• 한국벤처창업학회:학술대회논문집
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• 2021.11a
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• pp.41-45
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• 2021

대학생의 대부분은 진로에 대한 명확한 목표가 설정되지 않거나 졸업 후 진로 미결정으로 인해 어려움을 느낀다. 대학 생활 중 진로의 선택과 결정, 결정을 위해 정보를 수집하고 탐색하는 활동들은 매우 중요하며 대학에서는 진로를 '창업' 으로 결정한 학생들을 위해 다양한 창업교육 프로그램을 제공하고 있다. 정부의 적극적인 지원으로 창업교육은 양적 성장을 하였지만 실제 창업까지는 한계가 있다. 창업을 하기 위한 적극적 창업준비행동으로 이루어질 수 있는 창업교육의 개발이 필요하다. 본 연구는 대학생의 창업교육이 창업준비행동에 미치는 영향 관계를 분석하고 두 영향 관계에서 계획된 우연기술에 의한 기회인식의 매개적 효과를 조사·연구하고자 한다. 이를 위해 대학에서 지원하는 창업교육의 참여 형태, 교육 만족도, 참여횟수 등 창업교육의 실태를 파악하고 창업을 하기 위한 정보수집활동과 도구구비활동 및 목표달성을 위한 적극적 노력 등 창업준비행동을 분석한다. 본 연구를 통해 개인적 관점에서 '창업교육' 이라는 우연한 행동을 기회로 인식하고 창업준비에 긍정적으로 활용할 수 있도록 체계화된 창업교육의 필요성을 시사하며 대학의 취업만을 강조하는 교육 패러다임을 변화할 수 있는 창업교육의 질적 성장의 중요성을 제시하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2753-2758
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• 2009

The Navy is trying to develop a sonar-operation strategy that efficiently searches for underwater targets. To develop an efficient sonar-operation strategy, a simulation system, which can analyze the efficiency of various operation strategies, is needed. The simulation executes the strategical operation by collecting information of sea environment, destroyer, sonar, and target. Also, it should be able to provide diverse information according to its progression. In this study, the simulation system that can evaluate and analyze the effectiveness of the search strategy for underwater targets in different environments was designed and developed. The simulation system was developed, utilizing the sonar equation and the lateral-range-curve, and it portrays many patterns of realistic movements of a target. This system will contribute to developing and improving efficient sonar-operation strategies to find underwater targets in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.761-764
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• 2020

본 논문에서는 라이프로그 데이터를 기반으로 한 행동인식 결과로부터 일상생활의 활동유형을 분석하는 기술에 대해 제안한다. 실제 일상생활 중에 수집한 가속도 센서 데이터만을 이용하여 분석한 행동인식 결과를 정적-동적 행동으로 분류된 특징 벡터로 나타내었고, 이를 클러스터링하여 6개의 대표 활동유형으로 분류하였다. 50명의 사용자 데이터를 분석하여 정적-동적 활동의 비율에 따른 활동유형을 분류함으로써 실제 라이프로그 데이터로부터 일상생활 활동유형을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.215-218
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• 2002

본 논문에서 구현한 시스템은 비젼(vision)시스템을 이용하여 자율 이동로봇의 경로를 탐색하고 추출된 정보로부터 자율 이동로봇의 위치제어 성능을 제시하고자 한다. 일반적인 로봇시스템은 자신이 이동해야 할 목표 지점을 자율적으로 생성할 수 없으므로 기타 다른 시스템의 정보를 이용하여 미로를 탐색하거나 장애물을 인식하고 식별하여 자신의 제어전략을 수립한다. 그리고, 본 연구에서 제시한 시스템은 자율이동로봇의 행동 환경을 호스트 PC인 비젼시스템이 로봇의 현재 위치, 로봇이 이동해야 할 목표위치, 장애물의 위치와 형태 둥둥을 분석한다. 분석된 결과값을 RF-Module을 이용해서 로봇에 전송하면 로봇은 그 데이터를 받아서 동작하게 되며 로봇이 오동작 또는 장애물로 인해 정확한 목적지까지 도달하지 못할때 호스트 PC는 새로운 최단경로를 만들거나 장애물을 회피 할 전략을 로봇에게 보내준다. 본 연구에 적용한 알고리즘은 A* 알고리즘을 사용하였으며, 본 알고리즘은 매우 단순하면서도 실시간 처리에 적용가능하며, 자율 이동로봇의 충돌회피, 최단 경로 생성에 대한 성능을 실험을 통하여 제시한다.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.607-613
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• 2018

