• Clinical and Experimental Pediatrics
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• v.45 no.2
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• pp.174-182
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• 2002

Purpose : The purpose of this study was to investigate the prevalence of obese and underweight adolescents in Incheon area and to examine the relationship between serum cholesterol level and obesity, then to assess the nutritional condition of adolescents. Methods : With a questionnaire regarding their demographic characteristics, blood samples were obtained from apparently healthy students aged 12 to 24 years by venipuncture at April and May, 2000. We measured the obesity index using standard body weight and the body mass index(BMI) according to the criteria established by the Korean Pediatric Society in 1998. Obesity was defined as BMI more than 95 percentile, and underweight less than 15 percentile by age and sex. Results : A total of 1,456 students(M : F=685 : 771) aged 12 to 24 years were included in this study. The prevalence of obesity by standard body weight in adolescents in Incheon were 11.7% : mild obesity 6.5%, moderate 4.6%, and severe 0.5%. By BMI, the prevalence of obesity was 6.4% in males and 6.2% in females. In males, the prevalence of obesity in rural areas was 8.5%, lower than in urban areas(14.3%). The prevalence of underweight by obesity index was 34.1% in rural areas and 22.9% in urban areas. In females, the prevalence of obesity was 12.5% in rural areas and 19.6% in urban areas. There were no significant differences between the two regions(P=0.529). The prevalence of obesity increased with age till 16.3% of peak prevalence in 16 years of age, and then decreased. In males, the prevalence of obesity in academic and vocational school were 13.7% and 9.7%, respectively(P=0.116). In females of the academic and vocational school, the prevalence of obesity was 6.8% and 18.0%, respectively(P=0.001). In obese adolescents, serum total cholesterol was over 200 mg/dL in 6.2%. Conclusion : This study revealed that the prevalence of obesity in adolescents was about 12% and that the prevalence of underweight adolescents was considerably high. We think nutritional assessment and intervention are warranted for adolescent students.

• Clinical and Experimental Pediatrics
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• v.46 no.12
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• pp.1200-1206
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• 2003

Purpose : This study was conducted for the use of nasal continuous positive airway pressure (CPAP), by comparing the early use of non-invasive nasal CPAP with low intermittent mandatory ventilation(low IMV) and endotracheal CPAP in weaning a mechanical ventilator from infants with moderate respiratory distress syndrome(RDS). Methods : Thirty infants in the study group, with moderate RDS from November 2001 to June 2002, were administered surfactants and treated with the mechanical ventilator, and applied the nasal CPAP in weaning. Thirty infants of the control group, from January 1999 to September 2001, were applied low IMV and endoctracheal CPAP in weaning. Results : There were no significant differences in the characteristics, the severity of clinical symptoms, the initial laboratory findings and settings of the mechanical ventilator. After weaning, the study group showed no significant changes in $PaCO_2$. However, the control group showed a slight $CO_2$ retension after one and 12 hours. Twenty eight infants(93.3%) of the study group and 24 infants(80%) of the control group were successfully extubated. The primary cause of failure was apnea. There were no significant differences in the duration of weaning and the mechanical ventilator treatment between the groups. Complications in weaning were related to the fixation of nasal CPAP and the mechanical problems caused by endotracheal tube. Conclusion : Aggressive weaning is possible for moderate RDS, in which the nasal CPAP was used without the low IMV and the endotracheal CPAP process. It had no difficulties. In conclusion, the nasal CPAP is an adequate weaning method for moderate RDS.

• Quality Improvement in Health Care
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• v.7 no.2
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• pp.138-155
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• 2000

Background: Factors related to waiting and staying time for patient care in emergency care center (ECC) were examined during 1 month from Apr. 1 to Apr. 30, 1997 at an ECC of Yeungnam university hospital in Taegu metropolitan city, to obtain the baseline data on the strategy of effective management of emergency patients. Method: The study subjects consisted of the 1,742 patients who visited at ECC and the data were obtained from the medical records of ECC and direct surveys. Results: The mean interval between ECC admission time and initial care time by each ECC duty residents was 83.1 minutes for male patients and 84.9 minutes for female patients, and mean ECC staying time (time interval between admission and final disposition from ECC) was 718.0 minutes in men and 670.5 minutes in women. As the results, the mean staying time in ECC was higher in older age, and especially the both of initial care time and staying time were highest in patients of medical aid, and shortest in patients of worker's accident compensation insurance. The on admission or not, previously endotracheal-intubation state of patient. The ECC staying ti initial care time was much more delayed in patients of not having previous medical records and the ECC staying time was higher in referred patients from out-patient department, in transferred patients from the other hospitals and patients having previous records, and in patients partly used the order-communicating system. The factors associated with the initial care time were the numbers of ECC patients and the existence of any true emergent patients, being cardiopulmonary resuscitation (CPR) statusme was much more longer in patients of drug intoxication, in CPR patients, in medical department patients, in transfused patients and in patients related to 3 or more departments. And according to the numbers of duty internships, the ECC staying time for four internships was more longer than for five internships and after admission ordering was done, also-more longer in status being of no available beds. As above mentioned results, the factors for the ECC staying time were thought to be statistically significant (P<0.01) according to the patient's age and the laboratory orders and the X-ray films checked. And also the factor for the ECC staying time were thought to be statistically significant (P<0.01) according to the status being of no available beds, the laboratory orders and/or the special laboratory orders, the X-ray films checked, final disposing department, transferred to other hospital or not, home medication or not, admission or not, the grades of beds, the year grades of residents, the causes of ECC visit, the being CPR status on admission or not, the surgical operation or not, being known personells in our hospital. Conclution: Authors concluded that the relieving method of long-staying time in ECC was being establishing the legally proved apparatus which could differentiate the true emergency or non-emergency patients, and that the methods of shortening ECC staying time were doing definitely necessary laboratory orders and managing beds more flexibly to admit for ECC patients and finally this methods were thought to be a method of unloading for ECC personnels and improving the quality of care in emergency patients.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.1
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• pp.107-119
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• 2014

