• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1017-1018
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• 2006

We measured EEG, extracted the feature vectors using alpha and beta rhythm from the measured EEG and pattern recognition was simulated by using the feature vector and the algorithms which are conventional LVQ and Forward only Counter Propagation Networks. And then the successful rate of pattern class of EEG data had about 76 %.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.93-100
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• 2000

Target signal feature parameters are very important to classify target by active sonar. Two highly correlated broad band pulses separated by time T have a time separation pitch(TSP) of 1/T Hz which is equal to the trough-to-trough or peak-to-peak spacing of its spectrum. In this study, TSP informations which represent feature of each target signal were effectively extracted by the FFT. The extracted TSP feature parameters were also applied to the pattern recognition algorithm to classify target and to analyze their properties.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.117-124
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• 2008

This paper introduces a novel approach for automated mapping of a map feature that is vinyl green house in high spatial resolution imagery Some map features have their unique spatial patterns. These patterns are normally detected in high spatial resolution remotely sensed data by human recognition system. When spatial patterns can be applied to map feature identification, it will improve image classification accuracy and will be contributed a lot to feature identification. In this study, an automated feature identification approach using spatial aucorrelation is developed, specifically for the vinyl green house that has distinctive spatial pattern in its array. The algorithm aimed to develop the method without any human intervention such as digitizing. The method can investigate the characteristics of repeated spatial pattern of vinyl green house. The repeated spatial pattern comes from the orderly array of vinyl green house. For this, object-based approaches are essential because the pattern is recognized when the shapes that are consists of the groups of pixels are involved. The experimental result shows very effective vinyl house extraction. The targeted three vinyl green houses were exactly identified in the IKONOS image for a part of Jeju area.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.75-83
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• 2018

Recently, autonomous vehicles have been actively studied. Pedestrian detection and recognition technology is important in autonomous vehicles. Pedestrian detection using CNN(Convolutional Neural Netwrok), which is mainly used recently, generally shows good performance, but there is a performance degradation depending on the environment of the image. In this paper, we propose a pedestrian detection system applying long-term memory structure of hippocampal neural network based on CNN network with LGP-FL (Local Gradient Pattern-Feature Layer) added. First, change the input image to a size of $227{\times}227$. Then, the feature is extracted through a total of 5 layers of convolution layer. In the process, LGP-FL adds the LGP feature pattern and stores the high-frequency pattern in the long-term memory. In the detection process, it is possible to detect the pedestrian more accurately by detecting using the LGP feature pattern information robust to brightness and color change. A comparison of the existing methods and the proposed method confirmed the increase of detection rate of about 1~4%.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.319-327
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• 2012

In this paper, we present a sign language recognition model which does not use any wearable devices for object tracking. The system design issues and implementation issues such as data representation, feature extraction and pattern classification methods are discussed. The proposed data representation method for sign language patterns is robust for spatio-temporal variances of feature points. We present a feature extraction technique which can improve the computation speed by reducing the amount of feature data. A neural network model which is capable of incremental learning is described and the behaviors and learning algorithm of the model are introduced. We have defined a measure which reflects the relevance between the feature values and the pattern classes. The measure makes it possible to select more effective features without any degradation of performance. Through the experiments using six types of sign language patterns, the proposed model is evaluated empirically.

• Korean Journal of Computational Design and Engineering
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• v.6 no.4
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• pp.263-270
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• 2001

In stamping-die manufacturing, the first step is to build die patterns for lost wax casting process. A recent industry trend is to manufacture the die pattern using 3-axis NC machining. This study identifies technical considerations of the pattern machining caused by the characteristics of Styrofoam material, and proposes technical methods related to establishing a process plan and generating tool paths for optimizing the pattern machining. In this paper, the process plan includes the fellowing three items: 1) deter-mining a global machining sequence-a sequence of profile, top, bottom machining and two set-ups, 2) extracting machining features from a pattern model and merging them, and 3) determining a machining sequence of machining features. To each machining feature, this study determines the machining start point, generates the approach tool path, and proposes a tool path linking method fur reducing the distance of the cutter rapid motion. Finally, a smooth tool path generation and an automatic feedrate adjustment (AFA) method are introduced far raising the machining efficiency.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.95-104
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• 1993

IPA(interpattern association) model is a method of feature extraction using a neural network. Even in the case that the reference patterns are simuklar to one another, this model can recover the reference patterns effectively. However, when the pattern whose feature pixels are lost is used as input, this model can not guarantee perfect recovery of the reference pattern. It is proposed a improved interpattern association(IPA) model for the feature extraction using neural network. The improved IPA model that combines the first interconnection weight matrix of the IPA model with the second additional weight matrix is proposed here to overcome the recovery problem of the original IPA model. The results of computer simulation and optical experiment are advanced.