to evaluate the torsional and cyclic fatigue resistance of the new Hyflex EDM OneFile (Coltene/Whaledent AG, Altstatten, Switzerland) manufactured by electrical discharge machining and compare the findings with the ones of Reciproc R25 (VDW, Munich, Germany) and WaveOne Primary (Dentsply Maillefer, Ballaigues, Switzerland). One hundred-twenty new Hyflex EDM OneFile (#25/0.08), Reciproc R25, and WaveOne Primary files were used. Torque and angle of rotation at failure of new instruments (n = 20) were measured according to ISO 3630-1 for each brand. Cyclic fatigue resistance was tested measuring the number of cycles to failure in an artificial stainless steel canal with a 60° angle and a 3-mm radius of curvature. Data were analyzed using the analysis of variance test and the Student-Newman-Keuls test for multiple comparisons. The fracture surface of each fragment was examined with a scanning electron microscope. The cyclic fatigue of Hyflex EDM was significantly higher than the one of Reciproc R25 and WaveOne Primary (P  .05). The new Hyflex EDM instruments (controlled memory wire) have higher cyclic fatigue resistance and angle of rotation to fracture but lower torque to failure than Reciproc R25 and WaveOne Primary files (M-wire for both files). Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  13. Pulsed DC Electric Field-Induced Differentiation of Cortical Neural Precursor Cells.

    Directory of Open Access Journals (Sweden)

    Hui-Fang Chang

    Full Text Available We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz. The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders.

  14. Pulsed DC Electric Field-Induced Differentiation of Cortical Neural Precursor Cells.

    Science.gov (United States)

    Chang, Hui-Fang; Lee, Ying-Shan; Tang, Tang K; Cheng, Ji-Yen

    2016-01-01

    We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC) pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs) could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz). The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders.

  15. Effect of hole geometry and Electric-Discharge Machining (EDM) on airflow rates through small diameter holes in turbine blade material

    Science.gov (United States)

    Hippensteele, S. A.; Cochran, R. P.

    1980-01-01

    The effects of two design parameters, electrode diameter and hole angle, and two machine parameters, electrode current and current-on time, on air flow rates through small-diameter (0.257 to 0.462 mm) electric-discharge-machined holes were measured. The holes were machined individually in rows of 14 each through 1.6 mm thick IN-100 strips. The data showed linear increase in air flow rate with increases in electrode cross sectional area and current-on time and little change with changes in hole angle and electrode current. The average flow-rate deviation (from the mean flow rate for a given row) decreased linearly with electrode diameter and increased with hole angle. Burn time and finished hole diameter were also measured.

  16. Electrical Discharge Machining of Al (6351-5% SiC-10% B4C Hybrid Composite: A Grey Relational Approach

    Directory of Open Access Journals (Sweden)

    S. Suresh Kumar

    2014-01-01

    Full Text Available The goal of the present experimental work is to optimize the electrical discharge machining (EDM parameters of aluminum alloy (Al 6351 matrix reinforced with 5 wt.% silicon carbide (SiC and 10 wt.% boron carbide (B4C particles fabricated through the stir casting route. Multiresponse optimization was carried out through grey relational analysis (GRA with an objective to minimize the machining characteristics, namely electrode wear ratio (EWR, surface roughness (SR and power consumption (PC. The optimal combination of input parameters is identified, which shows the significant enhancement in process characteristics. Contributions of each machining parameter to the responses are calculated using analysis of variance (ANOVA. The result shows that the pulse current contributes more (83.94% to affecting the combined output responses.

  17. Analysis of machining and machine tools

    CERN Document Server

    Liang, Steven Y

    2016-01-01

    This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

  18. Single Stator Dual PM Rotor Synchronous Machine with two-frequency single-inverter control, for the propulsion of hybrid electric vehicles

    Directory of Open Access Journals (Sweden)

    Topor Marcel

    2017-01-01

    Full Text Available This paper introduces a novel brushless, single winding and single stator, dual PM rotor axial-air-gap machine capable to deliver independently torque at the two rotors by adequate dual vector control. The proposed topologies, the circuit model, controlled dynamics simulation and preliminary 3D FEM torque production on a case study constitute the core of the paper. The proposed dual mechanical port system should be instrumental in parallel (with planetary gears or series hybrid electric vehicles (HEV aiming at a more compact and efficient electric propulsion system solution.

