• Journal of the Korean Society for Aviation and Aeronautics
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• v.15 no.3
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• pp.57-62
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• 2007

In this paper, we attempted to produce the cloud forecast that use the numerical weather prediction(NWP) MM5 for objective cloud forecast. We presented two methods for cloud forecast. One of them used total cloud mixing ratio registered to sum(synthesis) of cloud-water and cloud-ice grain mixing ratio those are variables related to cloud among NWP result data and the other method that used relative humidity. An experiment was carried out period from 23th to 24th July 2004. According to the sequence of comparing the derived cloud forecast data with the observed value, it was indicated that both of those have a practical use possibility as cloud forecast method. Specially in this Case study, cloud forecast method that use total cloud mixing ratio indicated good forecast availability to forecast of the low level clouds as well as middle and high level clouds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4038-4056
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• 2018

Recently, the most frequently studied topics related to the fourth industrial revolution (FIR) are Big data, AI, Cloud Computing and Internet of Things- these four components are collectively known as the main components of the FIR (henceforth MCs). The MCs have a wide range of effects on workers' performance. As such it is imperative that these components are properly understood. This understanding will lead to a proper recognition of the attitudes that workers need to adopt to the MCs. Specifically, the attitudes of workers to several variables need to be examined, including importance, intention to use, belief in improvement, efficacy to use, and negative cognition. Each of these variables plays a role in determining how worker's performance in the FIR era will change. The performance-related variables such as self-efficacy, expectations, and acceptance of change are also crucial. These variables are related to creation of new opportunities, and can greatly influence performance in the FIR era. This study explains how specific attitudes to MCs improve performance-related factors for FIR. The adoption of these attitudes will ultimately lead to more successful adaption to the FIR era.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.99-124
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• 2012

Our society has long been talking about necessity for innovation. Since companies in particular need to carry out business innovation in their overall processes, they have attempted to apply many innovation factors on sites and become to pay more attention to their innovation. In order to achieve this goal, companies has applied various information technologies (IT) on sites as a means of innovation, and consequently IT have been greatly developed. It is natural for the field of IT to have faced another revolution which is called cloud computing, which is expected to result in innovative changes in software application via the Internet, data storing, the use of devices, and their operations. As a vehicle of innovation, cloud computing is expected to lead the changes and advancement of our society and the business world. Although many scholars have researched on a variety of topics regarding the innovation via IT, few studies have dealt with the issue of could computing as IT. Thus, the purpose of this paper is to set the variables of innovation attributes based on the previous articles as the characteristic variables and clarify how these variables affect "Performance Expectancy" of companies and the intention of using cloud computing. The result from the analysis of data collected in this study is as follows. The study utilized a research model developed on the innovation diffusion theory to identify influences on the adaptation and spreading IT for cloud computing services. Second, this study summarized the characteristics of cloud computing services as a new concept that introduces innovation at its early stage of adaptation for companies. Third, a theoretical model is provided that relates to the future innovation by suggesting variables for innovation characteristics to adopt cloud computing services. Finally, this study identified the factors affecting expectation and the intention to use the cloud computing service for the companies that consider adopting the cloud computing service. As the parameter and dependent variable respectively, the study deploys the independent variables that are aligned with the characteristics of the cloud computing services based on the innovation diffusion model, and utilizes the expectation for performance and Intention to Use based on the UTAUT theory. Independent variables for the research model include Relative Advantage, Complexity, Compatibility, Cost Saving, Trialability, and Observability. In addition, 'Acceptance for Adaptation' is applied as an adjustment variable to verify the influences on the expected performances from the cloud computing service. The validity of the research model was secured by performing factor analysis and reliability analysis. After confirmatory factor analysis is conducted using AMOS 7.0, the 20 hypotheses are verified through the analysis of the structural equation model, accepting 12 hypotheses among 20. For example, Relative Advantage turned out to have the positive effect both on Individual Performance and on Strategic Performance from the verification of hypothesis, while it showed meaningful correlation to affect Intention to Use directly. This indicates that many articles on the diffusion related Relative Advantage as the most important factor to predict the rate to accept innovation. From the viewpoint of the influence on Performance Expectancy among Compatibility and Cost Saving, Compatibility has the positive effect on both Individual Performance and on Strategic Performance, while it showed meaningful correlation with Intention to Use. However, the topic of the cloud computing service has become a strategic issue for adoption in companies, Cost Saving turns out to affect Individual Performance without a significant influence on Intention to Use. This indicates that companies expect practical performances such as time and cost saving and financial improvements through the adoption of the cloud computing service in the environment of the budget squeezing from the global economic crisis from 2008. Likewise, this positively affects the strategic performance in companies. In terms of effects, Trialability is proved to give no effects on Performance Expectancy. This indicates that the participants of the survey are willing to afford the risk from the high uncertainty caused by innovation, because they positively pursue information about new ideas as innovators and early adopter. In addition, they believe it is unnecessary to test the cloud computing service before the adoption, because there are various types of the cloud computing service. However, Observability positively affected both Individual Performance and Strategic Performance. It also showed meaningful correlation with Intention to Use. From the analysis of the direct effects on Intention to Use by innovative characteristics for the cloud computing service except the parameters, the innovative characteristics for the cloud computing service showed the positive influence on Relative Advantage, Compatibility and Observability while Complexity, Cost saving and the likelihood for the attempt did not affect Intention to Use. While the practical verification that was believed to be the most important factor on Performance Expectancy by characteristics for cloud computing service, Relative Advantage, Compatibility and Observability showed significant correlation with the various causes and effect analysis. Cost Saving showed a significant relation with Strategic Performance in companies, which indicates that the cost to build and operate IT is the burden of the management. Thus, the cloud computing service reflected the expectation as an alternative to reduce the investment and operational cost for IT infrastructure due to the recent economic crisis. The cloud computing service is not pervasive in the business world, but it is rapidly spreading all over the world, because of its inherited merits and benefits. Moreover, results of this research regarding the diffusion innovation are more or less different from those of the existing articles. This seems to be caused by the fact that the cloud computing service has a strong innovative factor that results in a new paradigm shift while most IT that are based on the theory of innovation diffusion are limited to companies and organizations. In addition, the participants in this study are believed to play an important role as innovators and early adapters to introduce the cloud computing service and to have competency to afford higher uncertainty for innovation. In conclusion, the introduction of the cloud computing service is a critical issue in the business world.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.89-104
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• 2024

