• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.178-183
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• 2013

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• Fashion & Textile Research Journal
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• v.18 no.4
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• pp.468-476
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• 2016

Recent advancements in optics technology enable us to realize fast scans of hands using two-dimensional (2D) image scanners. In this paper, we propose an automatic hand measurement system using 2D image scanners for customized glove production. To develop the automatic hand measurement system, firstly hand scanning devices has been constructed. The devices are designed to block external lights and have user interface to guide hand posture during scanning. After hands are scanned, hand contour is extracted using binary image processing, noise elimination and outline tracing. And then, 19 hand landmarks are automatically detected using an automatic hand landmark detection algorithm based on geometric feature analysis. Then, automatic hand measurement program is executed based on the automatically extracted landmarks and measurement algorithms. The automatic hand measurement algorithms have been developed for 18 hand measurements required for custom-made glove pattern making. The program has been coded using the C++ programming language. We have implemented experiments to demonstrate the validity of the system using 11 subjects (8 males, 3 females) by comparing automatic 2D scan measurements with manual measurements. The result shows that the automatic 2D scan measurements are acceptable in the customized glove making industry. Our evaluation results confirm its effectiveness and robustness.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.96-105
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• 2015

This paper proposes a DSP optimization solution of rain detection and removal algorithm. We propose rain detection and removal algorithms considering camera motion, and also presents optimization results in algorithm level and DSP level. At algorithm level, this paper utilizes a block level binary pattern analysis, and reduces the operation time by using the fast motion estimation algorithm. Also, the algorithm is optimized at DSP level through inter memory optimization, EDMA, and software pipelining for real-time operation. Experiment results show that the proposed algorithm is superior to the other algorithms in terms of visual quality as well as processing speed.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.63-71
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• 2005

Equilibrium geometries, electronic structures, and energies of borocarbon clusters (binary compounds of carbon and boron), an unexplored class of molecules with highly unusual characteristics and potential for further development, have been investigated by means of B3LYP/6-311+G$^*$ density functional theory computations. A large number of B$_7$C${_1}^{1-}$, B$_6C_2$, and B$_5C_{3}\,^{1+}$ clusters with planar and non-planar monocyclic and polycyclic rings, as well as cage structures, have been systematically studied. Unexpectedly, planar forms are predicted not only to be the most stable structures, but also, in many cases, to have unprecedented planar heptacoordinate boron (p-heptaB) and planar heptacoordinate carbon (p-heptaC) arrangements. All these pheptaB and p-heptaC have 6π electrons and are aromatic according to the nucleus independent chemical shift (NICS). This novel bonding pattern is analyzed in terms of natural bond orbital (NBO) analysis. For virtually all possible B$_7$C${_1}^{1-}$, B$_6C_2$, and B$_5C_{3}\,^{1+}$ combinations, the p-heptaB arrangements are the more stable than other type structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.2
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• pp.112-123
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• 1990

This paper presents a vertex-detecting of Hanguel patterns using nested contour shape. Inputed binary character patterns are transformed by distance transformation method and make a new file of transferred data by analysis of charactersitcs. A new vertex-detecting algorithm for recognizing Hanguel patterns using the two data files is proposed. This algorithm is able to reduce the projecting parts of Hanguel pattern, separate the connecting parts between different strokes, set the code number by transformed value of coorked features. It makes the output of results in order to apply the Hanguel recognition.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.1
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• pp.69-76
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• 1999

This paper proposed a design of intelligent supervisory control systems for maintenance of highway traffic information equiprrent and processing algorithm of equiprrent fault data. The fault data of highway traffic equipment are transmitted from rerrnte supervisory controller to central supervisory system by real time, the transmitted fault data are anaIyzed the characteristic using evaluation algorithm of fault data in central supervisory system. The evaluation algorithm includes a neural network and fault knowlOOge-base for processing the multi-generated fault data. For validating the evaluation algorithm of intelligent supervisory control systems, the rrethod of analysis used to the five pattern of binary signal by transmitted real time and the opTclting user-interface constructed in central supervisory system.

• Journal of Sasang Constitutional Medicine
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• v.21 no.1
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• pp.197-216
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• 2009

1. Objectives This study is to investigate the relationship between type A behavior pattern(TABP) and diabetes according to Sasang Constitution. 2. Methods 162 persons(81 IGM persons vs 81 normal persons) out of 666 persons, more than 40 years old, who participated the community based cohort in Wonju City of South Korea from July 2nd to August 30th in 2006, were randomly selected and analyzed. Framingham Type A score, Social Support index, glucose related laboratory results were measured and analyzed according to Sasang Constitution. 3. Results Soeumin in IGM group had significantly high scores in FTA scales compared with Soyangin and Taeeumin, bur in female IGM group and normal group there was no significant difference in FTA scales and TABP frequency by Sasang Constitution. There was no significant difference in social support index in IGM and normal group by Sasang Constitution. There was no significant difference in glucose-related values between TABP/TBBP in IGM group and normal group. Soyangin in female IGM group had significantly high values in insulin(fasting) and HOMA-IR in TABP group, Soeumin group had significantly high values in FBS in TABP group. According to binary logistic regression analysis for IGM, Sasang Constitution was a significant risk factor and the ORs of Taeeumin and Soyangin were significantly higher than that of Soeumin. Social Support index was significantly higher only in female group. 4. Conclusions Adequate questionnaire of TABP for our country or a research of another subjects are needed. And Sasang Constitution is thought to be the reasonable intervention to control diabetes in terms of prevention, treatment and regimen.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.9-15
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• 2019

The purpose of present study is to assess the maximum width of the surface cracks using the histogram analysis of image processing techniques in concrete structures. For this purpose, the concrete crack image is acquired by the camera. The image is Grayscale coded and Binary coded. After Binary coded image is Dilate and Erode coded, the image is then recognized as separated objects by applying Labeling techniques. Over time, dust and stains may occur naturally on the surface of concrete. The crack image of concrete may include shadows and reflections by lighting depending on a surrounding conditions. In general, concrete cracks occur in a continuous pattern and noise of image appears in the form of shot noises. Bilateral Blurring and Adaptive Threshold apply to the Grayscale image to eliminate these effects. The remaining noises are removed by the object area ratio to the Labeled area. The maximum numbers of pixels and its positions in the crack objects without noises are calculated in x-direction and y-direction by Histogram analysis. The widths of the crack are estimated by trigonometric ratio at the positions of the pixels maximum numbers for the Labeled objects. Finally, the maximum crack width estimated by the proposed method is compared to the crack width measured with the crack gauge. The proposed method by the present study may increase the reliability for the estimation of maximum crack width using image processing techniques in concrete surface images.