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

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....

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

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.

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...

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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).

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.

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

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.,

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.

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.

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 ...

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

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.

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.

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.

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, ...

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.

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

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.

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

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

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...

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

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.

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.

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.

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.

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...

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...

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.

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.

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...

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.

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.

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.

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

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)

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.

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.

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.

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

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

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.

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)

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.

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.

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

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.

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

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 ...

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.

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

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.

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.

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)

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.

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.

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.

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...

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.

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

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, ...

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...

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.

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.

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...

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.

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

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.

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

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.

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...

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

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.

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)

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.

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

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.

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.

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

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.

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.

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

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...

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.

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.

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)

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.

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

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.

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.

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...

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...

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...

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...

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

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

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)

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.

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....

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.

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.

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

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.

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

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.

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.

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

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.

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.

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)

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.

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.

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.

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)

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.

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.

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

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...

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.

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

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

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.

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

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.

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.

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

Brain machine interfaces combining microelectrode arrays with nanostructured optical biochemical sensors

Science.gov (United States)

Hajj-Hassan, Mohamad; Gonzalez, Timothy; Ghafer-Zadeh, Ebrahim; Chodavarapu, Vamsy; Musallam, Sam; Andrews, Mark

2009-02-01

Neural microelectrodes are an important component of neural prosthetic systems which assist paralyzed patients by allowing them to operate computers or robots using their neural activity. These microelectrodes are also used in clinical settings to localize the locus of seizure initiation in epilepsy or to stimulate sub-cortical structures in patients with Parkinson's disease. In neural prosthetic systems, implanted microelectrodes record the electrical potential generated by specific thoughts and relay the signals to algorithms trained to interpret these thoughts. In this paper, we describe novel elongated multi-site neural electrodes that can record electrical signals and specific neural biomarkers and that can reach depths greater than 8mm in the sulcus of non-human primates (monkeys). We hypothesize that additional signals recorded by the multimodal probes will increase the information yield when compared to standard probes that record just electropotentials. We describe integration of optical biochemical sensors with neural microelectrodes. The sensors are made using sol-gel derived xerogel thin films that encapsulate specific biomarker responsive luminophores in their nanostructured pores. The desired neural biomarkers are O2, pH, K+, and Na+ ions. As a prototype, we demonstrate direct-write patterning to create oxygen-responsive xerogel waveguide structures on the neural microelectrodes. The recording of neural biomarkers along with electrical activity could help the development of intelligent and more userfriendly neural prosthesis/brain machine interfaces as well as aid in providing answers to complex brain diseases and disorders.

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

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.

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.

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

Classification of electrical discharges in DC Accelerators

Energy Technology Data Exchange (ETDEWEB)

Banerjee, Srutarshi, E-mail: sruban.stephens@gmail.com [Accelerator and Pulse Power Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400085 (India); Deb, A.K. [Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India); Rajan, Rehim N. [Accelerator and Pulse Power Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400085 (India); Kishore, N.K. [Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India)

2016-08-11

Controlled electrical discharge aids in conditioning of the system while uncontrolled discharges damage its electronic components. DC Accelerator being a high voltage system is no exception. It is useful to classify electrical discharges according to the severity. Experimental prototypes of the accelerator discharges are developed. Photomultiplier Tubes (PMTs) are used to detect the signals from these discharges. Time and Frequency domain characteristics of the detected discharges are used to extract features. Machine Learning approaches like Fuzzy Logic, Neural Network and Least Squares Support Vector Machine (LSSVM) are employed to classify the discharges. This aids in detecting the severity of the discharges.

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.

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.

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.

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.

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

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)

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.

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).

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.

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.

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.

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

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.

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.

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.

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)

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…

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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)

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.

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

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.

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

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.

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)

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)

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.

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.

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.

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.

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.

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...

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...

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.

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

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.

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…

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.

Using human brain activity to guide machine learning.

Science.gov (United States)

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

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...

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

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.

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.

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.

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

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

Deep Neural Network Based Demand Side Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Seunghyoung Ryu

2016-12-01

Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

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.

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.

