• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.76-83
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• 2007

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.

• Applied Microscopy
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• v.46 no.2
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• pp.100-104
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• 2016

Electron microscopy is currently the only available technique with a spatial resolution sufficient to identify fine neuronal processes and synaptic structures in densely packed neuropil. For large-scale volume reconstruction of neuronal connectivity, serial block-face scanning electron microscopy allows us to acquire thousands of serial images in an automated fashion and reconstruct neural circuits faster by reducing the alignment task. Here we introduce the whole reconstruction procedure of synaptic network in the rat hippocampal CA1 area and discuss technical issues to be resolved for improving image quality and segmentation. Compared to the serial section transmission electron microscopy, serial block-face scanning electron microscopy produced much reliable three-dimensional data sets and accelerated reconstruction by reducing the need of alignment and distortion adjustment. This approach will generate invaluable information on organizational features of our connectomes as well as diverse neurological disorders caused by synaptic impairments.

• Textile Coloration and Finishing
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• v.19 no.6
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• pp.7-11
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• 2007

A prediction model for yarn density profile was developed using the neural network methodology. The neural network model developed traces mass densities of a yarn within a section and predicts the mass profiles of the next yarn segment yet to be measured. The model does not require an assumption on the existence of a relationship between the past and future data sets. Four high-draft yarns made under different processing conditions were employed in order to test the performance of the model developed. It was shown that the model could predict the yarn density profiles without a significant error.

• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1364-1371
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• 1990

We propose a time-optimal path planning scheme for the robot manipulator using Hopfield neural network. The time-optimal path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to apply the neural networke technique, which can allow the parallel computation, to the minimum time problem. This paper proposes an approach for solving the time-optimal path planning by using Hopfield neural network. The effectiveness of the proposed method is demonstrarted using a PUMA 560 manipulator.

• Wind and Structures
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• v.5 no.1
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• pp.1-14
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• 2002

Artificial Neural Networks (ANN) have the capability to develop functional relationships between input-output patterns obtained from any source. Thus ANN can be conveniently used to develop a generalised relationship from limited and sometimes inconsistent data, and can therefore also be applied to tackle the data obtained from wind tunnel tests on building models with large number of variables. In this paper ANN model has been developed for predicting wind induced pressures in various zones of a Gable Building from limited test data. The procedure is also extended to a case wherein interference effects on a gable roof building by a similar building are studied. It is found that the Artificial Neural Network modelling is seen to predict successfully, the pressure coefficients for any roof slope that has not been covered by the experimental study. It is seen that ANN modelling can lead to a reduction of the wind tunnel testing effort for interference studies to almost half.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.299-306
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• 2020

Image recognition using neural networks is widely used in various fields. In this study, we investigated licensed / unlicensed vehicle recognition systems necessary for vehicle number recognition and control when entering and exiting a specific area. This system is equipped with the function of recognizing the image, so it checks all the information on the vehicle number and adds the function to accurately recognize the vehicle number plate. In addition, it is possible to check the vehicle number more quickly using a neural network.

• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.322-325
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• 2007

Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully utilized by accumulating a lot of input feature vectors and weight vectors, then converting the many inner-product operations into one matrix operation. Further research areas include benchmarking the performance with various hardware and GPU-aware learning algorithms. (c) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.8
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• pp.361-367
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• 2002

Many failures in high voltage equipment are preceded by partial discharge activity. In this paper deals with the application of the high frequency partial discharge measurement technique in motorette. HFPD measurement is very effective method to detect the PD occurred in motorette which is the called name of test specimen for accelerating test of stator winding[1] In this study, CT type HFPD sensor is used to detect the partial discharges and a measured HFPD pattern is analyzed by fractal mathematics. The neural network algorithm is used to pattern recognition and ageing diagnosis. As a result of this study, the fractal dimensions are increased along to applied voltage and HFPD pattern recognition using neural network shown excellent recognition rate. Also, the ageing diagnosis of motorette has been Possible.