WorldWideScience

Sample records for neural electric machine

  1. 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.

  2. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    Directory of Open Access Journals (Sweden)

    Huixia Zhao

    Full Text Available The insect-machine interface (IMI is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L. via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe, ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

  3. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    Science.gov (United States)

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect-machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

  4. Neuromechanism Study of Insect–Machine Interface: Flight Control by Neural Electrical Stimulation

    Science.gov (United States)

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A.; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect–machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee–machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control. PMID:25409523

  5. 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.

  6. 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.

  7. Electricity of machine tool

    International Nuclear Information System (INIS)

    Gijeon media editorial department

    1977-10-01

    This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

  8. 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...

  9. 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

  10. 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

  11. 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

  12. The Bearingless Electrical Machine

    Science.gov (United States)

    Bichsel, J.

    1992-01-01

    Electromagnetic bearings allow the suspension of solids. For rotary applications, the most important physical effect is the force of a magnetic circuit to a high permeable armature, called the MAXWELL force. Contrary to the commonly used MAXWELL bearings, the bearingless electrical machine will take advantage of the reaction force of a conductor carrying a current in a magnetic field. This kind of force, called Lorentz force, generates the torque in direct current, asynchronous and synchronous machines. The magnetic field, which already exists in electrical machines and helps to build up the torque, can also be used for the suspension of the rotor. Besides the normal winding of the stator, a special winding was added, which generates forces for levitation. So a radial bearing, which is integrated directly in the active part of the machine, and the motor use the laminated core simultaneously. The winding was constructed for the levitating forces in a special way so that commercially available standard ac inverters for drives can be used. Besides wholly magnetic suspended machines, there is a wide range of applications for normal drives with ball bearings. Resonances of the rotor, especially critical speeds, can be damped actively.

  13. 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

  14. 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

  15. 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

  16. 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.

  17. Permutation parity machines for neural synchronization

    International Nuclear Information System (INIS)

    Reyes, O M; Kopitzke, I; Zimmermann, K-H

    2009-01-01

    Synchronization of neural networks has been studied in recent years as an alternative to cryptographic applications such as the realization of symmetric key exchange protocols. This paper presents a first view of the so-called permutation parity machine, an artificial neural network proposed as a binary variant of the tree parity machine. The dynamics of the synchronization process by mutual learning between permutation parity machines is analytically studied and the results are compared with those of tree parity machines. It will turn out that for neural synchronization, permutation parity machines form a viable alternative to tree parity machines

  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. Permutation parity machines for neural cryptography

    International Nuclear Information System (INIS)

    Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

    2010-01-01

    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.

  20. 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 (ENTM...

  1. Axial gap rotating electrical machine

    Science.gov (United States)

    None

    2016-02-23

    Direct drive rotating electrical machines with axial air gaps are disclosed. In these machines, a rotor ring and stator ring define an axial air gap between them. Sets of gap-maintaining rolling supports bear between the rotor ring and the stator ring at their peripheries to maintain the axial air gap. Also disclosed are wind turbines using these generators, and structures and methods for mounting direct drive rotating electrical generators to the hubs of wind turbines. In particular, the rotor ring of the generator may be carried directly by the hub of a wind turbine to rotate relative to a shaft without being mounted directly to the shaft.

  2. Compound induction electric rotating machine

    Energy Technology Data Exchange (ETDEWEB)

    Decesare, D

    1987-07-28

    The present invention generally relates to dynamo-electric machines cabable of operating in a generator mode or in a motor mode and more specifically, to increased efficiency compound interaction AC and/or DC dynamo-electric machines. This patent describes such a machine having a distributed armature winding in a cylindrical rotor wound to form axial and substantially radial winding portions and including permanent and/or electromagnets to couple magnetic flux into the peripheral or circumferential surface of the rotor, and to provide interaction between a magnetic field formed beyond the rotor axial surfaces and the rotor to thereby enhance the total induction of flux into the rotor for improved, more efficient operation. 28 figs.,

  3. 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

  4. 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.

  5. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  6. Power Electronics and Electric Machines Facilities | Transportation

    Science.gov (United States)

    Research | NREL Facilities Power Electronics and Electric Machines Facilities NREL's power electronics and electric machines thermal management experimentation facilities feature a wide range of four researchers in discussion around a piece of laboratory equipment. Power electronics researchers

  7. Power Electronics and Electric Machines Publications | Transportation

    Science.gov (United States)

    Research | NREL and Electric Machines Publications Power Electronics and Electric Machines Publications NREL and its partners have produced many papers and presentations related to power electronics and from power electronics and electric machines research are available to the public. Photo by Pat Corkery

  8. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  9. Rotating electrical machines: Poynting flow

    International Nuclear Information System (INIS)

    Donaghy-Spargo, C

    2017-01-01

    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. (paper)

  10. Integrated Inverter For Driving Multiple Electric Machines

    Science.gov (United States)

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

    2006-04-04

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

  11. Structured Memory for Neural Turing Machines

    OpenAIRE

    Zhang, Wei; Yu, Yang; Zhou, Bowen

    2015-01-01

    Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this...

  12. Electrical machining method of insulating ceramics

    International Nuclear Information System (INIS)

    Fukuzawa, Y.; Mohri, N.; Tani, T.

    1999-01-01

    This paper describes a new electrical discharge machining method for insulating ceramics using an assisting electrode with either a sinking electrical discharge machine or a wire electrical discharge machine. In this method, the metal sheet or mesh is attached to the ceramic surface as an assisting material for the discharge generation around the insulator surface. When the machining condition changes from the attached material to the workpiece, a cracked carbon layer is formed on the workpiece surface. As this layer has an electrical conductivity, electrical discharge occurs in working oil between the tool electrode and the surface of the workpiece. The carbon is formed from the working oil during this electrical discharge. Even after the material is machined, an electrical discharge occurs in the gap region between the tool electrode and the ceramic because an electrically conductive layer is generated continuously. Insulating ceramics can be machined by the electrical discharge machining method using the above mentioned surface modification phenomenon. In this paper the authors show a machined example demonstrating that the proposed method is available for machining a complex shape on insulating ceramics. Copyright (1999) AD-TECH - International Foundation for the Advancement of Technology Ltd

  13. Superconducting magnetic systems and electrical machines

    International Nuclear Information System (INIS)

    Glebov, I.A.

    1975-01-01

    The use of superconductors for magnets and electrical machines attracts close attention of designers and scientists. A description is given of an ongoing research program to create superconductive magnetic systems, commutator motors, homopolar machines, topological generators and turbogenerators with superconductive field windings. All the machines are tentative experimental models and serve as a basis for further developments

  14. Electrical Discharge Machining (EDM: A Review

    Directory of Open Access Journals (Sweden)

    Asfana Banu

    2016-09-01

    Full Text Available Electro discharge machining (EDM process is a non-conventional and non-contact machining operation which is used in industry for high precision products. EDM is known for machining hard and brittle conductivematerials since it can melt any electrically conductive material regardless of its hardness. The workpiece machined by EDM depends on thermal conductivity, electrical resistivity, and melting points of the materials. The tool and the workpiece are adequately both immersed in a dielectric medium, such as, kerosene, deionised water or any other suitable fluid. This paper is reviewed comprehensively on types of EDM operation. A brief discussion is also done on the machining responses and mathematical modelling.

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

    OpenAIRE

    Ming Cheng; Le Sun; Giuseppe Buja; Lihua Song

    2015-01-01

    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 perm...

  16. Efficiency trends in electric machines and drives

    International Nuclear Information System (INIS)

    Mecrow, B.C.; Jack, A.G.

    2008-01-01

    Almost all electricity in the UK is generated by rotating electrical generators, and approximately half of it is used to drive electrical motors. This means that efficiency improvements to electrical machines can have a very large impact on energy consumption. The key challenges to increased efficiency in systems driven by electrical machines lie in three areas: to extend the application of variable-speed electric drives into new areas through reduction of power electronic and control costs; to integrate the drive and the driven load to maximise system efficiency; and to increase the efficiency of the electrical drive itself. In the short to medium term, efficiency gains within electrical machines will result from the development of new materials and construction techniques. Approximately a quarter of new electrical machines are driven by variable-speed drives. These are a less mature product than electrical machines and should see larger efficiency gains over the next 50 years. Advances will occur, with new types of power electronic devices that reduce switching and conduction loss. With variable-speed drives, there is complete freedom to vary the speed of the driven load. Replacing fixed-speed machines with variable-speed drives for a high proportion of industrial loads could mean a 15-30% energy saving. This could save the UK 15 billion kWh of electricity per year which, when combined with motor and drive efficiency gains, would amount to a total annual saving of 24 billion kWh

  17. 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.

  18. Machinability of nickel based alloys using electrical discharge machining process

    Science.gov (United States)

    Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

    2018-04-01

    The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

  19. Machine integrated gearbox for electrical vehicles

    NARCIS (Netherlands)

    Gerrits, T.; Wijnands, C.G.E.; Paulides, J.J.H.; Duarte, J.L.; Lomonova, E.A.

    2014-01-01

    The speed range that can be obtained with an electric machine is initially limited due to the finite supply voltage of the inverter. A winding reconfiguration method named dynamic machine operation is presented, capable of increasing the speed range and speed ratio by altering the configuration of

  20. Deep neural mapping support vector machines.

    Science.gov (United States)

    Li, Yujian; Zhang, Ting

    2017-09-01

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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. Fundamental Study on Electrical Discharge Machining

    OpenAIRE

    Uno, Yoshiyuki; Nakajima, Toshikatsu; Endo, Osamu

    1989-01-01

    The generation mechanism of crater in electrical discharge machining is analyzed with a single pulse discharge device for alloy tool steel, black alumina ceramics, cermet and cemented carbide, investigating the gap voltage, the discharge current, the shape of crater, the wear of electrode and so on. The experimental analysis makes it clear that the shape of crater has a characteristic feature for the kind of workpiece. The shape of electrode, which changes with the wear by an electric spark, ...

  9. Development of electric machines with superconducting windings

    International Nuclear Information System (INIS)

    Glebov, I.A.; Novitskij, V.G.

    1977-01-01

    Some studies are discussed performed in the USSR with the aim to develop the most promising electrical machines with superconducting windings, i.e. powerful (more than 1 MW) cryoturbogenerators for power heat and nuclear plants, electric motors of more than 10,000 kW, reverse systems of an electric driver and unipolar generators for electrolysis industry. The design and performances of the simulator of a 1500 kW cryoturbogenerator are given. Problems of coooling and oscillations of the simulator rotor are considered

  10. 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

  11. 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

  12. Electrical discharge machining for vessel sample removal

    International Nuclear Information System (INIS)

    Litka, T.J.

    1993-01-01

    Due to aging-related problems or essential metallurgy information (plant-life extension or decommissioning) of nuclear plants, sample removal from vessels may be required as part of an examination. Vessel or cladding samples with cracks may be removed to determine the cause of cracking. Vessel weld samples may be removed to determine the weld metallurgy. In all cases, an engineering analysis must be done prior to sample removal to determine the vessel's integrity upon sample removal. Electrical discharge machining (EDM) is being used for in-vessel nuclear power plant vessel sampling. Machining operations in reactor coolant system (RCS) components must be accomplished while collecting machining chips that could cause damage if they become part of the flow stream. The debris from EDM is a fine talclike particulate (no chips), which can be collected by flushing and filtration

  13. 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

    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....

  14. 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 ...

  15. 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

  16. 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.

  17. 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.

  18. 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, ...

  19. What does Attention in Neural Machine Translation Pay Attention to?

    NARCIS (Netherlands)

    Ghader, H.; Monz, C.; Kondrak, G.; Watanabe, T.

    2017-01-01

    Attention in neural machine translation provides the possibility to encode relevant parts of the source sentence at each translation step. As a result, attention is considered to be an alignment model as well. However, there is no work that specifically studies attention and provides analysis of

  20. 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

  1. Rotating electric machine with fluid supported parts

    Science.gov (United States)

    Smith, Jr., Joseph L.; Kirtley, Jr., James L.

    1981-01-01

    A rotating electric machine in which the armature winding thereof and other parts are supported by a liquid to withstand the mechanical stresses applied during transient overloads and the like. In particular, a narrow gap is provided between the armature winding and the stator which supports it and this gap is filled with an externally pressurized viscous liquid. The liquid is externally pressurized sufficiently to balance the static loads on the armature winding. Transient mechanical loads which deform the armature winding alter the gap dimensions and thereby additionally pressurize the viscous liquid to oppose the armature winding deformation and more nearly uniformly to distribute the resulting mechanical stresses.

  2. Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines

    International Nuclear Information System (INIS)

    Frauenhofer, Joachim; Arndt, Tabea; Grundmann, Joern

    2013-01-01

    The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO 2 emissions.

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

    DEFF Research Database (Denmark)

    Matzen, Torben Nørregaard; Rasmussen, Peter Omand

    2009-01-01

    Electrical machines for traction in electric vehicles are an essential component which attract attention with respect to machine design and control as a part of the emerging renewable industry. For the hybrid electric machine to replace the familiar behaviour of the combustion engine torque......, 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...

  4. 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.

  5. Influence of Electric Discharges on Bearings of Electric Machines

    Directory of Open Access Journals (Sweden)

    Karel Chmelik

    2006-01-01

    Full Text Available I the last time many articles were found out discussed about shaft voltage, bearing currents and their influence on lifetime and reliability of electric machines bearings. This is associated with extension of use of static converters for control drives for DC motors feeding in the past and for induction motors feeding from frequency converters in the last time. It is known from our own experiences that not all failures assigned to bearing currents were their real reason and we also know how hardly the mentioned currents can be measured on real machines and how work-intensive and expensive is to detect real reason of the failure on damaged bearing. We will not concern with basics of classical bearing currents in this paper, because they were known and studied in the beginning of the last century but our own investigations will be presented.

  6. 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.

  7. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

    2017-07-01

    To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

  8. 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

  9. 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.

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

    African Journals Online (AJOL)

    Electrical-thermal coupling of induction machine for improved thermal performance. ... Nigerian Journal of Technology ... The interaction of its electrical and mechanical parts leads to an increase in temperature which if not properly monitored ...

  11. 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.

  12. 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.

  13. Machine vision inspection of lace using a neural network

    Science.gov (United States)

    Sanby, Christopher; Norton-Wayne, Leonard

    1995-03-01

    Lace is particularly difficult to inspect using machine vision since it comprises a fine and complex pattern of threads which must be verified, on line and in real time. Small distortions in the pattern are unavoidable. This paper describes instrumentation for inspecting lace actually on the knitting machine. A CCD linescan camera synchronized to machine motions grabs an image of the lace. Differences between this lace image and a perfect prototype image are detected by comparison methods, thresholding techniques, and finally, a neural network (to distinguish real defects from false alarms). Though produced originally in a laboratory on SUN Sparc work-stations, the processing has subsequently been implemented on a 50 Mhz 486 PC-look-alike. Successful operation has been demonstrated in a factory, but over a restricted width. Full width coverage awaits provision of faster processing.

  14. Power Electronics and Electric Machines | Transportation Research | NREL

    Science.gov (United States)

    Power Electronics and Electric Machines NREL's power electronics and electric machines research helping boost the performance of power electronics components and systems, while driving down size, weight technical barriers to EDV commercialization. EDVs rely heavily on power electronics to distribute the proper

  15. The development on electric discharge machine for hot cell usage

    International Nuclear Information System (INIS)

    Ahn, Sang Bok; Kim, Young Suk; Park, Dae Kyu; Choo, Yong Sun; Oh, Wan Ho

    1998-06-01

    The electric discharge machine(EDM) was developed for hot cell usages in IMEF. This machine will be used to fabricate specimen directly from irradiated components from NPP's. The detailed contents are as follows; 1. Outline of electric discharge machine 2. Specimen shape to be fabricated by EDM 3. Technical specification to manufacture EDM 4. Installation EDM in hot cell 5. Optimum discharge conditions to fabricate specimens from CANDU tube. (author). 4 tabs., 20 figs

  16. Sixth international conference on electrical machines and drives

    International Nuclear Information System (INIS)

    1993-01-01

    This volume contains 111 papers presented at the Sixth International Conference on Electrical Machines and Drives. The topics covered include: miniature and micro motors; induction motors; DC machines; reluctance motors; condition monitoring; synchronous machines and drives; induction machines; induction generators; simulation; design; and operating experience; linear machines; noise and vibration; special machines. Separate abstracts have been prepared for a paper on linear step motors for control rod drives and for a paper on a motor drive for gas filtration in gas-cooled reactors. (UK)

  17. Block-Module Electric Machines of Alternating Current

    Science.gov (United States)

    Zabora, I.

    2018-03-01

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

  18. Metal release from coffee machines and electric kettles.

    Science.gov (United States)

    Müller, Frederic D; Hackethal, Christin; Schmidt, Roman; Kappenstein, Oliver; Pfaff, Karla; Luch, Andreas

    2015-01-01

    The release of elemental ions from 8 coffee machines and 11 electric kettles into food simulants was investigated. Three different types of coffee machines were tested: portafilter espresso machines, pod machines and capsule machines. All machines were tested subsequently on 3 days before and on 3 days after decalcification. Decalcification of the machines was performed with agents according to procedures as specified in the respective manufacturer's manuals. The electric kettles showed only a low release of the elements analysed. For the coffee machines decreasing concentrations of elements were found from the first to the last sample taken in the course of 1 day. Metal release on consecutive days showed a decreasing trend as well. After decalcification a large increase in the amounts of elements released was encountered. In addition, the different machine types investigated clearly differed in their extent of element release. By far the highest leaching, both quantitatively and qualitatively, was found for the portafilter machines. With these products releases of Pb, Ni, Mn, Cr and Zn were in the range and beyond the release limits as proposed by the Council of Europe. Therefore, a careful rinsing routine, especially after decalcification, is recommended for these machines. The comparably lower extent of release of one particular portafilter machine demonstrates that metal release at levels above the threshold that triggers health concerns are technically avoidable.

  19. 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.

  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. Wire electric-discharge machining and other fabrication techniques

    Science.gov (United States)

    Morgan, W. H.

    1983-01-01

    Wire electric discharge machining and extrude honing were used to fabricate a two dimensional wing for cryogenic wind tunnel testing. Electric-discharge cutting is done with a moving wire electrode. The cut track is controlled by means of a punched-tape program and the cutting feed is regulated according to the progress of the work. Electric-discharge machining involves no contact with the work piece, and no mechanical force is exerted. Extrude hone is a process for honing finish-machined surfaces by the extrusion of an abrasive material (silly putty), which is forced through a restrictive fixture. The fabrication steps are described and production times are given.

  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. Unipolar Electric Machines with Liquid-Metal Current Pickup,

    Science.gov (United States)

    1984-03-08

    A new homopolar motor , e4ournal of the Franklin Institute*. 1954, v. 258, Ne 1. %4 144093, Bjo.1.leTeJb H3o6peTeHxA. 1962,. 14 1. 30. X oao p o a...VIII. Motor Mode of Unipolar Electrical Machine ............... 301 Chapter IX. Bases of Theory and Calculation of Nonpolar Dynamos without...unipolar electric motors . Are examined questions of the classification of acyclic machines, their electromagnetic field, calculation of magnetic circuit

  4. Forecasting of electricity prices with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

    2006-08-15

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

  5. Forecasting of electricity prices with neural networks

    International Nuclear Information System (INIS)

    Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

    2006-01-01

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

  6. 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...

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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

    . 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...... 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...

  13. Operating point resolved loss computation in electrical machines

    Directory of Open Access Journals (Sweden)

    Pfingsten Georg Von

    2016-03-01

    Full Text Available Magnetic circuits of electromagnetic energy converters, such as electrical machines, are nowadays highly utilized. This proposition is intrinsic for the magnetic as well as the electric circuit and depicts that significant enhancements of electrical machines are difficult to achieve in the absence of a detailed understanding of underlying effects. In order to improve the properties of electrical machines the accurate determination of the locally distributed iron losses based on idealized model assumptions solely is not sufficient. Other loss generating effects have to be considered and the possibility being able to distinguish between the causes of particular loss components is indispensable. Parasitic loss mechanisms additionally contributing to the total losses originating from field harmonics, non-linear material behaviour, rotational magnetizations, and detrimental effects caused by the manufacturing process or temperature, are not explicitly considered in the common iron-loss models, probably even not specifically contained in commonly used calibration factors. This paper presents a methodology being able to distinguish between different loss mechanisms and enables to individually consider particular loss mechanisms in the model of the electric machine. A sensitivity analysis of the model parameters can be performed to obtain information about which decisive loss origin for which working point has to be manipulated by the electromagnetic design or the control of the machine.

  14. Efficient forced vibration reanalysis method for rotating electric machines

    Science.gov (United States)

    Saito, Akira; Suzuki, Hiromitsu; Kuroishi, Masakatsu; Nakai, Hideo

    2015-01-01

    Rotating electric machines are subject to forced vibration by magnetic force excitation with wide-band frequency spectrum that are dependent on the operating conditions. Therefore, when designing the electric machines, it is inevitable to compute the vibration response of the machines at various operating conditions efficiently and accurately. This paper presents an efficient frequency-domain vibration analysis method for the electric machines. The method enables the efficient re-analysis of the vibration response of electric machines at various operating conditions without the necessity to re-compute the harmonic response by finite element analyses. Theoretical background of the proposed method is provided, which is based on the modal reduction of the magnetic force excitation by a set of amplitude-modulated standing-waves. The method is applied to the forced response vibration of the interior permanent magnet motor at a fixed operating condition. The results computed by the proposed method agree very well with those computed by the conventional harmonic response analysis by the FEA. The proposed method is then applied to the spin-up test condition to demonstrate its applicability to various operating conditions. It is observed that the proposed method can successfully be applied to the spin-up test conditions, and the measured dominant frequency peaks in the frequency response can be well captured by the proposed approach.

  15. Electrical localization of weakly electric fish using neural networks

    International Nuclear Information System (INIS)

    Kiar, Greg; Mamatjan, Yasin; Adler, Andy; Jun, James; Maler, Len

    2013-01-01

    Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.

  16. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

  17. Electric converters of electromagnetic strike machine with battery power

    Science.gov (United States)

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

    2018-03-01

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

  18. Electric Machine Topologies in Energy Storage Systems

    OpenAIRE

    Santiago, Juan De; Oliveira, Janaina Goncalves de

    2010-01-01

    Energy storage development is essential if intermittent renewable energy generation is to increase. Pumped hydro, CAES and flywheels are environmentally friendly and economical storage alternatives that required electric motor/generators. The popularization of power electronics is relatively new and therefore the technology is still under development. There is not a clear winner when comparing technologies and therefore the optimal alternative depends on the specific requirements of the appli...

