• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.197-206
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• 2023

Recently, the international community, including the International Maritime Organization (IMO), has strengthened regulations on air pollution emissions of ships, and eco-friendly ships are actively being developed to reduce exhaust gas emissions. Among them, rotor sail (RS), a wind-assisted ship propulsion system, is attracting attention again. RS is a cylindrical device installed on the ship deck, that generates hydrodynamic lift using a magnus effect. This is a next generation eco-friendly auxiliary propulsion technology, and Enercon company, which developed RS-applied ships, announced that fuel savings of more than 30% are possible. In this study, optimal installation conditions such as RS spacing and arrangement type were selected when multiple RSs were installed on ships. AR=5.1, SR=1.0, and D_e/D was fixed at 2.0 according to the RS arrangement, and the wind direction was considered only for the unidirectional +y-axis. Regarding arrangement conditions, five conditions were set at 3D intervals in the +x-axis direction from 3D to 15D and five conditions in the +y-axis direction from 5D to 25D. C_L, C_D and aerodynamic efficiency (C_L/C_D) were compared according to the square(□) and diamond(◇) shape arrangements. Consequently, the effect of RS on the longitudinal distance was not significantly different. However, in the case of RS flow characteristics according to the transverse distance, the interaction effect of RS was the greatest when the two RSs almost matched the wind direction. In the case of the RS flow characteristics according to the arrangement, notably, when the wind blew in the forward (0°) direction, the diamond (◇) arrangement was least affected by the backward flow between RSs.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.378-387
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• 2023

Petrochemical is an energy consuming industry that consumes about 30% of total industrial energy consumption and is a representative carbon dioxide (CO₂) emission source. Among them, the Naphtha Cracking Center (NCC), which produces ethylene, propylene, propane and mixed C4, consumes large amounts of energy and emits significant amounts of CO₂. For this reason, an integrated techno economic- environmental impact assessment aimed at reducing energy consumption and environmental impact factors is necessary to ensure efficiency in terms of economics and environment. This study aims to analyze the efficiency of the heat exchanger network used in the existing NCC base on the pinch analysis and select an improvement plan that can reduced energy consumption. In order to reduces the utility consumption in the process, an optimal heat exchanger network considering the high-temperature and low-temperature stream was derived, and the economic evaluation was conducted by considering the trade-off between the reduction in utility consumption and the increase in heat exchanger installation cost. In addition, an environmental impact assessment was conducted on the reduced CO₂ emission in consideration of the environmental aspect, and the economic environmental impact assessment used the payback period to recover the invested funds to come up with an energy saving plan that can be applied based on the actual process. As a result of considering the economic-environmental impact assessment, when the environmental impact assessment was not considered, it was 4.29 months, 3.21 months, and 3.39 months for each case, and when considering the environmental impact assessment, it was 4.24 months, 3.17 months, and 3.35 months for each case. These results appeared equally both when the environmental impact assessment was not include and when it was include. In addition, a sensitivity analysis was conducted for each case to determine how important factors affect the payback period. As a result of the sensitivity analysis, the cost of the heat exchanger was identified as a major factor influencing the overall cost.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Korean Journal of Poultry Science
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• v.50 no.4
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• pp.203-212
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• 2023

This study conducted a diallel-crossbreeding test using four Korean native chicken parent stock lines (YC, YD, CK, and CF) to develop a native commercial layer with high egg-laying performance. A total of 312 chickens in six combinations were examined for various traits, including livability, body weight, age at first egg-laying, hen-day, and hen-housed egg production, egg weight, and egg quality, from hatching to 60 weeks of age. The results showed that the average survival rate was 77.1±18.8% with the YDYC combination having the highest survival rate along with excellent specific combining ability. The YDYC combination exhibited significantly higher body weight compared to the other combinations (P<0.01). The average age at first egg-laying was 121.3±2.5 days, with no significant difference between the combinations. The average hen-day egg production was 74.0±6.4%, and the hen-housed egg production was 181.4±33.8 eggs with the YDCF and YCCK combinations demonstrating the highest laying performance, while the YDYC and CKCF combinations had the lowest (P<0.05). Laying performance was more influenced by specific combining ability than general combining ability. The eggs from the YDYC combination were significantly lighter and had the darkest shell color (P<0.01), whereas the YDCF combination exhibited the thickest eggshells. There was no difference in internal egg quality among combinations, except the YDCF combination had the darkest yolk color. Overall, we concluded that the YCCK combination, characterized by high laying performance and livability, and the YDCF combination with high laying performance and good egg quality are the most desirable combinations for Korean native commercial layers.

• Food Science and Preservation
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• v.30 no.1
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• pp.155-169
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• 2023

Jerusalem artichoke (JA, Helianthus tuberosus L.) has a great potential to enhance the quality of yakju due to the plentiful inulin content which is functional and indigestible carbohydrate in human. In this study, the optimal preparation conditions such as the added amount and steam treatment of JA were investigated to improve the quality of yakju. As the amount of JA added to yakju increased, alcohol production decreased, whereas fermentation was performed well when the steam-cooked JA was added to yakju. The pH and total acidity of yakju decreased and increased, respectively, when the amount of JA added to yakju increased, whereas pH and total acidity of yakju increased and decreased, respectively, when the steam-cooked JA was added to yakju. The free sugar and organic acid contents of yakju increased and decreased, respectively, when the amount of JA added to yakju increased, whereas those of yakju decreased when the steam-cooked JA was added to Yakju. Amino acid content of JA decreased as the amount of JA added to yakju increased and that of JA significantly decreased when the steam-cooked JA was added to yakju. In the sensory evaluation analysis, the addition of 10% unsteam-cooked JA to yakju was the best when considering sweetness, flavor, sourness, and overall preference of yakju supplemented with JA. Consequently, utilizing JA to yakju may contribute to the improvement of the quality of yakju.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of Korean Society of Forest Science
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• v.56 no.1
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• pp.66-76
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• 1982

