• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.511-522
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• 2014

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.184-192
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• 2017

This study was a pilot test of serious educational game content(named Roly Poly 160) designed to enhance self-care by elderly diabetics. Roly Poly 160 was developed in eight steps (literature review, demand survey and consultation, extraction of serious content using games, development of Roly Poly 160, pretest for users, workshop for health care workers, final completion of Roly Poly 160 and user satisfaction survey of Roly Poly 160). Roly Poly 160 is intuitively structured in three modules (self-management, card games, and quiz games) that can be self-managed by the elderly and is designed based on six principles. First, we constructed an intuitive interface considering the age of users. Second, the menu was selected as the main menu for Koreans and calorie learning was made at the same time. Third, the calories and nutrients (carbohydrate, fat, protein, calcium, sodium) of selected foods are analyzed, and all test data recorded in the questionnaire chart are graphically displayed by year, month and week so that the change trends can be grasped at a glance. Fourth, necessary data were saved and output and used as educational data. Fifth, user data are made compatible and aggregated and up to 100 million members can be registered. Sixth, it is designed to be developed as a mobile app if necessary. Using Roly Poly 160, 119 diabetic patients were diagnosed with diabetes, and the satisfaction score was 4.26 out of 5. These findings indicate that Roly Poly 160 is appropriate as a diabetes self-care tool and suggests that there is a possibility to use it as a program to educate people about diabetes in public health centers, hospitals and clinics.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Journal of Korea Technology Innovation Society
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• v.11 no.4
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• pp.565-591
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• 2008

Using theories of sectoral innovation system and supply chain management, this paper examines the status quo of Korean IT SoC industry's innovation system by comparing it with Taiwanese one. Taiwan IT SoC industry has accomplished a rapid growth on the basis of government policies that foster domestic firms after the establishment of Hsinchu Science Park. Cooperative networks between foundries firms and fablesses have been formed within the supply chain in this process. Therefore, Taiwan industry has possessed the possibility of the coevolution in sectoral innovation system. However, Korean IT SoC industry has failed to form cooperative networks, because of weak networks between related firms. In other words, there exists an interaction failure, which is a kind of the system failure, and it means a lack of linkage between actors as a result of insufficient use of complementarities and interactive learning. Therefore, Korean industry has little possibility of the coevolution in sectoral innovation system. The cooperative networks between actors are prerequisite towards evolution of innovation system of Korean IT SoC industry. Above all, the cooperative networks between fablesses and system companies need to be strengthened within the supply chain.

• Journal of Distribution Science
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• v.14 no.8
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• pp.113-121
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• 2016

Purpose - This paper aims to examine the impact of buyer-supplier cooperation on suppliers' trust on buyers and the moderating role of buyers' governance mechanism between the sharing activities and trust. Research design, data, and methodology - An integrated research model is designed to materialize the research hypotheses. First, the impact of buyer-supplier cooperation is empirically analyzed by looking into how the sharing activities, in the field of information, resource, and knowledge, of buyer with supplier will affect the trust of supplier on buyer. Second, the moderating effect of contract-based governance mechanism of buyer is empirically analyzed. Third, the influence of trust on innovation performance of suppler is empirically analyzed. Results - Our findings provide supporting evidence for some of our hypotheses. First, all of the sharing activities are significantly influential, but in different degree, to trust of supplier. Second, contract-based governance mechanism of buyer have a moderating effect on the relationship between sharing activities and trust, positively in resource-sharing activities, negatively in information-sharing activities, not significant in knowledge-sharing activities. Third, supplier's trust on buyer positively affects supplier's own innovation performance. Conclusions - The strategies applied in supply chain management have become important as the competition among firms has shifted from competition between individual firms to competition between supply chains. A customer's sharing activities with its supplier may contribute to an increase in innovation performance. The supplier's information-sharing activity with its customer could affect its information-sharing activities with its main supplier. Cooperative activity with a partner in the supply chain is cultivated and amassed into relationship knowledge, and this study shows that the cooperative relational knowledge related to information-sharing activities enables firms to participate in sharing activities with their main suppliers. Increasing evidence shows that sharing various activities between buyer and supplier improves trust and performance outcomes, and enables firms to maintain competitive advantage. From the perspective of knowledge theory, external knowledge is becoming more important in firms' innovation activities, because innovative knowledge is acquired primarily through interaction with another organization. In addition, relationship learning could be an important tool in absorbing the supplier's core technology, information, expertise, and core competencies, increasing relational value.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.357-364
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• 2000