  19. Rotating magnetizations in electrical machines: Measurements and modeling

    Directory of Open Access Journals (Sweden)

    Andreas Thul

    2018-05-01

    Full Text Available This paper studies the magnetization process in electrical steel sheets for rotational magnetizations as they occur in the magnetic circuit of electrical machines. A four-pole rotational single sheet tester is used to generate the rotating magnetic flux inside the sample. A field-oriented control scheme is implemented to improve the control performance. The magnetization process of different non-oriented materials is analyzed and compared.

  20. Rotating magnetizations in electrical machines: Measurements and modeling

    Science.gov (United States)

    Thul, Andreas; Steentjes, Simon; Schauerte, Benedikt; Klimczyk, Piotr; Denke, Patrick; Hameyer, Kay

    2018-05-01

    This paper studies the magnetization process in electrical steel sheets for rotational magnetizations as they occur in the magnetic circuit of electrical machines. A four-pole rotational single sheet tester is used to generate the rotating magnetic flux inside the sample. A field-oriented control scheme is implemented to improve the control performance. The magnetization process of different non-oriented materials is analyzed and compared.

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

    Science.gov (United States)

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

    2004-01-01

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

  2. 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.

  3. 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…

  4. Electric discharge machining device for laboratories and workshops

    International Nuclear Information System (INIS)

    Lanxner, M.; Berko, A.; Ron, N.

    1976-11-01

    A simple low power electric discharge machining (EDM) device for special uses in laboratories and workshops is presented. The device includes an RC generator, an electromechanical servo 3-axis work-tool alignment system and a closed dielectric fluid circulation loop

  5. THE INFLUENCE OF LIMITING FACTORS ON ELECTRIC MACHINES ELECTROMAGNETIC POWER

    Directory of Open Access Journals (Sweden)

    Mr. Konstantin K. Kim

    2016-06-01

    Full Text Available The increase of electromechanical transducers single capacity is one of the main ways of their efficiency upgrading. The article discusses the impact of the main electromagnetic and me-chanical factors on the power, transmitted through the air gap of DC electric machines.

  6. A Telescopic Binary Learning Machine for Training Neural Networks.

    Science.gov (United States)

    Brunato, Mauro; Battiti, Roberto

    2017-03-01

    This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.

  7. Development of an electrically operated cassava slicing machine

    Directory of Open Access Journals (Sweden)

    I. S. Aji

    2013-08-01

    Full Text Available Labor input in manual cassava chips processing is very high and product quality is low. This paper presents the design and construction of an electrically operated cassava slicing machine that requires only one person to operate. Efficiency, portability, ease of operation, corrosion prevention of slicing component of the machine, force required to slice a cassava tuber, capacity of 10 kg/min and uniformity in the size of the cassava chips were considered in the design and fabrication of the machine. The performance of the machine was evaluated with cassava of average length and diameter of 253 mm and 60 mm respectively at an average speed of 154 rpm. The machine produced 5.3 kg of chips of 10 mm length and 60 mm diameter in 1 minute. The efficiency of the machine was 95.6% with respect to the quantity of the input cassava. The chips were found to be well chipped to the designed thickness, shape and of generally similar size. Galvanized steel sheets were used in the cutting section to avoid corrosion of components. The machine is portable and easy to operate which can be adopted for cassava processing in a medium size industry.

  8. Prophylactic and thermovision measurements of electric machines and equipment

    International Nuclear Information System (INIS)

    Jedlicka, R.; Brestovansky, L.

    1996-01-01

    High-voltage measurements of generators, unit and service transformers and some significant motor drives used at a nuclear power plant are described in this paper. Thermovision measurements of electric machines and distribution systems are dealt with in the second part of the paper. Power electric equipment represent one of the most significant components of a nuclear power plant. Turbine mechanical energy is converted into the electrical energy within these equipment. Power generated by generators is transformed by transformers so that it can achieve appropriate parameters for both the transmission over the distribution system and the power plant service power supply. The service power supply switchboards and cables provide supply to motors and other consumers necessary for the nuclear power plant technological process. The whole complex of equipment has to be maintained in good technical conditions. It is necessary to make thermovision and prophylactic measurements to identify and verify the electric equipment technical condition. The mentioned measurements warn the operation staff in advance against both gradual deterioration of power connection contact resistances, i.e. power connections overheating, and the machine insulation systems condition deterioration. The operation staff try to prevent the electric equipment operation accidents by early removing the detected failures, thus, improving the nuclear safety. In order to provide the above-mentioned activities a special prophylactic measurement group was established at the NPP Bohunice in 1983. The group specialists make following types of measurements. 1. Prophylactic measurements of electric machines. Prophylactics of 220 MW generators and 6 MW service power generators; Prophylactics of both unit and service transformers and VHV bushings; Prophylactics of major 6 kV motor drives. 2. Thermovision measurements of current connections. Measurements enumarated in paragraph 1 are made on disconnected electric

  9. Neural network based photovoltaic electrical forecasting in south Algeria

    International Nuclear Information System (INIS)

    Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

    2014-01-01

    Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

  10. Introduction to neural networks with electric power applications

    International Nuclear Information System (INIS)

    Wildberger, A.M.; Hickok, K.A.

    1990-01-01

    This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

  11. Optimization of electrical parameters of windings used in axial flux electrical machines

    International Nuclear Information System (INIS)

    Uhrik, M.

    2012-01-01

    This paper deals with shape optimization of windings used in electrical machines with disc type construction. These machines have short axial length what makes them suitable for use in small wind-power turbines or in-wheel traction drives. Disc type construction of stator offers more possibilities for winding arrangements than are available in classical machines with cylindrical construction. To find out the best winding arrangement for the novel disc type machine construction a series of analytical calculations, simulations and experimental measurements were performed. (Authors)

  12. Sample preparation of metal alloys by electric discharge machining

    Science.gov (United States)

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

    1976-01-01

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

  13. 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

  14. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks

    OpenAIRE

    Airola, Rasmus; Hager, Kristoffer

    2017-01-01

    The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple of years in the light of an increasing need to handle big data and large information flows. Machine learning has a broad area of application, such as human-computer interaction, predicting stock prices, real-time translation, and self driving vehicles. Large companies such as Microsoft and Google have already implemented machine learning in some o...

  15. Prophylactic and thermovision measurements of electric machines and equipment

    Energy Technology Data Exchange (ETDEWEB)

    Jedlicka, R; Brestovansky, L [Atomova Elektraren Bohunice, Jaslovske Bohunice (Slovakia)

    1997-12-31

    High-voltage measurements of generators, unit and service transformers and some significant motor drives used at a nuclear power plant are described in this paper. Thermovision measurements of electric machines and distribution systems are dealt with in the second part of the paper. Power electric equipment represent one of the most significant components of a nuclear power plant. Turbine mechanical energy is converted into the electrical energy within these equipment. Power generated by generators is transformed by transformers so that it can achieve appropriate parameters for both the transmission over the distribution system and the power plant service power supply. The service power supply switchboards and cables provide supply to motors and other consumers necessary for the nuclear power plant technological process. The whole complex of equipment has to be maintained in good technical conditions. It is necessary to make thermovision and prophylactic measurements to identify and verify the electric equipment technical condition. The mentioned measurements warn the operation staff in advance against both gradual deterioration of power connection contact resistances, i.e. power connections overheating, and the machine insulation systems condition deterioration. The operation staff try to prevent the electric equipment operation accidents by early removing the detected failures, thus, improving the nuclear safety. In order to provide the above-mentioned activities a special prophylactic measurement group was established at the NPP Bohunice in 1983. The group specialists make following types of measurements. 1. Prophylactic measurements of electric machines. Prophylactics of 220 MW generators and 6 MW service power generators; Prophylactics of both unit and service transformers and VHV bushings; Prophylactics of major 6 kV motor drives. 2. Thermovision measurements of current connections. (Abstract Truncated)

  16. Apparatus For Laminating Segmented Core For Electric Machine

    Science.gov (United States)

    Lawrence, Robert Anthony; Stabel, Gerald R

    2003-06-17

    A segmented core for an electric machine includes segments stamped from coated electric steel. The segments each have a first end, a second end, and winding openings. A predetermined number of segments are placed end-to-end to form layers. The layers are stacked such that each of the layers is staggered from adjacent layers by a predetermined rotation angle. The winding openings of each of the layers are in vertical alignment with the winding openings of the adjacent layers. The stack of layers is secured to form the segmented core.

  17. 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

  18. Recent Educational Experiences in Electric Machine Maintenance Teaching

    Directory of Open Access Journals (Sweden)

    Jose Alfonso Antonino-Daviu

    2013-05-01

    Full Text Available Maintenance of electric machines and installations is a particularly important area; eventual faults in these devices may lead to significant losses in terms of time and money. The investment and concern in developing proper maintenance protocols have been gradually increasing over recent decades. As a consequence, there is a need to instruct future engineers in the electric machines and installations maintenance area. The subject "Maintenance of Electric Machines and Installations" has been designed under this idea. It is taught within an official master degree in Maintenance Engineering. This work describes the educational experiences reached during the initial years of the teaching of the subject. Aspects such as student profiles, subject approaches, design of the syllabus, methodology and structure of the laboratory sessions are remarked in the work. In addition, the paper discusses other educational strategies which are being introduced to increase the interest in the subject, such as integration of Information and Communication Technologies (ICT, promotion of the collaborative work, inclusion of the possibility of remote learning or development of new assessment systems.

  19. Electrical discharge machining of carbon nanomaterials in air: machining characteristics and the advanced field emission applications

    International Nuclear Information System (INIS)

    Ok, Jong Girl; Kim, Bo Hyun; Chung, Do Kwan; Sung, Woo Yong; Lee, Seung Min; Lee, Se Won; Kim, Wal Jun; Park, Jin Woo; Chu, Chong Nam; Kim, Yong Hyup

    2008-01-01

    A reliable and precise machining process, electrical discharge machining (EDM), was investigated in depth as a novel method for the engineering of carbon nanomaterials. The machining characteristics of EDM applied to carbon nanomaterials 'in air' were systematically examined using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), energy-dispersive x-ray spectroscopy (EDS), x-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. The EDM process turned out to 'melt' carbon nanomaterials with the thermal energy generated by electrical discharge, which makes both the materially and geometrically unrestricted machining of nanomaterials possible. Since the EDM process conducted in air requires neither direct contact nor chemical agents, it protects the carbon nanomaterial workpieces against physical damage and unnecessary contamination. From this EDM method, several advanced field emission applications including 'top-down' patterning and the creative lateral comb-type triode device were derived, while our previously reported study on emission uniformity enhancement by the EDM method was also referenced. The EDM method has great potential as a clean, effective and practical way to utilize carbon nanomaterials for various uses

  20. Online fouling detection in electrical circulation heaters using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Lalot, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Valenciennes (France). LME; Lecoeuche, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Lille (France). Laboratoire 13D

    2003-06-01

    Here is presented a method that is able to detect fouling during the service of a circulation electrical heater. The neural based technique is divided in two major steps: identification and classification. Each step uses a neural network, the connection weights of the first one being the inputs of the second network. Each step is detailed and the main characteristics and abilities of the two neural networks are given. It is shown that the method is able to discriminate fouling from viscosity modification that would lead to the same type of effect on the total heat transfer coefficient. (author)

  1. 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…

  2. Guest Editorial Electric Machines in Renewable Energy Applications

    Energy Technology Data Exchange (ETDEWEB)

    Aliprantis, Dionysios; El-Sharkawi, Mohamed; Muljadi, Eduard; Brown, Ian; Chiba, Akira; Dorrell, David; Erlich, Istvan; Kerszenbaum, Isidor Izzy; Levi, Emil; Mayor, Kevin; Mohammed, Osama; Papathanassiou, Stavros; Popescu, Mircea; Qiao, Wei; Wu, Dezheng

    2015-12-01

    The main objective of this special issue is to collect and disseminate publications that highlight recent advances and breakthroughs in the area of renewable energy resources. The use of these resources for production of electricity is increasing rapidly worldwide. As of 2015, a majority of countries have set renewable electricity targets in the 10%-40% range to be achieved by 2020-2030, with a few notable exceptions aiming for 100% generation by renewables. We are experiencing a truly unprecedented transition away from fossil fuels, driven by environmental, energy security, and socio-economic factors.Electric machines can be found in a wide range of renewable energy applications, such as wind turbines, hydropower and hydrokinetic systems, flywheel energy storage devices, and low-power energy harvesting systems. Hence, the design of reliable, efficient, cost-effective, and controllable electric machines is crucial in enabling even higher penetrations of renewable energy systems in the smart grid of the future. In addition, power electronic converter design and control is critical, as they provide essential controllability, flexibility, grid interface, and integration functions.

  3. 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

    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...... 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...

  4. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

  5. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

  6. Machine learning based switching model for electricity load forecasting

    International Nuclear Information System (INIS)

    Fan Shu; Chen Luonan; Lee, Weijen

    2008-01-01

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

  7. Micro Electro Discharge Machining of Electrically Nonconductive Ceramics

    International Nuclear Information System (INIS)

    Schubert, A.; Zeidler, H.; Hackert, M.; Wolf, N.

    2011-01-01

    EDM is a known process for machining of hard and brittle materials. Due to its noncontact and nearly forceless behaviour, it has been introduced into micro manufacturing and through constant development it is now an important means for producing high-precision micro geometries. One restriction of EDM is its limitation to electrically conducting materials.Today many applications, especially in the biomedical field, make use of the benefits of ceramic materials, such as high strength, very low wear and biocompatibility. Common ceramic materials such as Zirconium dioxide are, due to their hardness in the sintered state, difficult to machine with conventional cutting techniques. A demand for the introduction of EDM to these materials could so far not be satisfied because of their nonconductive nature.At the Chemnitz University of Technology and the Fraunhofer IWU, investigations in the applicability of micro-EDM for the machining of nonconductive ceramics are being conducted. Tests are undertaken using micro-EDM drilling with Tungsten carbide tool electrodes and ZrO 2 ceramic workpieces. A starting layer, in literature often referred to as 'assisting electrode' is used to set up a closed electric circuit to start the EDM process. Combining carbon hydride based dielectric and a specially designed low-frequency vibration setup to excite the workpiece, the process environment can be held within parameters to allow for a constant EDM process even after the starting layer is machined. In the experiments a cylindrical 120 μm diameter Tungsten carbide tool electrode and Y 2 O 3 - and MgO- stabilized ZrO 2 worpieces are used. The current and voltage signals of the discharges within the different stages of the process (machining of the starting layer, machining of the base material, transition stage) are recorded and their characteristics compared to discharges in metallic material. Additionally, the electrode feed is monitored. The influences of the process parameters are

  8. Implementation of neural networks on 'Connection Machine'

    International Nuclear Information System (INIS)

    Belmonte, Ghislain

    1990-12-01

    This report is a first approach to the notion of neural networks and their possible applications within the framework of artificial intelligence activities of the Department of Applied Mathematics of the Limeil-Valenton Research Center. The first part is an introduction to the field of neural networks; the main neural network models are described in this section. The applications of neural networks in the field of classification have mainly been studied because they could more particularly help to solve some of the decision support problems dealt with by the C.E.A. As the neural networks perform a large number of parallel operations, it was therefore logical to use a parallel architecture computer: the Connection Machine (which uses 16384 processors and is located at E.T.C.A. Arcueil). The second part presents some generalities on the parallelism and the Connection Machine, and two implementations of neural networks on Connection Machine. The first of these implementations concerns one of the most used algorithms to realize the learning of neural networks: the Gradient Retro-propagation algorithm. The second one, less common, concerns a network of neurons destined mainly to the recognition of forms: the Fukushima Neocognitron. The latter is studied by the C.E.A. of Bruyeres-le-Chatel in order to realize an embedded system (including hardened circuits) for the fast recognition of forms [fr

  9. 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...

  10. Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

    2014-03-01

    Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

  11. 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.

  12. 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.

  13. 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...

  14. Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

    Directory of Open Access Journals (Sweden)

    Gaoyan Zhong

    2017-12-01

    Full Text Available The present study proposes a back propagation artificial neural network (BPANN to provide improved precision for predicting the material removal rate (MRR in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.

  15. 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

  16. Stator for a rotating electrical machine having multiple control windings

    Science.gov (United States)

    Shah, Manoj R.; Lewandowski, Chad R.

    2001-07-17

    A rotating electric machine is provided which includes multiple independent control windings for compensating for rotor imbalances and for levitating/centering the rotor. The multiple independent control windings are placed at different axial locations along the rotor to oppose forces created by imbalances at different axial locations along the rotor. The multiple control windings can also be used to levitate/center the rotor with a relatively small magnetic field per unit area since the rotor and/or the main power winding provides the bias field.

  17. 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

  18. Evolving Neural Turing Machines for Reward-based Learning

    DEFF Research Database (Denmark)

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

    2016-01-01

    An unsolved problem in neuroevolution (NE) is to evolve artificial neural networks (ANN) that can store and use information to change their behavior online. While plastic neural networks have shown promise in this context, they have difficulties retaining information over longer periods of time...... 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....

  19. Electrical Discharge Platinum Machining Optimization Using Stefan Problem Solutions

    Directory of Open Access Journals (Sweden)

    I. B. Stavitskiy

    2015-01-01

    Full Text Available The article presents the theoretical study results of platinum workability by electrical discharge machining (EDM, based on the solution of the thermal problem of moving the boundary of material change phase, i.e. Stefan problem. The problem solution enables defining the surface melt penetration of the material under the heat flow proceeding from the time of its action and the physical properties of the processed material. To determine the rational EDM operating conditions of platinum the article suggests relating its workability with machinability of materials, for which the rational EDM operating conditions are, currently, defined. It is shown that at low densities of the heat flow corresponding to the finishing EDM operating conditions, the processing conditions used for steel 45 are appropriate for platinum machining; with EDM at higher heat flow densities (e.g. 50 GW / m2 for this purpose copper processing conditions are used; at the high heat flow densities corresponding to heavy roughing EDM it is reasonable to use tungsten processing conditions. The article also represents how the minimum width of the current pulses, at which platinum starts melting and, accordingly, the EDM process becomes possible, depends on the heat flow density. It is shown that the processing of platinum is expedient at a pulse width corresponding to the values, called the effective pulse width. Exceeding these values does not lead to a substantial increase in removal of material per pulse, but considerably reduces the maximum repetition rate and therefore, the EDM capacity. The paper shows the effective pulse width versus the heat flow density. It also presents the dependences of the maximum platinum surface melt penetration and the corresponding pulse width on the heat flow density. Results obtained using solutions of the Stephen heat problem can be used to optimize EDM operating conditions of platinum machining.

  20. 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.

  1. 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.

  2. 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...

  3. Practical aspects of the use of three-phase alternating current electric machines in electricity storage system

    Science.gov (United States)

    Ciucur, Violeta

    2015-02-01

    Of three-phase alternating current electric machines, it brings into question which of them is more advantageous to be used in electrical energy storage system by pumping water. The two major categories among which are given dispute are synchronous and the asynchronous machine. To consider the synchronous machine with permanent magnet configuration because it brings advantages compared with conventional synchronous machine, first by removing the necessary additional excitation winding. From the point of view of loss of the two types of machines, the optimal adjustment of the magnetic flux density is obtained to minimize the copper loss by hysteresis and eddy currents.

  4. 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.

  5. 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.

  6. [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.

  7. 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...

  8. 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

  9. The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

    Science.gov (United States)

    Tseng, Pai-Chung; Chen, Shen-Len

    The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

  10. 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....

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. Features of measurement and processing of vibration signals registered on the moving parts of electrical machines

    OpenAIRE

    Gyzhko, Yuri

    2011-01-01

    Measurement and processing of vibration signals registered on the moving parts of the electrical machines using the diagnostic information-measuring system that uses Bluetooth wireless standard for the transmission of the measured data from moving parts of electrical machine is discussed.

  16. 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.

  17. Device for delivering cryogen to rotary super-conducting winding of cryogen-cooled electrical machine

    International Nuclear Information System (INIS)

    Filippov, I.F.; Gorbunov, G.S.; Khutoretsky, G.M.; Popov, J.S.; Skachkov, J.V.; Vinokurov, A.A.

    1980-01-01

    A device is disclosed for delivering cryogen to a superconducting winding of a cryogen-cooled electrical machine comprising a pipe articulated along the axis of the electrical machine and intended to deliver cryogen. One end of said pipe is located in a rotary chamber which communicates through channels with the space of the electrical machine, and said space accommodating its superconducting winding. The said chamber accommodates a needle installed along the chamber axis, and the length of said needle is of sufficient length such that in the advanced position of said cryogen delivering pipe said needle reaches the end of the pipe. The layout of the electrical machine increases the reliability and effectiveness of the device for delivering cryogen to the superconducting winding, simplifies the design of the device and raises the efficiency of the electrical machine

  18. 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...

  19. Electricity price forecasting using Enhanced Probability Neural Network

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the ''spikes'' can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment. (author)

  20. Machine learning of radial basis function neural network based on Kalman filter: Introduction

    Directory of Open Access Journals (Sweden)

    Vuković Najdan L.

    2014-01-01

    Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.

  1. A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine

    OpenAIRE

    ASLAN, Muhammet Fatih; SABANCI, Kadir; YİĞİT, Enes; KAYABAŞI, Ahmet; TOKTAŞ, Abdurrahim; DUYSAK, Hüseyin

    2018-01-01

    In this study, classification of two types of wheat grainsinto bread and durum was carried out. The species of wheat grains in thisdataset are bread and durum and these species have equal samples in the datasetas 100 instances. Seven features, including width, height, area, perimeter,roundness, width and perimeter/area were extracted from each wheat grains. Classificationwas separately conducted by Artificial Neural Network (ANN) and Extreme Learning Machine (ELM)artificial intelligence techn...

  2. A new method of machine vision reprocessing based on cellular neural networks

    International Nuclear Information System (INIS)

    Jianhua, W.; Liping, Z.; Fenfang, Z.; Guojian, H.

    1996-01-01

    This paper proposed a method of image preprocessing in machine vision based on Cellular Neural Network (CNN). CNN is introduced to design image smoothing, image recovering, image boundary detecting and other image preprocessing problems. The proposed methods are so simple that the speed of algorithms are increased greatly to suit the needs of real-time image processing. The experimental results show a satisfactory reply

  3. 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.

  4. Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

    Science.gov (United States)

    Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

    2018-02-22

    Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

  5. 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.

  6. 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.

