• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.560-569
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• 2016

Purpose The purpose of this study was not only to explore the factors associated with the survival of OHCA(Out-of-hospital Cardiac Arrest), but to provide ideas for improving the operation of emergency medical system in Korea. Method 90,734 OHCAs(Out-of-hospital Cardiac Arrest) with a cardiac etiology, who had been transported by 119 EMS ambulances for seven years from 2006 to 2012 in Korea, were analyzed. The data had a multilevel structure in that patient's survival in the same region is interrelated, so two-level (patient-region) logistic regression analysis was applied to adjust this correlation. Results The adjusted OR in group who were given CPR(Cardiopulmonary Resuscitation) by a bystander were 1.40 for survival to discharge. In addition, the adjusted OR in the group with an implementation of AED (automated external defibrillator) before arriving in hospital was 2.98 for survival to discharge. we categorized some continuous variables (number of emergency physician, OHCAs volume fo hospital, area deprivation level) into five quintiles. The adjusted OR in the number of emergency physician compared with Q1(lowest) was 1.29(Q2), 2.89(Q3), 3.39(Q4), 4.07(Q5), respectively. the adjusted OR in OHCAs volume of each hospital compared with Q1(lowest) was 2.06(Q2), 3.06(Q3), 3.46(Q4), 4.36(Q5), respectively. Lastly, the adjusted OR in deprivation level compared with Q1(least deprived area) was 0.72(Q4), 0.64(Q5) so that the adjusted OR of survival to discharge tended to decrease in more deprived districts. Conclusion The survival to discharge was better significantly in group given CPR by a bystander and with the implementation of AED before arriving in hospital. The survival to discharge tended to be significantly better in hospitals with a larger number of emergency physicians and higher volume of OHCAs in less deprived districts.

• Korean Journal of Environmental Agriculture
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• v.42 no.1
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• pp.52-62
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• 2023

This study focused on determining the specific causes and prevention methods of mass fish deaths occurred in five reservoirs (Gagugi, Neupgokgi, Danggokgi, Sagokji, and Hangokji) in Andong-si. For this purpose, a survey of agricultural land and livestock in the upper part of the reservoirs and analysis of water quality in the reservoir irrespective of whether it rains or not were conducted. We attempted to examine the changes in dissolved oxygen (DO) in the surface and bottom layers of reservoirs and changes in DO depending on the amount of livestock compost and time. Based on the above investigations, treatment plans were established to efficiently control the inflow of contaminated water into reservoirs. The rainfall and farmland areas in the upper part of the reservoir were investigated using Google and aviation data provided by the Ministry of Land, Infrastructure, and Transport. The current status of livestock farms distributed around the reservoirs was also examined because compost from these farms can flow into the reservoir when it rains. Various water quality parameters, such as phosphate phosphorus (PO₄-P) and ammonium nitrogen (NH₃-N), were analyzed and compared for each reservoir during the rainy season. Changes in the DO concentration and electrical conductivity (EC) were also observed at the inlet of the reservoir during raining using an automated instrument. In addition, DO was measured until the concentration reached 0 ppm in 10 min by adding livestock compost at various concentrations (0.05%, 0.1%, 0.3%, and 0.5% by wt.), where the concentration of the livestock compost represents the relative weight of rainwater. The DO concentration in the surface layer of reservoirs was 3.7 to 5.3 ppm, which is sufficient for fish survival. However, the fish could not survive at the bottom layer with DO concentration of 0.0-2.1 ppm. When the livestock compost was 0.3%, DO required 10-19 h to reach 0 ppm. Considering these results, it was confirmed that the DO in the bottom layer of the reservoir could easily change to an anaerobic state within 24 h when the livestock compost in the rainwater exceeds 0.3%. The results show that the direct cause of fish mortality is the inflow of excessive livestock compost into reservoirs during the first rainfall in spring. All the surveyed reservoirs had relatively good topographical features for the inflow of compost generated from livestock farms. This keeps the bottom layer of the reservoir free of oxygen. Therefore, to prevent fish death due to insufficient DO in the reservoir, measures should be undertaken to limit the amount of livestock compost flowing into the reservoir within 0.3%, which has been experimentally determined. As a basic countermeasure, minerals such as limestone, dolomite, and magnesia containing calcium and magnesium should be added to the compost of livestock farms around the reservoir. These minerals have excellent pollutant removal capabilities when sprayed onto the compost. In addition, measures should be taken to prevent fish death according to the characteristics of each reservoir.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.201-208
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• 2007

Purpose: We investigated whether the diagnostic performance of SPECT scintimammography (SMM) can be improved by adding computer-aided diagnosis (CAD) of ultrasonography (US). Materials and methods: We reviewed breast SPECT SMM images and corresponding US images from 40 patients with breast masses (21 malignant and 19 benign tumors). The quantitative data of SPECT SMM were obtained as the uptake ratio of lesion to contralateral normal breast. The morphologic features of the breast lesions on US were extracted and quantitated using the automated CAD software program. The diagnostic performance of SPECT SMM and CAD of US alone was determined using receiver operating characteristic (ROC) curve analysis. The best discriminating parameter (D-value) combining SPECT SMM and the CAD of US was created. The sensitivity, specificity and accuracy of combined two diagnostic modalities were compared to those of a single one. Results: Both SPECT SMM and CAD of US showed a relatively good diagnostic performance (area under curve = 0.846 and 0.831, respectively). Combining the results of SPECT SMM and CAD of US resulted in improved diagnostic performance (area under curve =0.860), but there was no statistical differerence in sensitivity, specificity and accuracy between the combined method and a single modality. Conclusion: It seems that combining the results of SPECT SMM and CAD of breast US do not significantly improve the diagnostic performance for diagnosis of breast cancer, compared with that of SPECT SMM alone. However, SPECT SMM and CAD of US may complement each other in differential diagnosis of breast cancer.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.162-173
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• 2009

Information on the expected geographical shift of suitable zones for growing crops under future climate is a starting point of adaptation planning in agriculture and is attracting much concern from policy makers as well as researchers. Few practical schemes have been developed, however, because of the difficulty in implementing the site-selection concept at an analytical level. In this study, we suggest site-selection criteria for quality Fuji apple production and integrate geospatial data and information available in public domains (e.g., digital elevation model, digital soil maps, digital climate maps, and predictive models for agroclimate and fruit quality) to implement this concept on a GIS platform. Primary criterion for selecting sites suitable for Fuji apple production includes land cover, topography, and soil texture. When the primary criterion is satisfied, climatic conditions such as the length of frost free season, freezing risk during the overwintering period, and the late frost risk in spring are tested as the secondary criterion. Finally, the third criterion checks for fruit quality such as color and shape. Land attributes related to these factors in each criterion were implemented in ArcGIS environment as relevant raster layers for spatial analysis, and retrieval procedures were automated by writing programs compatible with ArcGIS. This scheme was applied to the A1B projected climates for South Korea in the future normal years (2011-2040, 2041-2070, and 2071-2100) as well as the current climate condition observed in 1971-2000 for selecting the sites suitable for quality Fuji apple production in each period. Results showed that this scheme can figure out the geographical shift of suitable zones at landscape scales as well as the latitudinal shift of northern limit for cultivation at national or regional scales.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.