Soil harness represents such physical properties as porosity, amount of water, bulk density and soil texture. It is very important to know the mechanical properties of soil as well as the chemical in order to research the fundamental phenomena in the growth and the distribution of tree roots. The writer intended to grip soil hardness by soil layer and also to grasp the root distribution and the correlation between soil hardness and the root distribution of Pinus riguda Mill. planted on the denuded hillside with sooding works by soil layer on soil profile. The site investigated is situated at Peongchang-ri 13, Kocksung county, Chon-nam Province. The area is consisted of 3.63 ha having on elevation of 167.5-207.5 m. Soil texture is sandy loam and parant rock in granite. Average slope of the area is $17^{\circ}-30^{\circ}$. Soil moisture condition is dry. Main exposure of the area is NW or SW. The total number of plots investigated was 24 plots. It divided into two groups by direction each 12 plots in NW and SW and divided into three groups by the position of mountain plots in foot of mountain, in hillside, and in summit of mountain, respectively. Each sampling tree was selected as specimen by purposive sampling and soil profile was made at the downward distance of 50cm form the sampling tree at each plot. Soil hardness, soil layer surveying, root distribution of the tree and vegetation were measured and investigated at the each plot. The soil hardness measured by the Yamanaka Soil Hardness Tester in mm unit. the results are as follows: 1) Soil hardness increases gradually in conformity with the increment of soil depth. The average soil indicator hardness by soil layer are as follows: 14.6mm in I - soil layer (0-10cm in depth from soil surface), 16.2mm in II - soil layer (10-20cm), 17.2 in III - soil layer (20-30cm), 18.3mm in IV - soil layer(30-40cm), 19.8mm in V - soil layer (4.50mm). 2) The tree roots (less than 20mm in diameter) distribute more in the surface layer than in the subsoil layer and decrease gradually according to the increment of soil depth. The ratio of the root distribution can be illustrated by comparing with each of five soil layers from surface to subsoil layer as follows: I - soil layer; 31%, II - soil layer; 26%, III - soil layer; 18%, IV - soil layer; 12%, V - soil layer; 13%, 3) Soil hardness and tree root distribution (less than 20mm in diameter) of Pinus rigida Mill. correlate negatively each other; the more soil hardness increases, the most root distribution decreases. The correlation coefficients between soil hardness and distribution of tree roots by soil layer are as follows: I - soil layer; -0.3675 (at the 10% significance level), II - soil layer; -0.5299 (at the 1% significance level), III - soil layer; -0.5573 (at the 2% significance level), IV - soil layer; -0.6922 (at the 5% significance level), V - soil layer; -0.7325 (at the 2% significance level). 4) the most suitable range of soil hardness for the growth of Pinus rigida Mill is the range of 12-14.9mm in soil indicator hardness. In this range of soil indicator hardness, the root distribution of this tree amounts to 41.8% in spite of 33% in soil harness and under the 20.9mm of soil indicator hardness, the distribution amounts to 93.2% in spite of 82% in soil hardness. Judging from above facts, the roots of Pinus rigida can easily grow within the soil condition of 20.9mm in soil indicator hardness. 5) The soil layers are classified by their depths from the surface soil.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Applied Biological Chemistry
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• v.18 no.1
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• pp.16-22
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• 1975

This experiment was carried out to investigate the physiological characteristics of two original yeasts, 5-Y-5 and 6-Y-6, which selected from 24 Takju yeasts and three mutants, 30-24,30-81 and 40-27. induced from two original yeasts by the irradiation of UV light. The results were summarized as follows. 1) Alcohol tolerances of three mutants were decreased in some degree as compared with those of original yeasts. 2) Tolerances of lactic and citric acids of acid producing mutant 30-81, was increased than those of original yeasts. 3) In the case of using ammonium sulfate as a nitrogen source, two original yeasts and three mutants required Ca-pantothenate as a essential growth factor and four strains of yeasts except the mutant, 30-81, required biotin as a stimulated growth factor, When asparagine was used as a nitrogen source, two original yeasts and three mutants showed the same as above result but the stimulated effect of biotin was far less. 4) Propagation powers of the mutants were weaken than those of original yeasts, particular that of acid producing mutant, 30-81, was the weakest in the three mutants. 5) The optimum temperature for fermentation of original yeasts were $30^{\circ}C\;to\;35^{\circ}C$ but three mutants were $25^{\circ}C\;to\;30^{\circ}C$. 6) The optimum pH for fermentation of original yeasts were pH 5 to 6, and there is no appreciable difference between original yeasts and three mutants. The fermentation power of mutant,30-81, was decreased more rapidly than those of other mutants according to approach neutral. Three mutants were more sensible to heat than original yeasts. 7) Two original yeasts and three mutants were inhibited more over 20 percent of sugar for fermentation and three mutants were more sensible to sugar concentration than original yeasts.