• Journal of the Korea institute for structural maintenance and inspection
• /
• v.17 no.5
• /
• pp.13-20
• /
• 2013

In order to reduce seismic responses of a structure, additional dampers and vibration control devices are generally considered. Usually, control performance of additional devices are investigated for optimal design without variation of characteristics of a structure. In this study, multi-objective integrated optimization of structure-smart control device is conducted and possibility of reduction of structural resources of a building structure with smart top-story isolation system has been investigated. To this end, 20-story example building structure was selected and an MR damper and low damping elastomeric bearings were used to compose a smart base isolation system. Artificial earthquakes generated based on design spectrum of low-to-moderate seismicity regions are used for structural analyses. Based on numerical simulation results, it has been shown that a smart top-story isolation system can effectively reduce both structural responses and isolation story drifts of the building structure in low-to-moderate seismicity regions. The integrated optimal design method proposed in this study can provide various optimal designs that presents good control performance by appropriately reducing the amount of structural material and damping device.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.3
• /
• pp.43-50
• /
• 2019

Effects of tensile strength and aspect ratio of steel fiber on compressive and flexural behavior of steel fiber-reinforced concrete (SFRC) with high- and normal-strength were investigated. Also, this study explores compressive behavior of SFRC with different loading rate. For this purpose, four types of steel fiber were used for SFRC with specified compressive strength of 35 and 60 MPa, respectively. Cylindrical specimens with a diameter of 150 mm and height of 300 mm were made for compression test, and prismatic specimens with a $150{\times}150mm$ cross-section and 450 mm span length were made for flexural test. Test results from compression and flexural tests indicated that the toughness of concrete significant increased with steel fibers. Especially, using steel fiber with high tensile strength and aspect ratio can be lead to performance improvement of high-strength SFRC. In this study, equations are suggested to predict compressive toughness ratio of SFRC from flexural toughness ratio.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.1
• /
• pp.154-162
• /
• 2019

Large scale damage has been globally increased due to natural disasters such as earthquake. Although a variety of studies secured seismic performance of buildings, casualties and economic loss have occurred because of poor security of seismic performance in non-structural components. Structure's location on which non-structural components are installed and characteristics of vibration occurring on each position of structures are varied, so a response spectrum is required for each position of structures. In addition, a response spectrum occurring in a structure is different, depending on the form of it and positions on which it is installed. Therefore, selection of a response spectrum is important, so a definite method for calculating the response spectrum which acts on non-structural components is necessary. A method for choosing a response spectrum is suggested in this paper, and a structural analysis was conducted with the suggested method, by selecting a ground response spectrum and a structural system, which may occur in Korea. Moreover, it helps create a response spectrum necessary for a seismic test of non-structural components, by suggesting the method for deduction it, with a simple formula.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.5
• /
• pp.825-832
• /
• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Journal of Internet Computing and Services
• /
• v.24 no.1
• /
• pp.49-59
• /
• 2023

Recently, attention to the pandemic situation represented by COVID-19 emerged problems caused by unexpected shortage of medical personnel. In this paper, we present a method for diagnosing the presence or absence of lesional sign on PA chest X-ray images as computer vision solution to support diagnosis tasks. Method for visual anomaly detection based on feature modeling can be also applied to X-ray images. With extracting feature vectors from PA chest X-ray images and divide to patch unit, region-specific abnormality can be detected. As preliminary experiment, we created simulation data set containing multiple objects and present results of the comparative experiments in this paper. We present method to improve both efficiency and performance of the process through hard masking of patch features to aligned images. By summing up regional specificity and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to previous studies. By aggregating region-specific and global anomaly detection results, it shows improved performance by 0.069 AUROC compared to our last study.

• The Korean Journal of Applied Statistics
• /
• v.22 no.4
• /
• pp.771-780
• /
• 2009

Cataract is the main cause of blindness and visual impairment, especially, age-related cataract accounts for about half of the 32 million cases of blindness worldwide. As the life expectancy and the expansion of the elderly population are increasing, the cases of cataract increase as well, which causes a serious economic and social problem throughout the country. However, the incidence of cataract can be reduced dramatically through early diagnosis and prevention. In this study, we developed a prediction model of cataracts for early diagnosis using hospital data of 3,237 subjects who received the screening test first and then later visited medical center for cataract check-ups cataract between 1994 and 2005. To develop the prediction model, we used random forests and compared the predictive performance of this model with other common discriminant models such as logistic regression, discriminant model, decision tree, naive Bayes, and two popular ensemble model, bagging and arcing. The accuracy of random forests was 67.16%, sensitivity was 72.28%, and main factors included in this model were age, diabetes, WBC, platelet, triglyceride, BMI and so on. The results showed that it could predict about 70% of cataract existence by screening test without any information from direct eye examination by ophthalmologist. We expect that our model may contribute to diagnose cataract and help preventing cataract in early stages.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.19 no.2
• /
• pp.60-67
• /
• 2015

