• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.178-191
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• 2020

Purpose: This paper proposes a performance evaluation method for state-of-the-art facility inspection/diagnostic equipment through a trend survey of equipment and standardization systems of US, Japan, and Korea. This paper also suggests the priority of developing a performance evaluation method through expert interviews and surveys. Method: In this study, report for the last 5 years of FMS, state-of-the-art equipment of facility maintenance companies/safety diagnosis specialist agencies and papers/research reports/patents of NTIS were analyzed to identify recent trends of facility inspection/diagnostic equipment usages. standardization system of US, Japan, and Korea were analyzed to figure out a suitable form of a performance evaluation method for the domestic situation. And expert interview and survey were conducted to identify the priority of developing a performance evaluation method. Result: The performance evaluation method must be developed by the shape that only evaluates performance, regardless of types of equipment, on inspection item level for creative technology development. The priority of developing the performance evaluation method was identified as crack detection of concrete for durability evaluation and displacement/deformation/fatigue detection of concrete and steel for stability evaluation. Conclusion: The performance evaluation method will be developed firstly for the crack detection of concrete for durability evaluation and displacement/deformation/fatigue detection of concrete/steel for stability evaluation. In order to promote creative technology development, the performance evaluation method should be developed in a form that provides standardized specimens or testbeds and can be applied regardless of types of technologies.

• Journal of Oriental Neuropsychiatry
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• v.21 no.4
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• pp.21-39
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• 2010

Objectives : This study was to investigate the characteristics of the heart rate variability(HRV), types of insomnia and accompanying symptoms of 175 insomnia patients. Methods : For this study we carried out HRV, Inbody of 175 patients with insomnia who have come to Dongeui oriental hospital of Dongeui university from the period of Janaury 2008 to September 2010. We studied the association of the insomnia patients' age and gender with HRV, accompanying symptoms and the types of insomnia. The information of the accompanying symptoms and types of insomnia was based on each patients' progress note. Results : l. There was no significant differences in the characteristics of HRV between male and female. 2. The most frequent symptom shown among the insmonia patents' was headache(41.14%). 3. Comparing the symptoms between the gender, female patients had more dry mouth, alternative periodic chill and fever, and hot flush symptoms than the male patients. 4. Comparing the symptoms between the age groups(divided in two), the lower age group (20~59) had more dizziness and constipation symptoms than the higher age group (over 60). 5. Comparing the types of insomma between the age group, the higher age group (over 60) had more termination insomnia. 6. The types of insomnia of the entire insomnia patients were onset insomnia (73.1%), maintenance insomnia(20.6%), termination insomma(l7.1 %), shallow sleep (52.6%), listed by order of frequency. 7. The patients who takes hypnotics had more anorexia symptoms than the patients who doesn't take hypnotics. 8. There was no visible differences of the average fractal portrait between male and female. The age group of 20~30yrs. had the highest average fractal portrait, and the age group of 30~59yrs. the second highest, and the age group over 60yrs. the lowest. 9. The average of the regulation reserves of the autonomic nervous system(B2) - was lower than the regulation level of the ANS at the present (Bl). 10. When compared the priority of the function of the ANS, it showed that the proportion of HF($38.61{\pm}29.19%$)was the most, and than VLF($30.65{\pm}23.36%$), LF($20.04{\pm}19.13%$) the least. 11. The average of the compensation level of the central nervous system at present(Cl) - was lower than the compensation reserves of the CNS(C2). 12. The average of the control reserves of the cerebrum(D2) - was lower than the control level of the cerebrum at the present (Dl). 13. There was no visible differences between different sexes and ages in pulse rate. 14. The abdomen fat ratio above the line of the insomnia patients was 77.97% in male and 93.1% in female. Both sexes showed that insomnia patients had more abdomen fat that the standard, and female patients had more abd. fat than male patients. Conclusions : This study shows that the HRV of insomnia patients had no significant differences between gender. Fractal portrait, HF, LF and VLF of the insomnia patients are in inverse proportion to the age. The study of the Body Component Analysis showed that female had more abdomen fat than male, and both gender showed more abdomen fat than the standard. When looked into the accompanying symptoms of the insomnia patients, the symptoms show differences according to gender, age and hypnotics taking, as shown as below. In the entire patients, Headache was the most accompanying symptom. Female had more dry mouth, alternative periodic chill and fever, and hot flush symptoms than male. Higher age groups had more dizziness and constipation as accompanying symptoms than lower age groups. Patients who takes hypnotics had more anorexia than those who dont.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.3
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• pp.279-286
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• 2009

This study carried out to find out feed value of barley plus pea mixture with different ratio and cutting date to got basic information when introduced the mixture as new cropping system in middle part of Korean peninsular. Dry matter (DM) yield increased as barley seeding rate was higher and showed the highest yield in the plots with barley 85% plus 15% ratio when harvested on May 16. There was no different in crude protein, available protein and digestible protein cutting on April 25 in every mixture, but the content increased with higher pea mixture rate after May 2. The content of acid detergent fiber (ADF) and neutral detergent fiber (NDF) increase coincided with higher barley rate and late cutting dates. But relative feed value (RFV) resulted in opposite trend. Higher pea ratio influenced increased content of total digestible nuterients (TDN), but decreased before May 9 cutting and increased after the next cutting regime. There was no statistical difference in P and Mg between sowing rate, but Ca increased at higher pea ratio and P, Ca, K decreased in all plots as harvests were delayed. The content of estimated net energy (ENE), net energy maintenance (NEM) and net energy gain (NEG) significantly increased with higher pea rate and earlier cutting. But net energy lactation (NEL) was no significant differences between seeding rates and cutting dates. In conclusion, mineral yield such as P, Ca, K and Mg showed the highest yield at barley plus pea ratio of 75 : 25 and energy yield of ENE, NEL, NEM, NEG and TDN was the highest at 85 to 15 mixture plots and DM yield, TDN yield, mineral yield such as P, Ca, K and Mg and energy yield of ENE, NEL, NEM, NEG were the highest on each treatment cutting on May 16.

