The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

Technology innovation companies are focusing on contributing to business performance by R&D project as a strategic tool. Successful R&D leads to corporate competitiveness enhancement, national industrial development, but there are high uncertainty and risks in R&D. Public and private R&D projects are carried out to achieve various purposes. It was verified how the risk management and benefit management of the R&D project affect the detailed R&D project performance between the Public and private domain. The impact of Project Leadership on R&D performance was also analyzed. Those who have participated in the Public and Private R&D projects at companies or research institutes were surveyed. First, it was found that project risk and benefit management have partially an effect on R&D project performance. Second, Public and private R&D Project Leadership showed partially a interaction effect between project management and project performance.

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

This study identified the core competencies of aircraft maintenance quality engineers and compared the importance and retention of core competencies. Through literature research, 21 core competencies were derived in three areas of management technology, elemenal technology and collaboration technology, and a survey was conducted on the importance and retention of core competencies for 42 aircraft maintenance quality engineers. As a result of the survey, the importance of all core competencies of aircraft maintenance quality engineers is 3.95/5 on average, and the retention of all core competencies is 3.99/5 on average. 'Risk Management’, ‘Creating Document’, ‘Honesty/Moral’ were identified as the most important competencies in each area, and ‘Quality Management’, ‘Language’, ‘Honesty/Moral’ were identified as the most possessed competencies in each area. An IPA (Importance-Performance Analysis) was performed to analyze the details. Through IPA, ‘Risk Management’ and ‘Safety Management’ were evaluated as having a low degree of retention compared to a high level of importance. Therefore, they were identified as a core competencies that need to be improved first. In addition, the characteristics of each core competency and the recognition level in the field were also identified. This study will be helpful in defining the roles and functions of aircraft maintenance quality engineers to improve flight quality and prevent aviation accidents.

In the recent era of the fourth industrial revolution, many industries aim to maximize the efficiency of products and services by introducing cutting-edge technologies such as artificial intelligence and big data. In this situation, organizational culture is changing a lot due to the influx of the MZ generation with strong individualistic tendencies and the decreased face-to-face communication between members. However, active communication with colleagues is still essential to maximize performance, and the margins created by simplifying work processes and automating processes must be used for creating work performance. This requires cooperation and commitment through the job immersion of members who have an active attitude. This study analyzed how the organization's autonomous work environment and trust among members, which are creative work performance conditions, affect job immersion using raw data from the Occupational Safety and Health Research Institute. As a result, it was found that both the organization's autonomous working environment and trust among members significantly effected the members' job immersion. in order to achieve productivity and value improvement in companies, efforts are needed to increase workers' job immersion by building an autonomous working environment and trust among members. The results of this study are expected to contribute significantly to the search for ways to increase workers’ job commitment to improve organizational productivity.