• 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 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.

• Journal of Korean Academy of Nursing
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• v.11 no.2
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• pp.5-8
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• 1981

This study concerns itself with the development of a new model of comprehensive health service for rural communities of Korea. The study was conceived to resolve the problems of both underservice in rural communities and underutilization of valuable health manpower, namely the nurses, the disenchanted elite health personnel in Korea. On review of the current situation, the greatest deficiencies in the Korean health care system were found in the availability of primary care at the peripheries of md communities, in the dissemination of knowledge of disease prevention and health care, and in the induction of and guidance for active participation by the clientele in health maintenance at the personal, family and community level Abundant untapped health resources were identified that could be brough to bear upon the national effort to extend health services to every member of the Korean Population. Therefore, it was Postulated that the problem of underservice in rural communities of Korea can be structurcturally resolved by the effective mobilization and organization of untapped health resources, and that. a primary care Nursing Service System offers the best possibility for fulfillment of rural health service goals within the current health man-power situation. In order to identify appropriate strategies to combat the present difficulties in Korean rural health services and to utilize nurses and other health personnel in community-centered health programs, a search was made for examples of innovative service models throughout the world. An extensive literature survey and field visits to project sites both in Korea and in the United States were made. Experts in the field of world health, health service, planners, administrators, and medical and nursing practitioners in Korea, in the United States as well as visitors from other Asian countries were widely consulted. On the basis of information and inputs from these experts a new rural health service model has been constructed within the conceptual framework of community development, especially of the innovation diffusion Model. It is considered especially important that citizens in each community develop capacities for self-care with assistance and supports from available health professionals and participate in health service-related decisions that affect their own well-being. The proposed model is based upon the regionalization of health care planning utilizing a comprehensive Nursing Service System at the immediate delivery level The model features: (1) a health administration unit at each administrative level; (2) mechanisms for community participation; (3) a continuous source of primary health care at the local community level; (4) relative centralization of specialty care and provision of tertiary or super-specialty care only at major national metropolitan centers; and (5) a system for patient referral to the appropriate level of care. This model has been built around professional nurses as the key community health workers because their training is particularly suited and because large numbers of well-trained nurses are currently available and being trained. The special element in this model is a professional nurse-guided, self-care facilitating primary care Community Nursing Service System. This is supported by a Nursing Extension Service as a new training and support structure. (See attached diagrams). A broad spectrum of programs was proposed for the Community Nursing Service System. These were designed to establish a balance of activities between the clinic-centered individual care component and the field activity-centered educational and supportive component of health care services. Examples of possible program alternatives and proposed guidelines for health care in specific situations were presented, as well as the roles and functions of the key health personnel within the Community Nursing Service System. This Rural Health Service Model was proposed as a real alternative to the maldistributed, inequitable, uncoordinated solo-practice, physician-centered fee-for-service health care available to Koreans today.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.125-155
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• 2010