Neural Network Modeling of Cutting Fluid Impact on Energy Consumption during Turning

Directory of Open Access Journals (Sweden)

M. Bachraty

2016-06-01

Full Text Available This paper presents a part of research on power consumption differences between various cutting fluids used during turning operations. An attempt was made to study the possibility of artificial neural network to model the behavior function and predicting the electrical power consumption. Friction factor of examined cutting fluids was also measured to describe a more complete picture of investigated cutting fluids characteristics. It was discovered that wide spectrum of characteristics is present in today’s market and that artificial neural networks are suitable for purpose of modeling the power consumption of the lathe during machining. This paper could be used as a foundation for later database building where it would be possible to predict how certain cutting fluid will behave in a specific machining parameter combination.

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.

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.

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.

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.

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.

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

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.

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

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.

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

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.

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)

Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair

Directory of Open Access Journals (Sweden)

Sara L Gonzalez Andino

2011-07-01

Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

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)

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

Radial basis function neural network for power system load-flow

International Nuclear Information System (INIS)

Karami, A.; Mohammadi, M.S.

2008-01-01

This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

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.

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.

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.

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)

Using adaptive network based fuzzy inference system to forecast regional electricity loads

International Nuclear Information System (INIS)

Ying, L.-C.; Pan, M.-C.

2008-01-01

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

Using adaptive network based fuzzy inference system to forecast regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

2008-02-15

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

A comparison of neural network architectures for the prediction of MRR in EDM

Science.gov (United States)

Jena, A. R.; Das, Raja

2017-11-01

The aim of the research work is to predict the material removal rate of a work-piece in electrical discharge machining (EDM). Here, an effort has been made to predict the material removal rate through back-propagation neural network (BPN) and radial basis function neural network (RBFN) for a work-piece of AISI D2 steel. The input parameters for the architecture are discharge-current (Ip), pulse-duration (Ton), and duty-cycle (τ) taken for consideration to obtained the output for material removal rate of the work-piece. In the architecture, it has been observed that radial basis function neural network is comparatively faster than back-propagation neural network but logically back-propagation neural network results more real value. Therefore BPN may consider as a better process in this architecture for consistent prediction to save time and money for conducting experiments.

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.

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.

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

A novel method for extraction of neural response from single channel cochlear implant auditory evoked potentials.

Science.gov (United States)

Sinkiewicz, Daniel; Friesen, Lendra; Ghoraani, Behnaz

2017-02-01

Cortical auditory evoked potentials (CAEP) are used to evaluate cochlear implant (CI) patient auditory pathways, but the CI device produces an electrical artifact, which obscures the relevant information in the neural response. Currently there are multiple methods, which attempt to recover the neural response from the contaminated CAEP, but there is no gold standard, which can quantitatively confirm the effectiveness of these methods. To address this crucial shortcoming, we develop a wavelet-based method to quantify the amount of artifact energy in the neural response. In addition, a novel technique for extracting the neural response from single channel CAEPs is proposed. The new method uses matching pursuit (MP) based feature extraction to represent the contaminated CAEP in a feature space, and support vector machines (SVM) to classify the components as normal hearing (NH) or artifact. The NH components are combined to recover the neural response without artifact energy, as verified using the evaluation tool. Although it needs some further evaluation, this approach is a promising method of electrical artifact removal from CAEPs. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

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.

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…

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.

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.

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.

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

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.

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.

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.

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.

Short-term electric load forecasting using computational intelligence methods

OpenAIRE

Jurado, Sergio; Peralta, J.; Nebot, Àngela; Mugica, Francisco; Cortez, Paulo

2013-01-01

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out...

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

Introduction to AC machine design

CERN Document Server

Lipo, Thomas A

2018-01-01

AC electrical machine design is a key skill set for developing competitive electric motors and generators for applications in industry, aerospace, and defense. This book presents a thorough treatment of AC machine design, starting from basic electromagnetic principles and continuing through the various design aspects of an induction machine. Introduction to AC Machine Design includes one chapter each on the design of permanent magnet machines, synchronous machines, and thermal design. It also offers a basic treatment of the use of finite elements to compute the magnetic field within a machine without interfering with the initial comprehension of the core subject matter. Based on the author's notes, as well as after years of classroom instruction, Introduction to AC Machine Design: * Brings to light more advanced principles of machine design--not just the basic principles of AC and DC machine behavior * Introduces electrical machine design to neophytes while also being a resource for experienced designers * ...

