• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.4
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• pp.289-297
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• 2007

Normaly, the map generlization methode has been used for the making of small scale map using a large scale map. The object join methode is consumed of a lots of processing time and the manual process. The object Join technique used in the NGI based on the digital map ver. 1.0 have problems of poor Joining rate and a lots of processing time. This study has improved the object Join technique considering of the geometry and attribute information for the digital map ver. 2.0. Using improved technique increased joining rate of object and reduced the processing time.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.275-281
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• 2015

The recent health care is growing rapidly want to receive offers users a variety of medical services, can be exploited easily exposed to a third party information on the role of the patient's hospital staff (doctors, nurses, pharmacists, etc.) depending on the patient clearly may have to be classified. In this paper, in order to ensure safe use by third parties in the health care environment, classify the attributes of patient information and patient privacy protection technique using hierarchical multi-property rights proposed to classify information according to the role of patient hospital officials The. Hospital patients and to prevent the proposed method is represented by a mathematical model, the information (the data consumer, time, sensor, an object, duty, and the delegation circumstances, and so on) the privacy attribute of a patient from being exploited illegally patient information from a third party the prevention of the leakage of the privacy information of the patient in synchronization with the attribute information between the parties.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.3
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• pp.297-320
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• 2018

This study aims to improve the access and retrieval of images and to find a way to effectively generate tags as a tool for providing explanation of images. To do this, this study investigated the features of human tagging and machine tagging, and compare and analyze them. Machine tags had the highest general attributes, some specific attributes and visual elements, and few abstract attributes. The general attribute of the human tag was the highest, but the specific attribute was high for the object and scene where the human tag constructor can recognize the name. In addition, sentiments and emotions, as well as subjects of abstract concepts, events, places, time, and relationships are represented by various tags. The tag set generated through this study can be used as basic data for constructing training data set to improve the machine learning algorithm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.223-233
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• 2014

This study empirically analyzes the relationships among the road and traffic experts' personal characteristics, the driving behavior and factors being expected to have an effect on the satisfaction about roundabout operation. The factors are drawn and grouped through the principle component analysis to clarify driving environment satisfaction on roundabout operation. Each group is named as personal attribute, driving behavior attribute, and satisfaction. After the variables are refined by confirmatory factor analysis, satisfaction model is developed with personal attribute and driving behavior attributes as exogenous variables and roundabout driving awareness and emotion attributes as endogenous variables. As a result, driving satisfaction of roundabout operation is directly influenced by delay reduction, safety improvement, capacity increase, sight improvement, severity accident reduction, and bicycle convenience and indirectly gender, age, driving time, and driving experience. Law obeyance, driving concession, traffic sign obeyance, and interposition do not statistically shows significant on satisfaction. As a result of Analytical Hierarchy Process (AHP), the turning radius of geometry and the driving behavior are important elements for roundabout safety.

• Journal of the Society of Disaster Information
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• v.14 no.2
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• pp.182-193
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• 2018

Purpose: The purpose of the study is to analyze the relationship between travel behavior and urban spatial characteristics in a hierarchical manner. Method: This study analyzed the relationship between traffic patterns and urban spatial characteristics for 83 cities in Korea by using a hierarchical linear model. Results: It was found that the urban spatial characteristics influenced the choice of transportation mode and travel time with personal attributes. However, the degree of influence on the choice of the means and the time required is relatively low through the policy of changing the city attribute, so the policy effect of mobilizing the land use policy for the traffic is theoretically, but the scale is not bigger than expected. Conclusion: In high density or the bigger scale of the city, the mass transportation system is widely supplied and used, but it does not overcome the drawback that it takes more time than the autos.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.175-177
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• 2003

연관규칙은 데이터 안에 존재하는 항목들간의 종속 관계를 찾아내는 것이다. 기존의 연구에서는 연관규칙 탐사 과정에서 발견항목 자체에만 관심을 두고 연구되어 왔다. 즉, 연관규칙 생성을 위한 후보 항목은 수량을 배제한 항목 대 수량비가 1:1인 상태에서 규칙을 발견하는 연구였다. 이것은 항목의 구매 수량에 관계없이 같은 가중치로 규칙을 발견하는 문제점을 갖고 있다. 두 번째 문제점은 연관규칙은 시간적 연장선상에서 발견되는 규칙이라 할 수 있다. 즉, 규칙을 발견하는 과정에서 모든 자료를 동일한 시간적 가중치를 두어 취급하는 것이다. 본 논문에서는 각각의 아이템을 (아이템, 수량)의 묶음 단위로 후보항목을 만들어 수량적 속성이 포함된 아이템 대 수량 비 1:n의 관계에서 규칙을 발견하는 방법을 제안한다. 또한 과거의 자료들을 이용하여 예측할 때 모든 자료를 동일하게 취급하기보다는 최근의 자료에 더 큰 비중을 주는 예측법을 사용하여 연관규칙 발견의 신뢰성을 높인다. 성능평가는 기존의 알고리즘과 비교하여 제안한 알고리즘의 성능향상 및 타당성을 보인다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.867-876
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• 2023

In this study, the attributes of the NHPP software development cost model applying the Gamma family distribution (Erlang, Log-Logistic, Rayleigh) were newly analyzed, and after comparing with the Goel-Okumoto basic model to verify the properties of the model, the optimal model was also presented based on this. To analyze software reliability, failure time data that occurred randomly during system operation was used, and the calculation of the parameters was solved using the maximum likelihood estimation. As a result of comprehensive evaluation through various attribute analysis (mean value function, development cost, optimal release time), it was confirmed that the Rayleigh model had the best performance. Through this study, the attributes of the software development cost model applying the Gamma family distribution, which has no previous research case, were newly identified. Also, basic design data could also be presented so that developers can efficiently utilize this research data at an early stage.

• Culinary science and hospitality research
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• v.16 no.3
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• pp.94-113
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• 2010

Despite a number of previous studies about cultural tourism festivals, studies on food menus in the cultural tourism festival setting have often been neglected. Considering the importance of food menus, identifying major selection attributes that satisfy visitors in a festival setting is vital. Using conjoint analysis, this study demonstrated that price was the most influential selection attributes to attract visitors. The time required between ordering and receiving food was found to be the second important selection attribute, followed by menu and place. Cluster analysis identified two distinct segments that take different sets of elements into account when making their selection decision. Conjoint simulation estimated the most preferred foodservice form in cultural tourism festivals setting would have 21.18% potential market share. The implications gained from this study provided an important starting point for determining key selection attributes in establishing strategies to enhance visitors' level of satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.27-38
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• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• Journal of the Korea Society for Simulation
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• v.28 no.3
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• pp.25-40
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• 2019

Today, container terminals are fiercely competing to attract an increasing number of containers. As a way to improve terminal productivity, this study proposes two dispatching rules for yard truck allocation priorities. First, Multi-Attribute Dispatching Rule(MADR) is an allocation method to calculate the weighted sum of multiple factors affecting container terminal productivity and priority them. Especially, the workload of the quay crane was considered one of the factors to reduce the residence time of the ship. Second, Cycling Dispatching Rule(CDR) is the effective way to increase the number of double cycles that directly affect terminal productivity. To identify the effects of combinations of pooling and dispatching, a comparative experiments was performed on 8 scenarios that combined them. A simulation environment has been developed for experiments and the results have demonstrated that the combination of terminal level pooling and Multi-attribute Dispatching could be an excellent combination in KPIs consisting of GCR and delayed departure of ships, etc.