Proposal of Joint Planning Working Group for Development of Korean Space Telescopes (한국형 우주망원경 개발을 위한 공동기획 Working Group 제안)
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- Journal of Space Technology and Applications
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- v.1 no.3
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- pp.283-301
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- 2021
In order to satisfy the intellectual curiosity of mankind to explore the unknown, National Aeronautics and Space Administration (NASA) in the United States and European Space Agency (ESA) in Europe are embarking on various R&D under the motto of the grand dream of pioneering space into a safe and sustainable environment. In the 2020s and 30s, it is expected that advanced giant observation equipment will be in operation, such as the development of a 10-meter-class telescope in space. In Korea, following the development of the 0.15 m Near-Infrared Imaging Spectrometer (NISS), Korea Astronomy and Space Science Institute (KASI) is also participating a 0.2 m Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) as an international cooperation partner in small exploration telescope. However, domestic experience in the development and operation of the space telescopes is still insufficient, and there is no plan with long-term prospects for constructing telescopes. In order to answer questions about the unknown world that mankind has not experienced using our own equipment, planning and preparation for the construction of a space telescope through close cooperation among industry-university-institute-government is urgently needed. In this paper, the necessity, background, development goals, and expected effects of the development of the Korean Space Telescope are summarized conceptually, and a working group (WG) is also proposed. In the WG activities, Korea shall take the lead in establishing the Korean-style space telescope development plan, and will start a valuable step to establish the national direction in the field of space astronomy and related technologies. We hope that the WG will be another milestone in Korea's space development.
One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.
The purpose of study is to examine the effectiveness of exhibition sales promotions and purchase intention for reasonable visitors. Perceived behavior control determining moderating effects on the relationship among their sales promotions and purchase intention is used as a predictive variable of unexpected impulsive purchases or negative purchase intention contrary to business intention. A total of 315 visitors who experienced the sales promotions of G-Star 2016 in Busan respond to the questionnaire and 259 forms are used to analyze the data. The main results of this study were as follows. First, except to value-added sales promotion, all of sales promotions positively impact on visitors' purchase intention. Second, as a result of analyzing the moderating effects of the perceived behavioral control consisting of control belief and perceived power on the relationships among the sales promotions and purchase intention, the control belief moderated the sales promotions such as price-off and education on purchase intention. In addition, the perceived power moderated the sales promotions such as escape and entertainment on purchase intention. In a nutshell, the degree of perceived behavior control makes critically impact on the effectiveness of exhibition sales promotions. Based on this results, it yields new insights into the way of developing various sales promotion strategies according to different features of visitors.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used