  19. A multichannel integrated circuit for electrical recording of neural activity, with independent channel programmability.

    Science.gov (United States)

    Mora Lopez, Carolina; Prodanov, Dimiter; Braeken, Dries; Gligorijevic, Ivan; Eberle, Wolfgang; Bartic, Carmen; Puers, Robert; Gielen, Georges

    2012-04-01

    Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1-6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 nV/√Hz, a NEF of 5.1, a CMRR > 60 dB, a THD noise ratios.

  20. Vertical electric field stimulated neural cell functionality on porous amorphous carbon electrodes.

    Science.gov (United States)

    Jain, Shilpee; Sharma, Ashutosh; Basu, Bikramjit

    2013-12-01

    We demonstrate the efficacy of amorphous macroporous carbon substrates as electrodes to support neuronal cell proliferation and differentiation in electric field mediated culture conditions. The electric field was applied perpendicular to carbon substrate electrode, while growing mouse neuroblastoma (N2a) cells in vitro. The placement of the second electrode outside of the cell culture medium allows the investigation of cell response to electric field without the concurrent complexities of submerged electrodes such as potentially toxic electrode reactions, electro-kinetic flows and charge transfer (electrical current) in the cell medium. The macroporous carbon electrodes are uniquely characterized by a higher specific charge storage capacity (0.2 mC/cm(2)) and low impedance (3.3 kΩ at 1 kHz). The optimal window of electric field stimulation for better cell viability and neurite outgrowth is established. When a uniform or a gradient electric field was applied perpendicular to the amorphous carbon substrate, it was found that the N2a cell viability and neurite length were higher at low electric field strengths (≤ 2.5 V/cm) compared to that measured without an applied field (0 V/cm). While the cell viability was assessed by two complementary biochemical assays (MTT and LDH), the differentiation was studied by indirect immunostaining. Overall, the results of the present study unambiguously establish the uniform/gradient vertical electric field based culture protocol to either enhance or to restrict neurite outgrowth respectively at lower or higher field strengths, when neuroblastoma cells are cultured on porous glassy carbon electrodes having a desired combination of electrochemical properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Tissue heterogeneity as a mechanism for localized neural stimulation by applied electric fields

    Energy Technology Data Exchange (ETDEWEB)

    Miranda, P C [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Correia, L [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Salvador, R [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Basser, P J [Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD 20892-1428 (United States)

    2007-09-21

    We investigate the heterogeneity of electrical conductivity as a new mechanism to stimulate excitable tissues via applied electric fields. In particular, we show that stimulation of axons crossing internal boundaries can occur at boundaries where the electric conductivity of the volume conductor changes abruptly. The effectiveness of this and other stimulation mechanisms was compared by means of models and computer simulations in the context of transcranial magnetic stimulation. While, for a given stimulation intensity, the largest membrane depolarization occurred where an axon terminates or bends sharply in a high electric field region, a slightly smaller membrane depolarization, still sufficient to generate action potentials, also occurred at an internal boundary where the conductivity jumped from 0.143 S m{sup -1} to 0.333 S m{sup -1}, simulating a white-matter-grey-matter interface. Tissue heterogeneity can also give rise to local electric field gradients that are considerably stronger and more focal than those impressed by the stimulation coil and that can affect the membrane potential, albeit to a lesser extent than the two mechanisms mentioned above. Tissue heterogeneity may play an important role in electric and magnetic 'far-field' stimulation.

  2. Targeted therapies using electrical and magnetic neural stimulation for the treatment of chronic pain in spinal cord injury.

