• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제54권12호
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• pp.578-584
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• 2005

This paper describes development of management software for transformers based on artificial intelligent analysis technology of dissolved gases in oil. Fault interpretation using the artificial intelligent analysis is performed by the artificial neural network and a rule based on the analysis of dissolved gases. The used gases are acetylene($C_{2}H_{2}$), hydrogen($H_2$), ethylene($C_{2}H_{4}$), methane($CH_4$), ethane($C_{2}H_{6}$), carbon monoxide(CO) and carbon dioxide($CO_2$). This software is mainly composed of gases input, fault's causes, expected fault's phenomena in detail, the decision on maintenance as well as report and gas trend windows. It is indicated that this is very powerful software for the efficient management of oil-immersed transformers using data analysis of gas components.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5095-5099
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• 2015

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materials and Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (${\geq}65$ years), use of antibiotics, low serum albumin concentrations (${\leq}37.18g/L$), radiotherapy, surgery, low hemoglobin hyperlipidemia (${\leq}93.67g/L$), long time of hospitalization (${\geq}14$days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model($0.829{\pm}0.019$)was higher than that of LR model ($0.756{\pm}0.021$). Conclusions: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

• 한국지반공학회논문집
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• 제32권2호
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• pp.5-17
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• 2016

터널에서의 붕괴는 터널 구조물의 특수성 및 예상치 못한 지반조건의 변화로 인해 언제 어디서든 발생될 수 있다. 그로 인한 경제적인 손실과 인명피해를 줄이기 위하여 사고를 미연에 방지하기 위한 방안에 대한 다양한 연구들이 계속 진행되고 있는 실정이다. 본 연구에서는 붕괴예측을 위하여 국내 터널 붕괴 현장 56개소의 시공데이터를 분석하고 인공신경망 기법에 적용할 입력인자를 민감도 분석으로 선정하였다. 또한 인공신경망 모델 설계는 선정된 입력인자로 학습을 수행하고 터널 붕괴 유형 예측에 최적화된 모델을 결정하였다. 이 모델을 이용하여 붕괴가 발생된 총 12개소에 적용성 평가를 실시하여 터널 붕괴 유형 예측 가능성을 검증하였다. 이러한 결과는 터널 시공 현장에서 붕괴 예방을 위한 기초 자료로서 활용 될 수 있을 것이다.

• 설비공학논문집
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• 제24권8호
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• 한국수산과학회지
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• 제49권2호
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• pp.224-233
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• 2016

The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli, black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea. An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.

• 소성∙가공
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• 제27권6호
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• pp.331-338
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• 2018

Electrohydraulic forming (EHF) process is a high speed forming process that utilizes the electric energy discharge in fluid-filled chamber to deform a sheet material. This process is completed in a very short time of less than 1ms. Therefore, finite element analysis is essential to observe the deformation mechanism of the material in detail. In addition, to perform the numerical simulation of EHF, the material properties obtained from the high-speed status, not quasi static conditions, should be applied. In this study, to obtain the parameters in the constitutive equation of Al 6061-T6 at high strain rate condition, a surrogate model using an artificial neural network (ANN) technique was employed. Using the results of the numerical simulation with free-bulging die in LS-DYNA, the surrogate model was constructed by ANN technique. By comparing the z-displacement with respect to the x-axis position in the experiment with the z-displacement in the ANN model, the parameters for the smallest error are obtained. Finally, the acquired parameters were validated by comparing the results of the finite element analysis, the ANN model and the experiment.

• 대한토목학회논문집
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• 제26권6C호
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• pp.421-429
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• 2006

본 연구에서는 국내 연약지반의 신뢰성 있는 강성지수 추정을 위하여 인공신경망기법을 적용하였다. 실내시험을 통한 물성치결과들을 통하여 인공신경망을 위한 입력자료를 확보한 뒤 이를 이용하여 모델을 학습시킨 후 모델검증을 실시하였다. 개발된 모델의 검증결과 측정값과 예측값의 상관관계가 매우 높게 나타났으며 이를 통하여 수학적 모델 수립이 곤란한 국내 연약지반의 신뢰성 있는 강성지수 추정의 전반적인 고찰의 기초를 확립하였다.

• Geomechanics and Engineering
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• 제34권3호
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• pp.329-339
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• 2023

The objective of this study is to develop a Stability Evaluation System for retaining walls to assess their safety in real-time during excavation. A ground investigation is typically conducted before construction to gather information about the soil properties and predict wall stability. However, these properties may not accurately reflect the actual ground being excavated. To address this issue, the study employed a differential evolution algorithm to estimate the soil parameters of the actual ground. The estimated results were then used as input for an artificial neural network to evaluate the stability of the retaining walls. The study achieved an average accuracy of over 90% in predicting differential settlement, wall displacement, anchor force, and structural stability of the retaining walls. If implemented at actual excavation sites, this approach would enable real-time prediction of wall stability and facilitate effective safety management. Overall, the developed Stability Evaluation System offers a promising solution for ensuring the stability of retaining walls during construction. By incorporating real-time soil parameter analysis, it enhances the accuracy of stability predictions and contributes to proactive safety management in excavation projects.

• Corrosion Science and Technology
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• 제22권5호
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• pp.377-386
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• 2023

Aircraft washing is performed periodically for corrosion control. Currently, the aircraft washing interval is qualitatively set according to the geographical conditions of each base. We developed a washing interval determination algorithm based on atmospheric corrosion environment monitoring data at the Republic of Korea Air Force (ROKAF) bases and United States Air Force (USAF) bases to determine the optimal interval. The main factors of the washing interval decision algorithm were identified through hierarchical clustering, sensitivity analysis, and analysis of variance, and criteria were derived. To improve the classification accuracy, we developed a washing interval decision model based on an artificial neural network (ANN). The ANN model was calibrated and validated using the atmospheric corrosion environment monitoring data and washing intervals of the USAF bases. The new algorithm returned a three-level washing interval, depending on the corrosion rate of steel and the results of the ANN model. A new base-specific aircraft washing interval was proposed by inputting the atmospheric corrosion environment monitoring results of the ROKAF bases into the algorithm.

• 한국연초학회지
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• 제17권1호
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• pp.5-14
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• 1995

Efficient algorithms for the automatic classification of flue-cured tovacco ripeness and grading have been developed The ripeness of the tobacco was classified into 4 levels vased on the color. The lab-built simple RGB color measuring system was utilized for detecting the light reflectance of the tobacco leaves. The measured data were used far training the artificial neural network The performance of the trained network was also tested far the untrained samples. The spectrophotometer was used to detect the light reflectance and absorption of the graded tobacco leaves in the frequency ranges of the visible light The measured data and the statistical analysis was performed to investigate the light characteristics of the graded samples. The measured data were obtained from samples of 5 different grades directly without considering the leaf positions. Those data were used far training the artificial neural network The performance of the trained network was also tested far the untrained samples. The neural network based sensor information processing showed successful results for grading of tobacco leaves.