  7. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  8. Electrical Quality Assurance of the Superconducting Circuits during LHC Machine Assembly

    CERN Document Server

    Bozzini, D; Desebe, O; Mess, K H; Russenschuck, Stephan; Bednarek, M; Dworak, D; Górnicki, E; Jurkiewicz, P; Kapusta, P; Kotarba, A; Ludwin, J; Olek, S; Talach, M; Zieblinski, M; Klisch, M; Prochal, B

    2008-01-01

    Based on the LHC powering reference database, all-together 1750 superconducting circuits were connected in the various cryogenic transfer lines of the LHC machine. Testing the continuity, magnet polarity, and the quality of the electrical insulation were the main tasks of the Electrical Quality Assurance (ELQA) activities during the LHC machine assembly. With the assembly of the LHC now complete, the paper reviews the work flow, resources, and the qualification results including the different types of electrical non-conformities.

  9. Variable cross-section windings for efficiency improvement of electric machines

    Science.gov (United States)

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

    2018-02-01

    Implementation of energy-saving technologies in industry is impossible without efficiency improvement of electric machines. The article considers the ways of efficiency improvement and mass and dimensions reduction of electric machines with electronic control. Features of compact winding design for stators and armatures are described. Influence of compact winding on thermal and electrical process is given. Finite element method was used in computer simulation.

  10. DESIGN ANALYSIS OF ELECTRICAL MACHINES THROUGH INTEGRATED NUMERICAL APPROACH

    Directory of Open Access Journals (Sweden)

    ARAVIND C.V.

    2016-02-01

    Full Text Available An integrated design platform for the newer type of machines is presented in this work. The machine parameters are evaluated out using developed modelling tool. With the machine parameters, the machine is modelled using computer aided tool. The designed machine is brought to simulation tool to perform electromagnetic and electromechanical analysis. In the simulation, conditions setting are performed to setup the materials, meshes, rotational speed and the excitation circuit. Electromagnetic analysis is carried out to predict the behavior of the machine based on the movement of flux in the machines. Besides, electromechanical analysis is carried out to analyse the speed-torque characteristic, the current-torque characteristic and the phase angle-torque characteristic. After all the results are analysed, the designed machine is used to generate S block function that is compatible with MATLAB/SIMULINK tool for the dynamic operational characteristics. This allows the integration of existing drive system into the new machines designed in the modelling tool. An example of the machine design is presented to validate the usage of such a tool.

  11. Forecasting electricity market pricing using artificial neural networks

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien

    2007-01-01

    Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long

  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. High power linear electric machine - made possible by gas springs

    Energy Technology Data Exchange (ETDEWEB)

    Hoff, E.; Brennvall, J.E.; Nilssen, R.; Norum, L.

    2004-07-01

    In some applications, such as compressors, free piston linear machines have several advantages compared to rotating machines. The power level of linear machines has been limited, mainly due to difficulties with the spring. A solution for this has now been found and will be described in this paper. It can open up new areas of applications, where the power level exceeds the present power limit of about 2 kW. This machine needs special regulators in order to work efficiently. Two regulator algorithms for piston phase and one for position amplitude are therefore implemented for this prototype. (author)

  14. Increase of energy efficiency of testing of traction electric machines of direct and pulsating current

    Directory of Open Access Journals (Sweden)

    A.M. Afanasov

    2015-03-01

    Full Text Available The results of the analysis of the effect of the load current of traction electric machines when tested for heating on the total electricity consumption for the test are presented. It is shown that increase of load current at the heating test permits to significantly reduce the consumption of electrical energy, and reduce the testing time without reducing its quality.

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

    International Nuclear Information System (INIS)

    Durand, D.M.

    2003-01-01

    Uniform electric fields applied to neural tissue can modulate neuronal excitability with a threshold value of about 1mV mm -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 -1 . These results suggest that the threshold for this effect is clearly smaller than 1mV mm -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 ∼1mmV mm -. (author)

  16. Effect of electrical discharge machining on surface characteristics and machining damage of AISI D2 tool steel

    International Nuclear Information System (INIS)

    Guu, Y.H.; Hocheng, H.; Chou, C.Y.; Deng, C.S.

    2003-01-01

    In this work the electrical discharge machining (EDM) of AISI D2 tool steel was investigated. The surface characteristics and machining damage caused by EDM were studied in terms of machining parameters. Based on the experimental data, an empirical model of the tool steel was also proposed. A new damage variable was used to study the EDM damage. The workpiece surface and re-solidified layers were examined by a scanning electron microscopy. Surface roughness was determined with a surface profilometer. The residual stress acting on the EDM specimen was measured by the X-ray diffraction technique. Experimental results indicate that the thickness of the recast layer, and surface roughness are proportional to the power input. The EDM process introduces tensile residual stress on the machined surface. The EDM damage leads to strength degradation

  17. Machine and component residual life estimation through the application of neural networks

    International Nuclear Information System (INIS)

    Herzog, M.A.; Marwala, T.; Heyns, P.S.

    2009-01-01

    This paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset represents the renewal case where the failed unit is repaired and restored to a good-as-new condition. Data were collected in the laboratory by subjecting a series of similar test pieces to fatigue loading with a hydraulic actuator. The average prediction error of the various neural networks being compared varied from 431 to 841 s on this dataset, where test pieces had a characteristic life of 8971 s. The second dataset were collected from a group of pumps used to circulate a water and magnetite solution within a plant. The data therefore originated from a repaired system affected by reliability degradation. When optimized, the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum-of-squares error within 11.1% of each other for the renewal dataset. The small number of inputs and poorly mapped input space on the second dataset meant that much larger errors were recorded on some of the test data. The potential for using neural networks for residual life prediction and the advantage of incorporating condition-based data into the model was nevertheless proven for both examples

  18. 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.

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

    Science.gov (United States)

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

    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.

  20. 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 ...

  1. Electrical gearbox equivalent by means of dynamic machine operation

    NARCIS (Netherlands)

    Gerrits, T.; Wijnands, C.G.E.; Paulides, J.J.H.; Duarte, J.L.

    2011-01-01

    A dynamic propulsion system using variable torque/speed characteristics, designed to realize a machine integrated equivalent of a gearbox is presented. This is achieved through adaption of the machine characteristics, and driven by specialized power electronics, leading to a reconfigurable stator

  2. T-wave end detection using neural networks and Support Vector Machines.

    Science.gov (United States)

    Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

    2018-05-01

    In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. 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.

  4. 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...

  5. Design of an axial-flux permanent magnet machine for a solar-powered electric vehicle

    NARCIS (Netherlands)

    Friedrich, L.A.J.; Bastiaens, K.; Gysen, B.L.J.; Krop, D.C.J.; Lomonova, E.A.

    2018-01-01

    This paper concerns the design optimization of two axial-flux permanent magnet (AFPM) machines, aimed to be used as a direct drive in-wheel motor for the propulsion of a solar-powered electric vehicle. The internal stator twin external rotor AFPM machine topology having either a distributed or

  6. Vibration Prediction Method of Electric Machines by using Experimental Transfer Function and Magnetostatic Finite Element Analysis

    International Nuclear Information System (INIS)

    Saito, A; Kuroishi, M; Nakai, H

    2016-01-01

    This paper concerns the noise and structural vibration caused by rotating electric machines. Special attention is given to the magnetic-force induced vibration response of interior-permanent magnet machines. In general, to accurately predict and control the vibration response caused by the electric machines, it is inevitable to model not only the magnetic force induced by the fluctuation of magnetic fields, but also the structural dynamic characteristics of the electric machines and surrounding structural components. However, due to complicated boundary conditions and material properties of the components, such as laminated magnetic cores and varnished windings, it has been a challenge to compute accurate vibration response caused by the electric machines even after their physical models are available. In this paper, we propose a highly-accurate vibration prediction method that couples experimentally-obtained discrete structural transfer functions and numerically-obtained distributed magnetic-forces. The proposed vibration synthesis methodology has been applied to predict vibration responses of an interior permanent magnet machine. The results show that the predicted vibration response of the electric machine agrees very well with the measured vibration response for several load conditions, for wide frequency ranges. (paper)

  7. Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines; Energieeffiziente elektrische Maschinen durch neue Materialien. Supraleitung in grossen elektrischen Maschinen

    Energy Technology Data Exchange (ETDEWEB)

    Frauenhofer, Joachim [Siemens, Nuernberg (Germany); Arndt, Tabea; Grundmann, Joern [Siemens, Erlangen (Germany)

    2013-07-01

    The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO{sub 2} emissions.

  8. Low temperature wetting and cleanup of alkali metal-advanced electrical machine systems

    International Nuclear Information System (INIS)

    Gass, W.R.; Witkowski, R.E.; Burrow, G.C.

    1980-01-01

    Advanced homopolar electrical machines employing high electrical current density, liquid metal sliprings for current transfer utilize NaK/sub 78/ (78 w/o potassium, 22 w/o sodium) for the conducting fluid. Experiments have been performed to improve alkali metal/oxide clean-up procedures. Studies have also confirmed chemical and materials compatibility between barium doped NaK/sub 78/ and typical machine structural materials. 4 refs

  9. Performances of the PCA method in electrical machines diagnosis using Matlab

    OpenAIRE

    Ramahaleomiarantsoa , J.F.; Sambatra , Eric Jean Roy; Heraud , Nicolas; Razafimahenina , Jean Marie

    2012-01-01

    Nowadays, faults diagnosis is almost an inevitable step to be maintained in the optimal safety operating of every physical system. Electrical machines, main elements of every electromechanical system, are among the research topics of many academic and industrial laboratories because of the importance of their roles in the industrial process. Lots of technologies of these machines are old and well controlled. However, they remain the seat of several electrical and mechanical faults [1-4]. Thus...

  10. 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.

  11. electrical-thermal coupling of induction machine for improved

    African Journals Online (AJOL)

    user

    parameter method was used in the thermal model of the machine. The system of ... Thermal modeling is important for design purpose, fault detection ... dependent problems are challenging both in software development ... numerical solution.

  12. 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.

  13. A neural network image reconstruction technique for electrical impedance tomography

    International Nuclear Information System (INIS)

    Adler, A.; Guardo, R.

    1994-01-01

    Reconstruction of Images in Electrical Impedance Tomography requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. This paper presents a reconstruction algorithm using neural network techniques which calculates a linear approximation of the inverse problem directly from finite element simulations of the forward problem. This inverse is adapted to the geometry of the medium and the signal-to-noise ratio (SNR) used during network training. Results show good conductivity reconstruction where measurement SNR is similar to the training conditions. The advantages of this method are its conceptual simplicity and ease of implementation, and the ability to control the compromise between the noise performance and resolution of the image reconstruction

  14. 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)

  15. 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.

  16. 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.

  17. 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

  18. Optimal methodology for a machining process scheduling in spot electricity markets

    International Nuclear Information System (INIS)

    Yusta, J.M.; Torres, F.; Khodr, H.M.

    2010-01-01

    Electricity spot markets have introduced hourly variations in the price of electricity. These variations allow the increase of the energy efficiency by the appropriate scheduling and adaptation of the industrial production to the hourly cost of electricity in order to obtain the maximum profit for the industry. In this article a mathematical optimization model simulates costs and the electricity demand of a machining process. The resultant problem is solved using the generalized reduced gradient approach, to find the optimum production schedule that maximizes the industry profit considering the hourly variations of the price of electricity in the spot market. Different price scenarios are studied to analyze the impact of the spot market prices for electricity on the optimal scheduling of the machining process and on the industry profit. The convenience of the application of the proposed model is shown especially in cases of very high electricity prices.

  19. 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.

  20. 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.

  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

    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. 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.

  3. Inappropriate shock delivery by implantable cardioverter defibrillator due to electrical interference with washing machine.

    Science.gov (United States)

    Chongtham, Dhanaraj Singh; Bahl, Ajay; Kumar, Rohit Manoj; Talwar, K K

    2007-05-31

    We report a patient with hypertrophic cardiomyopathy who received an inappropriate implantable cardioverter defibrillator shock due to electrical interference from a washing machine. This electrical interference was detected as an episode of ventricular fibrillation with delivery of shock without warning symptoms.

  4. Field weakening performance of flux-switching machines for hybrid/electric vehicles

    NARCIS (Netherlands)

    Tang, Y.; Paulides, J.J.H.; Lomonova, E.A.

    2015-01-01

    Flux-switching machines (FSMs) are a viable candidate for electric propulsion of hybrid/electric vehicles. This paper investigates the field weakening performance of FSMs. The investigation starts with general torque and voltage expressions, which reveal the relationships between certain parameters

  5. 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.

  6. Method of Relative Magnitudes for Calculating Magnetic Fluxes in Electrical Machine

    Directory of Open Access Journals (Sweden)

    Oleg A.

    2018-03-01

    Full Text Available Introduction: The article presents the study results of the model of an asynchronous electric motor carried out by the author within the framework of the Priorities Research Program “Research and development in the priority areas of development of Russia’s scientific and technical complex for 2014–2020”. Materials and Methods: A model of an idealized asynchronous machine (with sinusoidal distribution of magnetic induction in air gap is used in vector control systems. It is impossible to create windings for this machine. The basis of the new calculation approach was the Conductivity of Teeth Contours Method, developed at the Electrical Machines Chair of the Moscow Power Engineering Institute (MPEI. Unlike this method, the author used not absolute values, but relative magnitudes of magnetic fluxes. This solution fundamentally improved the method’s capabilities. The relative magnitudes of the magnetic fluxes of the teeth contours do not required the additional consideration for exact structure of magnetic field of tooth and adjacent slots. These structures are identical for all the teeth of the machine and differ only in magnitude. The purpose of the calculations was not traditional harmonic analysis of magnetic induction distribution in air gap of machine, but a refinement of the equations of electric machine model. The vector control researchers used only the cos(θ function as a value of mutual magnetic coupling coefficient between the windings. Results: The author has developed a way to take into account the design of the windings of a real machine by using imaginary measuring winding with the same winding design as a real phase winding. The imaginary winding can be placed in the position of any machine windings. The calculation of the relative magnetic fluxes of this winding helped to estimate the real values of the magnetic coupling coefficients between the windings, and find the correction functions for the model of an idealized

  7. Equivalent model of a dually-fed machine for electric drive control systems

    Science.gov (United States)

    Ostrovlyanchik, I. Yu; Popolzin, I. Yu

    2018-05-01

    The article shows that the mathematical model of a dually-fed machine is complicated because of the presence of a controlled voltage source in the rotor circuit. As a method of obtaining a mathematical model, the method of a generalized two-phase electric machine is applied and a rotating orthogonal coordinate system is chosen that is associated with the representing vector of a stator current. In the chosen coordinate system in the operator form the differential equations of electric equilibrium for the windings of the generalized machine (the Kirchhoff equation) are written together with the expression for the moment, which determines the electromechanical energy transformation in the machine. Equations are transformed so that they connect the currents of the windings, that determine the moment of the machine, and the voltages on these windings. The structural diagram of the machine is assigned to the written equations. Based on the written equations and accepted assumptions, expressions were obtained for the balancing the EMF of windings, and on the basis of these expressions an equivalent mathematical model of a dually-fed machine is proposed, convenient for use in electric drive control systems.

  8. Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

    Science.gov (United States)

    Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

    2018-04-01

    The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

  9. Support vector machine based battery model for electric vehicles

    International Nuclear Information System (INIS)

    Wang Junping; Chen Quanshi; Cao Binggang

    2006-01-01

    The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

  10. Superconductor Armature Winding for High Performance Electrical Machines

    Science.gov (United States)

    2016-12-05

    eddy -induced currents used for shielding. 3.1 SOLID SHIELD. The frequency of the induced current for our machines ... eddy   current  shields)   •  SuperSat     •  switch  reluctance  generators   •  AC  Homopolar   • Toroidal  (Gramme...higher than expected, due probably to highly conducting Nb sheath around the MgB2 filaments (the measured losses were coupling or eddy current

  11. Australia's long-term electricity demand forecasting using deep neural networks

    OpenAIRE

    Hamedmoghadam, Homayoun; Joorabloo, Nima; Jalili, Mahdi

    2018-01-01

    Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in...

  12. Electric converters of electromagnetic strike machine with capacitor supply

    Science.gov (United States)

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

    2018-03-01

    The application of pulse linear electromagnetic engines in small power strike machines (energy impact is 0.01...1.0 kJ), where the characteristic mode of rare beats (pulse seismic vibrator, the arch crash device bins bulk materials), is quite effective. At the same time, the technical and economic performance of such machines is largely determined by the ability of the power source to provide a large instantaneous power of the supply pulses in the winding of the linear electromagnetic motor. The use of intermediate energy storage devices in power systems of rare-shock LEME makes it possible to obtain easily large instantaneous powers, forced energy conversion, and increase the performance of the machine. A capacitor power supply of a pulsed source of seismic waves is proposed for the exploration of shallow depths. The sections of the capacitor storage (CS) are connected to the winding of the linear electromagnetic motor by thyristor dischargers, the sequence of activation of which is determined by the control device. The charge of the capacitors to the required voltage is made directly from the battery source, or through the converter from a battery source with a smaller number of batteries.

  13. 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.

  14. 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)

  15. 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.

  16. Catalytic aided electrical discharge machining of polycrystalline diamond - parameter analysis of finishing condition

    Science.gov (United States)

    Haikal Ahmad, M. A.; Zulafif Rahim, M.; Fauzi, M. F. Mohd; Abdullah, Aslam; Omar, Z.; Ding, Songlin; Ismail, A. E.; Rasidi Ibrahim, M.

    2018-01-01

    Polycrystalline diamond (PCD) is regarded as among the hardest material in the world. Electrical Discharge Machining (EDM) typically used to machine this material because of its non-contact process nature. This investigation was purposely done to compare the EDM performances of PCD when using normal electrode of copper (Cu) and newly proposed graphitization catalyst electrode of copper nickel (CuNi). Two level full factorial design of experiment with 4 center points technique was used to study the influence of main and interaction effects of the machining parameter namely; pulse-on, pulse-off, sparking current, and electrode materials (categorical factor). The paper shows interesting discovery in which the newly proposed electrode presented positive impact to the machining performance. With the same machining parameters of finishing, CuNi delivered more than 100% better in Ra and MRR than ordinary Cu electrode.

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

    International Nuclear Information System (INIS)

    Hill, Mary Ann; Dombrowski, David E.; Clarke, Kester Diederik; Forsyth, Robert Thomas; Aikin, Robert M.; Alexander, David John; Tegtmeier, Eric Lee; Robison, Jeffrey Curt; Beard, Timothy Vance; Edwards, Randall Lynn; Mauro, Michael Ernest; Scott, Jeffrey E.; Strandy, Matthew Thomas

    2016-01-01

    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. 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.

  19. 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.

  20. 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.

  1. Participation in the ABWR Man-Machine interface design. Applicability to the Spanish Electrical Sector

    International Nuclear Information System (INIS)

    Rodriguez, C.; Manrique Martin, A.; Nunez, J.

    1997-01-01

    Project coordinated by DTN within the advanced reactor programme. Participation in the design activities for the Advanced Boiling Water Reactor (ABWR) man-machine interface was divided into two phases: Phase I: Preparation of drawings for designing, developing and assessing the advanced control room Phase II: Application of these drawings in design activities Participation in this programme has led to the following possible future applications to the electrical sector: 1. Design and implementation of man-machine interfaces 2. Human factor criteria 3. Assessment of man-machine interfaces 4. Functional specification, computerised operating procedures 5. Computerised alarm prototypes. (Author)

  2. Electric motor assisted bicycle as an aerobic exercise machine.

    Science.gov (United States)

    Nagata, T; Okada, S; Makikawa, M

    2012-01-01

    The goal of this study is to maintain a continuous level of exercise intensity around the aerobic threshold (AT) during riding on an electric motor assisted bicycle using a new control system of electrical motor assistance which uses the efficient pedaling rate of popular bicycles. Five male subjects participated in the experiment, and the oxygen uptake was measured during cycling exercise using this new pedaling rate control system of electrical motor assistance, which could maintain the pedaling rate within a specific range, similar to that in previous type of electrically assisted bicycles. Results showed that this new pedaling rate control system at 65 rpm ensured continuous aerobic exercise intensity around the AT in two subjects, and this intensity level was higher than that observed in previous type. However, certain subjects were unable to maintain the expected exercise intensity because of their particular cycling preferences such as the pedaling rate. It is necessary to adjust the specific pedaling rate range of the electrical motor assist control according to the preferred pedaling rate, so that this system becomes applicable to anyone who want continuous aerobic exercise.

  3. 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...... a general form while the corresponding computational algorithms are described briefly. The authors present a new training algorithm of sub-networks named “'Expert in one thing and good at many' (EOGM).” In this algorithm, every sub-network is trained on a primary dataset with some of its near neighbors...... as the accessorial datasets. Simulated results with a kind of dynamic integration methods show the effectiveness of these algorithms, where the performance of the algorithm with EOGM is better than that of the algorithm with a common training method....

  4. Global detection of live virtual machine migration based on cellular neural networks.

    Science.gov (United States)

    Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

    2014-01-01

    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.

  5. Adjustment and Prediction of Machine Factors Based on Neural Artificial Intelligence

    International Nuclear Information System (INIS)

    Hussein, A.Z.; Amin, E.S.; Ibrahim, M.S.

    2009-01-01

    Since the discovery of x-ray, it is use in examination has become an integral part of medical diagnostic radiology. The use of X-ray is harmful to human beings but recent technological advances and regulatory constraints have made the medical Xray much safer than they were at the beginning of the 20th century. However, the potential benefits of the engineered safety features can not be fully realized unless the operators are aware of these safety features. The aim of this work is to adjust and predict x-ray machine factors (current and voltage) using neural artificial network in order to obtain effective dose within the range of dose limitation system and assure radiological safety.

  6. 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.

  7. Adjustment and Prediction of X-Ray Machine Factors Based on Neural Artificial Inculcating

    International Nuclear Information System (INIS)

    Hussein, A.Z.; Amin, E.S.; Ibrahim, M.S.

    2009-01-01

    Since the discovery of X-rays, their use in examination has become an integral part of medical diagnostic radiology. The use of X-rays is harmful to human beings but recent technological advances and regulatory constraints have made the medical X-rays much safer than they were at the beginning of the 20th century. However, the potential benefits of the engineered safety features can not be fully realized unless the operators are aware of these safety features. The aim of this work is to adjust and predict X-ray machine factors (current and voltage) using neural artificial network in order to obtain effective dose within the range of dose limitation system and assure radiological safety.

  8. 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.

  9. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

    2009-01-01

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  10. Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning.

    Science.gov (United States)

    Takada, Kenji

    2016-09-01

    New approach for the diagnosis of extractions with neural network machine learning. Seok-Ki Jung and Tae-Woo Kim. Am J Orthod Dentofacial Orthop 2016;149:127-33. Not reported. Mathematical modeling. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. 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…

  12. 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…

  13. 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.

  14. 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%.

  15. Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

    Science.gov (United States)

    Khellal, Atmane; Ma, Hongbin; Fei, Qing

    2018-05-09

    The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

  16. 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.