    Science.gov (United States)

    Moreno-Duarte, Ingrid; Morse, Leslie R; Alam, Mahtab; Bikson, Marom; Zafonte, Ross; Fregni, Felipe

    2014-01-15

    Chronic neuropathic pain is one of the most common and disabling symptoms in individuals with spinal cord injury (SCI). Over two-thirds of subjects with SCI suffer from chronic pain influencing quality of life, rehabilitation, and recovery. Given the refractoriness of chronic pain to most pharmacological treatments, the majority of individuals with SCI report worsening of this condition over time. Moreover, only 4-6% of patients in this cohort report improvement. Novel treatments targeting mechanisms associated with pain-maladaptive plasticity, such as electromagnetic neural stimulation, may be desirable to improve outcomes. To date, few, small clinical trials have assessed the effects of invasive and noninvasive nervous system stimulation on pain after SCI. We aimed to review initial efficacy, safety and potential predictors of response by assessing the effects of neural stimulation techniques to treat SCI pain. A literature search was performed using the PubMed database including studies using the following targeted stimulation strategies: transcranial Direct Current Stimulation (tDCS), High Definition tDCS (HD-tDCS), repetitive Transcranial Magnetical Stimulation (rTMS), Cranial Electrotherapy Stimulation (CES), Transcutaneous Electrical Nerve Stimulation (TENS), Spinal Cord Stimulation (SCS) and Motor Cortex Stimulation (MCS), published prior to June of 2012. We included studies from 1998 to 2012. Eight clinical trials and one naturalistic observational study (nine studies in total) met the inclusion criteria. Among the clinical trials, three studies assessed the effects of tDCS, two of CES, two of rTMS and one of TENS. The naturalistic study investigated the analgesic effects of SCS. No clinical trials for epidural motor cortex stimulation (MCS) or HD-tDCS were found. Parameters of stimulation and also clinical characteristics varied significantly across studies. Three out of eight studies showed larger effects sizes (0.73, 0.88 and 1.86 respectively) for pain

  3. SENSITIVITY ANALYSIS BY ARTIFICIAL NEURAL NETWORK (ANN OF VARIABLES THAT INFLUENCE THE DIAGONAL TWIST IN A PAPERBOARD INDUSTRIAL MACHINE

    Directory of Open Access Journals (Sweden)

    Guinter Neutzling Schneid

    2016-01-01

    Full Text Available The dimensional stability of the paper may change due to middle exchange moisture, releasing the latent stress acquired into the manufacturing process. One result of this tension release is the diagonal curl. This study aims to conduct a sensitivity analysis of the different input’s variables of an industrial paper machine, along with some laboratory measurements, in order to identify the importance in production of paperboard quality control and relate to the property of the paper called twist. A survey was made of the production history, relating to 2012, to observe the products with the highest quality losses. From this, they were correlated with the critical points of measurement profile in the machine cross direction and consequently with the paper. It was found some changes once the variables correlated with twist, referring to the three analyzes of the profile (tender side, middle and drive side. It was revealed, from the sensitivity analysis, that the most important and sensitive variables, respectively for the tender side, middle and drive side, were total flow from the top layer, vapor pressure in the 6th group of drying cylinders and mass flow side of the bottom layer of the formation of paperboard.

  4. Time disparity sensitive behavior and its neural substrates of a pulse-type gymnotiform electric fish, Brachyhypopomus gauderio.

    Science.gov (United States)

    Matsushita, Atsuko; Pyon, Grace; Kawasaki, Masashi

    2013-07-01

    Roles of the time coding electrosensory system in the novelty responses of a pulse-type gymnotiform electric fish, Brachyhypopomus, were examined behaviorally, physiologically, and anatomically. Brachyhypopomus responded with the novelty responses to small changes (100 μs) in time difference between electrosensory stimulus pulses applied to different parts of the body, as long as these pulses were given within a time period of ~500 μs. Physiological recording revealed neurons in the hindbrain and midbrain that fire action potentials time-locked to stimulus pulses with short latency (500-900 μs). These time-locked neurons, along with other types of neurons, were labeled with intracellular and extracellular marker injection techniques. Light and electron microscopy of the labeled materials revealed neural connectivity within the time coding system. Two types of time-locked neurons, the pear-shaped cells and the large cells converge onto the small cells in a hypertrophied structure, the mesencephalic magnocellular nucleus. The small cells receive a calyx synapse from a large cell at their somata and an input from a pear-shaped cell at the tip of their dendrites via synaptic islands. The small cells project to the torus semicircularis. We hypothesized that the time-locked neural signals conveyed by the pear-shaped cells and the large cells are decoded by the small cells for detection of time shifts occurring across body areas.