기존의 웹 디자인은 웹이라는 매체의 특성 상 디자인적인 요소가 매우 중요함에도 불구하고 디자인은 위한 구체적인 방법론이 미약하다. 특히, 많은 소비자들을 유인하고 구매를 촉발시켜야 하는 인터넷 쇼핑몰의 경우에는 더욱 더 그럼하에도 불구하고 이를 위한 전략적인 방법론이 부족하다. 즉, 기존 연구들은 제품의 다양성, 서비스, 촉진, 항해량, 편리성, 사용자 인터페이스 등이 중요하다고 하였지만 실제 인터넷 쇼핑몰을 디자인하는 입장에서는 활용하기가 상당히 애매하다. 그 이유는 이들 요인들은 서로 영향관계를 가지고 있어서 사용자 인터페이스가 복잡하면 항해량이 늘어나 편리성이 감소하고, 제품이 늘어나더라도 검색엔진을 사용하면 상대적으로 항해량이 감소하게 되어 편리성이 증가한다. 따라서, 이들 요인을 활용하여 인터넷 쇼핑몰을 구축하려면 요인간의 영향관계를 면밀히 파악하고 이 영향요인이 소비자의 구매행동에 어떠한 영향을 주는지가 충분히 검토되어야 한다.이에 본 연구에서는 퍼지인식도를 이용하여 인터넷 쇼핑몰 상에서 소비자의 구매행동에 영향을 주는 요인을 추출하고 이들 요인간의 인과관계를 도출하여 보다 구체적이고 전략적으로 인터넷 쇼핑몰을 디자인할 수 있는 방법으로 web-Cogmulator를 제시한다. Web-Cogmulator는 소비자의 쇼핑몰에 대한 암묵지식 형태의 구매행동을 형태지식화하여 지식베이스 형태로 가지고 있기 때문에 인터넷 쇼핑몰의 다양한 요인의 변화에 따른 소비자의 구매행동을 추론 시뮬레이션하는 것이 가능하다. 이에 본 연구에서는 기본적인 인터넷 쇼핑몰 시나리오를 바탕으로 추론 시뮬레이션을 실시하여 Web-Cogmulator의 유용성을 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computati

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.89-105
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• 1998

Modern organizations, both complicated and complex, operate in an economic climate of· turbulence and rapid change. And Today's healthcare environment is changing, driven by demographic, environmental. social, political and technological forces. In actual practice, the organization usually depend on several factors such as economic state of organization, managerial strategies, a synthesis of several theories that reflect individual biases, specific circumstances, and practical realities. These rapidly changing healthcare environment and professional nursing practice need a strategy for the organizational development and goal attainment. An understanding of organizational culture could help managers enhance or expand their management strategy, thus increasing the probability of their success in the organization. Organizational culture is an abstract, yet potent managerial concept. With roots in several disciplines, several perspectives and definitions of organizational culture have emerged. The concept of organizational culture has been rapidly introduced into the academic and organizational world, with the much attention to the excellent companies that have continued rapid grow th despite the overall world economic recession in the late of 1970s. Organizational culture is the combination of the symbols, language, assumptions, and behaviors that overtly manifest an organization's norm and values. It is the taken-for-granted and shared meanings people assign to their social surroundings that can have a profound effect on an organizaitonal decision making and performance. For attaining a organizational goal and developing organization, it is necessary to put emphasis on developing organizational culture. It has to set organizational culture well understood by its members as an instrument to achieve the organizational goals. Both Manager and staff can focus and act on the values identified. Also, managers will exhibit better decision making capabilities because they are guided by perception of the organizational values. Therefore, understanding of organizational culture could give a strategy for organizational development that assist hiring personnel, orienting new comers, facilitating organizational change and promoting learning and so on. But their is few study on nursing organizational culture in Korea. Moreover they have not had a clear definition of Korean nursing organizational culture. Therefore, it is necessary to lay down definition of Korean nursing organizational culture and fine out real factor of Korean nursing culture. For defining a definition of Korean nursing organizational culture, this study assessed several definitions of organizational culture, factors of culture, types of culture, and functions of culture through book review.