Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.
The purpose of this study is to present the effective smoke control flow rate and mode for securing safety through quantitative risk assessment according to the smoke control flow rate and mode (supply or exhaust) of the platform when a train fire occurs at the subway platform. To this end, a fire outbreak scenario was created using a side platform with a central staircase as a model and fire analysis was performed for each scenario to compare and analyze fire propagation characteristics and ASET, evacuation analysis was performed to predict the number of deaths. In addition, a fire accident rate (F)/number of deaths (N) diagram (F/N diagram) was prepared for each scenario to compare and evaluate the risk according to the smoke control flow rate and mode. In the ASET analysis of harmful factors, carbon monoxide, temperature, and visible distance determined by performance-oriented design methods and standards for firefighting facilities, the effect of visible distance is the largest, In the case where the delay in entering the platform of the fire train was not taken into account, the ASET was analyzed to be about 800 seconds when the air flow rate was 4 × 833 m3/min. The estimated number of deaths varies greatly depending on the location of the vehicle of fire train, In the case of a fire occurring in a vehicle adjacent to the stairs, it is shown that the increase is up to three times that of the vehicle in the lead. In addition, when the smoke control flow rate increases, the number of fatalities decreases, and the reduction rate of the air supply method rather than the exhaust method increases. When the supply flow rate is 4 × 833 m3/min, the expected number of deaths is reduced to 13% compared to the case where ventilation is not performed. As a result of the risk assessment, it is found that the current social risk assessment criteria are satisfied when smoke control is performed, and the number of deaths is the flow rate 4 × 833 m3/min when smoke control is performed at 29.9 people in 10,000 year, It was analyzed that it decreased to 4.36 people.
As the 4th industrial revolution progresses, new technologies and services are being born, growing, and maturing. Now, beyond the mobile era, the metaverse is being discussed as a new paradigm. Therefore, in this study, in preparation for the metaverse era, we tried to analyze what factors have an important influence when consumers want to use new technologies. In particular, the research was conducted focusing on how the context in which consumers use the technology changes depending on whether they are B2C or B2B. For this, augmented reality (AR) was selected in the B2C context by linking the research subject with the metaverse era, and the smart factory was selected in the B2B context. The research model for the analysis was established by deriving and setting common influence variables by reflecting the characteristics of the research target technology based on the modified extended unified theory of acceptance and use of technology. A survey was conducted for empirical analysis, and 150 AR and 150 smart factory subjects were analyzed. The empirical study results are as follows. The relationship between performance expectancy and intention to use, technology readiness and intention to use was found to have a significant positive (+) effect on both AR and smart factory. On the other hand, it was found that effort expectancy, social influence, and trust had a positive (+) effect on intention to use only in AR. Only in smart factory, facilitating conditions had a significant positive (+) effect on intention to use. It was also found that the perceived risk had a significant negative (-) effect on the intention to use only in the smart factory. The results of this study are academically significant in that we empirically test that influencing factors of technology use varies depending on the context in which it is used by consumers. In practice, it provided an implication of what to focus on first is being implemented.
The purpose of this study is to survey startup founders within 7 years of founding the importance and satisfaction of the components of the scale-up entrepreneurial ecosystem at the national level in Korea and analyze the direction of scale-up policy by component using IPA (importance-performance analysis). Since the perception of founders, who are the subjects of the entrepreneurial ecosystem, affects the quantity and quality of start-ups, research is needed to analyze and diagnose the perception of scale-up components. For the development of the national economy and entrepreneurial ecosystem, companies that emerge from startups to scale-up and unicorns must be produced, and for this, elements for the scale-up entrepreneurial ecosystem are needed. The results of this study are as follows. First, the importance ranking of the components of the scale-up entrepreneurial ecosystem recognized by founders was in the order of "Financial support by growth stage," "Support for customized scale-up for enterprises," "Improvement of regulations," "Funds dedicated to scale-up," "large-scale investment," and "nurturing technical talents." Second, the factors that should be intensively improved in the importance-satisfaction matrix in the future were 'Pan-Government Integration Promotion Plan', 'Scale-Up Specialized Organization Operation', 'Company Customized Scale-Up Support', 'Regulatory Improvement', and 'Building a Korean Scale-Up Model'. As a result, various and large financial capital for the scale-up entrepreneurial ecosystem, diversification of scale-up programs by business sector, linkage of start-ups and scale-up support, deregulation of new technologies and new industries, strengthening corporate-tailored scale-up growth capabilities, and providing overseas networking opportunities can be derived. In addition, it is expected to contribute to policy practice and academic work with research that derives the components of the domestic scale-up entrepreneurial ecosystem and diagnoses its perception.