  5. Multi nodal load forecasting in electric power systems using a radial basis neural network; Previsao de carga multinodal em sistemas eletricos de potencia usando uma rede neural de base radial

    Energy Technology Data Exchange (ETDEWEB)

    Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br

    2009-07-01

    This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)

  6. Introduction to AC machine design

    CERN Document Server

    Lipo, Thomas A

    2018-01-01

    AC electrical machine design is a key skill set for developing competitive electric motors and generators for applications in industry, aerospace, and defense. This book presents a thorough treatment of AC machine design, starting from basic electromagnetic principles and continuing through the various design aspects of an induction machine. Introduction to AC Machine Design includes one chapter each on the design of permanent magnet machines, synchronous machines, and thermal design. It also offers a basic treatment of the use of finite elements to compute the magnetic field within a machine without interfering with the initial comprehension of the core subject matter. Based on the author's notes, as well as after years of classroom instruction, Introduction to AC Machine Design: * Brings to light more advanced principles of machine design--not just the basic principles of AC and DC machine behavior * Introduces electrical machine design to neophytes while also being a resource for experienced designers * ...

  7. Recycling rotating electrical machines

    Directory of Open Access Journals (Sweden)

    Rafael Hernández-Millán

    2017-01-01

    Full Text Available Este trabajo establece los principios de diseño para el reciclaje de máquinas eléctricas rotativas (sincrónicas y de inducción, en otras palabras, las máquinas eléctricas y sus componentes pueden ser reutilizados. Además, se cubren temas tecnológicos surgidos de las siguientes componentes de la máquina: núcleo del estator y rotor, devanados del estator y rotor, cojinetes, ejes, y carcasas. Los principios de diseño discutidos pueden extenderse a los transformadores. Este trabajo no consideró materiales de aislamiento en devanados de alta tensión. La economía de reciclaje no se discute ni consecuencias ambientales. Las máquinas rotativas consideradas en el presente estudio son de un rango de potencia entre 0,1 kW a 400 kW, frecuencias de 50 hertz y 60 hertz y polos 2, 4 y 6, aunque los conceptos generales podrían aplicarse a otras máquinas. Se discuten las normas de máquina necesarios para lograr estos objetivos, que abarca: velocidad, tensión nominal, capacidades, formas, dimensiones, de aislamiento, de los devanados, cojinetes, ejes y carcasas.

  8. Performance assessment of electric power generations using an adaptive neural network algorithm and fuzzy DEA

    Energy Technology Data Exchange (ETDEWEB)

    Javaheri, Zahra

    2010-09-15

    Modeling, evaluating and analyzing performance of Iranian thermal power plants is the main goal of this study which is based on multi variant methods analysis. These methods include fuzzy DEA and adaptive neural network algorithm. At first, we determine indicators, then data is collected, next we obtained values of ranking and efficiency by Fuzzy DEA, Case study is thermal power plants In view of the fact that investment to establish on power plant is very high, and maintenance of power plant causes an expensive expenditure, moreover using fossil fuel effected environment hence optimum produce of current power plants is important.

  9. Normative findings of electrically evoked compound action potential measurements using the neural response telemetry of the Nucleus CI24M cochlear implant system.

    NARCIS (Netherlands)

    Cafarelli-Dees, D.; Dillier, N.; Lai, W.K.; Wallenberg, E. von; Dijk, B. van; Akdas, F.; Aksit, M.; Batman, C.; Beynon, A.J.; Burdo, S.; Chanal, J.M.; Collet, L.; Conway, M.; Coudert, C.; Craddock, L.; Cullington, H.; Deggouj, N.; Fraysse, B.; Grabel, S.; Kiefer, J.; Kiss, J.G.; Lenarz, T.; Mair, A.; Maune, S.; Muller-Deile, J.; Piron, J.P.; Razza, S.; Tasche, C.; Thai-Van, H.; Toth, F.; Truy, E.; Uziel, A.; Smoorenburg, G.F.