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.

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

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

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.

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.

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,

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”.

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)

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.

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)

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

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.

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.

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...

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%.

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)

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.

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)

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

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

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.

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.

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....

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.

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)

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.

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.

Dynamics of neural cryptography.

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Dynamics of neural cryptography

International Nuclear Information System (INIS)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-01-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

Dynamics of neural cryptography

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

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.

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

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.

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.

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.

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....

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.

Design and Performance Improvement of AC Machines Sharing a Common Stator

Science.gov (United States)

Guo, Lusu

discussed in this dissertation. In the design stage, an optimization method based on orthogonal experimental design will be introduced. Besides, a universal current profiling technique is proposed to minimize the torque pulsation along with the stator copper losses in modular interior permanent magnet motors. Instead of sinusoidal current waveforms, this algorithm will calculate the proper currents which can minimize the torque pulsation. Finite element analysis and Matlab programing will be used to develop this optimal current profiling algorithm. Permanent magnet machines are becoming more attractive in some modern traction applications, such as traction motors and generators for an electrified vehicle. The operating speed or the load condition in these applications may be changing all the time. Compared to electric machines used to operate at a constant speed and constant load, better control performance is required. In this dissertation, a novel model reference adaptive control (MRAC) used on five-phase interior permanent magnet motor drives is presented. The primary controller is designed based on artificial neural network (ANN) to simulate the nonlinear characteristics of the system without knowledge of accurate motor model or parameters. The proposed motor drive decouples the torque and flux components of five-phase IPM motors by applying a multiple reference frame transformation. Therefore, the motor can be easily driven below the rated speed with the maximum torque per ampere (MTPA) operation or above the rated speed with the flux weakening operation. The ANN based primary controller consists of a radial basis function (RBF) network which is trained on-line to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment and implemented experimentally utilizing dSPACE DS1104 DSP board on a five-phase prototype IPM motor. The proposed model reference adaptive control method has been applied on the commons stator SynRM and IPM

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.

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.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

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

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...

The achievements of the Z-machine; Les exploits de la Z-machine

Energy Technology Data Exchange (ETDEWEB)

Larousserie, D

2008-03-15

The ZR-machine that represents the latest generation of Z-pinch machines has recently begun preliminary testing before its full commissioning in Albuquerque (Usa). During its test the machine has well operated with electrical currents whose intensities of 26 million Ampere are already 2 times as high as the intensity of the operating current of the previous Z-machine. In 2006 the Z-machine reached temperatures of 2 billions Kelvin while 100 million Kelvin would be sufficient to ignite thermonuclear fusion. In fact the concept of Z-pinch machines was imagined in the fifties but the technological breakthrough that has allowed this recent success and the reborn of Z-machine, was the replacement of gas by an array of metal wires through which the electrical current flows and vaporizes it creating an imploding plasma. It is not well understood why Z-pinch machines generate far more radiation than theoretically expected. (A.C.)

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.

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

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)

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.

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.

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)

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.

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.

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.

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.

Machine learning topological states

Science.gov (United States)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

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.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Directory of Open Access Journals (Sweden)

Chernoded Andrey

2017-01-01

Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Tomography and generative training with quantum Boltzmann machines

Science.gov (United States)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

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.

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.

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

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.

Polycrystalline-Diamond MEMS Biosensors Including Neural Microelectrode-Arrays

Directory of Open Access Journals (Sweden)

Donna H. Wang

2011-08-01

Full Text Available Diamond is a material of interest due to its unique combination of properties, including its chemical inertness and biocompatibility. Polycrystalline diamond (poly-C has been used in experimental biosensors that utilize electrochemical methods and antigen-antibody binding for the detection of biological molecules. Boron-doped poly-C electrodes have been found to be very advantageous for electrochemical applications due to their large potential window, low background current and noise, and low detection limits (as low as 500 fM. The biocompatibility of poly-C is found to be comparable, or superior to, other materials commonly used for implants, such as titanium and 316 stainless steel. We have developed a diamond-based, neural microelectrode-array (MEA, due to the desirability of poly-C as a biosensor. These diamond probes have been used for in vivo electrical recording and in vitro electrochemical detection. Poly-C electrodes have been used for electrical recording of neural activity. In vitro studies indicate that the diamond probe can detect norepinephrine at a 5 nM level. We propose a combination of diamond micro-machining and surface functionalization for manufacturing diamond pathogen-microsensors.