  17. Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

    Science.gov (United States)

    Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

    2018-02-01

    In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

  18. 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.

  19. Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines

    International Nuclear Information System (INIS)

    Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli

    2015-01-01

    Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis. (paper)

  20. 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.

  1. 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.

  2. 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 believe

  3. 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

  4. The influence of electric fields on hippocampal neural progenitor cells.

    Science.gov (United States)

    Ariza, Carlos Atico; Fleury, Asha T; Tormos, Christian J; Petruk, Vadim; Chawla, Sagar; Oh, Jisun; Sakaguchi, Donald S; Mallapragada, Surya K

    2010-12-01

    The differentiation and proliferation of neural stem/progenitor cells (NPCs) depend on various in vivo environmental factors or cues, which may include an endogenous electrical field (EF), as observed during nervous system development and repair. In this study, we investigate the morphologic, phenotypic, and mitotic alterations of adult hippocampal NPCs that occur when exposed to two EFs of estimated endogenous strengths. NPCs treated with a 437 mV/mm direct current (DC) EF aligned perpendicularly to the EF vector and had a greater tendency to differentiate into neurons, but not into oligodendrocytes or astrocytes, compared to controls. Furthermore, NPC process growth was promoted perpendicularly and inhibited anodally in the 437 mV/mm DC EF. Yet fewer cells were observed in the DC EF, which in part was due to a decrease in cell viability. The other EF applied was a 46 mV/mm alternating current (AC) EF. However, the 46 mV/mm AC EF showed no major differences in alignment or differentiation, compared to control conditions. For both EF treatments, the percent of mitotic cells during the last 14 h of the experiment were statistically similar to controls. Reported here, to our knowledge, is the first evidence of adult NPC differentiation affected in an EF in vitro. Further investigation and application of EFs on stem cells is warranted to elucidate the utility of EFs to control phenotypic behavior. With progress, the use of EFs may be engineered to control differentiation and target the growth of transplanted cells in a stem cell-based therapy to treat nervous system disorders.

  5. Analysis of neural activity in human motor cortex -- Towards brain machine interface system

    Science.gov (United States)

    Secundo, Lavi

    , the correlation of ECoG activity to kinematic parameters of arm movement is context-dependent, an important constraint to consider in future development of BMI systems. The third chapter delves into a fundamental organizational principle of the primate motor system---cortical control of contralateral limb movements. However, ipsilateral motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in ipsilateral primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. We also recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface.

  6. Surface structuring of boron doped CVD diamond by micro electrical discharge machining

    Science.gov (United States)

    Schubert, A.; Berger, T.; Martin, A.; Hackert-Oschätzchen, M.; Treffkorn, N.; Kühn, R.

    2018-05-01

    Boron doped diamond materials, which are generated by Chemical Vapor Deposition (CVD), offer a great potential for the application on highly stressed tools, e. g. in cutting or forming processes. As a result of the CVD process rough surfaces arise, which require a finishing treatment in particular for the application in forming tools. Cutting techniques such as milling and grinding are hardly applicable for the finish machining because of the high strength of diamond. Due to its process principle of ablating material by melting and evaporating, Electrical Discharge Machining (EDM) is independent of hardness, brittleness or toughness of the workpiece material. EDM is a suitable technology for machining and structuring CVD diamond, since boron doped CVD diamond is electrically conductive. In this study the ablation characteristics of boron doped CVD diamond by micro electrical discharge machining are investigated. Experiments were carried out to investigate the influence of different process parameters on the machining result. The impact of tool-polarity, voltage and discharge energy on the resulting erosion geometry and the tool wear was analyzed. A variation in path overlapping during the erosion of planar areas leads to different microstructures. The results show that micro EDM is a suitable technology for finishing of boron doped CVD diamond.

  7. 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.

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

    Science.gov (United States)

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

    2009-10-01

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

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  10. 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.

  11. Event-driven simulation of neural population synchronization facilitated by electrical coupling.

    Science.gov (United States)

    Carrillo, Richard R; Ros, Eduardo; Barbour, Boris; Boucheny, Christian; Coenen, Olivier

    2007-02-01

    Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.

  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. Numerical approach for optimum electromagnetic parameters of electrical machines used in vehicle traction applications

    International Nuclear Information System (INIS)

    Fodorean, D.; Giurgea, S.; Djerdir, A.; Miraoui, A.

    2009-01-01

    A large speed variation is an essential request in the automobile industry. In order to compete with diesel engines, the flux weakening technique has to be employed on the electrical machines. In this way, appropriate electromagnetic and geometrical parameters can give the desired speed. Using the inverse problem method coupled with numerical analysis by finite element method (FEM), the authors propose an optimum parameters configuration that maximizes the speed domain operation. Several types of electrical machines are under study: induction, synchronous permanent magnet, variable reluctance and transverse flux machines, respectively. With a proper non-linear model, by using analytical and numerical calculation, the authors propose an optimum solution for the speed variation of the studied drives, which will be standing for a final comparison.

  14. Availability of the electric drive systems containing flux switching permanent magnet machines

    NARCIS (Netherlands)

    Wang, L.; Sfakianakis, G.; Paulides, J.J.H.; Lomonova, E.A.

    2016-01-01

    This paper investigates how to improve availability of an electrical drive containing a 3-phase 12/10 (12 stator tooth/10 rotor poles) flux switching permanent magnet machine. In this respect, Field-Oriented Control and Space-Vector Pulse-Width-Modulation strategies will be applied with 3-phase

  15. Hybrid propulsion testing using direct-drive electrical machines for super yacht and inland shipping

    NARCIS (Netherlands)

    Paulides, J.J.H.; Djukic, N.; de Roon, J.A.; Encica, L.

    2016-01-01

    Hybrid or full electric propulsions for inland ships are becoming more popular. In these application, direct-drive PM propulsion motors are a preferred machine configuration. This paper discusses the challenges to determine the losses, as estimated with simulations, during the testing procedures of

  16. Electrical Machines: Turn-to-Turn Capacitance in Formed Windings with Rectangular Cross-Section Wire

    NARCIS (Netherlands)

    Djukic, Nenad; Encica, L.; Paulides, Johan

    2015-01-01

    Calculation of turn-to-turn capacitance (Ctt) in electrical machines (EMs) with formed windings with rectangular cross-section wire is presented. Three calculation methods are used for the calculation of Ctt in case of rectangular conductors – finite element (FE) method and two previously published

  17. Traveling wire electrode increases productivity of Electrical Discharge Machining /EDM/ equipment

    Science.gov (United States)

    Kotora, J., Jr.; Smith, S. V.

    1967-01-01

    Traveling wire electrode on electrical discharge machining /EDM/ equipment reduces the time requirements for precision cutting. This device enables cutting with a minimum of lost material and without inducing stress beyond that inherent in the material. The use of wire increases accuracy and enables tighter tolerances to be maintained.

  18. Magnetic saturation in semi-analytical harmonic modeling for electric machine analysis

    NARCIS (Netherlands)

    Sprangers, R.L.J.; Paulides, J.J.H.; Gysen, B.L.J.; Lomonova, E.

    2016-01-01

    A semi-analytical method based on the harmonic modeling (HM) technique is presented for the analysis of the magneto-static field distribution in the slotted structure of rotating electric machines. In contrast to the existing literature, the proposed model does not require the assumption of infinite

  19. 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.

  20. Halbach array motor/generators: A novel generalized electric machine

    Energy Technology Data Exchange (ETDEWEB)

    Merritt, B.T.; Post, R.F.; Dreifuerst, G.R.; Bender, D.A. [Lawrence Livermore National Lab., CA (United States)

    1995-02-01

    For many years Klaus Halbach has been investigating novel designs for permanent magnet arrays, using advanced analytical approaches and employing a keen insight into such systems. One of his motivations for this research was to find more efficient means for the utilization of permanent magnets for use in particle accelerators and in the control of particle beams. As a result of his pioneering work, high power free-electron laser systems, such as the ones built at the Lawrence Livermore Laboratory, became feasible, and his arrays have been incorporated into other particle-focusing systems of various types. This paper reports another, quite different, application of Klaus` work, in the design of high power, high efficiency, electric generators and motors. When tested, these motor/generator systems display some rather remarkable properties. Their success derives from the special properties which these arrays, which the authors choose to call {open_quotes}Halbach arrays,{close_quotes} possess.

  1. 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.

  2. 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.

  3. Converters and electric machines. Solid insulating materials. Electrical characteristics; Convertisseurs et machines electriques. Materiaux isolants solides. Caracteristiques electriques

    Energy Technology Data Exchange (ETDEWEB)

    Anton, A. [Institut National Superieur de Chimie Industrielle, 76 - Rouen (France)

    2003-08-01

    The aim of this article is to allow a preselection of a solid insulating material using the most common electrical characteristics: tangent of the loss angle, relative permittivity, dielectric rigidity, superficial resistivity, transverse resistivity, resistance to high voltage creeping spark currents, index of creeping resistance. The characteristics of the main solid insulating materials are presented in tables for: thermoplastics, thermosetting materials, natural insulating materials, mineral insulating materials, rubber and synthetic elastomers, stratified insulating materials, thermoplastic films, composite synthetic papers. A comparison is made between the different materials using the three properties: tangent of the loss angle, relative permittivity and resistance to HV spark creeping currents. (J.S.)

  4. Prediction of Electricity Usage Using Convolutional Neural Networks

    OpenAIRE

    Hansen, Martin

    2017-01-01

    Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...

  5. Tracking Single Units in Chronic, Large Scale, Neural Recordings for Brain Machine Interface Applications

    Directory of Open Access Journals (Sweden)

    Ahmed eEleryan

    2014-07-01

    Full Text Available In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs. Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 seconds processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions.

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

    Science.gov (United States)

    Baffes, P. T.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  7. Design and market considerations for axial flux superconducting electric machine design

    Science.gov (United States)

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

    2014-05-01

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

  8. 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.

  9. Evaluating the electrical discharge machining (EDM) parameters with using carbon nanotubes

    Science.gov (United States)

    Sari, M. M.; Noordin, M. Y.; Brusa, E.

    2012-09-01

    Electrical discharge machining (EDM) is one of the most accurate non traditional manufacturing processes available for creating tiny apertures, complex or simple shapes and geometries within parts and assemblies. Performance of the EDM process is usually evaluated in terms of surface roughness, existence of cracks, voids and recast layer on the surface of product, after machining. Unfortunately, the high heat generated on the electrically discharged material during the EDM process decreases the quality of products. Carbon nanotubes display unexpected strength and unique electrical and thermal properties. Multi-wall carbon nanotubes are therefore on purpose added to the dielectric used in the EDM process to improve its performance when machining the AISI H13 tool steel, by means of copper electrodes. Some EDM parameters such as material removal rate, electrode wear rate, surface roughness and recast layer are here first evaluated, then compared to the outcome of EDM performed without using nanotubes mixed to the dielectric. Independent variables investigated are pulse on time, peak current and interval time. Experimental evidences show that EDM process operated by mixing multi-wall carbon nanotubes within the dielectric looks more efficient, particularly if machining parameters are set at low pulse of energy.

  10. Evaluating the electrical discharge machining (EDM) parameters with using carbon nanotubes

    International Nuclear Information System (INIS)

    Sari, M M; Brusa, E; Noordin, M Y

    2012-01-01

    Electrical discharge machining (EDM) is one of the most accurate non traditional manufacturing processes available for creating tiny apertures, complex or simple shapes and geometries within parts and assemblies. Performance of the EDM process is usually evaluated in terms of surface roughness, existence of cracks, voids and recast layer on the surface of product, after machining. Unfortunately, the high heat generated on the electrically discharged material during the EDM process decreases the quality of products. Carbon nanotubes display unexpected strength and unique electrical and thermal properties. Multi-wall carbon nanotubes are therefore on purpose added to the dielectric used in the EDM process to improve its performance when machining the AISI H13 tool steel, by means of copper electrodes. Some EDM parameters such as material removal rate, electrode wear rate, surface roughness and recast layer are here first evaluated, then compared to the outcome of EDM performed without using nanotubes mixed to the dielectric. Independent variables investigated are pulse on time, peak current and interval time. Experimental evidences show that EDM process operated by mixing multi-wall carbon nanotubes within the dielectric looks more efficient, particularly if machining parameters are set at low pulse of energy.

  11. 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.

  12. 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.

  13. Comparative Study Between Support Vector Machines And Neural Networks For Lithological Discrimination Using Hyper spectral Data

    International Nuclear Information System (INIS)

    Naguib, A.M.; Abd Elwahab, M.S.; Farag, M.A.; Yahia, M.A.; Ramadan, H.H.

    2009-01-01

    Remote sensing hyper spectral data has many applications especially in the field of , earth science. Utilization of this technology has shown a rapid increase in many areas of economic and scientific significance. Hyper spectral sensors capture the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Classification, clustering, and visualization of these very high dimensional signatures need untraditional methods. Different approaches for spectral image interpretation have been studied using Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to meet the challenge of high dimensionality. The study used SVMs for geological mapping of hyper spectral imagery at Abu Zenima area, western Sinai, Egypt, the hyper spectral data has been captured in 2003 by Hyperion instrument on the United States Geological survey (USGS) Earth Observing 1 (EO-I) satellite. Precisely the study compares between the use of SVMs and a neural network built on the concept of SVMs, this network uses the Kernel-Adatron algorithm with the Gaussian kernel for the process of training. The SVMs also uses the Gaussian kernel with different bandwidths to enhance the performance of the interpretation process; the results are compared in details. The Neural Network was trained with four data sets, the first consists of 11310 samples, gives recognition rate of 84%, the second has 22620 samples, recognition rate was 91.5%; the third has 33930 samples, recognition rate was 94.6%; finally the fourth has 45240 samples, recognition rate of 99.2%. The previous results fall in comparison with the results of SVMs which use two algorithms for training the first is the one against one algorithm which gave a recognition rate of 84% for the first data set, a recognition rate of 76.9% for the second data set, a recognition rate of 95.2% for the third one and 98.5% for the fourth one. and the other is one against many algorithms which gave a recognition

  14. Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

    International Nuclear Information System (INIS)

    Strušnik, Dušan; Marčič, Milan; Golob, Marjan; Hribernik, Aleš; Živić, Marija; Avsec, Jurij

    2016-01-01

    Highlights: • Steam ejector pump and electric liquid ring vacuum pump are analysed and modelled. • A supervised machine learning models by using real process data are applied. • The equation of ejector pumped mass flow from steam turbine condenser was solved. • The loss of specific energy capable of work in a SEPS or LRVP component was analysed. • The economic efficiency analysis per different coal heating values was made. - Abstract: This paper compares the vapour ejector and electric vacuum pump power consumptions with machine learning algorithms by using real process data and presents some novelty guideline for the selection of an appropriate condenser vacuum pump system of a steam turbine power plant. The machine learning algorithms are made by using the supervised machine learning methods such as artificial neural network model and local linear neuro-fuzzy models. The proposed non-linear models are designed by using a wide range of real process operation data sets from the CHP system in the thermal power plant. The novelty guideline for the selection of an appropriate condenser vacuum pumps system is expressed in the comparative analysis of the energy consumption and use of specific energy capable of work. Furthermore, the novelty is expressed in the economic efficiency analysis of the investment taking into consideration the operating costs of the vacuum pump systems and may serve as basic guidelines for the selection of an appropriate condenser vacuum pump system of a steam turbine.

  15. Effect of machining parameters on surface integrity of silicon carbide ceramic using end electric discharge milling and mechanical grinding hybrid machining

    International Nuclear Information System (INIS)

    Ji, Renjie; Liu, Yonghong; Zhang, Yanzhen; Cai, Baoping; Li, Xiaopeng; Zheng, Chao

    2013-01-01

    A novel hybrid process that integrates end electric discharge (ED) milling and mechanical grinding is proposed. The process is able to effectively machine a large surface area on SiC ceramic with good surface quality and fine working environmental practice. The polarity, pulse on-time, and peak current are varied to explore their effects on the surface integrity, such as surface morphology, surface roughness, micro-cracks, and composition on the machined surface. The results show that positive tool polarity, short pulse on-time, and low peak current cause a fine surface finish. During the hybrid machining of SiC ceramic, the material is mainly removed by end ED milling at rough machining mode, whereas it is mainly removed by mechanical grinding at finish machining mode. Moreover, the material from the tool can transfer to the workpiece, and a combination reaction takes place during machining.

  16. 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.

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

    Science.gov (United States)

    Kim, Lok-Won

    2018-05-01

    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 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).

  18. 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.

  19. Modeling of thermal spalling during electrical discharge machining of titanium diboride

    International Nuclear Information System (INIS)

    Gadalla, A.M.; Bozkurt, B.; Faulk, N.M.

    1991-01-01

    Erosion in electrical discharge machining has been described as occurring by melting and flushing the liquid formed. Recently, however, thermal spalling was reported as the mechanism for machining refractory materials with low thermal conductivity and high thermal expansion. The process is described in this paper by a model based on a ceramic surface exposed to a constant circular heating source which supplied a constant flux over the pulse duration. The calculations were based on TiB 2 mechanical properties along a and c directions. Theoretical predictions were verified by machining hexagonal TiB 2 . Large flakes of TiB 2 with sizes close to grain size and maximum thickness close to the predicted values were collected, together with spherical particles of Cu and Zn eroded from cutting wire. The cutting surfaces consist of cleavage planes sometimes contaminated with Cu, Zn, and impurities from the dielectric fluid

  20. Design Analysis of a Dual Rotor Permanent Magnet Machine driven Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Izzat Bin Zainuddin Mohd

    2018-01-01

    Full Text Available Electric bike in urban countries such as Europe and China commonly used the brushless direct current machine (BLDC as it able to produce high torque to transport the user from one place to another. However, BLDC torque density can’t be improving due to limitation magnetic flux generated by the permanent magnet. Therefore, the performance of electric bike can’t be improved. Outer rotor BLDC machine design able to improve the torque density of the motor due to increase radius of the motor which can be explained by simple physics equation (Torque = Force x radius. However, an outer rotor machine only generates constant speed, which is not suitable for operating under tractive load condition, especially electric bike. The proposed model is a new novel of double layer outer rotor BLDCPM machine which able to amplify the magnetic flux density and improve the torque density of the machine. The mutual magnetic coupling between the inner and outer rotor of the proposed model increase the magnetic flux intensity as both of them acts as individual parts. Thus, the magnetic flux generated by both rotors are double which resulted in improving the performance of the E-bike. Designing parameters and analysing the performance of the proposed 2D model is done using FEA tools. Evaluation of the conventional and proposed model by comparing torque performance, magnetic flux density and motor constant square density. Other than that, speed torque graph also is evaluated to justify either it can operate similarly to ICE engine with gears. Two model is designed which is Single Outer Rotor Brushless Direct Current (SORBLDC and Double Outer Rotor Brushless Direct Current (DORBLDC operated with the same cases of 27 Amp current supplied to it and operate under various speed from 500 rpm to 2000 rpm. The average torque produce by the conventional and proposed model are 2.045439 Nm and 3.102648 Nm. Furthermore, improvement of the proposed model to conventional model in

  1. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

    International Nuclear Information System (INIS)

    Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

    2009-01-01

    This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

  2. 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.

  3. Aging Detection of Electrical Point Machines Based on Support Vector Data Description

    Directory of Open Access Journals (Sweden)

    Jaewon Sa

    2017-11-01

    Full Text Available Electrical point machines (EPM must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of “aged” and “not-yet-aged” equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy.

  4. 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.

  5. An overview of technology and research in electrode design and manufacturing in sinking electrical discharge machining

    Directory of Open Access Journals (Sweden)

    Bhola Jha

    2014-06-01

    Full Text Available Electrical discharge machining (EDM is one of the earliest non-traditional machining processes, based on thermoelectric energy between the workpiece and an electrode. In this process, the material is removed electro thermally by a series of successive discrete discharges between two electrically conductive objects, i.e., the electrode and the workpiece. The performance of the process, to a large extent, depends on the material, design and manufacturing method of the electrodes. Electrode design and method of its manufacturing also affect on the cost of electrode. Researchers have explored a number of ways to improve electrode design and devised various ways of manufacturing. The paper reports a review on the research relating to EDM electrode design and its manufacturing for improving and optimizing performance measures and reducing time and cost of manufacturing. The final part of the paper discusses these developments and outlines the trends for future research work.

  6. 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.

  7. Electric-Discharge Machining Techniques for Evaluating Tritium Effects on Materials

    International Nuclear Information System (INIS)

    Morgan, M.J.

    2003-01-01

    In this investigation, new ways to evaluate the long-term effects of tritium on the structural properties of components were developed. Electric-discharge machining (EDM) techniques for cutting tensile and fracture toughness samples from tritium exposed regions of returned reservoirs were demonstrated. An existing electric discharge machine was used to cut sub-size tensile and fracture toughness samples from the inside surfaces of reservoir mockups. Tensile properties from the EDM tensile samples were similar to those measured using full-size samples cut from similar stock. Although the existing equipment could not be used for machining tritium-exposed hardware, off-the shelf EDM units are available that could. With the right equipment and the required radiological controls in place, similar machining and testing techniques could be used to directly measure the effects of tritium on the properties of material cut from reservoir returns. Stress-strain properties from tritium-exposed reservoirs would improve finite element modeling of reservoir performance because the data would be representative of the true state of the reservoir material in the field. Tensile data from samples cut directly from reservoirs would also complement existing shelf storage and burst test data of the Life Storage Program and help answer questions about a specific reservoir's processing history and properties

  8. A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand

    International Nuclear Information System (INIS)

    Mostafavi, Elham Sadat; Mostafavi, Seyyed Iman; Jaafari, Arefeh; Hosseinpour, Fariba

    2013-01-01

    Highlights: • A hybrid approach is presented for the estimation of the electricity demand. • The proposed method integrates the capabilities of GP and SA. • The GSA model makes accurate predictions of the electricity demand. - Abstract: This study proposes an innovative hybrid approach for the estimation of the long-term electricity demand. A new prediction equation was developed for the electricity demand using an integrated search method of genetic programming and simulated annealing, called GSA. The annual electricity demand was formulated in terms of population, gross domestic product (GDP), stock index, and total revenue from exporting industrial products of the same year. A comprehensive database containing total electricity demand in Thailand from 1986 to 2009 was used to develop the model. The generalization of the model was verified using a separate testing data. A sensitivity analysis was conducted to investigate the contribution of the parameters affecting the electricity demand. The GSA model provides accurate predictions of the electricity demand. Furthermore, the proposed model outperforms a regression and artificial neural network-based models

  9. 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...