Ontologies have been adopted in various business and scientific communities as a key component of the Semantic Web. Despite the increasing importance of ontologies, ontology developers still perceive construction tasks as a challenge. A clearly defined and well-structured methodology can reduce the time required to develop an ontology and increase the probability of success of a project. However, no reliable knowledge-engineering methodology for ontology development currently exists; every methodology has been tailored toward the development of a particular ontology. In this study, we developed a Graduation Screen Ontology (GSO). The graduation screen domain was chosen for the several reasons. First, the graduation screen process is a complicated task requiring a complex reasoning process. Second, GSO may be reused for other universities because the graduation screen process is similar for most universities. Finally, GSO can be built within a given period because the size of the selected domain is reasonable. No standard ontology development methodology exists; thus, one of the existing ontology development methodologies had to be chosen. The most important considerations for selecting the ontology development methodology of GSO included whether it can be applied to a new domain; whether it covers a broader set of development tasks; and whether it gives sufficient explanation of each development task. We evaluated various ontology development methodologies based on the evaluation framework proposed by G$\acute{o}$mez-P$\acute{e}$rez et al. We concluded that METHONTOLOGY was the most applicable to the building of GSO for this study. METHONTOLOGY was derived from the experience of developing Chemical Ontology at the Polytechnic University of Madrid by Fern$\acute{a}$ndez-L$\acute{o}$pez et al. and is regarded as the most mature ontology development methodology. METHONTOLOGY describes a very detailed approach for building an ontology under a centralized development environment at the conceptual level. This methodology consists of three broad processes, with each process containing specific sub-processes: management (scheduling, control, and quality assurance); development (specification, conceptualization, formalization, implementation, and maintenance); and support process (knowledge acquisition, evaluation, documentation, configuration management, and integration). An ontology development language and ontology development tool for GSO construction also had to be selected. We adopted OWL-DL as the ontology development language. OWL was selected because of its computational quality of consistency in checking and classification, which is crucial in developing coherent and useful ontological models for very complex domains. In addition, Protege-OWL was chosen for an ontology development tool because it is supported by METHONTOLOGY and is widely used because of its platform-independent characteristics. Based on the GSO development experience of the researchers, some issues relating to the METHONTOLOGY, OWL-DL, and Prot$\acute{e}$g$\acute{e}$-OWL were identified. We focused on presenting drawbacks of METHONTOLOGY and discussing how each weakness could be addressed. First, METHONTOLOGY insists that domain experts who do not have ontology construction experience can easily build ontologies. However, it is still difficult for these domain experts to develop a sophisticated ontology, especially if they have insufficient background knowledge related to the ontology. Second, METHONTOLOGY does not include a development stage called the "feasibility study." This pre-development stage helps developers ensure not only that a planned ontology is necessary and sufficiently valuable to begin an ontology building project, but also to determine whether the project will be successful. Third, METHONTOLOGY excludes an explanation on the use and integration of existing ontologies. If an additional stage for considering reuse is introduced, developers might share benefits of reuse. Fourth, METHONTOLOGY fails to address the importance of collaboration. This methodology needs to explain the allocation of specific tasks to different developer groups, and how to combine these tasks once specific given jobs are completed. Fifth, METHONTOLOGY fails to suggest the methods and techniques applied in the conceptualization stage sufficiently. Introducing methods of concept extraction from multiple informal sources or methods of identifying relations may enhance the quality of ontologies. Sixth, METHONTOLOGY does not provide an evaluation process to confirm whether WebODE perfectly transforms a conceptual ontology into a formal ontology. It also does not guarantee whether the outcomes of the conceptualization stage are completely reflected in the implementation stage. Seventh, METHONTOLOGY needs to add criteria for user evaluation of the actual use of the constructed ontology under user environments. Eighth, although METHONTOLOGY allows continual knowledge acquisition while working on the ontology development process, consistent updates can be difficult for developers. Ninth, METHONTOLOGY demands that developers complete various documents during the conceptualization stage; thus, it can be considered a heavy methodology. Adopting an agile methodology will result in reinforcing active communication among developers and reducing the burden of documentation completion. Finally, this study concludes with contributions and practical implications. No previous research has addressed issues related to METHONTOLOGY from empirical experiences; this study is an initial attempt. In addition, several lessons learned from the development experience are discussed. This study also affords some insights for ontology methodology researchers who want to design a more advanced ontology development methodology.

• Research in Community and Public Health Nursing
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• v.11 no.1
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• pp.21-36
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• 2000

