• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.459-469
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• 2010

This study is to develop a grid-based daily runoff model considering seasonal vegetation canopy condition. The model simulates the temporal and spatial variation of runoff components (surface, interflow, and baseflow), evapotranspiration (ET) and soil moisture contents of each grid element. The model is composed of three main modules of runoff, ET, and soil moisture. The total runoff was simulated by using soil water storage capacity of the day, and was allocated by introducing recession curves of each runoff component. The ET was calculated by Penman-Monteith method considering MODIS leaf area index (LAI). The daily soil moisture was routed by soil water balance equation. The model was evaluated for 930 $km^2$ Yongdam watershed. The model uses 1 km spatial data on landuse, soil, boundary, MODIS LAI. The daily weather data was built using IDW method (2000-2008). Model calibration was carried out to compare with the observed streamflow at the watershed outlet. The Nash-Sutcliffe model efficiency was 0.78~0.93. The watershed soil moisture was sensitive to precipitation and soil texture, consequently affected the streamflow, and the evapotranspiration responded to landuse type.

• Asia Marketing Journal
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• v.8 no.1
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• pp.1-17
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• 2006

A couple of previous studies have warned against the use of homogeneous choice models in assessing the effect of reference price since unaccounted for response heterogeneity may result in spurious reference price effects(Chang, Siddarth and Weinberg 1999; Bell and Lattin 2000). According to Meyer and Kahn(1991), not accounting for consideration set heterogeneity may also bias the effect parameters in the choice model. Therefore, failure to account for these two sources of bias, in fact, have cast doubt on the empirical support for reference price effects in general. In view of aforementioned potential sources of bias, the author investigates the robustness of loss aversion effect in the reference-dependent model after accounting for heterogeneity in response as well as consideration set. The proposed model defines individual household's consideration set based on the posterior distribution of preference obtained from the Empirical Bayes approach. In addition, the same posterior distribution is used to form household-specific reference prices. Response heterogeneity correction is carried out via the Latent Class approach. The proposed model outperforms the Reference-Dependent model that includes the reference price measure most often employed in the previous studies. This implies that as a way of simplifying decision task, consumers restrict their consideration set to a subset of available brands not only in making a brand choice but also in forming reference prices.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2022.09a
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• pp.85-85
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• 2022

영농형 태양광발전은 태양의 일사량을 전기발전과 영농에 공유하는(solar-sharing) 방식이다. 본 연구는 신재생에너지의 활용의 극대화를 위하여 추적식 영농형 태양광발전시스템을 구축하고 시설하부에서 일정 기간 재배중인 작물의 하부 환경과 생육을 조사하여 영농형태양광 하부작물개발을 위한 기초자료를 확보하고자 하였다. 구축한 추적식 영농형 태양광발전시스템은 4열 6단의 24장 모듈(8m × 6m)을 가지며, 발전시설 중심축 기둥 간 중심간격 14m로 단일지주식 스크루 공법으로 순천대학교 부속농장 답작포(순천시 죽평리)에 설치하여 하부 환경과 하부작물의 생육을 조사하였다. 태양광발전시설 하부작물의 생육을 조사하기 위하여 순천 농협육묘장에서 벼(신동진)를 육묘하여 2022년 6월 16일 이앙하였다. 태양광발전시스템 하부 지역을 4방위 방향에 따라 강음영(중심축으로부터 1~3m), 중음영(5m), 약음영(7~9m) 구역으로 설정하여 생육을 조사한 결과, 방위에 따른 초장은 남쪽에서 음영간 차이가 상대적으로 낮게 나타났으며, 1번기 태양광 발전시설에 의하여 음영이 중첩된 2번기 시설의 동쪽에서 대조구 대비 초장이 상대적으로 낮은 경향을 나타내었다. 음영강도에 따른 초장은 대체로 강음영구에서 낮게 나타났으며, 약음영구로 갈수록 높게 나타났다. 엽수는 방위에 따라서, 그리고 음영의 강도에 따른 차이가 초장에 비하여 작게 나타났다. 출수기의 경우 방위별로는 남쪽에서 음영별 차이가 작게 나타났으며, 음영강도에 따라서 차이를 보였다. 또한 태양광시설 하부에 데이터수집장치(Model 1650, Spctrum Technonogies, USA)를 설치하여 음영에 따른 토양전도도, 토양함수량, 토양온도, par light 등 생육환경을 조사, 비교하였다.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1427-1435
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• 2023

Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, the anisotropic properties of the crop canopy surface can govern spectral reflectance and vegetation indices. In this study, we applied an ensemble of bidirectional reflectance distribution function (BRDF) models to high-resolution Sentinel-2 satellite imagery and compared the differences between correction results before and after reflectance. In the red and near-infrared (NIR) band reflectance images, BRDF-corrected outlier values appeared in certain urban and paddy fields of farmland areas and forest shadow areas. These effects were equally observed when calculating the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2). Furthermore, the outlier values in corrected NIR band were shown in pixels shadowed by mountain terrain. These results are expected to contribute to the development and improvement of BRDF models in high-resolution satellite images.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.73-86
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• 2024