  10. Experience with the Quality Assurance of the Superconducting Electrical Circuits of the LHC Machine

    CERN Document Server

    Bozzini, D; Kotarba, A; Mess, Karl Hubert; Olek, S; Russenschuck, Stephan

    2006-01-01

    The coherence between the powering reference database for the LHC and the Electrical Quality Assurance (ELQA) is guaranteed on the procedural level. However, a challenge remains the coherence between the database, the magnet test and assembly procedures, and the connection of all superconducting circuits in the LHC machine. In this paper, the methods, tooling, and procedures for the ELQA during the assembly phase of the LHC will be presented in view of the practical experience gained in the LHC tunnel. Some examples of detected polarity errors and electrical non-conformities will be presented. The parameters measured at ambient temperature, such as the dielectric insulation of circuits, will be discussed.

  11. Parametric and non-parametric models for lifespan modeling of insulation systems in electrical machines

    OpenAIRE

    Salameh , Farah; Picot , Antoine; Chabert , Marie; Maussion , Pascal

    2017-01-01

    International audience; This paper describes an original statistical approach for the lifespan modeling of electric machine insulation materials. The presented models aim to study the effect of three main stress factors (voltage, frequency and temperature) and their interactions on the insulation lifespan. The proposed methodology is applied to two different insulation materials tested in partial discharge regime. Accelerated ageing tests are organized according to experimental optimization m...

  12. 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, ...

  13. 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.

  14. A general electromagnetic excitation model for electrical machines considering the magnetic saturation and rub impact

    Science.gov (United States)

    Xu, Xueping; Han, Qinkai; Chu, Fulei

    2018-03-01

    The electromagnetic vibration of electrical machines with an eccentric rotor has been extensively investigated. However, magnetic saturation was often neglected. Moreover, the rub impact between the rotor and stator is inevitable when the amplitude of the rotor vibration exceeds the air-gap. This paper aims to propose a general electromagnetic excitation model for electrical machines. First, a general model which takes the magnetic saturation and rub impact into consideration is proposed and validated by the finite element method and reference. The dynamic equations of a Jeffcott rotor system with electromagnetic excitation and mass imbalance are presented. Then, the effects of pole-pair number and rubbing parameters on vibration amplitude are studied and approaches restraining the amplitude are put forward. Finally, the influences of mass eccentricity, resultant magnetomotive force (MMF), stiffness coefficient, damping coefficient, contact stiffness and friction coefficient on the stability of the rotor system are investigated through the Floquet theory, respectively. The amplitude jumping phenomenon is observed in a synchronous generator for different pole-pair numbers. The changes of design parameters can alter the stability states of the rotor system and the range of parameter values forms the zone of stability, which lays helpful suggestions for the design and application of the electrical machines.

  15. 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

  16. 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.

  17. 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.

  18. Monthly electric energy demand forecasting with neural networks and Fourier series

    International Nuclear Information System (INIS)

    Gonzalez-Romera, E.; Jaramillo-Moran, M.A.; Carmona-Fernandez, D.

    2008-01-01

    Medium-term electric energy demand forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose. (author)

  19. 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.

  20. Response surface modelling of tool electrode wear rate and material removal rate in micro electrical discharge machining of Inconel 718

    DEFF Research Database (Denmark)

    Puthumana, Govindan

    2017-01-01

    conductivity and high strength causing it extremely difficult tomachine. Micro-Electrical Discharge Machining (Micro-EDM) is a non-conventional method that has a potential toovercome these restrictions for machining of Inconel 718. Response Surface Method (RSM) was used for modelling thetool Electrode Wear...

  1. Short-term electricity prices forecasting in a competitive market: A neural network approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

    2007-01-01

    This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

  2. 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

  3. 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.

  4. Effects of fluid flow on heat transfer in large rotating electrical machines

    International Nuclear Information System (INIS)

    Lancial, Nicolas

    2014-01-01

    EDF operates a large number of electrical rotating machines in its electricity generation capacity. Thermal stresses which affect them can cause local heating, sufficient to damage their integrity. The present work contributes to provide methodologies for detecting hot spots in these machines, better understanding the topology of rotating flows and identifying their effects on heat transfer. Several experimental scale model were used by increasing their complexity to understand and validate the numerical simulations. A first study on a turbulent wall jet over a non-confined backward-facing step (half-pole hydro-generator) notes significant differences compared to results from confined case: both of them are present in an hydro-generator. A second study was done on a small confined rotating scale model to determinate the effects of a Taylor-Couette-Poiseuille on temperature distribution and position of hot spots on the heated rotor, by studying the overall flow regimes flow. These studies have helped to obtain a reliable method based on conjugate heat transfer (CHT) simulations. Another method, based on FEM coupled with the use of an inverse method, has been studied on a large model of hydraulic generator so as to solve the computation time issue of the first methodology. It numerically calculates the convective heat transfer from temperature measurements, but depends on the availability of experimental data. This work has also developed new no-contact measurement techniques as the use of a high-frequency pyrometer which can be applied on rotating machines for monitoring temperature. (author)

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    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

  6. 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.

  7. HTS machines as enabling technology for all-electric airborne vehicles

    International Nuclear Information System (INIS)

    Masson, P J; Brown, G V; Soban, D S; Luongo, C A

    2007-01-01

    Environmental protection has now become paramount as evidence mounts to support the thesis of human activity-driven global warming. A global reduction of the emissions of pollutants into the atmosphere is therefore needed and new technologies have to be considered. A large part of the emissions come from transportation vehicles, including cars, trucks and airplanes, due to the nature of their combustion-based propulsion systems. Our team has been working for several years on the development of high power density superconducting motors for aircraft propulsion and fuel cell based power systems for aircraft. This paper investigates the feasibility of all-electric aircraft based on currently available technology. Electric propulsion would require the development of high power density electric propulsion motors, generators, power management and distribution systems. The requirements in terms of weight and volume of these components cannot be achieved with conventional technologies; however, the use of superconductors associated with hydrogen-based power plants makes possible the design of a reasonably light power system and would therefore enable the development of all-electric aero-vehicles. A system sizing has been performed both for actuators and for primary propulsion. Many advantages would come from electrical propulsion such as better controllability of the propulsion, higher efficiency, higher availability and less maintenance needs. Superconducting machines may very well be the enabling technology for all-electric aircraft development

  8. HTS machines as enabling technology for all-electric airborne vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Masson, P J [FAMU-FSU College of Engineering and the Center for Advanced Power Systems, Tallahassee, FL 32310 (United States); Brown, G V [NASA Glenn Research Center, Cleveland, OH (United States); Soban, D S [Aerospace System Design Laboratory/Georgia Tech, Atlanta, GA 32332 (United States); Luongo, C A [FAMU-FSU College of Engineering and the Center for Advanced Power Systems, Tallahassee, FL 32310 (United States)

    2007-08-15

    Environmental protection has now become paramount as evidence mounts to support the thesis of human activity-driven global warming. A global reduction of the emissions of pollutants into the atmosphere is therefore needed and new technologies have to be considered. A large part of the emissions come from transportation vehicles, including cars, trucks and airplanes, due to the nature of their combustion-based propulsion systems. Our team has been working for several years on the development of high power density superconducting motors for aircraft propulsion and fuel cell based power systems for aircraft. This paper investigates the feasibility of all-electric aircraft based on currently available technology. Electric propulsion would require the development of high power density electric propulsion motors, generators, power management and distribution systems. The requirements in terms of weight and volume of these components cannot be achieved with conventional technologies; however, the use of superconductors associated with hydrogen-based power plants makes possible the design of a reasonably light power system and would therefore enable the development of all-electric aero-vehicles. A system sizing has been performed both for actuators and for primary propulsion. Many advantages would come from electrical propulsion such as better controllability of the propulsion, higher efficiency, higher availability and less maintenance needs. Superconducting machines may very well be the enabling technology for all-electric aircraft development.

  9. The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

    Science.gov (United States)

    Ivanin, O. A.; Direktor, L. B.

    2018-05-01

    The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

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

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... self organizing feature map; which is back-propagating in nature. ... distribution scheduling. ... electricity demand with lead times that range from ... become increasingly vital since the rise of the ... implemented for advanced control, data and sensor ... inspired methods of computing are thought to be the.

  11. An on-line monitoring system for a micro electrical discharge machining (micro-EDM) process

    International Nuclear Information System (INIS)

    Liao, Y S; Chang, T Y; Chuang, T J

    2008-01-01

    A pulse-type discriminating system to monitor the process of micro electrical discharge machining (micro-EDM) is developed and implemented. The specific features are extracted and the pulses from a RC-type power source are classified into normal, effective arc, transient short circuit and complex types. An approach to discriminate the pulse type according to three durations measured at three pre-determined voltage levels of a pulse is proposed. The developed system is verified by using simulated signals. Discrimination of the pulse trains in actual machining processes shows that the pulses are mainly the normal type for micro wire-EDM and micro-EDM milling. The pulse-type distribution varies during the micro-EDM drilling process. The percentage of complex-type pulse increases monotonically with the drilling depth. It starts to drop when the gap condition is seriously deteriorated. Accordingly, an on-line monitoring strategy for the micro-EDM drilling process is proposed

  12. 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.

  13. 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.

  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

    2017-01-29

    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.

  15. Isogeometric analysis and harmonic stator-rotor coupling for simulating electric machines

    Science.gov (United States)

    Bontinck, Zeger; Corno, Jacopo; Schöps, Sebastian; De Gersem, Herbert

    2018-06-01

    This work proposes Isogeometric Analysis as an alternative to classical finite elements for simulating electric machines. Through the spline-based Isogeometric discretization it is possible to parametrize the circular arcs exactly, thereby avoiding any geometrical error in the representation of the air gap where a high accuracy is mandatory. To increase the generality of the method, and to allow rotation, the rotor and the stator computational domains are constructed independently as multipatch entities. The two subdomains are then coupled using harmonic basis functions at the interface which gives rise to a saddle-point problem. The properties of Isogeometric Analysis combined with harmonic stator-rotor coupling are presented. The results and performance of the new approach are compared to the ones for a classical finite element method using a permanent magnet synchronous machine as an example.

  16. 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

  17. Engineering of Machine tool’s High-precision electric drives

    Science.gov (United States)

    Khayatov, E. S.; Korzhavin, M. E.; Naumovich, N. I.

    2018-03-01

    In the article it is shown that in mechanisms with numerical program control, high quality of processes can be achieved only in systems that provide adjustment of the working element’s position with high accuracy, and this requires an expansion of the regulation range by the torque. In particular, the use of synchronous reactive machines with independent excitation control makes it possible to substantially increase the moment overload in the sequential excitation circuit. Using mathematical and physical modeling methods, it is shown that in the electric drive with a synchronous reactive machine with independent excitation in a circuit with sequential excitation, it is possible to significantly expand the range of regulation by the torque and this is achieved by the effect of sequential excitation, which makes it possible to compensate for the transverse reaction of the armature.

  18. Comparative Study of White Layer Characteristics for Static and Rotating Workpiece during Electric Discharge Machining

    Directory of Open Access Journals (Sweden)

    SHAHID MEHMOOD

    2017-10-01

    Full Text Available EDMed (Electric Discharge Machined surfaces are unique in their appearance and metallurgical characteristics, which depend on different parameter such as electric parameters, flushing method, and dielectric type. Conventionally, in static workpiece method the EDM (Electric Discharge Machining is performed by submerging both of the tool and workpiece in dielectric liquid and side flushing is provided by impinging pressurized dielectric liquid into the gap. Another flushing method has been investigated in this study, in which, instead of side flushing the rotation motion is provided to the workpiece. Surface characteristics for both flushing methods are determined and compared in this study. The investigated surface characteristics are: surface roughness, crater size, surface morphology, white layer thickness and composition. These investigations are performed using optical and SEM (Scanning Electron Microscope. Statistical confidence limits are determined for scattered data of surface roughness. It is found that the white layer thickness and surface roughness are directly proportional to discharge current for both flushing methods. The comparison has shown that the side flushing of statics workpiece gives thicker white layer and lower surface finish as compared to the flushing caused by the rotation of workpiece

  19. Comparative study of white layer characteristics for static and rotating workpiece during electric discharge machining

    International Nuclear Information System (INIS)

    Mehmood, S.; Shah, M.; Anjum, N.A.

    2017-01-01

    EDMed (Electric Discharge Machined) surfaces are unique in their appearance and metallurgical characteristics, which depend on different parameter such as electric parameters, flushing method, and dielectric type. Conventionally, in static workpiece method the EDM (Electric Discharge Machining) is performed by submerging both of the tool and workpiece in dielectric liquid and side flushing is provided by impinging pressurized dielectric liquid into the gap. Another flushing method has been investigated in this study, in which, instead of side flushing the rotation motion is provided to the workpiece. Surface characteristics for both flushing methods are determined and compared in this study. The investigated surface characteristics are: surface roughness, crater size, surface morphology, white layer thickness and composition. These investigations are performed using optical and SEM (Scanning Electron Microscope). Statistical confidence limits are determined for scattered data of surface roughness. It is found that the white layer thickness and surface roughness are directly proportional to discharge current for both flushing methods. The comparison has shown that the side flushing of statics workpiece gives thicker white layer and lower surface finish as compared to the flushing caused by the rotation of workpiece. (author)

  20. Hardness and structure changes at surface in electrical discharge machined steel C 3840

    International Nuclear Information System (INIS)

    Karastojkovic, Z.; Janjusevic, Z.

    2003-01-01

    The electrical discharge machining (EDM) of both hard and soft materials became an important technique in industrial applications. This technique has an advantage in producing of structural/tool parts of complex geometry. The EDM is based on electrical phenomena, when the treated surface undergoes to erosion. The first step in EDM, the melting of thin surface layer, frequently is neglected. In this paper the changes of hardness and structure at surface layer, after EDM is applied on steel C 3840, will be discussed. The steel C- 3840 was quenched and tempered to hardness of 63 HRC, at surface, and than machined by electrical discharging. The changed, white, layer is just a product of melting and decarburization processes. The white layer is registered at surface by using a metallographic investigation. Hardness profile is measured from surface to the interior of material. The achievement of local high temperatures during EDM is resulting on melt and erosion of material. Besides of these effects, during EDM were happened some minor but not a neglectible effects, primary on structure changes on treated surface. It would be expected that melting, even an evaporation of melted metal, and further the phase transformation have an important influence on the starting structure. (Original)

  1. Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine

    Science.gov (United States)

    Laib dit Leksir, Y.; Mansour, M.; Moussaoui, A.

    2018-03-01

    Analysis and processing of databases obtained from infrared thermal inspections made on electrical installations require the development of new tools to obtain more information to visual inspections. Consequently, methods based on the capture of thermal images show a great potential and are increasingly employed in this field. However, there is a need for the development of effective techniques to analyse these databases in order to extract significant information relating to the state of the infrastructures. This paper presents a technique explaining how this approach can be implemented and proposes a system that can help to detect faults in thermal images of electrical installations. The proposed method classifies and identifies the region of interest (ROI). The identification is conducted using support vector machine (SVM) algorithm. The aim here is to capture the faults that exist in electrical equipments during an inspection of some machines using A40 FLIR camera. After that, binarization techniques are employed to select the region of interest. Later the comparative analysis of the obtained misclassification errors using the proposed method with Fuzzy c means and Ostu, has also be addressed.

  2. 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.

  3. PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

    Directory of Open Access Journals (Sweden)

    Yuanchang Zhong

    2014-01-01

    Full Text Available The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

  4. 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.

  5. Analysis of Effects of Cutting Parameters of Wire Electrical Discharge Machining on Material Removal Rate and Surface Integrity

    International Nuclear Information System (INIS)

    Tonday, H. R.; Tigga, A. M.

    2016-01-01

    As wire electrical discharge machining is pioneered as a vigorous, efficient and precise and complex nontraditional machining technique, research is needed in this area for efficient machining. In this paper, the influence of various input factors of wire electrical discharge machining (WEDM) on output variable has been analyzed by using Taguchi technique and analysis of variance. The design of experiments has been done and by applying L8 orthogonal arrays method and experiments have been conducted and collected required data. The objectives of the research are to maximize the material removal rate and to minimize the surface roughness value (Ra). Surface morphology of machined workpiece has been obtained and examined by employing scanning electron microscopy (SEM) technique. (paper)

  6. Analysis of Effects of Cutting Parameters of Wire Electrical Discharge Machining on Material Removal Rate and Surface Integrity

    Science.gov (United States)

    Tonday, H. R.; Tigga, A. M.

    2016-02-01

    As wire electrical discharge machining is pioneered as a vigorous, efficient and precise and complex nontraditional machining technique, research is needed in this area for efficient machining. In this paper, the influence of various input factors of wire electrical discharge machining (WEDM) on output variable has been analyzed by using Taguchi technique and analysis of variance. The design of experiments has been done and by applying L8 orthogonal arrays method and experiments have been conducted and collected required data. The objectives of the research are to maximize the material removal rate and to minimize the surface roughness value (Ra). Surface morphology of machined workpiece has been obtained and examined by employing scanning electron microscopy (SEM) technique.

  7. Alternating current electric field effects on neural stem cell viability and differentiation.

    Science.gov (United States)

    Matos, Marvi A; Cicerone, Marcus T

    2010-01-01

    Methods utilizing stem cells hold tremendous promise for tissue engineering applications; however, many issues must be worked out before these therapies can be routinely applied. Utilization of external cues for preimplantation expansion and differentiation offers a potentially viable approach to the use of stem cells in tissue engineering. The studies reported here focus on the response of murine neural stem cells encapsulated in alginate hydrogel beads to alternating current electric fields. Cell viability and differentiation was studied as a function of electric field magnitude and frequency. We applied fields of frequency (0.1-10) Hz, and found a marked peak in neural stem cell viability under oscillatory electric fields with a frequency of 1 Hz. We also found an enhanced propensity for astrocyte differentiation over neuronal differentiation in the 1 Hz cultures, as compared to the other field frequencies we studied. Published 2010 American Institute of Chemical Engineers

  8. Development of automated system based on neural network algorithm for detecting defects on molds installed on casting machines

    Science.gov (United States)

    Bazhin, V. Yu; Danilov, I. V.; Petrov, P. A.

    2018-05-01

    During the casting of light alloys and ligatures based on aluminum and magnesium, problems of the qualitative distribution of the metal and its crystallization in the mold arise. To monitor the defects of molds on the casting conveyor, a camera with a resolution of 780 x 580 pixels and a shooting rate of 75 frames per second was selected. Images of molds from casting machines were used as input data for neural network algorithm. On the preparation of a digital database and its analytical evaluation stage, the architecture of the convolutional neural network was chosen for the algorithm. The information flow from the local controller is transferred to the OPC server and then to the SCADA system of foundry. After the training, accuracy of neural network defect recognition was about 95.1% on a validation split. After the training, weight coefficients of the neural network were used on testing split and algorithm had identical accuracy with validation images. The proposed technical solutions make it possible to increase the efficiency of the automated process control system in the foundry by expanding the digital database.

  9. Controlling corrosion rate of Magnesium alloy using powder mixed electrical discharge machining

    Science.gov (United States)

    Razak, M. A.; Rani, A. M. A.; Saad, N. M.; Littlefair, G.; Aliyu, A. A.

    2018-04-01

    Biomedical implant can be divided into permanent and temporary employment. The duration of a temporary implant applied to children and adult is different due to different bone healing rate among the children and adult. Magnesium and its alloys are compatible for the biodegradable implanting application. Nevertheless, it is difficult to control the degradation rate of magnesium alloy to suit the application on both the children and adult. Powder mixed electrical discharge machining (PM-EDM) method, a modified EDM process, has high capability to improve the EDM process efficiency and machined surface quality. The objective of this paper is to establish a formula to control the degradation rate of magnesium alloy using the PM-EDM method. The different corrosion rate of machined surface is hypothesized to be obtained by having different combinations of PM-EDM operation inputs. PM-EDM experiments are conducted using an opened-loop PM-EDM system and the in-vitro corrosion tests are carried out on the machined surface of each specimen. There are four operation inputs investigated in this study which are zinc powder concentration, peak current, pulse on-time and pulse off-time. The results indicate that zinc powder concentration is significantly affecting the response with 2 g/l of zinc powder concentration obtaining the lowest corrosion rate. The high localized temperature at the cutting zone in spark erosion process causes some of the zinc particles get deposited on the machined surface, hence improving the surface characteristics. The suspended zinc particles in the dielectric fluid have also improve the sparking efficiency and the uniformity of sparks distribution. From the statistical analysis, a formula was developed to control the corrosion rate of magnesium alloy within the range from 0.000183 mm/year to 0.001528 mm/year.

  10. Study on effect of tool electrodes on surface finish during electrical discharge machining of Nitinol

    Science.gov (United States)

    Sahu, Anshuman Kumar; Chatterjee, Suman; Nayak, Praveen Kumar; Sankar Mahapatra, Siba

    2018-03-01

    Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

  11. 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.

  12. Chronic behavior evaluation of a micro-machined neural implant with optimized design based on an experimentally derived model.

    Science.gov (United States)

    Andrei, Alexandru; Welkenhuysen, Marleen; Ameye, Lieveke; Nuttin, Bart; Eberle, Wolfgang

    2011-01-01

    Understanding the mechanical interactions between implants and the surrounding tissue is known to have an important role for improving the bio-compatibility of such devices. Using a recently developed model, a particular micro-machined neural implant design aiming the reduction of insertion forces dependence on the insertion speed was optimized. Implantations with 10 and 100 μm/s insertion speeds showed excellent agreement with the predicted behavior. Lesion size, gliosis (GFAP), inflammation (ED1) and neuronal cells density (NeuN) was evaluated after 6 week of chronic implantation showing no insertion speed dependence.

  13. Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013

    International Nuclear Information System (INIS)

    Dev, Priya; Martin, Michael A.

    2014-01-01

    Highlights: • Neural nets are unable to properly capture spiky price behavior found in the electricity market. • We modeled electricity price data from the Australian National Electricity Market over 15 years. • Neural nets need to be augmented with other modeling techniques to capture price spikes. • We fit a Generalized Pareto Distribution to price spikes using a peaks-over-thresholds approach. - Abstract: Competitors in the electricity supply industry desire accurate predictions of electricity spot prices to hedge against financial risks. Neural networks are commonly used for forecasting such prices, but certain features of spot price series, such as extreme price spikes, present critical challenges for such modeling. We investigate the predictive capacity of neural networks for electricity spot prices using Australian National Electricity Market data. Following neural net modeling of the data, we explore extreme price spikes through extreme value modeling, fitting a Generalized Pareto Distribution to price peaks over an estimated threshold. While neural nets capture the smoother aspects of spot price data, they are unable to capture local, volatile features that characterize electricity spot price data. Price spikes can be modeled successfully through extreme value modeling

  14. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Vibration of high-voltage electric machines with rotors on rolling bearings

    Science.gov (United States)

    Shekyan, H. G.; Gevorgyan, A. V.