The finite element (FE) model updating is a commonly used approach in civil engineering, enabling damage detection, design verification, and load capacity identification. In the FE model updating, acceleration responses are generally employed to determine modal properties of a structure, which are subsequently used to update the initial FE model. While the acceleration-based model updating has been successful in finding better approximations of the physical systems including material and sectional properties, the boundary conditions have been considered yet to be difficult to accurately estimate as the acceleration responses only correspond to translational degree-of-freedoms (DOF). Recent advancement in the sensor technology has enabled low-cost, high-precision gyroscopes that can be adopted in the FE model updating to provide angular information of a structure. This study proposes a FE model updating strategy based on data fusion of acceleration and angular velocity. The usage of both acceleration and angular velocity gives richer information than the sole use of acceleration, allowing the enhanced performance particularly in determining the boundary conditions. A numerical simulation on a simply supported beam is presented to demonstrate the proposed FE model updating approach.

• Journal of Korea Multimedia Society
• /
• v.14 no.11
• /
• pp.1383-1391
• /
• 2011

Following the appearance of the latest medical equipment with improved function, the importance of image analysis which enables effective image processing and analysis consistent with the hardware performance is on the rise. As well as, ongoing study is being done on the 2D medical image processing and 3D reconstruction. This paper segments chest CT images into each stage and finally shows 3D reconstruction of each segmented result. Among various image segmentation methods, Region Growing and apply sharpening and Gamma Controller as for image improvement for effective segmentation, image segmentation in order of bronchus and lung, bronchus, lung. Human organs image of segmented is use VTK(Visualization Toolkit) to make 3D reconstruction, two and three-dimensional medical image processing and analysis for lesions diagnosis are able to utilized.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.386-389
• /
• 2016

최근 남녀노소를 불문하고 피부 관리에 대한 관심이 증가하면서, 피부 개선에 효과적인 화장품의 선택에 관심이 높아지고 있다. 하지만 다양한 화장품들을 대상으로 자동화된 고객 맞춤형 화장품 추천은 그 발전이 더디고, 이와 관련된 연구 또한 아직 미미한 실정이다. 또한, 다양한 특성을 가지는 고객 피부 데이터 셋의 확보가 어려운 상황에서, 소수의 데이터 표본만을 이용하여 화장품 추천이 진행되고 있어 추천의 정확도를 확보하지 못하고 있다. 본 논문은 스마트폰용 휴대용 카메라를 이용하여 고객의 피부 상태를 진단한 후, 고객의 피부 개선에 적합한 화장품을 자동으로 추천하는 기법을 제안한다. 먼저, 화장품 추천을 위해 사용자의 SNS 데이터와 피부 데이터를 수집 및 분석하여 추천 리스트를 생성한다. 이를 기반으로, 추천된 각 화장품의 스코어를 계산한다. 그 다음, 피부 개선 순위와 스코어 기반의 화장품 특성 순위 간의 상관계수를 이용하여 가장 높은 상관계수의 화장품을 우선 추천한다. 성능 평가를 위해 실제 화장품 회사에서 제시한 화장품 추천 리스트와 본 논문에서 제안한 방법을 적용한 화장품 추천 리스트를 비교함으로써 효용성과 타당성을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.1373-1376
• /
• 2012

복무적합도 검사는 정신질환이나 사고가능성이 있는 병사를 감별하고, 입대 후 적응문제로 조기 전역할 수 있는 집단을 예측하는 신인성검사 중 하나로, 현재 군에서 징병 및 입영단계에 실시하는 인성검사이다. 이는 전체 검사대상자를 상대로 정신과적 문제 식별을 위한 개별면담이 불가능하기 때문에 위 검사를 통해 대상자를 효율적으로 선별하기 위함이다. 본 연구는 데이터 마이닝을 통해 복무적합도 검사의 판정을 예측 할 수 있을지 확인하고자 하였다. 이를 위해 데이터 마이닝의 기법 중 회귀분석의 로지스틱 회귀분석 기법이 복무적합도검사 판정에 우수한 성능을 보임을 확인하였고, 로지스틱 회귀분석의 추정된 회귀계수를 이용하여 만든 반응확률에 대한 예측 모형식은 높은 정분류율을 보였고 평가 결과 통계적으로 의미가 있음을 증명하였다. 따라서 본 연구 결과를 활용하면 소수의 문항으로 복무적합도 검사 이전의 선별용 검사 개발이나 자가 진단용 검사 개발로 활용이 가능 할 것으로 기대한다.