The purpose of this study was to assess the needs of breast self examination education (BSEE) programs for developing nursing centers. A first, we reviewed the literature of activity and management-related factors of nursing centers: and, second. we used a questionnaire to discover the degree of knowledge, attitude, and practice on breast cancer, as well as an individual's intention to participate BSEE program. 1. Reviewing the literatures of nursing center activities. Nursing centers which were administered by a professional nurse are an ideal site for faculty and student practices. With the use of nursing models of health. professional nurses in nursing centers diagnose and treat human responses to potential and actual health problems and offer holistic, client-centered health service. In nursing centers professional nursing services include health education, health promotion, and health-related research. A nursing center is comprised of the advisory and exacutive commitee; the advisory commitee serves consultants and links community needs to the nursing center, while the director of the exacutive commitee identifies the potential resources to generate funds, support, and facilitate the activities of staffs in a nursing center. Nursing centers mobilize various financal resources for reimbursement of services from college and insurance companies, collect minimum service fees from the client, and further collect fees for providing programs to community groups, this also includes membership fees, and donations. The services provided by nursing centers focus on services related to primary prevention, health maintenance & health promotion, direct nursing care for acute & chronic diseases, and holistic care for actual and potential health problems. The client satisfaction for the services was high. Students also showed positive reponses for their clinical experiences and independent working conditions. 2. The degree of knowledge, attitudes, and practices for breast cancer. and an individual's intention to participate in the BSEE program. The subjects of this study were 308 females in K-city in the Republic of Korea. Data were collected using a self-administered questionnaire. The mean age of the respondents was 35.0 years old. Those who already participated in the BSEE were 64.9%, and those who had support and encouragement to practice BSE from significant others were 25.1 %. Clients intent to participate in the BSEE were 37.0%. The mean score of knowledge(2.4 out of 5 points) and practices(1.8 out of 5 points) for breast cancer were quite low, but the mean score of attitudes was relatively positive04.5 out of 20 point) for breast cancer. Those who already had BSEE showed significantly high scores in knowledge(t=6.48, p<0.01), attitudes (t=10.54, p<0.01). and practices(t=57.07, p<0.001) for breast cancer than those who had not participated in the BSEE. In all age groups no intention to participate in the BSEE was higher than who the intention to participate. These findings suggest some strategies should be developed to increase the awareness of breast cancer's early detection.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.107-119
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• 2014

Purpose : To efficiently evaluate phased array coil performance using a software tool box with which we can make visual comparison of the sensitivity of every coil element between the real experiment and EM simulation. Materials and Methods: We have developed a $C^{{+}{+}}$- and MATLAB-based software tool called Phased Array Coil Evaluator (PACE). PACE has the following functions: Building 3D models of the coil elements, importing the FDTD simulation results, and visualizing the coil sensitivity of each coil element on the ordinary Cartesian coordinate and the relative coil position coordinate. To build a 3D model of the phased array coil, we used an electromagnetic 3D tracker in a stylus form. After making the 3D model, we imported the 3D model into the FDTD electromagnetic field simulation tool. Results: An accurate comparison between the coil sensitivity simulation and real experiment on the tool box platform has been made through fine matching of the simulation and real experiment with aids of the 3D tracker. In the simulation and experiment, we used a 36-channel helmet-style phased array coil. At the 3D MRI data acquisition using the spoiled gradient echo sequence, we used the uniform cylindrical phantom that had the same geometry as the one in the FDTD simulation. In the tool box, we can conveniently choose the coil element of interest and we can compare the coil sensitivities element-by-element of the phased array coil. Conclusion: We expect the tool box can be greatly used for developing phased array coils of new geometry or for periodic maintenance of phased array coils in a more accurate and consistent manner.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.5
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• pp.64-72
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• 2014

This study quantified the storage and annual uptake of carbon by apple trees in orchards as a production-type greenspace, and computed the annual carbon emissions from apple cultivation. Tree individuals in the study orchards were sampled to include the range of stem diameter sizes. The study measured biomass for each part including the roots of sample trees through a direct harvesting method to compute total carbon storage per tree. Annual carbon uptake per tree was quantified by analyzing the radial growth rates of stem samples at ground level. Annual carbon emissions from management practices such as pruning, mowing, irrigation, fertilization, and use of pesticides and fungicides were estimated based on maintenance data, interviews with managers, and actual measurements. Regression models were developed using stem diameter at ground level (D) as an independent variable to easily estimate storage and annual uptake of the carbon. Storage and annual uptake of carbon per tree increased as D sizes got larger. Apple trees with D sizes of 10 and 15 cm stored 9.1 and 21.0 kg of carbon and annually sequestered 1.0 and 1.6 kg, respectively. Storage and annual uptake of carbon per unit area in study orchards were 3.81 t/ha and 0.42 t/ha/yr, respectively, and annual carbon emissions were 1.30 t/ha/yr. Thus, the carbon emissions were about 3 times greater than the annual carbon uptake. The study identified management practices to reduce the carbon footprint of production-type greenspace, including efficient uses of water, pesticides, fungicides, and fertilizers. It breaks new ground by including measured biomass of roots and a detailed inventory of carbon emissions.