    2018-04-01

    The paper presents an investigation of vibrational activity of electric machines due to high-harmonic vibrational loadings. It is shown that the vibrational loadings experienced by bearings may result in the interruption of their normal operation and even take them out of action. Therefore, the values of the vibrational speed-up leading to high harmonics are factors determining the admissible dynamic loading on the bearings. In the paper, an attempt is made to consider the factors which result in origination of high harmonics and to illustrate methods for their smoothing.

  20. Influence of electrical discharge machining on the tribological characteristics of WC-Co alloys

    International Nuclear Information System (INIS)

    Casas, B.; Martinez, E.; Esteve, J.; Anglada, M.; Llanes, L.

    2001-01-01

    The influence of electrical discharge machining (EDM) on the abrasive wear resistance of two WC-10 % w tCo cemented carbides with different carbide grain size has been studied. Different surface finish conditions were evaluated corresponding to sequential EDM as well as grinding and polishing with diamond. The abrasive wear resistance was determined through microscratch measurements using a nano indentation system. Contrary to the results obtained from hardness measurements, this techniques allows to discern tribological differences among the distinct surface finish conditions studied. Finally, the abrasive wear resistance degradation associated with sequential EDM is discussed as a function of microstructure in terms of a damage parameters. (Author) 9 refs

  1. 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.

  2. 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.

  3. 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.

  4. Upper limb functional electrical stimulation devices and their man-machine interfaces.

    Science.gov (United States)

    Venugopalan, L; Taylor, P N; Cobb, J E; Swain, I D

    2015-01-01

    Functional Electrical Stimulation (FES) is a technique that uses electricity to activate the nerves of a muscle that is paralysed due to hemiplegia, multiple sclerosis, Parkinson's disease or spinal cord injury (SCI). FES has been widely used to restore upper limb functions in people with hemiplegia and C5-C7 tetraplegia and has improved their ability to perform their activities of daily living (ADL). At the time of writing, a detailed literature review of the existing upper limb FES devices and their man-machine interfaces (MMI) showed that only the NESS H200 was commercially available. However, the rigid arm splint doesn't fit everyone and prevents the use of a tenodesis grip. Hence, a robust and versatile upper limb FES device that can be used by a wider group of people is required.

  5. Rotating electrical machines part 2 : methods for determining losses and efficiency of rotating electrical machinery form tests (excl. machines for traction vehicles)

    CERN Document Server

    International Electrotechnical Commission. Geneva

    1972-01-01

    Applies to d.c. machines and to a.c. synchronous and induction machines. The principles can be applied to other types of machines such as rotary converters, a.c. commutator motors and single-phase induction motors for which other methods of determining losses are used.

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

    Directory of Open Access Journals (Sweden)

    Sébastien Martin

    Full Text Available 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.

  7. 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.

  8. 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.

  9. 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.

  10. Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

    International Nuclear Information System (INIS)

    Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

    2015-01-01

    Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

  11. 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.

  12. 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.

  13. Advances in three-dimensional field analysis and evaluation of performance parameters of electrical machines

    Science.gov (United States)

    Sivasubramaniam, Kiruba

    This thesis makes advances in three dimensional finite element analysis of electrical machines and the quantification of their parameters and performance. The principal objectives of the thesis are: (1)the development of a stable and accurate method of nonlinear three-dimensional field computation and application to electrical machinery and devices; and (2)improvement in the accuracy of determination of performance parameters, particularly forces and torque computed from finite elements. Contributions are made in two general areas: a more efficient formulation for three dimensional finite element analysis which saves time and improves accuracy, and new post-processing techniques to calculate flux density values from a given finite element solution. A novel three-dimensional magnetostatic solution based on a modified scalar potential method is implemented. This method has significant advantages over the traditional total scalar, reduced scalar or vector potential methods. The new method is applied to a 3D geometry of an iron core inductor and a permanent magnet motor. The results obtained are compared with those obtained from traditional methods, in terms of accuracy and speed of computation. A technique which has been observed to improve force computation in two dimensional analysis using a local solution of Laplace's equation in the airgap of machines is investigated and a similar method is implemented in the three dimensional analysis of electromagnetic devices. A new integral formulation to improve force calculation from a smoother flux-density profile is also explored and implemented. Comparisons are made and conclusions drawn as to how much improvement is obtained and at what cost. This thesis also demonstrates the use of finite element analysis to analyze torque ripples due to rotor eccentricity in permanent magnet BLDC motors. A new method for analyzing torque harmonics based on data obtained from a time stepping finite element analysis of the machine is

  14. Effect of Electrical Discharge Machining on Stress Concentration in Titanium Alloy Holes.

    Science.gov (United States)

    Hsu, Wei-Hsuan; Chien, Wan-Ting

    2016-11-24

    Titanium alloys have several advantages, such as a high strength-to-weight ratio. However, the machinability of titanium alloys is not as good as its mechanical properties. Many machining processes have been used to fabricate titanium alloys. Among these machining processes, electrical discharge machining (EDM) has the advantage of processing efficiency. EDM is based on thermoelectric energy between a workpiece and an electrode. A pulse discharge occurs in a small gap between the workpiece and electrode. Then, the material from the workpiece is removed through melting and vaporization. However, defects such as cracks and notches are often detected at the boundary of holes fabricated using EDM and the irregular profile of EDM holes reduces product quality. In this study, an innovative method was proposed to estimate the effect of EDM parameters on the surface quality of the holes. The method combining the finite element method and image processing can rapidly evaluate the stress concentration factor of a workpiece. The stress concentration factor was assumed as an index of EDM process performance for estimating the surface quality of EDM holes. In EDM manufacturing processes, Ti-6Al-4V was used as an experimental material and, as process parameters, pulse current and pulse on-time were taken into account. The results showed that finite element simulations can effectively analyze stress concentration in EDM holes. Using high energy during EDM leads to poor hole quality, and the stress concentration factor of a workpiece is correlated to hole quality. The maximum stress concentration factor for an EDM hole was more than four times that for the same diameter of the undamaged hole.

  15. Surface integrity and fatigue behaviour of electric discharged machined and milled austenitic stainless steel

    Energy Technology Data Exchange (ETDEWEB)

    Lundberg, Mattias, E-mail: mattias.lundberg@liu.se; Saarimäki, Jonas; Moverare, Johan J.; Calmunger, Mattias

    2017-02-15

    Machining of austenitic stainless steels can result in different surface integrities and different machining process parameters will have a great impact on the component fatigue life. Understanding how machining processes affect the cyclic behaviour and microstructure are of outmost importance in order to improve existing and new life estimation models. Milling and electrical discharge machining (EDM) have been used to manufacture rectangular four-point bend fatigue test samples; subjected to high cycle fatigue. Before fatigue testing, surface integrity characterisation of the two surface conditions was conducted using scanning electron microscopy, surface roughness, residual stress profiles, and hardness profiles. Differences in cyclic behaviour were observed between the two surface conditions by the fatigue testing. The milled samples exhibited a fatigue limit. EDM samples did not show the same behaviour due to ratcheting. Recrystallized nano sized grains were identified at the severely plastically deformed surface of the milled samples. Large amounts of bent mechanical twins were observed ~ 5 μm below the surface. Grain shearing and subsequent grain rotation from milling bent the mechanical twins. EDM samples showed much less plastic deformation at the surface. Surface tensile residual stresses of ~ 500 MPa and ~ 200 MPa for the milled and EDM samples respectively were measured. - Highlights: •Milled samples exhibit fatigue behaviour, but not EDM samples. •Four-point bending is not suitable for materials exhibiting pronounced ratcheting. •LAGB density can be used to quantitatively measure plastic deformation. •Grain shearing and rotation result in bent mechanical twins. •Nano sized grains evolve due to the heat of the operation.

  16. 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.

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

    Science.gov (United States)

    Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

    2015-01-01

    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.

  18. Neural net based determination of generator-shedding requirements in electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

    1992-09-01

    This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

  19. 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.

  20. 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.

  1. 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.

  2. An experimental and computational investigation of electrical resistivity imaging for prediction ahead of tunnel boring machines

    Science.gov (United States)

    Schaeffer, Kevin P.

    Tunnel boring machines (TBMs) are routinely used for the excavation of tunnels across a range of ground conditions, from hard rock to soft ground. In complex ground conditions and in urban environments, the TBM susceptible to damage due to uncertainty of what lies ahead of the tunnel face. The research presented here explores the application of electrical resistivity theory for use in the TBM tunneling environment to detect changing conditions ahead of the machine. Electrical resistivity offers a real-time and continuous imaging solution to increase the resolution of information along the tunnel alignment and may even unveil previously unknown geologic or man-made features ahead of the TBM. The studies presented herein, break down the tunneling environment and the electrical system to understand how its fundamental parameters can be isolated and tested, identifying how they influence the ability to predict changes ahead of the tunnel face. A proof-of-concept, scaled experimental model was constructed in order assess the ability of the model to predict a metal pipe (or rod) ahead of face as the TBM excavates through a saturated sand. The model shows that a prediction of up to three tunnel diameters could be achieved, but the unique presence of the pipe (or rod) could not be concluded with certainty. Full scale finite element models were developed in order evaluate the various influences on the ability to detect changing conditions ahead of the face. Results show that TBM/tunnel geometry, TBM type, and electrode geometry can drastically influence prediction ahead of the face by tens of meters. In certain conditions (i.e., small TBM diameter, low cover depth, large material contrasts), changes can be detected over 100 meters in front of the TBM. Various electrode arrays were considered and show that in order to better detect more finite differences (e.g., boulder, lens, pipe), the use of individual cutting tools as electrodes is highly advantageous to increase spatial

  3. A New Energy-Based Method for 3-D Finite-Element Nonlinear Flux Linkage computation of Electrical Machines

    DEFF Research Database (Denmark)

    Lu, Kaiyuan; Rasmussen, Peter Omand; Ritchie, Ewen

    2011-01-01

    This paper presents a new method for computation of the nonlinear flux linkage in 3-D finite-element models (FEMs) of electrical machines. Accurate computation of the nonlinear flux linkage in 3-D FEM is not an easy task. Compared to the existing energy-perturbation method, the new technique......-perturbation method. The new method proposed is validated using experimental results on two different permanent magnet machines....

  4. Electrically polarized PLLA nanofibers as neural tissue engineering scaffolds with improved neuritogenesis.

    Science.gov (United States)

    Barroca, Nathalie; Marote, Ana; Vieira, Sandra I; Almeida, Abílio; Fernandes, Maria H V; Vilarinho, Paula M; da Cruz E Silva, Odete A B

    2018-07-01

    Tissue engineering is evolving towards the production of smart platforms exhibiting stimulatory cues to guide tissue regeneration. This work explores the benefits of electrical polarization to produce more efficient neural tissue engineering platforms. Poly (l-lactic) acid (PLLA)-based scaffolds were prepared as solvent cast films and electrospun aligned nanofibers, and electrically polarized by an in-lab built corona poling device. The characterization of the platforms by thermally stimulated depolarization currents reveals a polarization of 60 × 10 -10 C cm -2 that is stable on poled electrospun nanofibers for up to 6 months. Further in vitro studies using neuroblastoma cells reveals that platforms' polarization potentiates Retinoic Acid-induced neuronal differentiation. Additionally, in differentiating embryonic cortical neurons, poled aligned nanofibers further increased neurite outgrowth by 30% (+70 μm) over non-poled aligned nanofibers, and by 50% (+100 μm) over control conditions. Therefore, the synergy of topographical cues and electrical polarization of poled aligned nanofibers places them as promising biocompatible and bioactive platforms for neural tissue regeneration. Given their long lasting induced polarization, these PLLA poled nanofibrous scaffolds can be envisaged as therapeutic devices of long shelf life for neural repair applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. 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.

  6. Ideology of a multiparametric system for estimating the insulation system of electric machines on the basis of absorption testing methods

    Science.gov (United States)

    Kislyakov, M. A.; Chernov, V. A.; Maksimkin, V. L.; Bozhin, Yu. M.

    2017-12-01

    The article deals with modern methods of monitoring the state and predicting the life of electric machines. In 50% of the cases of failure in the performance of electric machines is associated with insulation damage. As promising, nondestructive methods of control, methods based on the investigation of the processes of polarization occurring in insulating materials are proposed. To improve the accuracy of determining the state of insulation, a multiparametric approach is considered, which is a basis for the development of an expert system for estimating the state of health.

  7. Analytical approximation of the erosion rate and electrode wear in micro electrical discharge machining

    International Nuclear Information System (INIS)

    Kurnia, W; Tan, P C; Yeo, S H; Wong, M

    2008-01-01

    Theoretical models have been used to predict process performance measures in electrical discharge machining (EDM), namely the material removal rate (MRR), tool wear ratio (TWR) and surface roughness (SR). However, these contributions are mainly applicable to conventional EDM due to limits on the range of energy and pulse-on-time adopted by the models. This paper proposes an analytical approximation of micro-EDM performance measures, based on the crater prediction using a developed theoretical model. The results show that the analytical approximation of the MRR and TWR is able to provide a close approximation with the experimental data. The approximation results for the MRR and TWR are found to have a variation of up to 30% and 24%, respectively, from their associated experimental values. Since the voltage and current input used in the computation are captured in real time, the method can be applied as a reliable online monitoring system for the micro-EDM process

  8. 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.

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

    International Nuclear Information System (INIS)

    Rodriguez-Mateos, F.; Coull, L.; Dahlerup-Petersen, K.; Hagedorn, D.; Krainz, G.; Rijllart, A.; McInturff, A.

    1996-01-01

    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

  10. Statistical investigations into the erosion of material from the tool in micro-electrical discharge machining

    DEFF Research Database (Denmark)

    Puthumana, Govindan

    2018-01-01

    This paper presents a statistical study of the erosion of material from the tool electrode in a micro-electrical discharge machining process. The work involves analysis of variance and analysis of means approaches on the results of the tool electrode wear rate obtained based on design...... current (Id) and discharge frequency (fd) control the erosion of material from the tool electrode. The material erosion from the tool electrode (Me) increases linearly with the discharge frequency. As the current index increases from 20 to 35, the Me decreases linearly by 29%, and then increases by of 36......%. The current index of 35 gives the minimum material erosion from the tool. It is observed that none of the two-factor interactions are significant in controlling the erosion of the material from the tool....

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

    International Nuclear Information System (INIS)

    Rodriguez-Mateos, F.; Coull, L.; Dahlerup-Petersen, K.; Hagedorn, D.; Krainz, G.; Rijllart, A.; McInturff, A.

    1995-01-01

    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

  12. 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.

  13. A Hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

    Energy Technology Data Exchange (ETDEWEB)

    Morton, April M [ORNL; Nagle, Nicholas N [ORNL; Piburn, Jesse O [ORNL; Stewart, Robert N [ORNL; McManamay, Ryan A [ORNL

    2017-01-01

    As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.

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

    Science.gov (United States)

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

    2016-01-01

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

  15. 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.

  16. 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.

  17. 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

  18. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

    International Nuclear Information System (INIS)

    Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

    2004-01-01

    This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

  19. Stretchable Transparent Electrode Arrays for Simultaneous Electrical and Optical Interrogation of Neural Circuits in Vivo.

    Science.gov (United States)

    Zhang, Jing; Liu, Xiaojun; Xu, Wenjing; Luo, Wenhan; Li, Ming; Chu, Fangbing; Xu, Lu; Cao, Anyuan; Guan, Jisong; Tang, Shiming; Duan, Xiaojie

    2018-04-09

    Recent developments of transparent electrode arrays provide a unique capability for simultaneous optical and electrical interrogation of neural circuits in the brain. However, none of these electrode arrays possess the stretchability highly desired for interfacing with mechanically active neural systems, such as the brain under injury, the spinal cord, and the peripheral nervous system (PNS). Here, we report a stretchable transparent electrode array from carbon nanotube (CNT) web-like thin films that retains excellent electrochemical performance and broad-band optical transparency under stretching and is highly durable under cyclic stretching deformation. We show that the CNT electrodes record well-defined neuronal response signals with negligible light-induced artifacts from cortical surfaces under optogenetic stimulation. Simultaneous two-photon calcium imaging through the transparent CNT electrodes from cortical surfaces of GCaMP-expressing mice with epilepsy shows individual activated neurons in brain regions from which the concurrent electrical recording is taken, thus providing complementary cellular information in addition to the high-temporal-resolution electrical recording. Notably, the studies on rats show that the CNT electrodes remain operational during and after brain contusion that involves the rapid deformation of both the electrode array and brain tissue. This enables real-time, continuous electrophysiological monitoring of cortical activity under traumatic brain injury. These results highlight the potential application of the stretchable transparent CNT electrode arrays in combining electrical and optical modalities to study neural circuits, especially under mechanically active conditions, which could potentially provide important new insights into the local circuit dynamics of the spinal cord and PNS as well as the mechanism underlying traumatic injuries of the nervous system.

  20. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    Science.gov (United States)

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. 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.

  2. 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”.

  3. 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.

  4. Online Vibration Monitoring of a Water Pump Machine to Detect Its Malfunction Components Based on Artificial Neural Network

    Science.gov (United States)

    Rahmawati, P.; Prajitno, P.

    2018-04-01

    Vibration monitoring is a measurement instrument used to identify, predict, and prevent failures in machine instruments[6]. This is very needed in the industrial applications, cause any problem with the equipment or plant translates into economical loss and they are mostly monitored component off-line[2]. In this research, a system has been developed to detect the malfunction of the components of Shimizu PS-128BT water pump machine, such as capacitor, bearing and impeller by online measurements. The malfunction components are detected by taking vibration data using a Micro-Electro-Mechanical System(MEMS)-based accelerometer that are acquired by using Raspberry Pi microcomputer and then the data are converted into the form of Relative Power Ratio(RPR). In this form the signal acquired from different components conditions have different patterns. The collected RPR used as the base of classification process for recognizing the damage components of the water pump that are conducted by Artificial Neural Network(ANN). Finally, the damage test result will be sent via text message using GSM module that are connected to Raspberry Pi microcomputer. The results, with several measurement readings, with each reading in 10 minutes duration for each different component conditions, all cases yield 100% of accuracies while in the case of defective capacitor yields 90% of accuracy.

  5. 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.

  6. 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.

  7. Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

    Science.gov (United States)

    Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

    2013-06-01

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

  8. Fault-tolerant electric drive and space-phasor modulation of flux-switching permanent magnet machine for aerospace application

    NARCIS (Netherlands)

    Wang, L.; Aleksandrov, S.; Tang, Y.; Paulides, J.J.H.; Lomonova, E.A.

    2017-01-01

    This study investigates how to improve the fault tolerance or availability of an electrical drive containing a three-phase 12 stator teeth/10 rotor poles (12/10) the flux-switching permanent magnet machine. In this respect, space-vector modulation and space-phasor modulation will be analysed in this

  9. 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.

  10. 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,

  11. 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.

  12. Adult subependymal neural precursors, but not differentiated cells, undergo rapid cathodal migration in the presence of direct current electric fields.

    Directory of Open Access Journals (Sweden)

    Robart Babona-Pilipos

    Full Text Available BACKGROUND: The existence of neural stem and progenitor cells (together termed neural precursor cells in the adult mammalian brain has sparked great interest in utilizing these cells for regenerative medicine strategies. Endogenous neural precursors within the adult forebrain subependyma can be activated following injury, resulting in their proliferation and migration toward lesion sites where they differentiate into neural cells. The administration of growth factors and immunomodulatory agents following injury augments this activation and has been shown to result in behavioural functional recovery following stroke. METHODS AND FINDINGS: With the goal of enhancing neural precursor migration to facilitate the repair process we report that externally applied direct current electric fields induce rapid and directed cathodal migration of pure populations of undifferentiated adult subependyma-derived neural precursors. Using time-lapse imaging microscopy in vitro we performed an extensive single-cell kinematic analysis demonstrating that this galvanotactic phenomenon is a feature of undifferentiated precursors, and not differentiated phenotypes. Moreover, we have shown that the migratory response of the neural precursors is a direct effect of the electric field and not due to chemotactic gradients. We also identified that epidermal growth factor receptor (EGFR signaling plays a role in the galvanotactic response as blocking EGFR significantly attenuates the migratory behaviour. CONCLUSIONS: These findings suggest direct current electric fields may be implemented in endogenous repair paradigms to promote migration and tissue repair following neurotrauma.

  13. Classification of breast tumour using electrical impedance and machine learning techniques

    International Nuclear Information System (INIS)

    Amin, Abdullah Al; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

    2014-01-01

    When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy. (paper)

  14. Classification of breast tumour using electrical impedance and machine learning techniques.

    Science.gov (United States)

    Al Amin, Abdullah; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

    2014-06-01

    When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy.

  15. Electrical Stimulation Elicit Neural Stem Cells Activation:New Perspectives in CNS Repair

    Directory of Open Access Journals (Sweden)

    Ratrel eHuang

    2015-10-01

    Full Text Available Researchers are enthusiastically concerned about neural stem cell (NSC therapy in a wide array of diseases, including stroke, neurodegenerative disease, spinal cord injury (SCI and depression. Although enormous evidences have demonstrated that neurobehavioral improvement may benefit from NSC-supporting regeneration in animal models, approaches to endogenous and transplanted NSCs are blocked by hurdles of migration, proliferation, maturation and integration of NSCs. Electrical stimulation (ES may be a selective nondrug approach for mobilizing NSCs in the central nervous system (CNS. This technique is suitable for clinic application, because it is well established and its potential complications are manageable. Here, we provide a comprehensive review of the emerging positive role of different electrical cues in regulating NSC biology in vitro and in vivo, as well as biomaterial-based and chemical stimulation of NSCs. In the future, ES combined with stem cell therapy or other cues probably becomes an approach for promoting brain repair.

  16. 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.

  17. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

    Directory of Open Access Journals (Sweden)

    Farshid Keynia

    2011-03-01

    Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

  18. Method and system employing finite state machine modeling to identify one of a plurality of different electric load types

    Science.gov (United States)

    Du, Liang; Yang, Yi; Harley, Ronald Gordon; Habetler, Thomas G.; He, Dawei

    2016-08-09

    A system is for a plurality of different electric load types. The system includes a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor. The processor acquires a voltage and current waveform from the sensors for a corresponding one of the different electric load types; calculates a power or current RMS profile of the waveform; quantizes the power or current RMS profile into a set of quantized state-values; evaluates a state-duration for each of the quantized state-values; evaluates a plurality of state-types based on the power or current RMS profile and the quantized state-values; generates a state-sequence that describes a corresponding finite state machine model of a generalized load start-up or transient profile for the corresponding electric load type; and identifies the corresponding electric load type.

  19. 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 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...