A radial chart of Excel is very useful graphical method in delivering information for numerical data. However, it is not easy to discriminate or classify many individuals. In this case, after shaping each individual of a radial chart, we need to apply shape analysis. For a radial chart, since landmarks for shaping are formed as many as the number of variables representing the characteristics of the object, we consider a shape that connects them to a line. If the shape becomes complicated due to the large number of variables, it is difficult to easily grasp even if visualized using a radial chart. Principal component analysis (PCA) is performed on variables to create a visually effective shape. The classification table and classification rate are checked by applying the techniques of traditional discriminant analysis, support vector machine (SVM), and artificial neural network (ANN), before and after principal component analysis. In addition, the difference in discrimination between the two coordinates of generalized procrustes analysis (GPA) coordinates and Bookstein coordinates is compared. Bookstein coordinates are obtained by converting the position, rotation, and scale of the shape around the base landmarks, and show higher rate than GPA coordinates for the classification rate.

• Composites Research
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• v.37 no.3
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• pp.170-178
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• 2024

This study proposes a hybrid composite where a thin copper film (Cu film) is embedded in carbon fiber reinforced plastic (CFRP), and quantum annealing is applied to derive the combination of Cu film placement that maximizes the through-thickness thermal conductivity. The correlation between each ply of CFRP and the Cu film is analyzed through finite element analysis, and based on the results, a combination optimization problem is formulated. A formalization process is conducted to embed the defined problem into quantum annealing, resulting in the formulation of objective functions and constraints regarding the quantity of Cu films that can be inserted into each ply of CFRP. The formulated equations are programmed using Ocean SDK (Software Development Kit) and Leap to be embedded into D-Wave quantum annealer. Through the quantum annealing process, the optimal arrangement of Cu films that satisfies the maximum through-thickness thermal conductivity is determined. The resulting arrangements exhibit simpler patterns as the quantity of insertable Cu films decreases, while more intricate arrangements are observed as the quantity increases. The optimal combinations generated according to the quantity of Cu film placement illustrate the inherent thermal conductivity pathways in the thickness direction, indicating that the transverse placement freedom of the Cu film can significantly affect the results of through-thickness thermal conductivity.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.1-12
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• 2024

In this study, a determination method of structural member section based on Nonlinear behaviors of steel cable-stayed bridges and the Harmony Search algorithm was presented. The Harmony Search algorithm determines the structural member section of cable-stayed bridges by repeating the process of setting the initial value, initializing the harmony memory, configuring the new harmony memory, and updating the harmony memory to search for the optimal value. The nonlinear initial shape analysis of a three-dimensional steel cable-stayed bridge was performed with the cross-section of the main member selected by the Harmony Search algorithm, and the optimal cross-section of the main members of the cable-stayed bridge, such as pylons, girders, cross-beams, and cables, reflecting the complex behavior characteristics and the nonlinearity of each member was determined in consideration of the initial tension and shape. The total weight was used as the objective function for determining the cross-section of the main member of the cable-stayed bridges, and the load resistance ability and serviceability based on the ultimate state design method were used as the restraint conditions. The width and height ratio of the girder and cross-section were considered additional restraint conditions. The optimal sections of the main members were made possible to be determined by considering the geometry and material nonlinearity of the pylons, girders, and cross-sections and the nonlinearity of the cable members. As a result of determining the optimal cross-section, it was confirmed that the proposed analysis method can determine the optimal cross-section according to the various constraint conditions of the cable-stayed bridge, and the structural member section of the cable-stayed bridge considering the nonlinearity can be determined through the Harmony Search algorithm.

• The Mathematical Education
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• v.63 no.3
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• pp.549-571
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• 2024

As digital·AI-based teaching and learning is emphasized, discussions on the educational use of generative AI are becoming more active. This study analyzed the mathematical performance of ChatGPT 4, Claude 3 Opus, and Gemini Advanced on solving examples and problems from five first-year high school math textbooks. As a result of examining the overall correct answer rate and characteristics of each skill for a total of 1,317 questions, ChatGPT 4 had the highest overall correct answer rate of 0.85, followed by Claude 3 Opus at 0.67, and Gemini Advanced at 0.42. By skills, all three models showed high correct answer rates in 'Find functions' and 'Prove', while relatively low correct answer rates in 'Explain' and 'Draw graphs'. In particular, in 'Count', ChatGPT 4 and Claude 3 Opus had a correct answer rate of 1.00, while Gemini Advanced was low at 0.56. Additionally, all models had difficulty in explaining using Venn diagrams and creating images. Based on the research results, teachers should identify the strengths and limitations of each AI model and use them appropriately in class. This study is significant in that it suggested the possibility of use in actual classes by analyzing the mathematical performance of generative AI. It also provided important implications for redefining the role of teachers in mathematics education in the era of artificial intelligence. Further research is needed to develop a cooperative educational model between generative AI and teachers and to study individualized learning plans using AI.