• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.460-465
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• 2007

In this paper, we propose a novel pattern classification method based on statistical properties of the given data and fuzzy-rough set to minimize the coupling problem of the rules. In the proposed method, statistical properties is used by a selection criteria for deciding a partition number of antecedent fuzzy sets, and for minimizing an coupling problem of the generated rules. Moreover, rough set is used as a tool to remove unnecessary attributes between generated rules from the numerical data. In order to verify the validity of the proposed method, we compared the classification results (i.e, classification precision) of the proposed with the conventional pattern classification methods on the Fisher's IRIS data. From experiment results, we can conclude that the proposed method shows relatively better performance than those of the classification methods based on the conventional approaches.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Journal of the Ergonomics Society of Korea
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• v.26 no.1
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• pp.39-46
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• 2007

The purpose of this study was to develop a comprehensive posture classification scheme considering the effects of external load and motion repetition as well as those of working posture. The scheme was developed based on a series of existing empirical studies dealing with postural classification scheme, effects of external load and motion repetition. Ranges of joint motions, external load and motion repetition were divided into the groups with the same degree of discomforts. Each group was assigned a numerical relative discomfort score of code on the basis of discomfort values for the neutral position of elbow flexion. The criteria for evaluating stress of working postures were proposed based on the four distinct action categories, in order to enable practitioners to apply appropriate corrective actions. The proposed scheme was compared with OWAS, RULA and REBA. The comparison revealed that while the proposed scheme and RULA showed similar results for the working postures with light external load and non-repetitive postures, the former overestimated postural load for postures with moderate or heavy external load and repetitive postures than the latter.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.65-77
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• 2007

Site response analyses were performed based on equivalent linear technique using shear wave velocity profiles of 162 sites collected around the Korean peninsula. The site characteristics, particularly the shear wave velocities and the depth to the bedrock, are compared to those in the western United States. The results show that the site-response coefficients based on the mean shear velocity of the top 30m ($V_{S30}$) suggested in the current code underestimates the motion in short-period ranges and overestimates the motion in mid-period ranges. The current Korean code based on UBC is required to be modified considering site characteristics in Korea for the reliable estimation of site amplification. From the results of numerical estimations, new regression curves were derived between site coefficients ($F_{a}\;and\;F_{v}$) and the fundamental site periods, and site coefficients were grouped based on site periods with reasonable standard deviations compared to site classification based on $V_{S30}$. Finally, new site classification system and modification of design response spectra are recommended considering geotechnical characteristics in Korea.

• Journal of Industrial Technology
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• v.19
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• pp.155-162
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• 1999

In the last few years the automatic classification of morpholgical landforms using GSIS and DEM was investigated. Particular emphasis has been put on the morphological point attribute approaches and the extraction of drainage basin variables from digital elevation models. The automated derivation of landforms has become a neccessity for quantitative analysis in geomorphology. Furthermore, the application of GSIS technologies has become an important tool for data management and numerical data analysis for purpose of geomorphological mapping. A process developed by Dikau et al, which automates Hanmond's manual process, was applied to the pyoung chang of the kangwon. Although it produced a classification that has good resemblance to the landforms in the area, it had some problems. For example, it produced a progressive zonation when landform changes from plains to mountains, it does not distinguish open valleys from a plains mountain interface, and it was affected by micro relief. Although automating existing quantitative manual processes is an important step in the evolution automation, definition may need to be calibrated since the attributes are oftem measured differently. A new process is presented that partly solves these problems.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.449-460
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• 2022

Safe underground construction in a rock mass requires adequate ground investigation and effective determination of rock conditions. The estimation of rock mass behavior is difficult, because rock masses are innately anisotropic and heterogeneous at different scales and are affected by various environmental factors. Quantitative rock mass classification systems, such as the Q-system and rock mass rating, are widely used for characterization and engineering design. The measurement of rock classification parameters is subjective and can vary among observers, resulting in questionable accuracy. Geophysical investigation methods, such as seismic surveys, have also been used for ground characterization. Torsional shear wave propagation characteristics in cylindrical rods are equal to that in an infinite media. A probabilistic quantitative relationship between the Q-value and shear wave velocity is thus investigated considering long-wavelength wave propagation in equivalent continuum jointed rock masses. Individual Q-system parameters are correlated with stress-dependent shear wave velocities in jointed rocks using experimental and numerical methods. The relationship between the Q-value and the shear wave velocity is normalized using a defined reference condition. This relationship is further improved using probabilistic analysis to remove unrealistic data and to suggest a range of Q-values for a given wave velocity. The proposed probabilistic Q-value estimation is then compared with field measurements and cross-hole seismic test data to verify its applicability.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• Journal of the Korean Society of Clothing and Textiles
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• v.39 no.2
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• pp.217-232
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• 2015

Clothing design considers the concept of human body proportions. This paper proposes an anterior somatotype and body proportion modular by head ratio for 20s to 30s Korean males. Anterior somatotype is classified into 18-types with 13 ratios that consist of 7 heights and 6 widths divided by stature. A total of 18-types are expressed in numerical values that represent the percentage of the stature and geometrical figures represented on the grids one unit is 1% of the stature. The torso is divided by 6 modules that are side neck to shoulder, shoulder to axillary, axillary to waist, waist to navel, navel to hip, and hip to crotch. Head and leg can be estimated as numerical information of stature and torso. Morphological characteristics of anterior somatotype are analyzed by 4 factors based on results and 18-types named by simple signs. We develop a new classification of anterior somatotype that integrate vertical and horizontal characters of body proportions. The use of integrated classification in the clothing industry is expected to increase the clothing fit.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.442-445
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• 2004

The acoustic target strength (TS) of submarine is associated with its active detection, positioning and classification. That is, the survivability of submarine depends on its target strength. So it should be managed with all possible means. An anechoic coating to existing submarine or changing of curvature can be considered as major measures to reduce the TS of submarine. It is mainly based on the prediction of its TS. Under this circumstances, a study on the more accurate numerical methods becomes big topic for submarine design. In this paper, Kirchhoff approximation method was adopted as a numerical tool for the physical optics region. Secondly, the scaled models of submarine were built and tested in order to verify its performance. Through the comparison, it was found out that the Kirchhoff approximation method could be good design tool for the prediction of TS of submarine.