  20. Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihe Xi

    2017-11-01

    Full Text Available The extended range electric vehicle (EREV can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

  1. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

    Science.gov (United States)

    Mena, Gonzalo E; Grosberg, Lauren E; Madugula, Sasidhar; Hottowy, Paweł; Litke, Alan; Cunningham, John; Chichilnisky, E J; Paninski, Liam

    2017-11-01

    Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

  2. Rapid and Efficient Synthesis of Silver Nanofluid Using Electrical Discharge Machining

    Directory of Open Access Journals (Sweden)

    Kuo-Hsiung Tseng

    2013-01-01

    Full Text Available The electrical discharge machining (EDM system has been proven feasible as a rapid and efficient method for silver nanofluid preparation. This study prepared the silver nano-fluid via EDM and investigated the relationship between its process parameters and product characteristics. The prior study had found that the silver nano-fluid prepared by EDM contained both silver nanoparticles and silver ions. Silver ions had revealed the cause of the high suspension of the silver nanoparticles. To examine the relationship between the stability of silver nanofluid and the process parameters, this study quantified the relationship of process parameters to the material removal rate (MRR of silver electrode and silver ion output rate (IOR in the fluid, in order to achieve the most effective process parameter condition. Furthermore, the stability of silver nano-fluid was analyzed by various devices, including UV-Vis spectroscopy, size-distribution, and Zeta-potential analyzer. The effects of MRR, IOR, particle size, Zeta-potential, and optical properties of silver nanofluid under different process parameters are also discussed.

  3. 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.

  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. Hysteresis and reluctance electric machines with bulk HTS elements. Recent results and future development

    International Nuclear Information System (INIS)

    Kovalev, L.K.; Ilushin, K.V.; Penkin, V.T.; Kovalev, K.L.; Koneev, S.M.-A.; Poltavets, V.N.; Larionoff, A.E.; Modestov, K.A.; Larionoff, S.A.; Gawalek, W.; Habisreuther, T.; Oswald, B.; Best, K.-J.; Strasser, T.

    2000-01-01

    Two new types of HTS electric machine are considered. The first type is hysteresis motors and generators with cylindrical and disc rotors containing bulk HTS elements. The second type is reluctance motors with compound HTS-ferromagnetic rotors. The compound HTS-ferromagnetic rotors, consisting of joined alternating bulk HTS (YBCO) and ferromagnetic (iron) plates, provide a new active material for electromechanical purposes. Such rotors have anisotropic properties (ferromagnetic in one direction and diamagnetic in the perpendicular one). Theoretical and experimental results for HTS hysteresis and reluctance motors are presented. A series of hysteresis HTS motors with output power rating from 1 kW (at 50 Hz) up to 4 kW (at 400 Hz) and a series of reluctance HTS motors with output power 2-18.5 kW (at 50 Hz) were constructed and successfully tested. It was shown that HTS reluctance motors could reach two to five times better overall dimensions and specific power than conventional asynchronous motors of the same size and will have higher values of power factor (cos φ≥0.7 to 0.8). (author)

  6. Correlation of eddy current responses between fatigue cracks and electrical-discharge-machining notches

    Science.gov (United States)

    Seo, Sukho; Choi, Gyudong; Eom, Tae Jhoun; Lee, Bokwon; Lee, Soo Yeol

    2017-07-01

    The eddy current responses of Electrical Discharge Machining (EDM) notches and fatigue cracks are directly compared to verify the reliability of eddy current inspection. The fatigue crack growth tests using a constant load range control mode were conducted to obtain a variety of edge crack sizes, ranging from 0.9 to 6.6 mm for Al alloy and from 0.1 to 3 mm for Ti alloy. EDM notch specimens of Al and Ti alloys were accordingly prepared in lengths similar to that of the fatigued specimen. The crack length was determined by optical microscope and scanning electron microscope. The eddy current responses between the EDM and fatigued specimens with varying notch/crack length were examined using probe sensors at (100-500) kHz and (1-2) MHz for Al and Ti alloys, respectively. The results show a significant difference in the eddy current signal between the two specimens, based on the correlation between the eddy current response and notch/crack length. This suggests that eddy current inspection using the EDM reference specimen is inaccurate in determining the precise crack size, unless the eddy current response data base is obtained from a fatigue-cracked specimen.

  7. 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

  8. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

    International Nuclear Information System (INIS)

    Anbazhagan, S.; Kumarappan, N.

    2014-01-01

    Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

  9. 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

  10. Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

    Directory of Open Access Journals (Sweden)

    Salvador Gutiérrez

    Full Text Available 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

  11. Non-conventional rule of making a periodically varying different-pole magnetic field in low-power alternating current electrical machines with using ring coils in multiphase armature winding

    Science.gov (United States)

    Plastun, A. T.; Tikhonova, O. V.; Malygin, I. V.

    2018-02-01

    The paper presents methods of making a periodically varying different-pole magnetic field in low-power electrical machines. Authors consider classical designs of electrical machines and machines with ring windings in armature, structural features and calculated parameters of magnetic circuit for these machines.

  12. 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 [ R 2  = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM ( R 2  = 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 ( R 2  = 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.

  13. Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

    International Nuclear Information System (INIS)

    Chen, Xia; Hu, Hong-li; Liu, Fei; Gao, Xiang Xiang

    2011-01-01

    The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality

  14. A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

    2008-01-01

    This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to

  15. Neural network classification technique and machine vision for bread crumb grain evaluation

    Science.gov (United States)

    Zayas, Inna Y.; Chung, O. K.; Caley, M.

    1995-10-01

    Bread crumb grain was studied to develop a model for pattern recognition of bread baked at Hard Winter Wheat Quality Laboratory (HWWQL), Grain Marketing and Production Research Center (GMPRC). Images of bread slices were acquired with a scanner in a 512 multiplied by 512 format. Subimages in the central part of the slices were evaluated by several features such as mean, determinant, eigen values, shape of a slice and other crumb features. Derived features were used to describe slices and loaves. Neural network programs of MATLAB package were used for data analysis. Learning vector quantization method and multivariate discriminant analysis were applied to bread slices from what of different sources. A training and test sets of different bread crumb texture classes were obtained. The ranking of subimages was well correlated with visual judgement. The performance of different models on slice recognition rate was studied to choose the best model. The recognition of classes created according to human judgement with image features was low. Recognition of arbitrarily created classes, according to porosity patterns, with several feature patterns was approximately 90%. Correlation coefficient was approximately 0.7 between slice shape features and loaf volume.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  17. 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.

  18. 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.

  19. Surface treatment by electric discharge machining of Ti–6Al–4V alloy for potential application in orthopaedics

    Czech Academy of Sciences Publication Activity Database

    Harcuba, P.; Bačáková, Lucie; Stráský, J.; Bačáková, Markéta; Novotná, Katarína; Janeček, M.

    2012-01-01

    Roč. 7, MAR (2012), s. 96-105 ISSN 1751-6161. [Symposium on Biological Materials Science /7./. San Diego, 27.02.2011-03.03.2011] R&D Projects: GA TA ČR(CZ) TA01011141 Institutional research plan: CEZ:AV0Z50110509 Keywords : electric discharge machining * surface roughness * mechanical properties Subject RIV: FI - Traumatology, Orthopedics Impact factor: 2.368, year: 2012

  20. Application of wire electrodes in electric discharge machining of metal samples of reactor blocks of the operative atomic power station

    International Nuclear Information System (INIS)

    Gozhenko, S.V.

    2007-01-01

    Features of application of electroerosive methods are considered during the process of direct definition of properties of metal of the equipment of power units of the atomic power station. Results of development of a complex of the equipment for wire electric discharge machining of metal templet and its use are presented at the control of the basic metal of the main circulating pipelines over blocks of the atomic power station of Ukraine over long terms of operation

  1. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    Energy Technology Data Exchange (ETDEWEB)

    Mandal, Paras; Senjyu, Tomonobu [Department of Electrical and Electronics, University of the Ryukyus, 1 Senbaru, Nagakami Nishihara, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Tokyo 103-8515 (Japan)

    2006-09-15

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy. (author)

  2. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    International Nuclear Information System (INIS)

    Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

    2006-01-01

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6 h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy

  3. 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.

  4. Artificial neural networks applied to the prediction of spot prices in the market of electric energy

    International Nuclear Information System (INIS)

    Rodrigues, Alcantaro Lemes; Grimoni, Jose Aquiles Baesso

    2010-01-01

    The commercialization of electricity in Brazil as well as in the world has undergone several changes over the past 20 years. In order to achieve an economic balance between supply and demand of the good called electricity, stakeholders in this market follow both rules set by society (government, companies and consumers) and set by the laws of nature (hydrology). To deal with such complex issues, various studies have been conducted in the area of computational heuristics. This work aims to develop a software to forecast spot market prices in using artificial neural networks (ANN). ANNs are widely used in various applications especially in computational heuristics, where non-linear systems have computational challenges difficult to overcome because of the effect named 'curse of dimensionality'. This effect is due to the fact that the current computational power is not enough to handle problems with such a high combination of variables. The challenge of forecasting prices depends on factors such as: (a) foresee the demand evolution (electric load); (b) the forecast of supply (reservoirs, hydrology and climate), capacity factor; and (c) the balance of the economy (pricing, auctions, foreign markets influence, economic policy, government budget and government policy). These factors are considered be used in the forecasting model for spot market prices and the results of its effectiveness are tested and huge presented. (author)

  5. A new paradigm of electrical stimulation to enhance sensory neural function.

    Science.gov (United States)

    Breen, Paul P; ÓLaighin, Gearóid; McIntosh, Caroline; Dinneen, Sean F; Quinlan, Leo R; Serrador, Jorge M

    2014-08-01

    The ability to improve peripheral neural transmission would have significant therapeutic potential in medicine. A technology of this kind could be used to restore and/or enhance sensory function in individuals with depressed sensory function, such as older adults or patients with peripheral neuropathies. The goal of this study was to investigate if a new paradigm of subsensory electrical noise stimulation enhances somatosensory function. Vibration (50Hz) was applied with a Neurothesiometer to the plantar aspect of the foot in the presence or absence of subsensory electrical noise (1/f type). The noise was applied at a proximal site, on a defined region of the tibial nerve path above the ankle. Vibration perception thresholds (VPT) of younger adults were measured in control and experimental conditions, in the absence or presence of noise respectively. An improvement of ∼16% in VPT was found in the presence of noise. These are the first data to demonstrate that modulation of axonal transmission with externally applied electrical noise improves perception of tactile stimuli in humans. Copyright © 2014 IPEM. All rights reserved.

  6. Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

    Science.gov (United States)

    Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

    1997-04-01

    Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

  7. Sustainable Micro-Manufacturing of Micro-Components via Micro Electrical Discharge Machining

    Directory of Open Access Journals (Sweden)

    Valeria Marrocco

    2011-12-01

    Full Text Available Micro-manufacturing emerged in the last years as a new engineering area with the potential of increasing peoples’ quality of life through the production of innovative micro-devices to be used, for example, in the biomedical, micro-electronics or telecommunication sectors. The possibility to decrease the energy consumption makes the micro-manufacturing extremely appealing in terms of environmental protection. However, despite this common belief that the micro-scale implies a higher sustainability compared to traditional manufacturing processes, recent research shows that some factors can make micro-manufacturing processes not as sustainable as expected. In particular, the use of rare raw materials and the need of higher purity of processes, to preserve product quality and manufacturing equipment, can be a source for additional environmental burden and process costs. Consequently, research is needed to optimize micro-manufacturing processes in order to guarantee the minimum consumption of raw materials, consumables and energy. In this paper, the experimental results obtained by the micro-electrical discharge machining (micro-EDM of micro-channels made on Ni–Cr–Mo steel is reported. The aim of such investigation is to shed a light on the relation and dependence between the material removal process, identified in the evaluation of material removal rate (MRR and tool wear ratio (TWR, and some of the most important technological parameters (i.e., open voltage, discharge current, pulse width and frequency, in order to experimentally quantify the material waste produced and optimize the technological process in order to decrease it.

  8. Analysed a defective of the machine for a cap-tube nuclear fuel element ME-27 from its electricity point of view

    International Nuclear Information System (INIS)

    Achmad Suntoro

    2009-01-01

    It has been analysed a defective of the machine for a cap-tube nuclear fuel element ME-27 from its electricity point of view. The machine uses magnetic force resistance welding technique. A short circuit was happened within the machine because the nut for tightening high voltage cable for welding transformer was broken so that the cable touched the machine body and produced the short circuit. This condition made both the primary circuit breaker in the building down and produced high voltage pulse induction to the electronic circuit within the machine so that one of its electronic components was defective. This case becomes warnings on how important of tightening a nut according to its strength specification (using wrench torque) and the necessity of voltage transient limitation circuit to be installed. Both of the warnings are necessary for any equipment consuming high electric current oriented such as the ME-27 machine. (author)

  9. How single node dynamics enhances synchronization in neural networks with electrical coupling

    International Nuclear Information System (INIS)

    Bonacini, E.; Burioni, R.; Di Volo, M.; Groppi, M.; Soresina, C.; Vezzani, A.

    2016-01-01

    The stability of the completely synchronous state in neural networks with electrical coupling is analytically investigated applying both the Master Stability Function approach (MSF), developed by Pecora and Carroll (1998), and the Connection Graph Stability method (CGS) proposed by Belykh et al. (2004). The local dynamics is described by Morris–Lecar model for spiking neurons and by Hindmarsh–Rose model in spike, burst, irregular spike and irregular burst regimes. The combined application of both CGS and MSF methods provides an efficient estimate of the synchronization thresholds, namely bounds for the coupling strength ranges in which the synchronous state is stable. In all the considered cases, we observe that high values of coupling strength tend to synchronize the system. Furthermore, we observe a correlation between the single node attractor and the local stability properties given by MSF. The analytical results are compared with numerical simulations on a sample network, with excellent agreement.

  10. 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.

  11. A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Duo Zhang

    2014-07-01

    Full Text Available Vehicle active safety control is attracting ever increasing attention in the attempt to improve the stability and the maneuverability of electric vehicles. In this paper, a neural network combined inverse (NNCI controller is proposed, incorporating the merits of left-inversion and right-inversion. As the left-inversion soft-sensor can estimate the sideslip angle, while the right-inversion is utilized to decouple control. Then, the proposed NNCI controller not only linearizes and decouples the original nonlinear system, but also directly obtains immeasurable state feedback in constructing the right-inversion. Hence, the proposed controller is very practical in engineering applications. The proposed system is co-simulated based on the vehicle simulation package CarSim in connection with Matlab/Simulink. The results verify the effectiveness of the proposed control strategy.

  12. Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle

    Institute of Scientific and Technical Information of China (English)

    Theodore Amissah OCRAN; CAO Junyi; CAO Binggang; SUN Xinghua

    2005-01-01

    This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transistor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, temperature, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information supplied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lithium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.

  13. Pilot acute study of feedback-controlled retrograde peristalsis invoked by neural gastric electrical stimulation

    International Nuclear Information System (INIS)

    Aelen, P; Jurkov, A; Aulanier, A; Mintchev, M P

    2009-01-01

    Neural gastric electrical stimulation (NGES) is a new method for invoking gastric contractions under microprocessor control. However, optimization of this technique using feedback mechanisms to minimize power consumption and maximize effectiveness has been lacking. The present pilot study proposes a prototype feedback-controlled neural gastric electric stimulator for the treatment of obesity. Both force-based and inter-electrode impedance-based feedback neurostimulators were implemented and tested. Four mongrel dogs (2 M, 2 F, weight 14.9 ± 2.3 kg) underwent subserosal implantation of two-channel, 1 cm, bipolar electrode leads and two force transducers in the distal antrum. Two of the dogs were stimulated with a force feedback system utilizing the force transducers, and the other two animals were stimulated utilizing an inter-electrode impedance-based feedback system utilizing the proximal electrode leads. Both feedback systems were able to recognize erythromycin-driven contractions of the stomach and were capable of overriding them with NGES-invoked retrograde contractions which exceeded the magnitudes of the erythromycin-driven contractions by an average of 100.6 ± 33.5% in all animals. The NGES-invoked contractions blocked the erythromycin-driven contractions past the proximal electrode pair and induced temporary gastroparesis in the vicinity of the distal force transducer despite the continuing erythromycin infusion. The amplitudes of the erythromycin-invoked contractions in the vicinity of the proximal force transducer decreased abruptly by an average of 47.9 ± 6.3% in all four dogs after triggering-invoked retrograde contractions, regardless of the specific feedback-controlled mechanism. The proposed technique could be helpful for retaining food longer in the stomach, thus inducing early satiety and diminishing food intake

  14. 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

  15. Manipulation of food intake and weight dynamics using retrograde neural gastric electrical stimulation in a chronic canine model

    NARCIS (Netherlands)

    Aelen, P.; Neshev, E.; Cholette, M.; Crisanti, K.; Mitchell, P.; De bru, E.; Church, N.; Mintchev, M.P.

    2008-01-01

    Neural gastric electrical stimulation (NGES) could be a new technique for treating obesity. However, chronic animal experimentation exploring the efficacy of this therapy is lacking. In this study we investigated the utility of retrograde NGES in a chronic canine model. Nine mongrel dogs (26.8 ± 5.2

  16. Design of large permanent magnetized synchronous electric machines: Low speed, high torque machines - generator for direct driven wind turbine - motor for rim driven thruster

    Energy Technology Data Exchange (ETDEWEB)

    Kroevel, Oeystein

    2011-02-15

    This work presents the design of two prototype permanent magnetized electric machines for two different applications where large permanent magnet machines might be used. Existing technology have been used as the fundament for new design and adapted to new applications, contributing, hopefully, to the development of better and more environmental friendly energy conversion. The first application presented is represented with a prototype made in cooperation with the industry in which a PM-motor is integrated into a propeller unit. Both because of the industrial connection, and the integration between the PM-motor and the propeller, the choices made for the PM-motor are conservative trying to reduce the risk. The direct rim driven thruster prototype includes a surface mounted radial flux permanent magnet machine (SM RFPM) with fractional slot winding with a q around 1. Other engineering features were introduced to make the integration of propeller and motor feasible, but without the PM-machine the thruster would not have reached the performance demand. An important part of the project was to show that the SM RFPM enables this solution, providing high performance with a large air gap. The prototype has been tested in sea, under harsh conditions, and even though the magnets have been exposed directly to sea water and been visible corroded, the electric motor still performs well within the specifications. The second application is represented with a prototype PM-generator for wind turbines. This is an example of a new, very low speed high torque machine. The generator is built to test phenomena regarding concentrated coils, and as opposed to the first application, being a pure academic university project, its success is not connected to its performance, but with the prototype's ability to expose the phenomena in question. The prototype, or laboratory model, of the generator for direct driven wind turbines features SM RFPM with concentrated coils (CC). An opportunity

  17. 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

    2009-10-01

    Full Text Available 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.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.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 found to result in maximally predictive models are

  18. Employing Ti nano-powder dielectric to enhance surface characteristics in electrical discharge machining of AISI D2 steel

    Energy Technology Data Exchange (ETDEWEB)

    Marashi, Houriyeh, E-mail: houriyeh@marashi.co [Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur (Malaysia); Center of Advanced Manufacturing and Materials Processing (AMMP), 50603 Kuala Lumpur (Malaysia); Sarhan, Ahmed A.D., E-mail: ah_sarhan@yahoo.com [Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur (Malaysia); Center of Advanced Manufacturing and Materials Processing (AMMP), 50603 Kuala Lumpur (Malaysia); Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut 71516 (Egypt); Hamdi, Mohd [Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur (Malaysia); Center of Advanced Manufacturing and Materials Processing (AMMP), 50603 Kuala Lumpur (Malaysia)

    2015-12-01

    Graphical abstract: - Highlights: • We proposed adding Ti nano-powder to dielectric in EDM. • Average and peak-valley surface roughness was improved by 35 and 40%, respectively. • Improvement of up to 69% in material removal rate was obtained. • Enhanced surface morphology and formation of shallower craters were observed. - Abstract: Manufacturing components with superior surface characteristics is challenging when electrical discharge machining (EDM) is employed for mass production. The aim of this research is to enhance the characteristics of AISI D2 steel surface machined with EDM through adding Ti nano-powder to dielectric under various machining parameters, including discharge duration (T{sub on}) and peak current (I). Surface roughness profilometer, FESEM and AFM analysis were utilized to reveal the machined surface characteristics in terms of surface roughness, surface morphology and surface micro-defects. Moreover, EDX analysis was performed in order to evaluate the atomic deposition of Ti nano-powder on the surface. The concentration of Ti nano-powder in dielectric was also examined using ESEM and EDX. According to the results, the addition of Ti nano-powder to dielectric notably enhanced the surface morphology and surface roughness at all machining parameters except T{sub on} = 340 μs. Of these parameters, maximum enhancement was observed at T{sub on} = 210 μs, where the material removal rate and average surface roughness improved by ∼69 and ∼35% for peak current of 6 and 12 A, respectively. Elemental analysis signified negligible Ti deposition on the machined surface while the atomic concentration of Ti was increased around the crack areas.

  19. 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

  20. Biphasic electrical currents stimulation promotes both proliferation and differentiation of fetal neural stem cells.

    Directory of Open Access Journals (Sweden)

    Keun-A Chang

    2011-04-01

    Full Text Available The use of non-chemical methods to differentiate stem cells has attracted researchers from multiple disciplines, including the engineering and the biomedical fields. No doubt, growth factor based methods are still the most dominant of achieving some level of proliferation and differentiation control--however, chemical based methods are still limited by the quality, source, and amount of the utilized reagents. Well-defined non-chemical methods to differentiate stem cells allow stem cell scientists to control stem cell biology by precisely administering the pre-defined parameters, whether they are structural cues, substrate stiffness, or in the form of current flow. We have developed a culture system that allows normal stem cell growth and the option of applying continuous and defined levels of electric current to alter the cell biology of growing cells. This biphasic current stimulator chip employing ITO electrodes generates both positive and negative currents in the same culture chamber without affecting surface chemistry. We found that biphasic electrical currents (BECs significantly increased the proliferation of fetal neural stem cells (NSCs. Furthermore, BECs also promoted the differentiation of fetal NSCs into neuronal cells, as assessed using immunocytochemistry. Our results clearly show that BECs promote both the proliferation and neuronal differentiation of fetal NSCs. It may apply to the development of strategies that employ NSCs in the treatment of various neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases.