The purpose of this study is to examine the current status of vascular plants and naturalized plants growing in the Seongeup Folk Village in Jeju and to consider and compare their distribution patterns and the characteristics of emergence of naturalized plants in other folk villages and all parts of Jeju, thereby exploring measures to well manage naturalized plants. The result of this study is as follows.11) The total number of vascular plants growing in Seongeup Folk Village is identified to be 354 taxa which include 93 families, 260 genus, 298 species, 44 varieties and 12 breeds. Among them, the number of naturalized plants is 55 taxa in total including 22 families, 46 genus, 53 species, and 2 varieties, which accounts for 21.7% of the total of 254 taxa identified all over the region of Jeju. The rate of naturalization in Seongeup Folk Village is 15.5%, which is far higher than the rates of plant naturalization in Hahoi Village in Andong, Yangdong Village in Gyeongju, Hangae Village in Seongju, Wanggok Village in Goseong, and Oeam Village in Asan. Among the naturalized plants identified within the targeted villages, the number of those growing in Jeju is 9 taxa including Silene gallica, Modiola caroliniana, Oenothera laciniata, Oenothera stricta, Apium leptophyllum, Gnaphalium purpureum, Gnaphalium calviceps, Paspalum dilatatum and Sisyrinchium angustifolium. It is suggested that appropriate management measures that consider the characteristics of the gateway to import and the birthplace of the naturalized plants are necessary. In the meantime, 3 more taxa that have not been included in the reference list of Jeju have been identified for the first time in Seongeup Folk Village, which include Bromus sterilis, Cannabis sativa and Veronica hederaefolia. The number of naturalized plants identified within the gardens of unit-based cultural properties is 20 taxa, among which the rate of prevalence of Cerastium glomeratum is the highest at 62.5%. On the other hand, the communities of plants that require landscape management are Brassica napus and other naturalized plants, including Cosmos bipinnatus, Trifolium repens, Medicago lupulina, Oenothera stricta, O. laciniata, Lotus corniculatus, Lolium perenne, Silene gallica, Hypochaeris radicata, Plantago virginica, Bromus catharticus and Cerastium glomeratum. As a short-term measure to manage naturalized plants growing in Seongeup Folk Village, it is important to identify the current status of Cosmos bipinnatus and Brassica napus that have been planted for landscape agriculture, and explore how to use flowers during the blooming season. It is suggested that Ambrosia artemisiifolia and Hypochaeris radicata, designated as invasive alien plants by the Ministry of Health and Welfare, should be eradicated initially, followed by regular monitoring in case of further invasion, spread or expansion. As for Hypochaeris radicata, in particular, some physical prevention measures need to be explored, such as for example, identifying the habitat density and eradication of the plant. In addition, it is urgent to remove plants, such as Sonchus oleraceus, Houttuynia cordata, Crassocephalum crepidioides, Erigeron annuus and Lamium purpureum with high index of greenness visually, growing wild at around high Jeongyi town walls. At the same time, as the distribution and dominance value of the naturalized plants growing in deserted or empty houses are high, it is necessary to find measures to preserve and manage them and to use the houses as lodging places.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.3
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• pp.41-66
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• 2017