• Journal of Chest Surgery
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• v.36 no.2
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• pp.63-72
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• 2003

Although a variety of synthetic vascular grafts are available in modern vascular surgery, no ideal prosthesis ha,4 yet been developed. Small-caliber vascular grafts with low flow, as used in the lower extremity, continue to become thrombosed at unacceptable rates. We have developed and evaluated the new antithrombogenic blood contacting surfaces in canine model. Material and Method: Two now antithrombogenic blood contacting surfaces(Polyvinylalcohol -Polyurethane(PVA-PU) blend and natural Graphite-polyurethane(G-PU) blend) have been developed and evaluated in canine model, using vascular grafts and patches. The luminal surfaces of the test vascular grafts(5 mm ID) were fabricated by dipping a glass rod in PVA-PU blend solution(50 % PVA) using phase separation method. Mongrel dogs of either sex weighing 18-22 kg were anesthetized by endotracheal intubation using halothane and their lungs were ventilated with a volume-cycled ventilator, Maintenance anesthesia with 0.5-1.0% halothane and supplemental oxygen was used. Two pairs were used for comparison in the bilateral femoral arteries for both vascular grafts(PVA-PU vs. PU) and vascular patches(G-PU vs. PU). Bilateral groin incisions were made and the arteries were exposed and clamped. After an excision of 1 cm of the artery between clamps, a grail of 2.5 cm in length was implanted end-to-end using 6-0 polypropylene suture. The vascular patch was implanted as a form of on-lay patch. Animals were sacrificed at 1, 2, 4, 6, 8 and 16 weeks for vascular grafts and 1, 2. 4 and 6 weeks for vascular patches. Result The vascular grafts of PVA-PU blends showed patent lumina in the 2 and 16 weeks animals, while those of PU showed a patent lumen in 2 weeks animal. PVA-PU graft of 16 weeks showed a fairly clean luminal surface. A light microscopic finding of this graft demonstrated good tissue infiltration through porosity, The animals with vascular patches showed patent arteries in both groups except 2 weeks animal. Scanning electron microscopy of the luminal surfaces of G-PU patches in 4 and 6 weeks animals showed endothelial cell covering with microvilli. PU patches showed qualitatively less endothelial cell covering. Conclusion: In conclusion, PVA-PU and G-PU blends can be a promising blood contacting surfaces for application in a synthetic vascualr graft. However, further animal study is needed to determine the real long-term effects of these methods of surface modifications.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.5
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• pp.628-636
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• 2018

Because large buoys are mainly made of steel, they are heavy and vulnerable to corrosion by sea water. This makes buoy installation and maintenance difficult. Moreover, vessel collision accidents with buoys and damage to vessels due to the material of buoys (e.g., steel) are reported every year. Recently, light buoys adopting eco-friendly and lightweight materials have come into the spotlight in order to solve the previously-mentioned problems. In Korea, a new lightweight buoy with a 7-Nautical Mile lantern adopting expanded polypropylene (EPP) and aluminum to create a buoyant body and tower structure, respectively, was developed in 2017. When these light buoys are operated in the ocean, the visibility and angle of light from the lantern installed on the light buoys changes, which may cause them to function improperly. Therefore, research on the performance of light buoys is needed since the weight distribution and motion characteristics of these new buoys differ from conventional models. In this study, stability estimation and motion analyses for newly-developed buoys under various environmental conditions considering a mooring line were carried out using ANSYS AQWA. Numerical simulations for the estimation of wind and current loads were performed using commercial CFD software, Siemens STAR-CCM+, to increase the accuracy of motion analysis. By comparing the estimated maximum significant motions of the light buoys, it was found that waves and currents were more influential in the motion of the buoys. And, the estimated motions of the buoys became larger as the sea state became worser, which might be the reason that the peak frequencies of the wave spectra got closer to those of the buoys.