• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2039-2044
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• 2006

Since the bio-information now even exceeds the capability of human brain, the techniques of data mining and artificial intelligent are needed to deal with the information in this field. There are many researches about using DNA microarray technique which can obtain information from thousands of genes at once, for developing new methods of analyzing and predicting of diseases. Discovering the mechanisms of unknown genes by using these new method is expecting to develop the new drugs and new curing methods. In this Paper, We tested accuracy on classification of microarray in Bayesian method to compare normalization method's Performance after dividing data in two class that is a feature abstraction method through a normalization process which reduce or remove noise generating in microarray experiment by various factors. And We represented that it improve classification performance in 95.89% after Lowess normalization.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.133-138
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• 2022

Digital radiation inspection equipment is a medical device that deals with human life and requires stability and high reliability. However, this system is currently the most advanced technology and the domestic market is almost occupied by European products including Japan. Therefore, research and development are needed not only to replace domestic medical devices, which are largely dependent on expensive imported products, but also to develop more economical and user-oriented products that are easy to operate and produce devices that lead to accurate diagnosis. In particular, among the digital X-ray systems, the motor driving technology and the mechatronics technology related to the development of mechanical devices have matured to some extent in Korea. In this paper, selection of AC servomotor for digital radiation inspection suitable for imaging purpose, and application of conversion device and control method to check performance and improve problems.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.375-378
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• 2019

최근 딥러닝과 머신러닝 기법이 소프트웨어의 성능 향상에 도움이 되는 것이 입증됨에 따라, 의료 영상 진단 보조 소프트웨어를 개발하기 위한 시도가 활발해 지고 있다. 그 중 캡슐내시경은 소장 소화기관을 관찰할 수 있는 초소형 의료기기로, 기존의 내시경 검사와 다르게 이물감이 느껴지지 않고 의료보험 적용으로 최근 들어 널리 이용되고 있다. 일반적으로 캡슐 내시경은 8 시간 동안 소화기간을 촬영하며, 한 번의 검사 결과로 생성된 동영상 데이터 셋은 수 만장의 이미지를 포함하기 때문에, 방대한 양의 이미지들을 효율적으로 관리하기 위한 체계가 필요하다. 특히, 방대한 양의 캡슐내시경 이미지를 학습하는 경우, 수 만장의 이미지 속에서 유의미한 특징(촬영정보, 의사소견, 환자정보, 병변의 위치 및 크기 등)을 추출해내야 하므로 학습 데이터 레이블링을 위한 정보를 정확히 추출해야 하는 작업이 요구된다. 따라서 본 논문에서는 캡슐내시경 영상을 학습할 때, 학습 데이터 레이블 정보를 체계적으로 구축할 수 있게 하는 레이블 정보 추출 기법을 제안하고자 한다. 제안하는 기법은 병원에서 14년간 수집된 총 340명의 캡슐내시경 데이터(약 1,700 만장의 이미지)를 토대로 영상데이터를 구조적으로 분석하여 유의미한 정보를 추출하고 노이즈 데이터를 제거한 뒤, 빅데이터 저장소에 적재할 수 있음을 보였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.460-460
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• 2018

댐 수문학적 안전성평가는 "시설물의 안전 및 유지관리에 관한 특별벌(이하 시특법)"에 따른 댐시설물의 정밀안전진단의 안전성평가 중 가장 중요한 평가 항목으로 댐 시설물을 평가 수행 시 주요한 평가 항목이다. 기존의 수문학적 안전성평가는 가능최대강수량 발생 시 댐의 월류 및 여유고 확보여부에 대한 평가 여부만 판단하고 있으나, 본 연구에서는 기후변화를 고려하는 장기적 관점의 추가 평가항목을 도출하고자 한다. 현재 가능최대강수량으로 event적 평가를 수행하는 수문학적 안전성 평가에서 기존평가항목 뿐만 아니라, 기후변화 장기적 관점의 추가적인 기후영향인자를 도출하고 이를 함께 적용할 수 있는 평가 체계를 구축하고자한다. 장기적 관점의 기후영향인자라 함은 기상청에서 제공하는 기후변화 시나리오 결과에서 30년동안 장기적인 관점에서 대상 댐의 운영에 부담을 야기할 것으로 판단되는 인자를 말하는 것이며, 이때 기후변화 시나리오의 일자료를 활용하여 기후인자의 장기적 변동성을 추정하고자 하며, 이때 활용한 지표로는 월최대강수량, 연강우강도 및 댐 상태에 영향을 미칠 수 있는 최소기온을 사용하였다. 기후변화 시나리오의 불확실성을 최소화하기 위하여 월최대 강수량값을 산출하였고, 1년 동안 발생한 강우의 일수 및 강수량에 대한 영향을 고려하기 위하여 연강우강도값을 산출하였다. 또한 댐의 월류 및 여유고 확보여부 평가 시 댐 상태에 대하여 고려하기 때문에 댐의 외부상태에 영향을 주는 최소기온을 활용하여 댐별 평가를 수행하였다. 이때 2011~2040년(S1), 2041년~2070년(S2), 2071년~2100년(S3)기간으로 나누어 장기간 기후에 대한 영향 평가를 수행하여 1종 댐 시설물의 기후영향인자 값을 도출하였다. 도출된 기후영향인자를 기존 수문학적 안전성평가 항목과 함께 평가 될 수 있도록 AHP분석기법을 활용하여 각 인자에 대한 가중치를 재산출하였고, 기후영향인자를 고려하는 수문학적 안전성평가 체계를 구축하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.40-47
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• 2021

In this study, the structural behavior by the details of the lap region with the headed bar was estimated through finite element analysis. To solve the finite element analysis of the anchorage region with complex contact conditions and nonlinear behavior, a quasi-static analysis technique by explicit dynamic analysis was performed. The accuracy of the finite element model was verified by comparing the experimental results with the finite element analysis results. It was confirmed that the quasi-static analysis technique well reflected the behavior of enlarged headed bar connection. As a result of performing numerical analysis using 21 finite element models with various development lengths and transverse reinforcement indexes, it was confirmed that the increase of development length and transverse reinforcement index improved the maximum strength and ductility. However, to satisfy the structural performance, it should be confirmed that both design variables(development length and transverse reinforcement index) must be enough at the design criteria. In the recently revised design standard(KDS 14 20 52 :2021), a design formula of headed bar that considers both the development length and the transverse reinforcing bar index is presented. Also the results of this study confirmed that not only the development length but also transverse reinforcing bars have a very important effect.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.