• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.2
• /
• pp.157-164
• /
• 2012

The design strategy of the multi-functional structure is that the electrical components and the circuits are directly put on their supporting structural panel in which the radiation shields and the thermal control functions are integrated. Applying the multi-functional structure reduces the total mass and size of the space system and makes it possible to lower launch cost. In present study the performance of thermal radiation for six types of multi-functional structure are investigated by the numerical method. The effect of the rib configuration on heat transfer for the multi-functional-structure is not important alone but is meaningful considering with the structural stiffness, difficulty of manufacturing and mass increase. In heat spreading point of view, the thickness of the outer conductive layer is important rather than the rib configuration and the trade-off study with the mass and thickness is required for optimum design.

• Korean Journal of Agricultural Science
• /
• v.40 no.3
• /
• pp.177-182
• /
• 2013

A genus Camelina has been attracted as a promising oil crop, especially available in drought and marginal conditions. Due to more demands on arable land for bioenergy crops, price of agricultural products has been a challengeable issue. In that respect, development of Camelina crop with higher germination rate and germination energy can be a strategy to secure seedling establishment, nutrient uptake and long vegetative period. In order to be easily available in the field and laboratory conditions, Camelina seed needs to be optimized for its germination temperature. Germination temperature regime was in a range of 8 to $32^{\circ}C$ initially, and consecutively narrowed down to 8 to $20^{\circ}C$. Based on the temperature range, Camelina germinated greater than 96% at $8-16^{\circ}C$ in two weeks after sowing, but germination rate started to decrease at the higher than $24^{\circ}C$ and was significantly low at higher than $32^{\circ}C$. In terms of rapid time to reach the maximum germination rate and greater germination energy, temperature ranged from 12 to $16^{\circ}C$ was found to be desirable for Camelina germination. Although germinationa rate was greater at $16^{\circ}C$, lower temperature close to $12^{\circ}C$ would be favored for the field conditions where greater root growth leading to healthier seedlings and better nutrient or water availability is considerably demanded.

• IE interfaces
• /
• v.23 no.4
• /
• pp.311-318
• /
• 2010

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.6
• /
• pp.644-651
• /
• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on water quality such as turbidity, temperature, pH and alkalinity. However, the process of chemical reaction to improve water quality (e.g., turbidity) by chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts using Jar-test data. In this paper, a systematic control strategy is proposed to derive the optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride), using Jar-test results. A neural network model is developed for coagulant dosing and purifying process by means of six input variables (turbidity, temperature, pH, alkalinity of raw water, PAC feed rate, turbidity in flocculation) and one output variable, while considering the relationships to the reaction of coagulation and flocculation. The model is utilized to derive the optimum coagulant dosage (in the sense of minimizing turbidity of water in flocculator). The ability of the proposed control scheme validated through the field test has proved to be of considerable practical value.

• Environmental and Resource Economics Review
• /
• v.11 no.2
• /
• pp.211-231
• /
• 2002

This paper summarizes the results of a study that assess how a demand side management (DSM) system addresses key economic and environmental challenges facing in the Korean natural gas sector considering; ${\bullet}$ high discrepancies of seasonal consumption volume and of load factor in unmatured domestic LNG market, ${\bullet}$ unfavorable and volatile international LNG market, imposing with the contestable "take-or-pay" contract terms, ${\bullet}$ low profile of LNG and existence of market barriers against an optimal fuel mix status in the industrial energy sector. A particular focus of this study is to establish an 'extended' DSM system in the unmatured gas market, especially in industry sector, that could play a key role to assure an optimum fuel mix scheme. Under the concept of 'extended' DSM, a system dynamics modeling approach has been introduced to explore the option to maximize economic benefits in terms of the national energy system optimization, entailing different ways of commitments accounting for different DSM measures and time delay scenarios. The study concludes that policy options exist that can reduce inefficiencies in gas industry and end-use system at no net costs to national economy. The most scenarios find that, by the year 2015, it is possible to develop a substantial potential of increased industrial gas end-uses under more reliable and stable load patterns. Assessment of sensitivity analysis suggests that time delay factor, in formulating DSM scenarios, plays a key role to overcome various market barriers in domestic LNG market and provides a strong justification for the policy portfolios 'just in time' (time accurateness), which eventually contribute to establish an optimum fuel mix strategy. The study indicates also the needs of advanced studies based on SD approach to articulate uncertainty in unmatured energy market analysis, including gas.