Cloud-based e-learning (CBEL) represents a promising technological frontier. Existing literature has presented a diverse array of findings regarding the determinants that influence the adoption of CBEL. The primary objective of this study is to conduct an exhaustive examination of the available literature, aiming to determine the key predictors of CBEL utilization by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A comprehensive review of 35 articles was undertaken, shedding light on the status of CBEL as an evolving field. Notably, there has been a discernible downturn in related research output during the COVID-19 pandemic, underscoring the temporal dynamics of this subject. It is noteworthy that a significant portion of this research has emanated from the Asian continent. Furthermore, the dominance of the technology acceptance model (TAM) in research frameworks is affirmed by our findings. Through a rigorous thematic analysis, our study identified five overarching themes, each encompassing a diverse range of sub-themes. These themes encompass 1) technological factors, 2) individual factors, 3) organizational factors, 4) environmental factors, and 5) security factors. This categorization provides a structured framework for understanding the multifaceted nature of CBEL adoption determinants. Our study serves as a compass, guiding future research endeavours in this domain. It underscores the imperative for further investigations utilizing diverse theoretical frameworks, contextual settings, research methodologies, and variables. This call for diversity and expansion in research efforts reflects the dynamic nature of CBEL and the evolving landscape of e-learning technologies.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.53-62
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• 2020

This study is to evaluate the accuracy of the meteorological information provided for the aircraft operating at low altitude. At first, it is necessary to identify crucial elements of weather information closely related to flight safety during low altitude flights. The study conducted a survey of pilots of low altitude aircraft, divided into pre-flight and in-flight phases, and reached an opinion that wind direction, wind speed, cloud coverage and ceiling and visibility are important items. Related to these items, we compared and calculated the accuracy of TAFs and METARs from Taean Airfield, Seosan Airport and Gunsan Airport because of their high number of domestic low-altitude flights. Accuracy analysis evaluated the accuracy of two numerical variables, Mean Absolute Error(MAE) and Root Mean Square Error(RMSE), and the cloud coverage which is categorical variable was calculated and compared by accuracy. For numeric variables, one-way ANOVA, which is a parameter-test, was approached to identify differences between actual forecast values and observations based on absolute errors for each item derived from the results of MAE and RMSE accuracy analyses. To determine the satisfaction of both normality assumptions and equivalence variability assumptions, the Shapiro-Wilk test was performed to verify that they do not have a normality distribution for numerical variables, and for the non-parametric test, Kruscal-Wallis test was conducted to determine whether or not they are satisfied.

• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.21-30
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• 2022

In this study, This study focuses on exploring the major factors influencing the successful introduction of the cloud-based accounting information system, which is the top priority in the field of corporate digital transformation. Therefore, theories were summarized based on the company's cloud environment and related prior research, and the major performance factors of the company were analyzed by dividing them into organizational factors, business operation factors, and technical system factors. Considering that the cloud-based accounting information system is in the early stages of its introduction, the research analysis method ranks major success factors according to their importance using the Delphi targeting the expert panel, through the AHP method, the major performance variables were finally explored through the mutual importance analysis of each major factor. As a result of the analysis, organizational factors were analyzed as corporate sustainability, business operational factors were the business solutions, and system scalability factors were analyzed. This study will be able to provide additional useful information on the initial introduction strategy and operation for the introduction and operation of the cloud-based accounting information system.

• The Journal of Society for e-Business Studies
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• v.19 no.1
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• pp.15-42
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• 2014

Cloud storage service has the potential to be a core infrastructure for the future mobile and Internet service; thus related service providers have been investing in it and trying to attract as many users as possible. In addition, those need to find out what motivates the users to keep using their service not only to attract new customers but also to secure their subscribers. Therefore, this study will examine its relationship with user's motivation based on the extended TAM model with external variables for objective research about continuous use of cloud storage service. As a result, it was found that personal innovativeness, self efficacy, functional attributes, and psychological switching cost influence the continuous use of cloud storage service. Also, it is expected they can guide service providers to the right track when setting up their business strategy in the future.

• Atmosphere
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• v.27 no.2
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.