• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Smart Media Journal
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• v.11 no.8
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• pp.21-28
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• 2022

Currently, as we enter the 21st century, the level and interest of society, culture, economy, and science are rapidly developing, but science education is still struggling. In order to increase the efficiency of science education, it is most important to focus on elementary education based on basic science. Therefore, this study aims to analyze the main causes of Korea's science museum's regression as a museum focusing on experience, interest, and fun such as simple science experience centers and science theme parks. To this end, the influencing factors were identified by applying the algorithm of the actor-based model based on the data on the exhibition space and the exhibition movement of the science museum completed and operated in Korea over the past 5 years, and the problem of the visitor movement in the exhibition space was analyzed through the space system. In this study, it was confirmed that the exhibition environment was the best when the linear plot movement system and the picalesque plot were applied simultaneously in the museum's exhibition narrative theory, and the arrangement of major exhibition spaces, width of exhibition spaces, and separation of spaces for exhibition purposes were derived.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.95-103
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• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

• Current Photovoltaic Research
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• v.11 no.1
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• pp.27-33
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• 2023

In this paper, a machine learning model by using a regression algorithm is proposed to estimate the power generation performance of the BIPV system. The physical model formula for estimating the generation performance and the proposed model were compared and analyzed. For the physical model formula, simple efficiency model, temperature correction model, and regressive physics model for changing an irradiance were used. As a result, when comparing the regressive physics model for changing an irradiance and the proposed model with the actual generation measured data, the respective RMSE values are 0.1497 kW, 0.0451 kW and the accuracy values are 86.44%, and 96.56%. Therefore, the proposed model implemented in this experiment can be useful in estimating power generation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.162-162
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• 2020

토양수분은 TDR(Time Domain Reflectometry)이나 Tensiometer 등의 장비를 이용하여 측정을 시행하고 있으나, 이를 위해서는 많은 인력과 경제적 자원이 소비될 뿐만 아니라 시공간적으로 측정할 수 있는 범위에 한계가 있다. 지상 관측의 대안으로 MIRAS(Microwave Imaging Radiometer with Aperture Synthesis)나 SMAP(Soil Moisture Active Passive), AMSR2(Advanced Microwave Scanning Radiometer 2) 등의 수동 마이크로파 위성 센서를 이용한 공간 토양수분 관측이 수행되었으나, 낮은 공간 해상도(9~36km)는 지역 규모의 토양수분 분포를 나타내기 충분하지 않고, 높은 불확실성을 내포하고 있다. 본 연구에서는 금강 상류의 용담댐 유역(930.0㎢)을 대상으로 Sentinel-1 C-band SAR(Synthetic Aperture Radar) 영상을 이용한 토지 피복 및 토양 속성을 고려한 10m 해상도의 토양수분 산출을 수행하였다. 용담댐 유역은 산림 79.7%, 논 9.0%, 밭 5.4%, 주거지 2.9%의 토지 피복 비율을 가지며 토양은 사양토(66.6%)와 양토(20.9%)가 우세하다. Sentinel-1 C-band SAR 영상은 SeNtinel Application Platform(SNAP)을 이용하여 전처리 후, 후방산란계수로 변환하였다. 토양수분 알고리즘은 TU-Wien change detection algorithm과 Regression model을 활용하였고, 검증을 위한 실측 토양수분 자료는 한국수자원공사(K-water)에서 제공하는 5년(2014~2018)간의 토양수분 관측자료를 이용하였다. 산출된 토양수분은 결정계수(Coefficient of determination, R²) 및 평균제곱근오차(Root Mean Square Error, RMSE)를 이용하여 실측 토양수분과 비교하였다. Sentinel-1 C-band SAR 영상을 이용한 고해상도의 토양수분 산출은 토지 피복 및 토양 속성을 고려한 지역 규모의 공간 토양수분 분포 및 시간적 변화를 표현 가능할 것으로 판단된다.

• Journal of Powder Materials
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• v.30 no.3
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• pp.217-222
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• 2023

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

• Smart Media Journal
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• v.11 no.7
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• pp.19-27
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• 2022

The artificial intelligence software market is expected to grow sixfold from 2020 to 2025. However, the software development process is not standardized and there is no standard for calculating the cost. Accordingly, each AI software development company calculates the input man-hours according to their respective development procedures and presents this as the basis for the development cost. In this study, the development stage of "artificial intelligence-based software" that learns with a large amount of data and derives and applies an algorithm was defined, and the required labor was collected by conducting a survey on the number of man-hours required for each development stage targeting developers. Correlation analysis and regression analysis were performed between the collected man-hours for each development stage, and a model for predicting the man-hours for each development stage was derived. As a result of testing the model, it showed an accuracy of 92% compared to the collected airborne effort. The man-hour prediction model proposed in this study is expected to be a tool that can be used simply for estimating man-hours and costs.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.