• Computers and Concrete
• /
• v.32 no.2
• /
• pp.217-232
• /
• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• The Korean Journal of Ecology
• /
• v.16 no.4
• /
• pp.417-428
• /
• 1993

Branching growth pattern of Mugnolza denuduta is likely to be originated from two growth strategies: environment overcoming strategy and life maintenance strategy, which coexist in a tree. Growth rate of branches was strongly correlated with relative light intensity (P<0.001) and physical contact (P<0.01), however there is no significant correlation between growth rate and direction of branch. When relative light intensity is less than 1%, the growth was restricted by physical contact with the surrounding branches. In contrast, the growth was rarely restricted by physical contact when relative light intensitiy was 10% or more. The branching rate was significantly affected by the presence or absence of physical contact (P<0.05), but it was not significantly affected by relative light intensity nor by the direction of branch. In the beginning stage of the growth, the ratio of the material allocation from main branch to subbranch was large and varied with the influence of surrounding environment. These various growth rates, which implicate a variety of material allocation ratios (0.16~0.98), affect branch growth pattern through the optimum growth strategies. The growth and arrangement of branches of Magnolia denudata display the solar collectors to maximize the total amount of energy absorbed and to overcome the restriction of the environment.

• Journal of Environmental Science International
• /
• v.15 no.7
• /
• pp.677-688
• /
• 2006

Currently, the application of $TiO_2$ photocatalyst has been focused on purification and treatment of wastewater. However, the use of conventional $TiO_2$ slurry photocatalyst results in disadvantage of stirring during the reaction and of separation after the reaction. And the usage of artificial UV lamp has made the cost of photocatalyst treatment system high. Consequently, we studied that solar light/$TiO_2$ film system was designed and developed in order to examine disinfection characteristics of sewage wastewater treatment. The optimum conditions for disinfection such as solar light intensity, characteristic of sewage wastewater, amounts of $TiO_2$ and comparison of solar ligth/$TiO_2$ systems with UV light/$TiO_2$ system was examined. The results are as follows: (1) photocatalytic disinfection process with solar light in the presence of $TiO_2$ film more effectively killed total coliform (TC) than solar light or $TiO_2$ film absorption only. (2) The survival ratio of TC and residual ratio of organic material (BOD, CODcr) decreased with remain resistant material. (3) The survival ratio of TC and residual ratio of organic material (BOD, CODcr) decreased with the increase of amounts of $TiO_2$. (4) TC survival ratio decreased linearly with increasing UV light intensity. (5) The disinfection effect of solar light/$TiO_2$ slurry system decreased more than UV light/$TiO_2$ film systems. (6) The disinfection reaction followed first-order kinetics. We suggest that solar light instead of using artificial UV light was conducted to investigate the applicability of alternative energy source in the disinfection of TC and the degradation of organic material.

• Journal of Microbiology and Biotechnology
• /
• v.26 no.7
• /
• pp.1163-1172
• /
• 2016

The Ω-loop is a nonregular and flexible structure that plays an important role in molecular recognition, protein folding, and thermostability. In the present study, molecular dynamics simulation was carried out to assess the molecular stability and flexibility profile of the porcine trypsin structures. Two Ω-Loops (fragment 57-67 and fragment 78-91) were confirmed to represent the flexible region. Subsequently, glycosylation site-directed mutations (A73S, N84S, and R104S) were introduced within the Ω-loop region and its wing chain based on its potential N-glycosylation sites (Asn-Xaa-Ser/Thr consensus sequences) and structure information to improve the thermostability of trypsin. The result demonstrated that the half-life of the N84S mutant at 50℃ increased by 177.89 min when compared with that of the wild-type enzyme. Furthermore, the significant increase in the thermal stability of the N84S mutant has also been proven by an increase in the T_m values determined by circular dichroism. Additionally, the optimum temperatures of the wild-type enzyme and the N84S mutant were 75℃ and 80℃, respectively. In conclusion, we obtained the thermostability-improved enzyme N84S mutant, and the strategy used to design this mutant based on its structural information and N-linked glycosylation modification could be applied to engineer other enzymes to meet the needs of the biotechnological industry.

• Journal of Information Technology Services
• /
• v.19 no.2
• /
• pp.109-124
• /
• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.