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...

Neural Decoder for Topological Codes

Science.gov (United States)

Torlai, Giacomo; Melko, Roger G.

2017-07-01

We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine, of the type extensively used in deep learning. We provide a general prescription for the training of the network and a decoding strategy that is applicable to a wide variety of stabilizer codes with very little specialization. We demonstrate the neural decoder numerically on the well-known two-dimensional toric code with phase-flip errors.

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.

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.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Science.gov (United States)

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

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

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.

Multi nodal load forecasting in electric power systems using a radial basis neural network; Previsao de carga multinodal em sistemas eletricos de potencia usando uma rede neural de base radial

Energy Technology Data Exchange (ETDEWEB)

Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br

2009-07-01

This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)

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.

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.

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.

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.

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…

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.

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.

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.

[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.

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.

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.

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.

A neural approach for the numerical modeling of two-dimensional magnetic hysteresis

International Nuclear Information System (INIS)

Cardelli, E.; Faba, A.; Laudani, A.; Riganti Fulginei, F.; Salvini, A.

2015-01-01

This paper deals with a neural network approach to model magnetic hysteresis at macro-magnetic scale. Such approach to the problem seems promising in order to couple the numerical treatment of magnetic hysteresis to FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, making possible a full computer simulation in a reasonable time. The neural system proposed consists of four inputs representing the magnetic field and the magnetic inductions components at each time step and it is trained by 2-d measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the neural system returns the predicted value of the field H at the same time step. A suitable partitioning of the neural system, described in the paper, makes the computing process rather fast. Validations with experimental tests and simulations for non-symmetric and minor loops are presented

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.

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

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.

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.

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.

The cognitive approach to conscious machines

CERN Document Server

Haikonen, Pentti O

2003-01-01

Could a machine have an immaterial mind? The author argues that true conscious machines can be built, but rejects artificial intelligence and classical neural networks in favour of the emulation of the cognitive processes of the brain-the flow of inner speech, inner imagery and emotions. This results in a non-numeric meaning-processing machine with distributed information representation and system reactions. It is argued that this machine would be conscious; it would be aware of its own existence and its mental content and perceive this as immaterial. Novel views on consciousness and the mind-

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...

International Conference on Artificial Neural Networks (ICANN)

CERN Document Server

Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

2015-01-01

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

Deep learning: Using machine learning to study biological vision

OpenAIRE

Majaj, Najib; Pelli, Denis

2017-01-01

Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions.

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.

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

Electric Motor-Generator for a Hybrid Electric Vehicle

OpenAIRE

Odvářka, Erik; Mebarki, Abdeslam; Gerada, David; Brown, Neil; Ondrůšek, Čestmír

2009-01-01

Several topologies of electrical machines can be used to meet requirements for application in a hybrid electric vehicle. This paper describes process of an electric motor-generator selection, considering electromagnetic, thermal and basic control design. The requested electrical machine must develop 45 kW in continuous operation at 1300 rpm with field weakening capability up to 2500 rpm. Both radial and axial flux topologies are considered as potential candidates. A family of axial flux machi...

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

A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

Science.gov (United States)

Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

2017-08-01

Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

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

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.

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.

Axial flux permanent magnet brushless machines

CERN Document Server

Gieras, Jacek F; Kamper, Maarten J

2008-01-01

Axial Flux Permanent Magnet (AFPM) brushless machines are modern electrical machines with a lot of advantages over their conventional counterparts. They are being increasingly used in consumer electronics, public life, instrumentation and automation system, clinical engineering, industrial electromechanical drives, automobile manufacturing industry, electric and hybrid electric vehicles, marine vessels and toys. They are also used in more electric aircrafts and many other applications on larger scale. New applications have also emerged in distributed generation systems (wind turbine generators

Machine Learning an algorithmic perspective

CERN Document Server

Marsland, Stephen

2009-01-01

Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

Directory of Open Access Journals (Sweden)

Fabrizia Caiazzo

2018-03-01

Full Text Available Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning.

Science.gov (United States)

Caiazzo, Fabrizia; Caggiano, Alessandra

2018-03-19

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

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.