    2005-01-01

    One hundred and forty-seven adult recipients of the Nucleus 24 cochlear implant system, from 13 different European countries, were tested using neural response telemetry to measure the electrically evoked compound action potential (ECAP), according to a standardised postoperative measurement

  10. Electronic dura mater for long-term multimodal neural interfaces

    Science.gov (United States)

    Minev, Ivan R.; Musienko, Pavel; Hirsch, Arthur; Barraud, Quentin; Wenger, Nikolaus; Moraud, Eduardo Martin; Gandar, Jérôme; Capogrosso, Marco; Milekovic, Tomislav; Asboth, Léonie; Torres, Rafael Fajardo; Vachicouras, Nicolas; Liu, Qihan; Pavlova, Natalia; Duis, Simone; Larmagnac, Alexandre; Vörös, Janos; Micera, Silvestro; Suo, Zhigang; Courtine, Grégoire; Lacour, Stéphanie P.

    2015-01-01

    The mechanical mismatch between soft neural tissues and stiff neural implants hinders the long-term performance of implantable neuroprostheses. Here, we designed and fabricated soft neural implants with the shape and elasticity of dura mater, the protective membrane of the brain and spinal cord. The electronic dura mater, which we call e-dura, embeds interconnects, electrodes, and chemotrodes that sustain millions of mechanical stretch cycles, electrical stimulation pulses, and chemical injections. These integrated modalities enable multiple neuroprosthetic applications. The soft implants extracted cortical states in freely behaving animals for brain-machine interface and delivered electrochemical spinal neuromodulation that restored locomotion after paralyzing spinal cord injury.

  11. Classification of electrical discharges in DC Accelerators

    Energy Technology Data Exchange (ETDEWEB)

    Banerjee, Srutarshi, E-mail: sruban.stephens@gmail.com [Accelerator and Pulse Power Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400085 (India); Deb, A.K. [Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India); Rajan, Rehim N. [Accelerator and Pulse Power Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400085 (India); Kishore, N.K. [Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India)

    2016-08-11

    Controlled electrical discharge aids in conditioning of the system while uncontrolled discharges damage its electronic components. DC Accelerator being a high voltage system is no exception. It is useful to classify electrical discharges according to the severity. Experimental prototypes of the accelerator discharges are developed. Photomultiplier Tubes (PMTs) are used to detect the signals from these discharges. Time and Frequency domain characteristics of the detected discharges are used to extract features. Machine Learning approaches like Fuzzy Logic, Neural Network and Least Squares Support Vector Machine (LSSVM) are employed to classify the discharges. This aids in detecting the severity of the discharges.

  12. Classification of electrical discharges in DC Accelerators

    Science.gov (United States)

    Banerjee, Srutarshi; Deb, A. K.; Rajan, Rehim N.; Kishore, N. K.

    2016-08-01

    Controlled electrical discharge aids in conditioning of the system while uncontrolled discharges damage its electronic components. DC Accelerator being a high voltage system is no exception. It is useful to classify electrical discharges according to the severity. Experimental prototypes of the accelerator discharges are developed. Photomultiplier Tubes (PMTs) are used to detect the signals from these discharges. Time and Frequency domain characteristics of the detected discharges are used to extract features. Machine Learning approaches like Fuzzy Logic, Neural Network and Least Squares Support Vector Machine (LSSVM) are employed to classify the discharges. This aids in detecting the severity of the discharges.

  13. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

    In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

  14. Application of Neural Network Technologies for Price Forecasting in the Liberalized Electricity Market

    Science.gov (United States)

    Gerikh, Valentin; Kolosok, Irina; Kurbatsky, Victor; Tomin, Nikita

    2009-01-01

    The paper presents the results of experimental studies concerning calculation of electricity prices in different price zones in Russia and Europe. The calculations are based on the intelligent software "ANAPRO" that implements the approaches based on the modern methods of data analysis and artificial intelligence technologies.

  15. Neural correlates of heterotopic facilitation induced after high frequency electrical stimulation of nociceptive pathways

    NARCIS (Netherlands)

    Broeke, E.N. van den; Heck, C.H. van; Rijn, C.M. van; Wilder-Smith, O.H.G.