The aim of this study is to predict the exact time of parturition from analysis and classification of preliminary behavior based on parturition signals in sows. This study was conducted with 12 crossbred sows (with an average of 3.5 parities). Behavioral characteristics were analyzed for duration and the frequency of different behaviors on a checklist, which includes the duration of the basic behaviors (feeding, standing, lying down, and sitting). The frequency of specific behaviors (investigatory behavior, shame-chewing, scratching, and bar-biting) was also recorded. Image information was collected every two minutes for 24 hours before the first piglets were born. As a result, the basic behavior of a sows' standing time (22.6% of the time after 24 h, 24.9% after 12 h) and time lying down (55.9% after 24 h, 66.3% after 12 h) increased over the 12 h period before parturition, compared with the 24 h period before parturition (p<0.01). Feeding (13.42% after 24 h, 4.38% after 12 h) and sitting (8.2% after 24 h, 4.5% after 12 h) tended to decrease during the 12 h before parturition (p>0.05). The sows' investigatory behavior ($11.44{\pm}1.80$ after 24 h, $55.97{\pm}6.13$ after 12 h), scratching ($3.75{\pm}1.92$ after 24 h, $20.99{\pm}5.81$ after 12 h), and bar-biting ($0.69{\pm}0.15$ after 24 h, $3.71{\pm}1.53$ after 12 h) increased in the 12-hour period before parturition, compared with the 24-hour period before parturition (p<0.01). On the other hand, shame-chewing ($2.20{\pm}1.67$ after 24 h, $0.07{\pm}0.01$ after 12 h) decreased compared to the 12-hour period before parturition (p>0.05). Thus, standing, investigatory behavior, scratching, and bar-biting could be used as behaviors indicative of parturition in sows.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.231-236
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• 2012

According to consumer behavior theory, human propensity can be divided into two regulatory focus types: promotion and prevention. These two types have much influence on the consumer's decision in many diverse areas. In this research, we apply regulatory focus theory to personalized recommendation to minimize the cold start problem and to improve the performance of recommendation algorithms. To achieve this goal, we extract the consumer behavior variables and information exploration activity index from web shopping logs. We then use them for classifying regulatory focus of the consumer. This research has the contribution to show the possibility of systematization of consumer behavior theory as an interdisciplinary research tool of social science and information technology. Based on this attempt, we will extend the research to IT services adapting theories on other areas.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.309-313
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• 2008

This paper examine Citizenship Behavior of Police officer, Empowerment and Customer Orientation in the Police organization, and look for the method of the human resource management to draw behavior of local police. For that, this paper show the base of the basic study for empirical method, this study draws citizenship behavior toward police officer, influence factor of empowerment, and looks for the effective method of the human resource management to draw customer orientation of members

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.406-411
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• 2005

In this paper, we present the area-based Q-learning to search a target object using multiple robot. To search the target in Markovian space, the robots should recognize their surrounding at where they are located and generate some rules to act upon by themselves. Under area-based Q-learning, a robot, first of all, obtains 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to search for a target object while navigating in a unknown hallway where some obstacles were placed. In the end of this paper, we presents the results of three algorithms - a random search, area-based action making (ABAM), and hexagonal area-based Q-teaming.

• Proceedings of the Korea Society of Design Studies Conference
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• 2003.05a
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• pp.230-231
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• 2003