• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.233-242
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• 2014

A magnetically levitated vehicle (Maglev) system is under commercialization as a new transportation system in Korea. The Maglev is operated by an unmanned automatic control system. Therefore, the plan of train operation should be carefully established and validated in advance. In general, when making a train operation plan, statistically predicted traffic data is used. However, a traffic wave often occurs in real train service, and demand-driven simulation technology is required to review a train operation plan and service quality considering traffic waves. We propose a method and model to simulate Maglev operation considering continuous demand changes. For this purpose, we employed a discrete event model that is suitable for modeling the behavior of railway passenger transportation. We modeled the system hierarchically using discrete event system specification (DEVS) formalism. In addition, through implementation and an experiment using the DEVSim++ simulation environment, we tested the feasibility of the proposed model. Our experimental results also verified that our demand-driven simulation technology can be used for a priori review of train operation plans and strategies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.3
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• pp.457-465
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• 2018

This study proposes a novel approach to explore a "travel time budget (TTB)" using a mobile phone signaling data (MPSD), which are passively generated between a mobile phone and a base station. The data analyzied in this study were provided from KT for 8 days (from May 19 to 26 in 2016). They were about 45 million signals passively generated from users whose stay area during night was classified as three areas in Mapo-gu, Seoul and in the city of Sejong. The estmation of TTB was implemented with various pre-processing techniques on the MPSD data in a data-driven analysis. As a result, the TTBs of Mapo-gu, Seoul and Sejong were 82.94 and 80.70 minutes, respectively. The results in this study were also compared with those based on the traditional methods. The authors expect that this result will help transport experts improve the use of MPSD.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.1-9
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• 2009

It is essential for companies to manage massive data for dealing with large volume of transactions and customers' needs. To this end, the companies have operated data warehouse with many complex tools for data gathering and reporting to the end-users. However, the data from the heterogeneous tools at the various sources cannot be exchanged because of the different interfaces. Therefore, the data cannot be controlled with integrated manner, and furthermore the companies do not focus the quality of data resulting in the data quality problem. Thus, this study suggests how to manage massive data with a metadata. In particular, we investigate current status of metadata management, its appliance, and perspectives. The contribution of this research is to apply the metadata management system to the real world and to suggest its management procedure.

• Journal of Distribution Science
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• v.19 no.8
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• pp.69-80
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• 2021

Purpose: This study's purpose is to explore the relationship between entrepreneurial marketing orientation on new product development performance via marketing and innovation capabilities. Research design, data, and methodology: This research has applied a survey method which involved 159 respondents from food retailers among Michelin Guide Restaurants in Thailand. The literature's existing measurement scales were used to operationalize the constructs proposed in this study. The analyses were conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to test the hypotheses. Results: The results have shown that new product development performance received positive and direct impacts from entrepreneurial marketing orientation, particularly in three dimensions: customer value orientation, opportunity-driven initiatives, and leveraged resources. Likewise, new product development performance received a positive, indirect impact from opportunity-driven initiatives, risk management, customer value orientation, and innovation that is focused on marketing and innovation capabilities. Conclusions: The results are useful for Thai food retailers as to strategy formulation in order to attract tourists from all over the world to tourist destinations in Thailand. Therefore, this empirical study is extremely important for domestic economic development and the international economy. These findings provide theoretical and managerial contributions for developing competitive strategies which will lead to sustainable business practices, as well as for providing future research directions.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1587-1596
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• 2016

This paper presents an interactive locomotion controller using motion capture data and inverted pendulum model. Most of the data-driven character controller using motion capture data have two kinds of limitation. First, it needs many example motion capture data to generate realistic motion. Second, it is difficult to make natural-looking motion when characters navigate dynamic terrain. In this paper, we present a technique that uses dimension reduction technique to motion capture data together with the Gaussian process dynamical model (GPDM), and interpolates the low-dimensional data to make final motion. With the low-dimensional data, we can make realistic walking motion with few example motion capture data. In addition, we apply the inverted pendulum model (IPM) to calculate the root trajectory considering the real-time user input upon the dynamic terrain. Our method can be used in game, virtual training, and many real-time applications.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.105-113
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• 2022

