The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform

Liu Shu-ming - One of the best experts on this subject based on the ideXlab platform.

  • Research on Combustion Within Boiler Stability Based on Neural Network
    Computer Simulation, 2012
    Co-Authors: Liu Shu-ming
    Abstract:

    Research flame stability to improving the detection accuracy of the combustion.This paper presented a Neural Network-based detection methods of flame stability.The mothod selects characteristics related directly to flame stability as Neural Network Input vector,through training samples to remove the impurities caused by the burning effects of small pulse.Experiments show that this method can effectively avoid the effects of impurities on the combustion,accurately detect the combustion stability,and achieved satisfactory results.

Shu Qian Chen - One of the best experts on this subject based on the ideXlab platform.

  • Study of Glass Fiber Textile Control Based on Image Processing and Neural Network
    The Open Automation and Control Systems Journal, 2013
    Co-Authors: Shu Qian Chen, Yang Lie Fu
    Abstract:

    Research on weft fiber cut problems of glass fiber has improved the efficiency of textile production. Glass fiber textile machine is a major producer machine of glass fiber cloth. In production, detection of textile machines weft usually adopts the contact type, requiring the weft to maintain a certain pressure on the sensor. This way can make the glass fiber weft fluff, and produce glass fiber dust, and may also cause harm to the human health and damage to the textile machine. Using video monitoring method detection weft, speed and image identification rate will directly affect the stability of the system. This paper presents a detection method of glass fiber textile's weft fiber cut based on Neural Network, selecting multiple features which are directly related to the image with the weft as Neural Network Input vector, through repeated training samples to remove tiny ripple effects which are caused by weft textile jitter, overcome the traditional method de- tection accuracy is not high. Experiments show that this method can effectively avoid the weft jitter, making accurate de- tection of the weft fiber cut, and achieving satisfactory results.

  • Research of Glass Fiber Textile Monitor Image Recognition Based on Neural Network
    Advanced Materials Research, 2013
    Co-Authors: Shu Qian Chen, Yang Lie Fu
    Abstract:

    Researched on weft fiber cut problems of glass fiber, improved the efficiency of textile production. Glass fiber textile machine is a major producer machine of glass fiber cloth. Textile machines weft detection usually uses the contact type in production, requires that the weft maintains certain pressure to the sensor. Using this method will cause glass fiber weft bristling, and will produce glass fiber floating dust. Damage to the textile machine and has the harm to the human body health. Used video surveillance method to detection the weft, image recognition and speed directly affects the stability of the system. This paper presented a detection methods of glass fiber textiles weft fiber cut based on Neural Network-based, selected multiple features which were directly related to the image with the weft as Neural Network Input vector, through repeated training samples to remove tiny ripple effects which were caused by weft textile jitter, overcome the traditional method detection accuracy was not high. Experimental results show that this method can effectively avoid the weft jitter, making accurate detection of the weft fiber cut, and achieved satisfactory results.

  • Research on Flame Combustion Stability Based on Neural Network
    Advanced Materials Research, 2012
    Co-Authors: Gui Zhi Bai, Li Hong Zhang, Shu Qian Chen
    Abstract:

    For the use of boiler flame image analysis to detect the boiler flame combustion stability, when the combustion affected by coal, peaking , improper operation or other effects, the flame appeared short pulsation. In general, the traditional detection methods based on gray scale variance can not avoid the impact of flame pulsation on account of the inaccuracy of the boiler combustion stability detection. This paper presents a flame combustion instability detection method based on Neural Network and selects multiple features which are directly related to the flame stability as Neural Network Input vector. Experiments show that this method can fight off the tiny ripple influence caused by the impurities combustion or peak and simultaneously, greatly improve the detection accuracy and stability.