Electric engineering summary

International Nuclear Information System (INIS)

Kang, Sing Eun; Park, Seong Taek; Lim, Yong Un

1975-03-01

This book is made up six parts, which deals with circuit theory about sinusoidal alternating current, basic current circuit, wave power, distorted wave, two terminal network, distributed circuit, laplace transformation and transfer function, power engineering on line, failure analysis transmission of line, substation and protection device and hydroelectric power plant, electricity machine like DC machine, electric transformer, induction machine and rectifier, electromagnetic on dielectric substance current, electromagnetic, electricity application like lighting engineering, heat transfer and electricity chemistry, industry, industry math with integer, rational number, factorization, matrix and differential.

Neural networks and applications tutorial

Science.gov (United States)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

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.

Riemann-Theta Boltzmann Machine arXiv

CERN Document Server

Krefl, Daniel; Haghighat, Babak; Kahlen, Jens

A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, yielding a novel parametric density function involving a ratio of Riemann-Theta functions. The conditional expectation of a hidden state for given visible states can also be calculated analytically, yielding a derivative of the logarithmic Riemann-Theta function. The conditional expectation can be used as activation function in a feedforward neural network, thereby increasing the modelling capacity of the network. Both the Boltzmann machine and the derived feedforward neural network can be successfully trained via standard gradient- and non-gradient-based optimization techniques.

An Individual Claims History Simulation Machine

Directory of Open Access Journals (Sweden)

Andrea Gabrielli

2018-03-01

Full Text Available The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.

Directory of Open Access Journals (Sweden)

Monzurul Alam

Full Text Available A brain-machine interface (BMI is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.

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.

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.

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.

EXPERIMENTAL EVALUATION OF WEDM MACHINED SURFACE WAVINESS

Directory of Open Access Journals (Sweden)

Katerina Mouralova

2016-10-01

Full Text Available Wire Electrical Discharge Machining (WEDM an unconventional machining technology which has become indispensable in many industries. The typical morphology of a surface machined using the electrical discharge technology is characterized with a large number of craters caused by electro-spark discharges produced during the machining process. The study deals with an evaluation of the machine parameter setting on the profile parameters of surface waviness on samples made of two metal materials Al 99.5 and Ti-6Al-4V. Attention was also paid to an evaluation of the surface morphology using 3D colour filtered and non-filtered images.

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.

Induction machine handbook

CERN Document Server

Boldea, Ion

2002-01-01

Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to

The achievements of the Z-machine

International Nuclear Information System (INIS)

Larousserie, D.

2008-01-01

The ZR-machine that represents the latest generation of Z-pinch machines has recently begun preliminary testing before its full commissioning in Albuquerque (Usa). During its test the machine has well operated with electrical currents whose intensities of 26 million Ampere are already 2 times as high as the intensity of the operating current of the previous Z-machine. In 2006 the Z-machine reached temperatures of 2 billions Kelvin while 100 million Kelvin would be sufficient to ignite thermonuclear fusion. In fact the concept of Z-pinch machines was imagined in the fifties but the technological breakthrough that has allowed this recent success and the reborn of Z-machine, was the replacement of gas by an array of metal wires through which the electrical current flows and vaporizes it creating an imploding plasma. It is not well understood why Z-pinch machines generate far more radiation than theoretically expected. (A.C.)

Machine learning in virtual screening.

Science.gov (United States)

Melville, James L; Burke, Edmund K; Hirst, Jonathan D

2009-05-01

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

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

Adaptive optimization and control using neural networks

Energy Technology Data Exchange (ETDEWEB)

Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

1993-10-22

Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

Neural-Network Quantum States, String-Bond States, and Chiral Topological States

Science.gov (United States)

Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio

2018-01-01

Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

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.

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.

Observation of enhanced electric field in an RF-plugged sheet plasma in the RFC-XX-M open-ended machine

International Nuclear Information System (INIS)

Oda, T.; Takiyama, K.; Kadota, K.