We reevaluate the Burton equation (Burton et al. 1975) of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q) and the decay time (
Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.
Franchising is one of the fastest growing types of business. It is already popular and well-known in the U.S., and has been growing in many other countries including Korea. Furthermore, many Korean franchising companies have expanded their business overseas actively. According to the data by the Ministry of Industry and Resource, 82 companies out of a sample of 500 franchising companies are already operating in many foreign countries and 48% of them have started their foreign business since 2006. This clearly indicates the fast growing current trend of foreign operation by Korean franchising companies. In spite of the fast growing trend of foreign expansion in the industry, academic research on internationalization of franchising companies is extremely difficult to find. Accordingly, academic research on the issue is necessary and urgent in Korea. Among the various research questions on internationalization of franchising business, this study intends to investigate the difference in organizational factors between the franchising companies doing foreign operation and those doing business only domestically. More specifically, this research has the following purposes. First, considering the lack of theoretical basis of previous studies, resource-based theory and agency theory are employed as the theoretical bases. Second, this study explains the difference in internationalization based on organizational factors such as company size, history and growth rate. Third, the five hypotheses regarding the difference in organizational factors are presented and tested empirically, which is the first attempt in the area of this topic. Finally, the study attempts to clarify the conflicting implications among theories regarding some organizational factos such as growth rate. As the theoretical background, resource-based theory and agency theory are discussed. According to resource-based theory, a firm can grow continuously when it has competence and resource, and also the ability to develop them. The competence and resource can include capital, human resource, management skill, market information, ability to manage risk, etc. Meanwhile, agency theory views the relationship between franchisor and franchisee as an agency relationship. In agency theory, bonding capability and monitoring capability are the two key factors which promote internationalization of franchising companies. Based on the two theories, a conceptual model is designed. The model consists of two groups of variables. One is organizational factors including size, history, growth rate, price bonding and geographic dispersion. The other is whether a franchising company is operating overseas or not. We developed the following five research hypotheses basically describing the relationship between organizational factors and internationalization of franchising companies. H1: The size of franchising companies operating overseas is larger than that of franchising companies operating domestically. H2: The history of franchising companies operating overseas is longer than that of franchising companies operating domestically. H3: The growth rate of franchising companies operating overseas is higher than that of franchising companies operating domestically. H4: The price bonding of franchising companies operating overseas is higher than that of franchising companies operating domestically. H5: The geographic dispersion of franchising companies operating overseas is wider than that of franchising companies operating domestically. Data for the analyses are obtained from 2005 Korea Franchise Survey data co-generated by Ministry of Industry and Resource, GS1 Korea, and Korea Franchise Association. Out of 2,804 population companies, 2,489 companies are excluded for various reasons and 315 companies are selected as the final sample. Prior to hypotheses tests, validity and reliability of the measures of size, history, growth rate and price bonding are examined for further analyses. Geographic dispersion is not validated since it is measured using nominal data. A series of independent sample T-tests is used to find out whether there exists any significant difference between the companies internationalized and those operating only domestically for each organizational factor. Among the five factors, size and geographic dispersion show significant difference, growth rate and price bonding do not reveal any difference and, finally, history factor shows conflicting results in the difference depending on how to measure it.