• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.213-221
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• 2018

South Korea has been reluctant to open its geospatial information overseas to ensure security as a divided country. However, this cannot continue as the domestic and international environments related to geospatial information and the industrial ecosystem of information and communication technologies have been changing dramatically. Within this context, this study aims to analyze the causal relations among relevant variables and how they change and interact with time using a systems thinking process. First, causal maps were created for the domains of national security, map-based convergence service, and corporate competition. Then, the causal maps for each domain were integrated, based on which the points for policy intervention and dominant feedback loops were identified. The analysis results showed that securing the self-sufficiency of domestic geospatial businesses is a key element to determine the whole causal map, and the variable that changes the dominant feedback loop from a vicious circle to a virtuous one is the decision to open geospatial information overseas. In this study, I found the policy leverage that is a policy intervention point that can produce a great effect with little input by building a causal map of the interactions between major variables. This study is significant in that it identified and analyzed the dominant feedback loop as to which causal structure would dominate the system in the long term. The results of this study can be used to discuss not only the impacts of map data overseas opening on the national security and geospatial information industry, but also the interactions in the future when Google or other global companies request to release the geospatial information.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.559-571
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• 2003

ENUM(tElephon NUmbering Mapping) is protocol that brings convergence between PSTN Networks and IP Networks using a unique worldwide E.164 telephone number as an identifier between different communication infrastructure. The mechanism provides a bridge between two completely different environments with E.164 number; IP based application services used in PSTN networks, and PSTN based application services used in IP networks. We propose a new way to organize and handle ENUM Tier 2 name servers to improve performance at the name resolution process in ENUM based application service. We build an ENUM based network model when NAPTR(Naming Authority PoinTeR) resource record is registered and managed by area code at the initial registration step. ENUM promises convenience and flexibility to both PSTN and IP users, yet there is no evidence how much patience is required when users decide to use ENUM instead of non-ENUM based applications. We have estimated ENUM response time, and proved how to improve performance up to 3 times when resources are managed by the proposed mechanism. The proposition of this thesis favorably influences users and helps to establish the policy for Tier 2 name server management.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.165-175
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• 2009

With the current move towards adopting mechanistic-empirical concepts in the design of pavement structures, state-of-the-art mechanistic analysis methodologies are needed to determine accurate pavement responses, such as stress, strain, and deformation. Previous laboratory studies of pavement foundation geomaterials, i.e., unbound granular materials used in base/subbase layers and fine-grained soils of a prepared subgrade, have shown that the resilient responses followed by nonlinear, stress-dependent behavior under repeated wheel loading. This nonlinear behavior is commonly characterized by stress-dependent resilient modulus material models that need to be incorporated into finite element (FE) based mechanistic pavement analysis methods to predict more realistically predict pavement responses for a mechanistic pavement analysis. Developed user material subroutine using aforementioned resilient model with nonlinear solution technique and convergence scheme with proven performance were successfully employed in general-purpose FE program, ABAQUS. This numerical analysis was investigated in predicted critical responses and domain selection with specific mesh generation was implemented to evaluate better prediction of pavement responses. Results obtained from both axisymmetric and three-dimensional (3D) nonlinear FE analyses were compared and remarkable findings were described for nonlinear FE analysis. The UMAT subroutine performance was also validated with the instrumented full scale pavement test section study results from the Federal Aviation Administration's National Airport Pavement Test Facility (FAA's NAPTF).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.353-358
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• 2018

In this study, it is describe to an optimization analysis process for the weight reduction of the voltage converter in the electric vehicle charging systems. The optimization design is a technique that finds the optimal material distribution under a given material quantity constraint by combining the design sensitivity with the material properties and the mathematical optimization. Among the topology optimization, a lightweight design is performed by a solid isotropic material with penalization with simple formula and well-convergence. The lightweight design consists of three steps. As a first step, a finite element model for the basic design of the on-board voltage converter was constructed and static analysis was performed on the load. In the second step, the optimum shape is obtained for the lightweight by performing the topology optimization using the solid isotropic material with penalization applying the stiffness coefficient of the isotropic material to the static analysis result. As a final step, impact analysis was performed by applying a half-sinusoidal pulse shape impact load which satisfies the impact test standard of the vehicle-mounted part with respect to the optimum shape. In the topology optimization, the design domain was defined as the mounting bracket area, and the design technology was finally achieved by optimizing the mounting bracket to achieve a weight reduction of 20% over the basic design.

