• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.156-156
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• 2021

본 연구는 지역빈도해석을 기반으로한 인공신경망 모델과 기존에 널리 사용되는 방법인 홍수지수법의 성능을 몬테카를로 시뮬레이션을 이용하여 평가하였다. 컴퓨터 기술이 발달함에 따라 인공지능에 대한 접근성이 좋아지며 수문학을 포함한 다양한 분야에 적용되고 있다. 인공지능을 이용하여 강수량 및 유량 등 다양한 수문자료에 대한 예측이 이루어지고 있으나 빈도해석에 관한 연구는 비교적 적다. 본 연구에서 사용된 인공 지능 모델은 대상 지점의 지형학적 자료와 수문학적 자료를 이용하여 인공신경망을 통해 지점의 확률강우량(QRT-ANN) 및 확률분포형의 매개변수 (PRT-ANN)를 추정한다. 지형학적 자료로는 위도, 경도 그리고 고도가 사용되었으며 수문학적 자료로는 대상 지점의 최근 30년 일일연최대강우량을 사용하였다. 지역빈도해석의 정확도는 지역 내 통계적 특성이 비슷한 지점들이 포함되면 될수록 높아진다. 통계적 특성으로는 불일치 척도, 이질성 척도, 적합성 척도가 있으며 다양한 조건의 통계적 특성에 따른 세 개의 지역빈도해석 방법의 성능을 평가하고자 하였다. 대상 지역 내 n개의 지점이 있다고 가정하였을 때, 홍수지수법의 경우 n-1개의 지점으로 추정한 지역 성장곡선을 이용하여 나머지 1개 지점의 확률강우량을 산정할 수 있으며 인공신경망 모델들 또한 n-1개 지점들의 자료를 이용하여 모델을 구축한 뒤 나머지 지점의 확률강우량 및 확률분포형의 매개변수를 예측할 수 있다. PRT-ANN의 경우 예측된 매개변수를 이용하여 확률강우량을 산정하며 시뮬레이션 시행마다 발생시킨 자료의 지점빈도해석 결과에 대한 나머지 세 방법의 평균 제곱근 상대오차 (Relative root mean square error, RRMSE)를 계산하였다. 몬테카를로 시뮬레이션을 이용한 성능 분석을 통하여 관측값의 다양한 통계적 특성에 맞는 지역빈도해석 방법을 제시할 수 있을 것으로 판단된다.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.283-296
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• 2023

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• Earthquakes and Structures
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• v.23 no.2
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• pp.129-138
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• 2022

One of the objectives to prevent building pounding between two adjacentstructures is to considerseparation distance or decrease relative displacement during seismic excitation. Although the majority of building codes around the world have basically suggested some equations or approximately recommended various distances between structuresto avoid pounding hazard, but a lot of reportsin zone of pounding have obviously shown thatsafety situation or economic consideration are not always provided due to the collisions between buildings and the cost of land, respectively. For this purpose, a dynamic MDOF model by having base isolation system is numerically considered and using various earthquake records, relative displacements are mathematically investigated. Different equations to determine the value of damping ratio are collected and the results of evaluations are listed for comparison among them to present a new equation for determination of impact damping ratio. Presented equation is depends significantly on impact velocity before and after impact based on artificial neural network, which the accuracy of them is investigated and also confirmed. In order to select the optimum equation, hysteresisloop of impact between base of building and rubber bumper is considered and compared with the hysteresis loop of each impact, calculated by different equations. Finally, using representative equation, the effect of thickness, number and stiffness of rubber bumpers are numerically investigated. The results of analysis indicate that stiffness and number of bumpers have significantly affected in zone of impact force while the thickness of bumpers have not shown significant influence to calculate impact force during earthquake. For instance, increasing the number of bumpers, gap size between structures and also the value of stiffness is caused to decrease impact force between models. The final evaluation demonstrates that bumpers are able to decrease peak lateral displacement of top story during impact.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.55-66
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• 2012

The practical way to minimize the greenhouse gas is to reduce the emission of carbon dioxide. For this reason, CCS(Carbon Capture and Storage) technology, which could reduce carbon dioxide emission, has risen as a realistic alternative in recent years. In addition, the researcher is recently working into ways of applying CCS technologies with deep saline aquifer. In this study, the evaluation model on the feasibility of $CO_2$ sequestration in the deep saline aquifer using ANN(Artificial Neural Network) was developed. In order to develop the efficiency-evaluation model, basic model was created in the deep saline aquifer and sensitivity analysis was performed for the aquifer characteristics by utilizing the commercial simulator of GEM. Based on the sensitivity analysis, the factors and ranges affecting $CO_2$ sequestration in the deep saline aquifer were chosen. The result from ANN training scenario were confirmed $CO_2$ sequestration by solubility trapping and residual trapping mechanism. The result from ANN model evaluation indicated there is the increase of correlation coefficient up to 0.99. It has been confirmed that the developed model can be utilized in feasibility of $CO_2$ sequestration at deep saline aquifer.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.51-61
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• 2002

In this dissertation, We demonstrated the Travel Time forecasting model in the freeway of multi-section with regard of drives' attitude. Recently, the forecasted travel time that is furnished based on expected travel time data and advanced experiment isn't being able to reflect the time-lag phenomenon specially in case of long distance trip, so drivers don't believe any more forecasted travel time. And that's why the effects of ATIS(Advanced Traveler Information System) are reduced. Therefore, in this dissertation to forecast the travel time of the freeway of multi-section reflecting the time-lag phenomenon & the delay of tollgate, we used traffic volume data & TCS data that are collected by Korea Highway Cooperation. Also keep the data of mixed unusual to applicate real system. The applied model for forecasting is consisted of feed-forward structure which has three input units & two output units and the back-propagation is utilized as studying method. Furthermore, the optimal alternative was chosen through the twelve alternative ideas which is composed of the unit number of hidden-layer & repeating number which affect studying speed & forecasting capability. In order to compare the forecasting capability of developed ANN model. the algorithm which are currently used as an information source for freeway travel time. During the comparison with reference model, MSE, MARE, MAE & T-test were executed, as the result, the model which utilized the artificial neural network performed more superior forecasting capability among the comparison index. Moreover, the calculated through the particularity of data structure which was used in this experiment.