• Korean Journal of Materials Research
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• v.4 no.3
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• pp.301-311
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• 1994

The growth of amorphous and first crystalline phase, and phase sequence by solid state reaction were examined in Co/Si multilayer thin films by DSC and XRD. The experimental results were compared with the results expected by effective driving force models, PDF and effective heat of formation models.Amorphous phase growth was not observed in Co/Si system and it was consistent with the predicted result by effective driving force. It was observed that the first crystalline phase is CoSi. According to the PDF and effective heat of formation models, the first crystalline phases were CoSi and $CO_2Si$, respectively. The experiemental results were coincident with the PDF model considering structure factors. In case of the atomic concentration ratios of 2Co : 1Si and 1Co : 2Si, the phases sequences were $CoSi\to Co_2Si$ and $CoSi \to Co_2Si \to CoSi \to CoSi_2$, respectively and it was analysized through the effective heat of formation model. The formations of CoSi, $CO_2Si$ and $COSi_2$ in initial stage were controlled by nucleation and the activation energies for the nucleation of three phases were 1.71, 2.34 and 2.79eV.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.593-596
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• 1992

An evaluation method for the effective length of electromagnet - which bas U-shape in frontview and trapezoidal in side view - is presented. Using 2D FEM, 2 analysing models are introduced for calculating effective length of the magnet ; the front model is using the normalized equi-pole face area of the magnet and the side model using the normalized equi-magnetic circuit. The ratio of the effective length to the length of bottom plate (core) comes out 1.25 - 1.30. In addition, 3D FEM analysis has been done and a proto-type test model is manufactured to verify the analysing method. The ratio by the experiment appears 1.2, which is reasonably in good agreement with the suggested numerical results.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.12
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• pp.28-37
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• 2019

Due to their flexible tailoring qualities, composites have become fascinating materials for structural engineers. While the research area of fiber-reinforced composite materials was previously limited to synthetic materials, natural fibers have recently become the primary research focus as the best alternative to artificial fibers. The natural fibers are eco-friendly and relatively cheaper than synthetic fibers. The main concern of current research into natural fiber-reinforced composites is the prediction and enhancement of the effective material properties. In the present work, finite element analysis is used with a numerical homogenization approach to determine the effective material properties of jute fiber-reinforced epoxy composites with various volume fractions of fiber. The finite element analysis results for the jute fiber-reinforced epoxy composite are then compared with several well-known analytical models.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• KDI Journal of Economic Policy
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• v.40 no.3
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• pp.91-115
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• 2018

This paper aims to identify the most effective mode of development finance flows for the economic growth of middle-income developing and least developed countries, separately. It also attempts to confirm whether governance has any significant role in the causal relationship between development finance flows and economic growth. Policymakers in each developing country should select the most effective modality of development finance inflows among the different modalities (such as Official Development Assistance (ODA) grants, Official Development Assistance (ODA) loans, FDI, and international personal remittances) and expand it for their economic growth. Dynamic panel regression models were used on 48 least developed countries and 89 middle-income developing countries, respectively, during the Millennium Development Era: 2000-2015. The empirical analysis results show that ODA grants and remittances were most effective in promoting economic growth for least developed countries, while FDI was most effective for middle-income developing countries. These findings were not affected by the status of governance of the individual country.

• Structural Engineering and Mechanics
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• v.10 no.5
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• pp.463-478
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• 2000

An iterative procedure for the analysis of reinforced concrete frames with beams in cracked state is presented. ACI and CEB model equations are used for the effective moment of inertia of the cracked members. In the analysis, shear deformations are taken into account and reduced shear stiffness is considered by using effective shear modulus models available in the literature. Based on the aforementioned procedure, a computer program has been developed. The results of the computer program have been compared with the experimental results available in the literature and found to be in good agreement. Finally, a parametric study is carried out on a two story reinforced concrete frame.

• The Journal of Engineering Geology
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• v.29 no.4
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• pp.367-381
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• 2019

We investigate pore pressure conditions and reservoir compaction associated with oil and gas production using 3 different permeability models, which are all based on one-dimensional radial flow diffusion model, but differ in considering permeability evolution during production. Model 1 assumes the most simplistic constant and invariable permeability regardless of production; Model 2 considers permeability reduction associated with reservoir compaction only due to pore pressure drawdown during production; Model 3 also considers permeability reduction but due to the effects of both pore pressure drawdown and coupled pore pressure-stress process. We first derive a unified stress-permeability relation that can be used for various sandstones. We then apply this equation to calculate pore pressure and permeability changes in the reservoir due to fluid extraction using the three permeability models. All the three models yield pore pressure profiles in the form of pressure funnel with different amounts of drawdown. Model 1, assuming constant permeability, obviously predicts the least amount of drawdown with pore pressure condition highest among the three models investigated. Model 2 estimates the largest amount of drawdown and lowest pore pressure condition. Model 3 shows slightly higher pore pressure condition than Model 2 because stress-pore pressure coupling process reduces the effective stress increase due to pore pressure depletion. We compare field data of production rate with the results of the three models. While models 1 and 2 respectively overestimates and underestimates the production rate, Model 3 estimates the field data fairly well. Our result affirms that coupling process between stress and pore pressure occurs during production, and that it is important to incorporate the coupling process in the permeability modeling, especially for tight reservoir having low permeability.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.51-65
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• 2024

Accurate electrical load forecasting is important to the effective operation of power systems in smart grids. With the recent development in machine learning, artificial intelligence-based models for predicting power demand are being actively researched. However, since existing models get input variables as numerical features, the accuracy of the forecasting model may decrease because they do not reflect the semantic relationship between these features. In this paper, we propose a scheme for short-term load forecasting by using features extracted through the large language models for input data. We firstly convert input variables into a sentence-like prompt format. Then, we use the large language model with frozen weights to derive the embedding vectors that represent the features of the prompt. These vectors are used to train the forecasting model. Experimental results show that the proposed scheme outperformed models based on numerical data, and by visualizing the attention weights in the large language models on the prompts, we identified the information that significantly influences predictions.