• Journal of Science Education
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• v.43 no.2
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• pp.207-226
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• 2019

The purpose of this study is to investigate how fusion science program for undergraduate general education influences the understanding of Nature of Science (NOS) and STEAM Literacy of students majoring in science and engineering in college. The students participated in the pre/post NOS survey(Lee, 2013) and the STEAM literacy survey(Choi et al., 2013) in the program. The results of this study are as follows. First, the fusion program was effective in understanding the NOS because there is a statistically significant difference between the pre/post tests.(p < 0.01). Second, while there were no significant differences between genders, however, there is a significant difference in students' majors in NOS understanding(p < 0.05). Third, it showed that improvement in STEAM Literacy in Convergence and Creativity domains is significant(p < 0.01). By contrast, the Caring domain of STEAM Literacy was decreased statically significant(p < 0.01). In the end, there is no difference in STEAM Literacy between genders as well as among students' major(p < 0.05).

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.349-355
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• 2019

The virtual reality training has been suggested as an intervention to improve physical function. But, the effects of virtual reality training focused on improving ADL in patients with acute stroke are unclear. Purpose of this study was to examine the effect of virtual reality training on hand function and activities of daily living (ADL) in patients with acute stroke. Sixteen patients with acute stroke were included in this study. The experimental group (VRA group) received 30 minutes of virtual reality training focused on ADL for each session, while the control group received 30 minutes of conventional virtual reality training. To examine the hand function and ADL of the subjects, the study used Jebsen-Tylor Hand Function Test (JTHFT) and Korean Modified Barthel Index (K-MBI), respectively. Both groups showed significant improvements in hand function of affected and unaffected sides, and in K-MBI total score before and after the intervention. The experimental group showed significantly greater improvements in the self-care domain of K-MBI after intervention than the control group. These findings suggest that the virtual reality training focused on ADL may have a better effect on self-care than conventional virtual reality training in patients with acute stroke.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.29-36
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• 2020

The background of this study is that machine learning administrative services are recently attracting attention as a major policy tool for non-face-to-face administrative services in the post-corona era. This study investigated the types of work expected to be effective when introducing machine learning administrative services for Seoul Metropolitan Government officials who are piloting machine learning administrative services. The research method is a machine that can be introduced by organizational unit by distributing and collecting questionnaires for Seoul administrative organizations that have performed machine learning-based administrative services for one month in July 2020 targeting Seoul public officials using machine learning-based administrative services. By analyzing the learning administration service and application service, the business characteristics of each machine learning administration service type such as supervised learning work type, unsupervised learning work type, and reinforced learning work type were analyzed. As a result of the research analysis, it was found that there were significant differences in the characteristics of administrative tasks by supervised and unsupervised learning areas. In particular, it was found that the reinforcement learning domain contains the most appropriate business characteristics for machine learning administrative services. Implications were drawn. The results of this study can be provided as a reference material to practitioners who want to introduce machine learning administration services, and can be used as basic data for research to researchers who want to study machine learning administration services in the future.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.197-202
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• 2022

Gender neutral draft is the system that the level of manpower which military demands right now can be kept and the concept of gender equality can enlarge itself. This thesis draws implications on the necessity of optional review of draft and the amelioration of military organizational culture for the military through the example of Norway that has adopted gender neutral draft initially in Europe. The Military that is characterized by traditionally male led domain accelerates itself the openness to female by the aggrandizement of social participation and the improvement of human right for female. The various concerns about a necessary facilities, the period ofmilitary service, the level of salary, the keeping of combat power, etc just should be overcome in order to enlarge conscription to female in Korea. Situations such as the environment of national security, the level of social participation to female, the treatment to minority are different, so it is difficult for us to draw any conclusion whether the adoption of sex neutral conscription is right or not. Nonetheless, the national strategy that prepares for future may not be concluded easily and we can not stop it even though it is discomfortable truth. In Conclusion, we anticipate that the sound discourse on measures of including female in draft will be continued from the start with this thesis.