The purpose of this research is to empirically trace the junctures of Yigye-gugok managed by Gwan-am Hong Gyeong-mo, a grandson of Yigye Hong Yang-ho who originally designed Yigye-gugok, while reviewing the features of the forms and patterns of gugok. The results of the research are as follows. 1. Ui-dong was part of the domain of the capital during the Chosun dynasty, which also is located in the city of Seoul as a matter of administrative zone. Likewisely, Yigye-gugok is taken as a special meaning for it was one and only gugok. Starting with Mangyeong Waterfall as the $1^{st}$ gok, Yigye follows through the $2^{nd}$ gok of Jeokchwibyeong Rock, the $3^{rd}$ gok of Chanunbong Peak, the $4^{th}$ gok of Jinuigang Rock, the $5^{th}$ gok of Okkyeongdae Rock, the $6^{th}$ gok of Wolyeongdam Pond, the $7^{th}$ gok of Tagyeongam Rock, the $8^{th}$ gok of Myeongoktan Stream, and the $9^{th}$ gok of Jaeganjeong Pavilion. Of these, Mangyeong Waterfall, Chanunbong Peak, and Okkyeongdae Rock are distinct for their locations in as much as their features, while estimated locations for Jinuigang Rock, Wolyeongdam Pond, Myeongoktan Stream, and Jaeganjeong Pavilion were discovered. However, Jeokchwibyeong Rock and Tagyeongam Rock demonstrated multiple locations in close resemblance to documentary literatures within secretive proximity, whereas geography, scenery, and sighted objects were considered to evaluate the 1st estimated location. Through these endeavored, it was possible to identify the shipping routes and structures for the total distance of 2.1km running from the $1^{st}$ gok to the $9^{th}$ gok, which nears Gwanam's description of 5ri(里), or approximately 1.96km for gugok. 2. Set towards the end of the $18^{th}$ century, Yigye-gugok originated from a series of work shaping the space of Hong Yang-ho's tomb into a space for the family. Comparing Yigye-gugok to other gugoks, numerous differences are apparent from beyond the rather more general format such as adjoining the $8^{th}$ gok while paving through the lower directions from the upper directions of the water. This gives rises to the interpretation such that Yigye-gugok was positioned to separate the doman of the family from those of the other families in power, thereby taking over Ui-dong. Yet, the aspect of the possession of the space lends itself to the determination that the location positioned at the $8^{th}$ gok above Mangyeongpok Waterfall representing Wooyi-dong was a consequence of the centrifugal space creation efforts. 3. While writings and poetic works were manufactured in such large quantities in Yigye-gugok whose products of setters and managers seemed intended towards gugok-do and letters carved on the rocks among others, there is yet a tremendous lack of visual media in the same respect. 'Yigye-gugok Daejacheop' Specimens of Handwriting offers the traces of Gwanam's attempts to engrave gakja at the food of Yigye-gugok. This research was able to ascertain that 'Yigye-gugok Daejacheop' Specimens of Handwriting was a product of Hong Yang-ho's collections maintained under the auspices of the National Central Museum, which are renowned for Song Shi-yeol's penmanship.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.20 no.1
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• pp.41-51
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• 1987

This study was carried out to prepare powdered smoked-dried mackerel which can be used as a soup base, and to examine storage stability and the taste compounds of Products. Raw mackerel are filleted, toiled for 10 minutes and pressed to remove lipids, and then soaked in extract solution of skipjack meat. This soaked mackerel are smoked 3 times to $10-12\%$ moisture content at $80^{\circ}C$ for 8 hours. And the smoked-dried mackerel were pulverized to 50 mesh. Finally, the powdered smoked-dried mackerel were packed in a laminated film $bag(PET/Al\;foil/CPP:\;5{\mu}m/15{\mu}m/70{\mu}m,\;15\times17cm)$ with air(product C), nitrogen(product N) and oxygen absorber(product O), and then stored at room temperature for 100 days. The moisture and crude lipid content of powdered smoked-dried mackerel was $11.3-12.3\%,\;12\%$, respectively, and water activity is 0.52-0.56. And these values showed little changes during storage. The pH, VBN and amino nitrogen content increased slowly during storage. Hydrophilic and lipophilic brown pigment formation showed a tendency of increase in product(C) and showed little change in product(N) and (O). The TBA value, peroxide value and carbonyl value of product(N) and (O) were lower than those of product (C). The major fatty acids of products were 16:0, 18:1, 22:6, 18:0 and 20:5, and polyenoic acids decreased, while saturated and monoenoic acids increased during processing and storage of products. The IMP content in products were 420.2-454.2 mg/100 g and decreased slightly with storage period. And major non-volatile organic acids in products were lactic acid, succinic acid and $\alpha-ketoglutaric$ acid. In free amino acids and related compounds, major ones are histidine, alanine, hydroxyproline, lysine, glutamic acid and anserine, which occupied $80.8\%$ of total free amino acids. The taste compounds of powdered smoked-dried mackerel were free amino acids and related compounds (1,279.4 mg/100 g), non-volatile organic acids(948.1 mg/100 g), nucleotides and their related compounds (672.8 mg/100 g), total creatinine(430.4 ntg/100 g), tetaine(86.6 mg/100 g) and small amount of TMAO. The extraction condition of powdered smoked-dried mackerel in preparing soup stock is appropriate at $100^{\circ}C$ for 1 minute. Judging from the results of taste and sensory evaluation, it is concluded that the powdered smoked-dried mackerel can be used as natural flavoring substance in preparing soups and broth.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• 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.