  1. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

    2008-01-01

    This paper presents an artificial neural network (ANN) approach for annual electricity consumption in high energy consumption industrial sectors. Chemicals, basic metals and non-metal minerals industries are defined as high energy consuming industries. It is claimed that, due to high fluctuations of energy consumption in high energy consumption industries, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual consumption in such industries. Furthermore, the ANN approach based on a supervised multi-layer perceptron (MLP) is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) industries in Iran from 1979 to 2003 is used to illustrate the applicability of the ANN approach. This study shows the advantage of the ANN approach through analysis of variance (ANOVA). Furthermore, the ANN forecast is compared with actual data and the conventional regression model through ANOVA to show its superiority. This is the first study to present an algorithm based on the ANN and ANOVA for forecasting long term electricity consumption in high energy consuming industries

  2. Effect of dielectric fluid with surfactant and graphite powder on Electrical Discharge Machining of titanium alloy using Taguchi method

    Directory of Open Access Journals (Sweden)

    Murahari Kolli

    2015-12-01

    Full Text Available In this paper, Taguchi method was employed to optimize the surfactant and graphite powder concentration in dielectric fluid for the machining of Ti-6Al-4V using Electrical Discharge Machining (EDM. The process parameters such as discharge current, surfactant concentration and powder concentration were changed to explore their effects on Material Removal Rate (MRR, Surface Roughness (SR, Tool wear rate (TWR and Recast Layer Thickness (RLT. Detailed analysis of structural features of machined surface was carried out using Scanning Electron Microscope (SEM to observe the influence of surfactant and graphite powder on the machining process. It was observed from the experimental results that the graphite powder and surfactant added dielectric fluid significantly improved the MRR, reduces the SR, TWR and RLT at various conditions. Analysis of Variance (ANOVA and F-test of experimental data values related to the important process parameters of EDM revealed that discharge current and surfactant concentration has more percentage of contribution on the MRR and TWR whereas the SR, and RLT were found to be affected greatly by the discharge current and graphite powder concentration.

  3. Comparison of PMAC Machines for Starter-Generator Application in a Series Hybrid-Electric Bus

    OpenAIRE

    Sinisa Jurkovic; Elias G. Strangas

    2011-01-01

    This paper presents a comparative study of outer rotor PMAC machine candidates for starter-generator application in hybrid bus with series power train configuration. PMAC machines with interior and surface mount permanent magnets are considered and compared, although a complete analysis is only carried out for the SPM. Different design aspects such as concentrated versus distributed windings as well as interior and exterior rotor structures are evaluated. Different slot numbers per pole per p...

  4. New concept of electrical drives for paper and board machines based on energy efficiency principles

    Directory of Open Access Journals (Sweden)

    Jeftenić Borislav

    2006-01-01

    Full Text Available In this paper, it is described how the reconstruction of the facility of paper machine has been conducted, at the press and drying part of the machine in June 2001, as well as the expansion of the Paper Machine with the "third coating" introducing, that has been done in July 2002, in the board factory "Umka". The existing old drive of the press and the drive of drying groups were established as a Line Shaft Drive, 76 m long. The novel drive is developed on the basis of conventional squirrel cage induction motor application, with frequency converter. The system control is carried out with the programmable controller, and the communication between controllers, converters, and control boards is accomplished trough profibus. Reconstruction of the coating part of the machine, during technological reconstruction of this part of the machine, was being conducted with a purpose to improve performance of the machine by adding device for spreading "third coating". The demands for the power facility were to replace existing facility with the new one, based on energy efficiency principles and to provide adequate facility for new technological sections. Also, new part of the facility had to be connected with the remaining part of the machine, i.e. with the press and drying part, which have been reconstructed in 2001. It has to be stressed that energy efficiency principles means to realize new, modernized drive with better performances and greater capacity for the as small as possible amount of increased installed power of separate drives. In the paper are also, graphically presented achieved energy savings results, based on measurements performed on separate parts of paper machine, before and after reconstruction. .

  5. Drive Control Scheme of Electric Power Assisted Wheelchair Based on Neural Network Learning of Human Wheelchair Operation Characteristics

    Science.gov (United States)

    Tanohata, Naoki; Seki, Hirokazu

    This paper describes a novel drive control scheme of electric power assisted wheelchairs based on neural network learning of human wheelchair operation characteristics. “Electric power assisted wheelchair” which enhances the drive force of the operator by employing electric motors is expected to be widely used as a mobility support system for elderly and disabled people. However, some handicapped people with paralysis of the muscles of one side of the body cannot maneuver the wheelchair as desired because of the difference in the right and left input force. Therefore, this study proposes a neural network learning system of such human wheelchair operation characteristics and a drive control scheme with variable distribution and assistance ratios. Some driving experiments will be performed to confirm the effectiveness of the proposed control system.

  6. Development of a perpendicular vibration-induced electrical discharge machining process for fabrication of partially wavy inner structures

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Ju Chul; Park, Sang Hu; Min, June Kee; Ha, Man Yeong; Shin, Bo Sung [Pusan National University, Busan (Korea, Republic of); Cho, Jong Rae [Korea Maritime University, Busan (Korea, Republic of)

    2016-05-15

    Heat transfer enhancement is an important issue in energy systems. To improve the efficiency of a cooling channel used inside injection molds, turbine blades, and high-temperature devices, channels with various shapes, such as wavy, elliptical, and twisted, have been studied. A cooling channel with a partially wavy inner structure has shown outstanding cooling performance despite a small increase in friction factor. However, generating a partially wavy inner structure inside a channel through conventional machining processes is not easy. To address this problem, we developed a new process called Perpendicular vibration-induced electrical discharge machining (PV-EDM). A specific electrode and one- and random-directional vibrating devices controlled by a pneumatic load were designed for the PV-EDM process. Experimental results showed that local shaping on the inner wall of a channel is possible, which confirmed the possibility of application of this process to actual industrial problems.

  7. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  8. 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

  9. 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.

  10. Joint punching and frequency effects on practical magnetic characteristics of electrical steels for high-speed machines

    Science.gov (United States)

    Kedous-Lebouc, A.; Messal, O.; Youmssi, A.

    2017-03-01

    Mechanical punching of electrical steels causes a degradation of their magnetic characteristics which can extend several millimeters from the cut edge. So, in the field of industrial applications, particularly that of small electrical machines, the stator core made of rigid and thin teeth would be subject to more losses. Thus, this topic of the effect of punching has to be submitted to further deep characterization and development in order to give some insight into the different mechanisms. In this framework, this paper evaluates the combined effect of punching and frequency on the magnetization curve and iron losses in thin SiFe and CoFe soft magnetic sheets. These alloys are typically suitable for the manufacture of high-speed electrical machines used in on board applications (aircraft power generators, automotive, etc). Two SiFe alloys and a CoFe alloy have been investigated. First, different rectangular samples of variable width (15, 10, 5, 3 mm) have been industrially punched. Then, a dedicated magnetic characterization has been made, using basically a mini-Epstein frame. Measurements have been performed from 50 Hz to 1 kHz and from 0.3 T to near saturation. Both rolling and transverse directions have been considered. Finally, a first attempt to predict the degradation due to the punching is presented. A useful description of the magnetic permeability as a function of B and f is given and the degradation parameters are estimated based on the knowledge of the reference permeability.

  11. Joint punching and frequency effects on practical magnetic characteristics of electrical steels for high-speed machines

    Energy Technology Data Exchange (ETDEWEB)

    Kedous-Lebouc, A. [Univ. Grenoble Alpes, G2Elab, F-38000 Grenoble, France — CNRS, G2Elab, F-38000 Grenoble (France); Messal, O., E-mail: oualid.messal@g2elab.grenoble-inp.fr [Univ. Grenoble Alpes, G2Elab, F-38000 Grenoble, France — CNRS, G2Elab, F-38000 Grenoble (France); Youmssi, A. [Université de N’gaoundéré, BP. 455 N’Gaoundéré (Cameroon)

    2017-03-15

    Mechanical punching of electrical steels causes a degradation of their magnetic characteristics which can extend several millimeters from the cut edge. So, in the field of industrial applications, particularly that of small electrical machines, the stator core made of rigid and thin teeth would be subject to more losses. Thus, this topic of the effect of punching has to be submitted to further deep characterization and development in order to give some insight into the different mechanisms. In this framework, this paper evaluates the combined effect of punching and frequency on the magnetization curve and iron losses in thin SiFe and CoFe soft magnetic sheets. These alloys are typically suitable for the manufacture of high-speed electrical machines used in on board applications (aircraft power generators, automotive, etc). Two SiFe alloys and a CoFe alloy have been investigated. First, different rectangular samples of variable width (15, 10, 5, 3 mm) have been industrially punched. Then, a dedicated magnetic characterization has been made, using basically a mini-Epstein frame. Measurements have been performed from 50 Hz to 1 kHz and from 0.3 T to near saturation. Both rolling and transverse directions have been considered. Finally, a first attempt to predict the degradation due to the punching is presented. A useful description of the magnetic permeability as a function of B and f is given and the degradation parameters are estimated based on the knowledge of the reference permeability.

  12. Electrical Neural Stimulation and Simultaneous in Vivo Monitoring with Transparent Graphene Electrode Arrays Implanted in GCaMP6f Mice.

    Science.gov (United States)

    Park, Dong-Wook; Ness, Jared P; Brodnick, Sarah K; Esquibel, Corinne; Novello, Joseph; Atry, Farid; Baek, Dong-Hyun; Kim, Hyungsoo; Bong, Jihye; Swanson, Kyle I; Suminski, Aaron J; Otto, Kevin J; Pashaie, Ramin; Williams, Justin C; Ma, Zhenqiang

    2018-01-23

    Electrical stimulation using implantable electrodes is widely used to treat various neuronal disorders such as Parkinson's disease and epilepsy and is a widely used research tool in neuroscience studies. However, to date, devices that help better understand the mechanisms of electrical stimulation in neural tissues have been limited to opaque neural electrodes. Imaging spatiotemporal neural responses to electrical stimulation with minimal artifact could allow for various studies that are impossible with existing opaque electrodes. Here, we demonstrate electrical brain stimulation and simultaneous optical monitoring of the underlying neural tissues using carbon-based, fully transparent graphene electrodes implanted in GCaMP6f mice. Fluorescence imaging of neural activity for varying electrical stimulation parameters was conducted with minimal image artifact through transparent graphene electrodes. In addition, full-field imaging of electrical stimulation verified more efficient neural activation with cathode leading stimulation compared to anode leading stimulation. We have characterized the charge density limitation of capacitive four-layer graphene electrodes as 116.07-174.10 μC/cm 2 based on electrochemical impedance spectroscopy, cyclic voltammetry, failure bench testing, and in vivo testing. This study demonstrates the transparent ability of graphene neural electrodes and provides a method to further increase understanding and potentially improve therapeutic electrical stimulation in the central and peripheral nervous systems.

  13. 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.

  14. A novel design and driving strategy for a hybrid electric machine with torque performance enhancement both taking reluctance and electromagnetic attraction effects into account

    International Nuclear Information System (INIS)

    Huang, W.-N.; Chen, W.-P.; Teng, C.-C.; Chen, M.-P.

    2006-01-01

    A novel design, the hybrid electric machine, that owns improved competence for the output torque regulation as well as enlarged power density comparing to the conventional brushless machines by making use of the simultaneous performance overlapping concept based on magnetism is proposed in this paper. The developed design concept is focused on electric machine structure and its counterpart drive for applying two main magnetic-power transmitting paths by combination of both features of magnetic tendencies of flux generation that may flow in the path with minimum reluctance and direction owning the electromagnetic motive attraction. The verifications demonstrate that the outputted torque owns effective improvement by the presented concept of the electric machine based on the equivalent 3-hp frame than the conventional brushless motors

  15. Superconducting rotating machines

    International Nuclear Information System (INIS)

    Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

    1975-01-01

    The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

  16. Study of wire electrical discharge machined folded-up corner cube retroreflector with a tunable cantilever beam

    Science.gov (United States)

    Chen, Yu-Fan; Wang, Yen-Hung; Tsai, Jui-che

    2018-03-01

    This work has developed an approach to construct a corner cube retroreflector (CCR). A two-dimensional cutout pattern is first fabricated with wire electrical discharge machining process. It is then folded up into a three-dimensional CCR suspended on a cantilever beam. The folded-up CCR may be driven through external actuators for optical modulation; it can also mechanically respond to perturbation, acceleration, etc., to function as a sensor. Mechanical (static and dynamic modeling) and optical (ray tracing) analyses are also performed.

  17. Synergistic responses of superficial chemistry and micro topography of titanium created by wire-type electric discharge machining.

    Science.gov (United States)

    Kataoka, Yu; Tamaki, Yukimichi; Miyazaki, Takashi

    2011-01-01

    Wire-type electric discharge machining has been applied to the manufacture of endosseous titanium implants as this computer associated technique allows extremely accurate complex sample shaping with an optimal micro textured surface during the processing. Since the titanium oxide layer is sensitively altered by each processing, the authors hypothesized that this technique also up-regulates biological responses through the synergistic effects of the superficial chemistry and micro topography. To evaluate the respective in vitro cellular responses on the superficial chemistry and micro topography of titanium surface processed by wire-type electric discharge, we used titanium-coated epoxy resin replica of the surface. An oxide layer on the titanium surface processed by wire-type electric discharge activated the initial responses of osteoblastic cells through an integrin-mediated mechanism. Since the mRNA expression of ALP on those replicas was up-regulated compared to smooth titanium samples, the micro topography of a titanium surface processed by wire-type electric discharge promotes the osteogenic potential of cells. The synergistic response of the superficial chemistry and micro topography of titanium processed by wire-type electric discharge was demonstrated in this study.

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

    International Nuclear Information System (INIS)

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

    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, 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. (paper)

  19. 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

  20. 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...... characteristicsin micro-EDM process. A new approach with two novel factors anticipated to directly control the material removal mechanism from the tool electrode are proposed; using discharge energyfactor (DEf) and dielectric flushing factor (DFf). The results showed that the correlation between the tool wear rate...... (TWR) and the factors is poor. Thus, individual effects of each factor on TWR are analyzed. The factors selected for the study of individual effects are pulse on-time, discharge peak current, gap voltage and gap flushing pressure. The tool wear rate decreases linearly with an increase in the pulse on...

  1. Machine terms dictionary

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1979-04-15

    This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

  2. 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....

  3. 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.

  4. 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.

  5. 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.

  6. Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

    Science.gov (United States)

    Shastri, Niket; Pathak, Kamlesh

    2018-05-01

    The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

  7. 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.

  8. Neural correlates of mirth and laughter: a direct electrical cortical stimulation study.

    Science.gov (United States)

    Yamao, Yukihiro; Matsumoto, Riki; Kunieda, Takeharu; Shibata, Sumiya; Shimotake, Akihiro; Kikuchi, Takayuki; Satow, Takeshi; Mikuni, Nobuhiro; Fukuyama, Hidenao; Ikeda, Akio; Miyamoto, Susumu

    2015-05-01

    Laughter consists of both motor and emotional aspects. The emotional component, known as mirth, is usually associated with the motor component, namely, bilateral facial movements. Previous electrical cortical stimulation (ES) studies revealed that mirth was associated with the basal temporal cortex, inferior frontal cortex, and medial frontal cortex. Functional neuroimaging implicated a role for the left inferior frontal and bilateral temporal cortices in humor processing. However, the neural origins and pathways linking mirth with facial movements are still unclear. We hereby report two cases with temporal lobe epilepsy undergoing subdural electrode implantation in whom ES of the left basal temporal cortex elicited both mirth and laughter-related facial muscle movements. In one case with normal hippocampus, high-frequency ES consistently caused contralateral facial movement, followed by bilateral facial movements with mirth. In contrast, in another case with hippocampal sclerosis (HS), ES elicited only mirth at low intensity and short duration, and eventually laughter at higher intensity and longer duration. In both cases, the basal temporal language area (BTLA) was located within or adjacent to the cortex where ES produced mirth. In conclusion, the present direct ES study demonstrated that 1) mirth had a close relationship with language function, 2) intact mesial temporal structures were actively engaged in the beginning of facial movements associated with mirth, and 3) these emotion-related facial movements had contralateral dominance. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. 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.

  10. 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

  11. Neural network based inspection of voids and karst conduits in hydro-electric power station tunnels using GPR

    Science.gov (United States)

    Kilic, Gokhan; Eren, Levent

    2018-04-01

    This paper reports on the fundamental role played by Ground Penetrating Radar (GPR), alongside advanced processing and presentation methods, during the tunnel boring project at a Dam and Hydro-Electric Power Station. It identifies from collected GPR data such issues as incomplete grouting and the presence of karst conduits and voids and provides full details of the procedures adopted. In particular, the application of collected GPR data to the Neural Network (NN) method is discussed.

  12. Machined GRP laminates for components in heavy electrical engineering and their use at very low temperatures

    International Nuclear Information System (INIS)

    Fuchs, H.

    1982-01-01

    Safe and economical components can be produced from machined GRP laminates. Matrix system, fibre reinforcement and elastic properties are described. Onset of damage and long-term properties are given with detailed charting of tests. Application of the laminate studies at stresses of up to half their short-term strength can be made, provided creep strain and its dependence on time and temperature are considered

  13. 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.

  14. Prognostic and Fault Tolerant Reconfiguration Strategies for Aerospace Power Electronic Controllers and Electric Machines, Phase I

    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...

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. The effect of electric discharge machined notches on the fracture toughness of several structural alloys

    International Nuclear Information System (INIS)

    Joyce, J.A.; Link, R.E.

    1993-09-01

    Recent computational studies of the stress and strain fields at the tip of very sharp notches have shown that the stress and strain fields are very weakly dependent on the initial geometry of the notch once the notch has been blunted to a radius that is 6 to 10 times the initial root radius. It follows that if the fracture toughness of a material is sufficiently high so that fracture initiation does not occur in a specimen until the crack-tip opening displacement (CTOD) reaches a value from 6 to 10 times the size of the initial notch tip diameter, then the fracture toughness will be independent of whether a fatigue crack or a machined notch served as the initial crack. In this experimental program the fracture toughness (J Ic and J resistance (J-R) curve, and CTOD) for several structure alloys was measured using specimens with conventional fatigue cracks and with EDM machined notches. The results of this program have shown, in fact, that most structural materials do not achieve initiation CTOD values on the order of 6 to 10 times the radius of even the smallest EDM notch tip presently achievable. It is found furthermore that tougher materials do not seem to be less dependent on the type of notch tip present. Some materials are shown to be much more dependent on the type of notch tip used, but no simple pattern is found that relates this observed dependence to the material strength toughness, or strain hardening rate

  20. Application of rare-earth magnets in high-performance electric machines

    International Nuclear Information System (INIS)

    Ramsden, V.S.

    1998-01-01

    Some state of the art developments of high-performance machines using rare-earth magnets are reviewed with particular examples drawn from a number of novel machine designs developed jointly by the Faculty of Engineering, University of Technology, Sydney (UTS) and CSIRO Telecommunications and Industrial Physics. These designs include an 1800 W, 1060 rev/min, 98% efficient solar car in-wheel motor using a Halbach magnet array, axial flux, and ironless winding; a 1200 W, 3000 rev/min, 91% efficient solar-powered, water-filled, submersible, bore-hole pump motor using a surface magnet rotor; a 500 W, 10000 rev/min, 87% efficient, oil-filled, oil-well tractor motor using a 2-pole cylindrical magnet rotor and slotless winding; a 75 kW, 48000 rev/min, 97% efficient, high-speed compressor drive with 2-pole cylindrical magnet rotor, slotted stator, and refrigerant cooling; and a 20 kW, 211 rev/min, 87% efficient, direct-drive generator for wind turbines with very low starting torque using an outer rotor with surface magnets and a slotted stator. (orig.)

  1. 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.

  2. 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...

  3. 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…

  4. Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movements are promising for rehabilitation applications.

    Science.gov (United States)

    Ferrante, Simona; Pedrocchi, Alessandra; Iannò, Marco; De Momi, Elena; Ferrarin, Maurizio; Ferrigno, Giancarlo

    2004-01-01

    This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

  5. 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.

  6. Fabrication of Superhydrophobic Metallic Surface by Wire Electrical Discharge Machining for Seamless Roll-to-Roll Printing

    Directory of Open Access Journals (Sweden)

    Jin-Young So

    2018-04-01

    Full Text Available This paper presents a proposal of a direct one-step method to fabricate a multi-scale superhydrophobic metallic seamless roll mold. The mold was fabricated using the wire electrical discharge machining (WEDM technique for a roll-to-roll imprinting application to produce a large superhydrophobic surface. Taking advantage of the exfoliating characteristic of the metallic surface, nano-sized surface roughness was spontaneously formed while manufacturing the micro-sized structure: that is, a dual-scale hierarchical structure was easily produced in a simple one-step fabrication with a large area on the aluminum metal surface. This hierarchical structure showed superhydrophobicity without chemical coating. A roll-type seamless mold for the roll-to-roll process was fabricated through engraving the patterns on the cylindrical substrate, thereby enabling to make a continuous film with superhydrophobicity.

  7. 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.

  8. 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.

  9. 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.

  10. Multi-Response Optimization of Wire Electrical Discharge Machining for Titanium Grade-5 by Weighted Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Sachin Ashok Sonawane

    2018-04-01

    Full Text Available This paper reports the results of research to examine the effects of cutting parameters such as pulse-on time, pulse-off time, servo voltage, peak current, wire feed rate and cable tension on surface finish, overcut and metal removal rate (MRR during Wire Electrical Discharge Machining (WEDM of grade-5 titanium (Ti-6Al-4V. Taguchi’s L27 orthogonal design method is used for experimentation. Multi-response optimization is performed by applying weighted principal component analysis (WPCA. The optimum values of cutting variables are found as a pulse on time 118 μs, pulse off time 45 μs, servo voltage 40 volts, peak current 190 Amp. , wire feed rate 5 m/min and cable tension 5 gram. On the other hand, Analysis of Variance (ANOVA, simulation results indicate that pulse-on time is the primary influencing variable which affects the response characteristics contributing 76.00%. The results of verification experiments show improvement in the value of output characteristics at the optimal cutting variables settings. Scanning electron microscopic (SEM analysis of the surface after machining indicates the formation of craters, resolidified material, tool material transfer and increase in the thickness of recast layer at higher values of the pulse on time.

  11. 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.

  12. 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

  13. Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

    Science.gov (United States)

    Oprea, Simona-Vasilica; Pîrjan, Alexandru; Căruțașu, George; Petroșanu, Dana-Mihaela; Bâra, Adela; Stănică, Justina-Lavinia; Coculescu, Cristina

    2018-05-05

    In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.

  14. 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.

  15. 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...

  16. Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading

    Energy Technology Data Exchange (ETDEWEB)

    Cousineau, Justine E [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bennion, Kevin S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Chieduko, Victor [UQM Technologies, Inc.; Lall, Rajiv [UQM Technologies, Inc.; Gilbert, Alan [UQM Technologies, Inc.

    2018-05-08

    Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. The results were compared to currently available models for contact resistance, and one model was adapted for prediction of TCR in future motor designs.

  17. 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.

  18. 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.

  19. 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.

  20. 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.