Y. Kosugi - One of the best experts on this subject based on the ideXlab platform.

  • A Network inversion technique for estimating equivalent dipole description of visual evoked potential.
    Methods of Information in Medicine, 2000
    Co-Authors: Y. Hayashi, Y. Kosugi
    Abstract:

    For the activation study of the brain, dipole localization from the scalp potential is one of the most promising techniques to realize a reasonable temporal resolution which cannot be realized in functional MR and PET. The goal of our study is to estimate inversely the electrical brain activity in the form of several dipoles from the scalp potential, using a Network inversion technique. As a basic approach, we have inversely estimated several dipoles from the potential distribution on a spherical surface, in the homogeneous sphere model. In the training phase, by expanding the Neural Network Input dimensions being redundant, the Network can easily learn the forward mapping. In the inversion phase, the space of the expanded-Network-Input-vector can be narrowed by introducing a penalty term. Additionally, a consensus term was used to force several dipoles to have a similar orientation. We estimate that this is applicable to the localization of several dipoles that reflect the actual brain activity, especially in the visual evoked potentials.

Shamsul Huda - One of the best experts on this subject based on the ideXlab platform.

  • ACSC - Hybrid wrapper-filter approaches for Input feature selection using maximum relevance-minimum redundancy and artificial Neural Network Input gain measurement approximation (ANNIGMA)
    2011
    Co-Authors: Shamsul Huda, John Yearwood, Andrew Stranieri
    Abstract:

    Feature selection processes improve the accuracy, computational efficiency and scalability of classification process in data mining applications. This paper proposes two filter and wrapper hybrid approaches for feature selection techniques by combining the filter's feature ranking score in the wrapper stage. The first approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The second hybrid combines an improved version of MI based (Maximum Relevance and Minimum Redundancy; MaxRel-MinRed) filter ranking heuristic with the wrapper heuristic ANNIGMA (MaxRel-MinRed-ANNIGMA). The novelty of our approach is that we integrate the capability of wrapper approach to find better feature subset by combining filter's ranking score with the wrapper-heuristic's score that take advantages of both filter and wrapper heuristics. The performances of the hybrid approaches have been verified using synthetic, bench mark data sets and real life data set and compared to both independent filter and wrapper based approaches. Experimental results show that hybrid approaches (MR-ANNIGMA and MaxRel-MinRed-ANNIGMA) achieve more compact feature sets and higher accuracies than filter and wrapper approaches alone.

  • NSS - Hybrid Wrapper-Filter Approaches for Input Feature Selection Using Maximum Relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
    2010 Fourth International Conference on Network and System Security, 2010
    Co-Authors: Shamsul Huda, John Yearwood, Andrew Strainieri
    Abstract:

    Feature selection is an important research problem in machine learning and data mining applications. This paper proposes a hybrid wrapper and filter feature selection algorithm by introducing the filter’s feature ranking score in the wrapper stage to speed up the search process for wrapper and thereby finding a more compact feature subset. The approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The novelty of our approach is that we use hybrid of wrapper and filter methods that combines filter’s ranking score with the wrapper-heuristic’s score to take advantages of both filter and wrapper heuristics. Performance of the proposed MR-ANNIGMA has been verified using bench mark data sets and compared to both independent filter and wrapper based approaches. Experimental results show that MR-ANNIGMA achieves more compact feature sets and higher accuracies than both filter and wrapper approaches alone.

Liang Yang - One of the best experts on this subject based on the ideXlab platform.

  • modified Neural Network correlation of refrigerant mass flow rates through adiabatic capillary and short tubes extension to co2 transcritical flow
    International Journal of Refrigeration-revue Internationale Du Froid, 2009
    Co-Authors: Liang Yang, Chunlu Zhang
    Abstract:

    Abstract This paper presents a modified dimensionless Neural Network correlation of refrigerant mass flow rates through adiabatic capillary tubes and short tube orifices. In particular, CO2 transcritical flow is taken into account. The definition of Neural Network Input and output dimensionless parameters is grounded on the homogeneous equilibrium model and extended to supercritical inlet conditions. 2000 sets of experimental mass flow-rate data of R12, R22, R134a, R404A, R407C, R410A, R600a and CO2 (R744) in the open literature covering capillary and short tube geometries, subcritical and supercritical inlet conditions are collected for Neural Network training and testing. The comparison between the trained Neural Network and experimental data reports 0.65% average and 8.2% standard deviations; 85% data fall into ±10% error band. Particularly for CO2, the average and standard deviations are −2.5% and 6.0%, respectively. 90% data fall into ±10% error band.