    2011-01-01

    Background High frequency electrical stimulation (HFS) of primary nociceptive afferents in humans induce a heightened sensitivity in the surrounding non-stimulated skin area. Several studies suggest that this heterotopic effect is the result of central (spinal) plasticity. The aim of this study is

  16. Neural correlates of heterotopic facilitation induced after high frequency electrical stimulation of nociceptive pathways

    NARCIS (Netherlands)

    Broeke, E.N. van den; Heck, C.H. van; Rijn, C.M. van; Wilder-Smith, O.H.G.

    2011-01-01

    BACKGROUND: High frequency electrical stimulation (HFS) of primary nociceptive afferents in humans induce a heightened sensitivity in the surrounding non-stimulated skin area. Several studies suggest that this heterotopic effect is the result of central (spinal) plasticity. The aim of this study is

  17. Field weakening capability investigation of an axial flux permanent-magnet synchronous machine with radially sliding permanent magnets used for electric vehicles

    Science.gov (United States)

    Zhao, Jing; Cheng, Dansong; Zheng, Ping; Liu, Xiangdong; Tong, Chengde; Song, Zhiyi; Zhang, Lu

    2012-04-01

    Due to the advantage of high power density compared with the conventional radial flux machines, the axial flux permanent-magnet synchronous machines (PMSMs) are very suitable candidates for the power train of electric vehicles (EVs). In this paper, a new axial flux PMSM adopting radially sliding permanent magnets (PMs) to fulfill field-weakening control and to improve the operating speed range is investigated. The field-weakening structure and principle of the axial flux PMSM with radially sliding PMs are proposed and analyzed. The influence of radially sliding PMs on electromagnetic performances and parameters is analyzed based on FEM. The field-weakening method with radially sliding PMs, which is a mechanical method, is compared and combined with traditional electrical method. Due to the optimized combination of the two methods, the field-weakening capability of the machine is much improved and the maximum speed can reach up to six times of the base speed with constant power, which is very satisfying for EV drive application.

  18. HTS machine laboratory prototype

    DEFF Research Database (Denmark)

    High Temperature Superconducting (HTS) electrical machines have the potential to offer outstanding technical performance with regards to efficiency and power density. However, the industry needs to address a large number of challenges in the attempt to harvest the full potential of HTS machines...... machine. The machine comprises six stationary HTS field windings wound from both YBCO and BiSCOO tape operated at liquid nitrogen temperature and enclosed in a cryostat, and a three phase armature winding spinning at up to 300 rpm. This design has full functionality of HTS synchronous machines. The design...... details and experimental results are shown together with discussions about their implication for scaled up HTS machines....

  19. Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

    Directory of Open Access Journals (Sweden)

    Yi Liang

    2016-11-01

    Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.

  20. Contributions to muscle force and EMG by combined neural excitation and electrical stimulation

    Science.gov (United States)

    Crago, Patrick E.; Makowski, Nathaniel S.; Cole, Natalie M.

    2014-10-01

    Objective. Stimulation of muscle for research or clinical interventions is often superimposed on ongoing physiological activity without a quantitative understanding of the impact of the stimulation on the net muscle activity and the physiological response. Experimental studies show that total force during stimulation is less than the sum of the isolated voluntary and stimulated forces, but the occlusion mechanism is not understood. Approach. We develop a model of efferent motor activity elicited by superimposing stimulation during a physiologically activated contraction. The model combines action potential interactions due to collision block, source resetting, and refractory periods with previously published models of physiological motor unit recruitment, rate modulation, force production, and EMG generation in human first dorsal interosseous muscle to investigate the mechanisms and effectiveness of stimulation on the net muscle force and EMG. Main results. Stimulation during a physiological contraction demonstrates partial occlusion of force and the neural component of the EMG, due to action potential interactions in motor units activated by both sources. Depending on neural and stimulation firing rates as well as on force-frequency properties, individual motor unit forces can be greater, smaller, or unchanged by the stimulation. In contrast, voluntary motor unit EMG potentials in simultaneously stimulated motor units show progressive occlusion with increasing stimulus rate. The simulations predict that occlusion would be decreased by a reverse stimulation recruitment order. Significance. The results are consistent with and provide a mechanistic interpretation of previously published experimental evidence of force occlusion. The models also predict two effects that have not been reported previously—voluntary EMG occlusion and the advantages of a proximal stimulation site. This study provides a basis for the rational design of both future experiments and clinical