• Korean Journal of Agricultural Science
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• v.14 no.1
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• pp.164-185
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• 1987

The Korean economy has been developed rapidly in the course of implementing the five year economic development plans since 1962. Accordingly the industrial and employment structure have been changed from the traditional agriculture to modem industrial economy. In the course of implementing export oriented industrialization policies, rural farm economy has been encountered labour shortage owing to rural farm population drain to urban areas, rural wage hike and pressure on farm operation costs, and possibility of farm productivity decrease. To cope with the above problems the Korean government has supplied farm machinery such as power tillers, tractors, transplanters, binders, combines, dryers and etc. by means of the favorable credit support and subsidies. The main objectives of this study are to identify the investment effects of farm mechanization such as B/C and Internal Rate of Return by machinery and operation patterns, changes of labour requirement per 10a for rice culture since 1965, partial farm budget of rice with and without mechanization, and estimation labour input with full mechanization. To achieve the objectives Saemaeul farm mechanization groups, common ownership and operation, and farms with private ownership and operation were surveyed mainly in Nonsan granary area, Chungnam province. The results of this study are as follows 1. The national average of labor input per 10a of paddy has decreased from 150.1Hr in 1965 to 87.2Hr in 1985 which showes 42% decrease of labour inputs. On the other hand the hours of labour input in Nonsan area have also decreased from 150.1Hr to 92.8Hr, 38% of that in 1965, during the same periods. 2. The possible labor saving hours per 10a of Paddy was estimated at 60 hours by substituting machine power for labor forces in the works of plowing, puddling, transplanting, harvesting and threshing, transporting and drying The labor savings were derived from 92.8 hours in 1986 deducting 30 hours of labor input with full mechanization in Nonsan area. 3. Social benefits of farm mechanization were estimated at 124,734won/10a including increment of rice (10%): 34,064won,labour saving: 65,800won,savings of conventional farm implements: 18,000 won and savings of animal power: 6,870won. 4. Rental charges by works prevailing in the area were 12,000won for land preparation, 15,000won for transplanting with seedlings, 19,500won for combine works and 6,000won for drying paddy. 5. Farm income per 10a of paddy with and without mechanization were amounted to 247,278won and 224,768won respectively. 6. Social rate of return of the machinery were estimated at more than 50% in all operation patterns. On the other hand internal rate of return of the machinery except tractors were also more than 50% but IRR of tractors by operation patterns were equivalent to 0 to 9%. From the view point of farmers financial status, private owner-operation of tractors is considered uneconomical. Tractor operation by Saemaeul mechanization groups would be economical considering the government subsidy, 40% of tractor price. 7. Farmers recommendations for the government that gained through field operation of farm machinery are to train maintenance technology for rural youth, to standardize the necessary parts of machinery, to implement price tag system, to intercede spare parts and provide marketing information to farmers by rural institutions as RDA,NACF,GUN office and FLIA.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.141-160
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• 2020

The purpose of this study was to devise a specific plan for activating sports to promote health in old age against the prolonged COVID-19 pandemic. Through literature review, it also analyzed the association between health status and COVID-19 in old age, suggested health promotion policies and projects for elderly people, and presented a plan for activating sport to promote health in old age against COVID-19 era. First, it is necessary to revise the relevant laws, including the Sport Promotion Act and the Elderly Welfare Act, partially or entirely, make developmental and convergent legislations for elderly health and sports, and establish an institutional device as needed. Second, it is necessary to build an integrated digital platform for the elderly and make a supporting system that links facilities, programs, information, and job creation as part of a New Deal program in the field of sports on the basis of the Korean New Deal. Third, it is necessary to train elderly welfare professionals. Efforts should be made to establish more departments related to elderly sports in universities and make it compulsory to place elderly sports instructors at elderly leisure and welfare facilities. Fourth, it is necessary to develop contents related to health in old age. This means performing diverse movements by manipulating them through a virtual reality (VR) simulation. Fifth, it is necessary to make a greater investment in research and development related to elderly sports and relevant fields. This means the need to conduct constant research on healthy and active aging in a systematic and practical way through multidisciplinary cooperation. Sixth, it is necessary to establish and operate an elderly management agency (elderly health agency) under the influence of the Office of the Prime Minister. This means the need to secure independence in implementing the functions related to health promotion in old age and make comprehensive operation, which involves all the issues of health promotion in old age, daily function maintenance and rehabilitation, social adjustment, and long-term care, by establishing an elderly management agency in an effort to give lifelong health management to the elderly and cope with the untact, New Normal age.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.