• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.124-133
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• 2022

With the development of hardware technology and the advancement of numerical model methods, more precise weather forecasts can be carried out. In this paper, we propose a Unicon Optimization scheme combining Loop Vectorization, Dependency Vectorization, and Code Modernization to optimize and increase Maintainability the Unicon source contained in SCAM, a simplified version of CESM, and present an overall SCAM structure. This paper tested the unicorn optimization scheme in the SCAM structure, and compared to the existing source code, the loop vectorization resulted in a performance improvement of 3.086% and the dependency vectorization of 0.4572%. And in the case of Unicorn Optimization, which applied all of these, the performance improvement was 3.457% compared to the existing source code. This proves that the Unicorn Optimization technique proposed in this paper provides excellent performance.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.2
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• pp.118-125
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• 2012

World population has increased rapidly following the industrial revolution, reaching 7 billion in 2012. Several forecasts estimate that this number will rise to about 8 billion in 2025. Improvements of living standards in developing nations have also raised resource and energy demands worldwide. In consequences, human beings have faced many global and urgent problems, such as global warming, water and food shortages, resource and energy crises, and so on. Many ocean utilization technologies for avoiding or reducing such big problems have been developed, for examples $CO_2$ ocean sequestration, seawater desalination, artificial upwelling, deepwater mining, and ocean energies. It is important, however, to assess such technologies from the viewpoints of sustainability and public acceptancy, since the aims of those technologies are to develop sustainable social systems rather than conventional ones based on fossil resources. Inclusive Marine Pressure Assessment and Classification Technology Research Committee (generally called IMPACT Research Committee) of Japan Society of Naval Architects and Ocean Engineers, has proposed Inclusive Impact Index "Triple I" as an indicator, which can predict both environmental sustainability and economical feasibility, in order to assess the ocean utilization technologies from the viewpoints of sustainability and public acceptancy. This index was considered by combining Ecological Footprint and Environmental Risk Assessment. The Ecological Footprint and the Environmental Risk Assessment are introduced in the first part of this paper. Then the concept and the structure of the Triple I are explained in the second part of this paper. Finally, the economy-ecology conversion factor in Triple I accounting is considered.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.5
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• pp.1-14
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• 2021

Excessive development and urbanization have destroyed animal, plant, habitats and reduced biodiversity. In order to preserve species diversity, habitat prediction studies are have been conducted at home and overseas using various modeling techniques. This study was conducted to suggest optimal habitat modeling research by comparing HSI and MaxEnt, which are widely used among habitat modeling techniques. The study was targeted on the endangered species of Prionailurus bengalensis in nearby areas (5460.35km2) including Cheonan City, and the same data were used for analysis to compare those models. According to the HSI analysis, Prionailurus bengalensis's habitat probability was 74.65% for less than 0.5 and 25.34% for more than 0.5 and the top 30% were forest (99.07%). MaxEnt's analysis showed that 56.22% of those below 0.5 and 43.79% of those above 0.5 were found to have a high explanatory power of 78.3% of AUC. The Paired Wilcoxn test, which evaluated the significance of thoes models, confirmed that the mean difference between the two models was statistically significant (p<0.05). Analysis of the differences in the results of those models using the matrix table shows that score 24.43% HSI and MaxEnt was accordance,12.44% of the 0.0 to 0.2 section, 7.22% of the 0.2 to 0.4 section, 2.73% of the 0.4 to 0.6 section, 1.96% of the 0.6 to 0.8, and 0.08% of the 0.9 to 1.0. To verify where the score difference appears, the result values of those models were reset to values from 1 to 5 and overlaid. Overlapping analysis resulted in 30.26% of the Strongly agree values, 56.77% of the agree values, and 11.92% of the Disagree values. The places where the difference in scores occurs were analyzed in the order of forest (45.23%), agricultural land (34.57%), and urbanization area (7.65%). This confirmed that the analysis of the same target species within the same target site also has differences in forecasts depending on the modelling method. Therefore, a novel analysis method combining the advantages of each modeling in habitat prediction studies should be developed, and future study may be used to select Prionailurus bengalensis and species-protected areas and species protection areas in the future. Further research is judged to require higher accuracy studies through the use of various modeling techniques and on-site verification.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.