• Journal of Korean Institute of Industrial Engineers
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• v.42 no.3
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• pp.222-230
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• 2016

As the global game market has been more competitive, it has been important to analyze success factors of game products. In this paper, we applied a Bass Diffusion Model and Clustering Analysis to identify the success factors of games based on data from Steam, an international game platform. By using a diffusion model, we first categorize game products into two groups : successful and unsuccessful games. Then, each group has been analyzed by using clustering analysis based on product features such as genres, price, and minimum system requirements. As a result, success factors of a game have been identified. The result shows that customers in game industry appreciate sophisticated contents. Unlike many other industries, price is not considered as a key success factor in the game industry. Expecially, advanced independent video games (commonly referred to as indie games) with killer contents show competitiveness in the market.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.586-595
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• 2022

In this paper, we propose a formation control algorithm for multi-USVs according to COLREGs. First, we applied the Dynamic Window Approach algorithm that can reflect the kinematic characteristics for the path movement of USVs. Then, we propose a virtual structure-based virtual leader-follower method that applies the advantages of leader-follower and virtual structure methods among conventional formation control algorithms for stability. Next, we proposed a collision avoidance algorithm according to all COLREGs when encountering an opposing ship by adding COLREGs situational conditions to the virtual leader, and finally confirmed the feasibility of the proposed method through simulation.

• 한국벤처창업학회:학술대회논문집
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• 2020.11a
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• pp.131-153
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• 2020

'기업 생태계'에서 '유니콘'이란 표현법은 2013년 'Aileen Lee'에 의해 명명된 이래, 특히 한국에서 '스타트업 생태계'의 국제적 수준 비교의 차원에서 활발하게 다루어졌다. 정부 차원에서는 이를 정책적 목표로 설정하여, '2022년까지 유니콘 기업 20개 목표'를 제시한 바 있다. 이와 같이 '유니콘 클럽 기업'에 대한 현상이 정책적 목표 차원에서 다루어지며, 대중적으로 더욱 확산된데 반해, 이에 대한 실체적 및 본질적 이해 목적의 학술적 연구는 충분치 못하였다. 본 연구는, 첫째, 2018년 기준 '유니콘 클럽' 기업 326개 및 2019년 479개의 기업을 대상으로 이들의 특성을 심층적이고 다면적으로 분석하였다. 그동안 주로 국가 별 '유니콘 기업' 수 및 산업 분류 기준 일반현황 중심의 대중적 소개가 주된 내용이었다. 그러나, 본 연구는 투자자를 포함한 기초 현황을 상세 분석하였고, 사례분석을 포함한 질적 탐색을 수행하였다. 또한 군집분석, 판별분석, 다층 회귀분석 등 양적 탐색을 함께 수행하였다. 개별기업의 '기업가 요인-산업(시장)환경 요인-자원 요인-전략 요인', 즉 'ERIS 모델'에 기반하여 그 특성을 살펴보았다. 둘째, 기업가치에 영향을 미치는 요인들을 앞서 분석한 특성 요인 및 투자자 특성과 연계하여 살펴보았다. 그리고 마지막으로는 이들을 토대로 '기업 생태계' 관점에서 유니콘 현상'을 바르게 이해하고, 또한 정책적 측면에서 이를 생산적으로 활용하는 방향을 제시하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.5-7
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• 2021

최근 해양사고 발생 건수의 급격한 증가와 더불어 어선의 조업 중 안전사고로 인한 인명피해 또한 크게 증가하였다. 중앙해양안전심판원의 공식 통계에 따르면, 2017년 46명이었던 안전사고의 사망실종자는 2019년 38명으로 소폭 감소하였으나, 2020년 60명으로 크게 증가하였다. 그러나, 사망자가 감소하였던 2019년 안전사고로 인한 부상자는 164명으로 전년도 76명 대비 2배 이상 증가하며 어선원에 대한 안전재해 예방은 실효성을 갖지 못하는 실정이다. 국내 업종별 산업재해율을 비교해볼 때, 어업 재해율은 농업, 광업, 제조업, 건설업, 임업 등을 포괄한 전체 산업 평균 재해율의 약 10배에 이르며 어업인들의 안전이 큰 위협에 놓여있음을 시사한다. 본 연구에서는 2017년부터 2019년의 수협중앙회의 어선원 공제보험데이터를 활용하여 선박별, 재해자별 사고 현황과 발생 형태를 분석하였다. 특히, 교차분석과 연관규칙분석기법을 통해 승선 직책별 부상 부위와 사고발생 형태를 식별하였으며, 이에 따라 직책에 따른 부상 부위를 비교하여 맞춤형 예방대책 수립을 위한 지원과, 사고발생형태의 군집 분석을 통해 발생형태간의 연결고리를 도출하여, 스위스 치즈 모델에서 제안하는 취약점(Weakness)를 식별하고, 이러한 취약점을 보완하기 위한 방어 장벽(Protective barriers)을 제언한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.3
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• pp.71-84
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• 2024

Artificial intelligence can be cited as a key linkage technology for expanding drones' application fields, and drones combined with artificial intelligence are expected to improve drones' operational capabilities based on algorithms that can solve complex tasks through learning. The purpose of this study is to analyze various latest research cases that apply deep reinforcement learning to drones to solve limitations for performing swarm flight and to propose a new research direction that applies them to multi-agent communication optimization technology. The process of the research is to investigate and analyze the methods for efficient operation of control and communication technologies required for swarm flight to be successful, and to apply algorithms that have the advantage of exchanging richer feedback between agents and having less learning than conventional methods when learning deep reinforcement learning algorithms. It is expected that the efficiency and performance of learning communication protocols optimized for swarm flight will be improved, which will increase the efficiency of mission performance when exploring or scouting large areas through swarm flight in the future.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.883-912
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• 2013

This study examines network compositions of convergent research relationships in the field of high-tech convergence technology and investigates the relational linkages among various research fields. A network analysis was performed to evaluate the High-Tech Convergence Technology Development Program, a convergent research funding program of the National Research Foundation of Korea (NRF); the dataset covered the 2009-2011 period. The analytical results reveal the hidden network structure of convergent research relationships and demonstrate that the formation of convergent research might be enhanced by interdependent pressures placed on various research fields but also by accumulated research capabilities that each of these fields possessed and which could be used to converge specialized and heterogeneous research areas and interests.