1987-12-01

We report nonperturbing observation of the electric field in the sheet plasma for RF end-plugging on the RFC XX-M open-ended machine by using the Stark effect with a combined technique of beam-probe and laser-induced fluorescence. Under the optimum condition for the RF plugging, enhanced electric field is found in the sheet plasma by about 2.5 times with respect to the electric field when no plasma is produced. The field spatial profile is also measured, which is discussed in connection with the electrostatic eigenmode. (author)

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

Directory of Open Access Journals (Sweden)

Laura Cornejo-Bueno

2017-11-01

Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

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.

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

Residual life estimation of electrical insulation system for rotating equipment

International Nuclear Information System (INIS)

Vashishtha, Y.D.; Gupta, A.K.; Bhattacharyya, A.K.; Verma, A.K.

1994-01-01

Residual life assessment gains significance towards the end of designed life for granting plant life extensions and resource planning for costly equipment replacement. A critical review of all the diagnostic techniques presently used to assess either health of insulation system or to infer qualitatively the remaining life for rotating machines is presented. However more emphasis is required on developing quantitative methods. This paper also formulates the experimental plan for progressively censored ageing tests, measurement of partial discharge parameters, micro-structural study for delamination and electrical tree growth and measurement of electrical breakdown strength. Partial discharge (PD) patterns, electrical tree growth and time to failure data shall be taken as training set for the neural network learning which can be useful to predict residual life with only one candidate parameter i.e. PD patterns. (author). 9 refs

Electric engineering introduction

International Nuclear Information System (INIS)

An, Byeong Won; Eom, Sang Ho

1999-03-01

It is divided into nine chapters, which includes electricity theory such as structure of material and current, nature of electricity, static, magnetic force and magnetic attraction, attraction of current and a storage battery, electric circuit on a direct current circuit, single phase circuit and 3-phase current circuit electricity machine like DC generator, DC motor, alternator, electric transformer, single-phase induction motor, 3-phase induction motor, synchronous motor, synchro electric machine, semiconductor such as diode, transistor, FET, UJT, silicon symmetrical switch, electronic circuit like smoothing circuit and Bistable MV. circuit, automatic control, measurement of electricity, electric application and safety.

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.

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....

On the Application of TLS Techniques to AC Electrical Drives

Directory of Open Access Journals (Sweden)

M. Cirrincione

2005-03-01

Full Text Available This paper deals with the application of a new neuron, the TLS EXIN neuron, to AC induction motor drives. In particular, it addresses two important subjects of AC induction motor drives: the on-line estimation of the electrical parameters of the machine and the speed estimation in sensorless drives. On this basis, this work summarizes the parameter estimation and sensorless techniques already developed by the authors over these last few years, all based on the TLS EXIN. With regard to sensorless, two techniques are proposed: one based on the MRAS and the other based on the full-order Luenberger observer. The work show some of the most significant results obtained by the authors in these fields and stresses the important potentiality of this new neural technique in AC induction machine drives.

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.

Fundamentals of electrical drives

CERN Document Server

Veltman, André; De Doncker, Rik W

2007-01-01

Provides a comprehensive introduction to various aspects of electrical drive systems. This volume provides a presentation of dynamic generic models that cover all major electrical machine types and modulation/control components of a drive as well as dynamic and steady state analysis of transformers and electrical machines.

Infinite ensemble of support vector machines for prediction of ...

African Journals Online (AJOL)

Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used ...

Face Recognition in Humans and Machines

Science.gov (United States)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

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, ...

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).

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.

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.

Introduction to Concepts in Artificial Neural Networks

Science.gov (United States)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Tensor Basis Neural Network v. 1.0 (beta)

Energy Technology Data Exchange (ETDEWEB)

2017-03-28

This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.

Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia

Science.gov (United States)

Sharma, Gaurav; Friedenberg, David A.; Annetta, Nicholas; Glenn, Bradley; Bockbrader, Marcie; Majstorovic, Connor; Domas, Stephanie; Mysiw, W. Jerry; Rezai, Ali; Bouton, Chad

2016-09-01

Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.

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.

Machine learning molecular dynamics for the simulation of infrared spectra.

Science.gov (United States)

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

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

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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.

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Directory of Open Access Journals (Sweden)

Christopher Bergmeir

2012-01-01

Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

Predicting the dissolution kinetics of silicate glasses using machine learning

Science.gov (United States)

Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

2018-05-01

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

Approach to design neural cryptography: a generalized architecture and a heuristic rule.