Purpose - An increase in automation has been as a result of digital technologies. The data will be instrumental in the determination of the services that are more necessary so that more resources can be allocated for them. The purpose of the current research is to investigate how big data utilization will help increase the profitability in the industry 4.0 era. Research design, Data, and methodology - The present research has conducted the comprehensive literature content analysis. Quantitative approaches allow respondents to decide, but qualitative methods allow them to offer more information. In the next step, respondents are given data collection equipment, and information is collected. Result - The According to qualitative literature analysis, there are five ways in which big data utilization will help increase the profitability in the industry 4.0 era. The five solutions are (1) Better Customer Insight, (2) Increased Market Intelligence, (3) Smarter Recommendations and Audience Targeting, (4) Data-driven innovation, (5) Improved Business Operations. Conclusion - Modern companies have been seeking a competitive advantage so that they can have the edge over other companies in the same industries providing the same services and products. Big data is that technology that businesses have always wanted for an extended period of time to revolutionize their operations, making their businesses more profitable.

• The Journal of Korean Association of Computer Education
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• v.20 no.2
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• pp.57-65
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• 2017

The world's senior population is on the rise. In particular, unlike the past seniors who were in the digital insensitivity class, the smart seniors who want to continue to use smart devices and the Internet are emerging. Although the definition of senior is merely defined as a senior group, research on the characteristics of seniors has been done in psychology studies, but research using data based senior cognitive response is only at an early stage. In order to provide contents according to the cognitive characteristics of Smart Senior, there is a need to classify the cognitive characteristics of Smart Senior well. Therefore, this paper suggests a data - driven senior cognitive response modeling method that helps the enjoyment of culture for seniors through classification of cognitive responses to smart seniors.

• Smart Structures and Systems
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• v.26 no.4
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• The Journal of Advanced Prosthodontics
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• v.13 no.1
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• pp.12-23
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• 2021

Purpose. The aim of the study was to compare the lingualized implant placement creating a buccal cantilever with prosthetic-driven implant placement exhibiting excessive crown-to-implant ratio. Materials and Methods. Based on patient's CT scan data, two finite element models were created. Both models were composed of the severely resorbed posterior mandible with first premolar and second molar and missing second premolar and first molar, a two-unit prosthesis supported by two implants. The differences were in implants position and crown-to-implant ratio; lingualized implants creating lingually overcontoured prosthesis (Model CP2) and prosthetic-driven implants creating an excessive crown-to-implant ratio (Model PD2). A screw preload of 466.4 N and a buccal occlusal load of 262 N were applied. The contacts between the implant components were set to a frictional contact with a friction coefficient of 0.3. The maximum von Mises stress and strain and maximum equivalent plastic strain were analyzed and compared, as well as volumes of the materials under specified stress and strain ranges. Results. The results revealed that the highest maximum von Mises stress in each model was 1091 MPa for CP2 and 1085 MPa for PD2. In the cortical bone, CP2 showed a lower peak stress and a similar peak strain. Besides, volume calculation confirmed that CP2 presented lower volumes undergoing stress and strain. The stresses in implant components were slightly lower in value in PD2. However, CP2 exhibited a noticeably higher plastic strain. CONCLUSION. Prosthetic-driven implant placement might biomechanically be more advantageous than bone quantity-based implant placement that creates a buccal cantilever.

• Knowledge Management Research
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• v.18 no.3
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• pp.63-80
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• 2017

Big Data environment is established by accumulating vast amounts of data as users continuously share and provide personal information in online environment. Accordingly, the more data is accumulated in online environment, the more data is accessible easily by third parties without users' permissions compared to the past. By utilizing strategies based on data-driven, firms recently make it possible to predict customers' preferences and consuming propensity relatively exactly. This Big Data environment, on the other hand, establishes 'Dataveillance' which means anybody can watch or control users' behaviors by using data itself which is stored online. Main objective of this study is to identify the relationship between Dataveillance and users' online privacy protection behaviors. To achieve it, we first investigate perceived online service efficiency; loss of control on privacy; offline surveillance; necessity of regulation influences on users' perceived threats which is generated by Dataveillance.