• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.4
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• pp.239-246
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• 2005

In this study, a finite element analysis and a vibration test were performed to estimate the natural frequencies of mouse femurs with osteoporosis. Three groups of the femurs include the osteoporotic group, the treated group and the normal group. For the finite element analysis, the micro finite element model of the femur was reconstructed using the Micro-CT images and the Voxel mesh generation algorithm. In the vibration test, the natural frequencies were measured by the mobility test. from the results, the averaged natural frequencies in the osteoporotic group were the highest, followed by those in the treated group. The finite element models were validated within 15% errors by comparing the natural frequencies in the finite element analysis with those in the vibration test. The developed Micro-CT system, the Yokel mesh generation algorithm, the presented finite element analysis, and vibration test could be useful for the investigation of the structural change of the bone tissue, and the diagnosis and the treatment in the osteoporosis.

• Informatization Policy
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• v.18 no.2
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• pp.3-22
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• 2011

The study of online social movements is basically concerned about the impact of the Internet on the existing social movements. More specifically, researchers have paid attention to changes in participants, leadership style and movement strategies caused by the Internet. Due to the Internet, networks of the individuals who are geographically scattered or a network of networks have emerged as new movement agents. Researchers have also analyzed a repertoire of collective action adopted by the online social movements. The increase in online social movements calls for a new interpretation of the existing social movement theories such as resource mobilization, collective identity and political opportunity structure. There are still a lot of debate about the impact of the internet on social movement and the resulting changes. Not only the early debate of cyber-optimism and cyber-scepticism, many studies done by the mid-range perspective also suggested different arguments on the impact of the Internet. This discrepancy comes from a relatively short history of online social movement study, which leads to a limited number of case studies and a shortage of date accumulations. In the future, researchers need to place more attention on the unique characteristics of different technologies and comparative studies of online social movements. The study should also extend its focus to a wide range of political systems in order to explain the impact of online social movements on political intermediary organizations and the democracy itself.

• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.21-31
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• 2014

In this article, data mining simulation is applied to find a proper approach and results of analysis for study of variables related to organization. Also, turnover intention and organizational commitment are used as target (dependent) variables in this simulation. Classification and regression tree (CART) with ensemble methods are used in this study for simulation. Human capital corporate panel data of Korea Research Institute for Vocation Education & Training (KRIVET) is used. The panel data is collected in 2005, 2007, and 2009. Organizational commitment variables are analyzed with combined measure variables which are created after investigation of reliability and single dimensionality for multiple-item measurement details. The results of this study are as follows. First, major determinants of turnover intention are trust, communication, and talent management-oriented trend. Second, the main determining factors for organizational commitment are trust, the number of years worked, innovation, communication. CART with ensemble methods has two ensemble CART methods which are CART with Bagging and CART with Arcing. Comparing two methods, CART with Arcing (Arc-x4) extracted scenarios with very high coefficients of determination. In this study, a scenario with maximum coefficient of determinant and minimum error is obtained and practical implications are presented. Using one of data mining methods, CART with ensemble method. Also, the limitation and future research are discussed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.102-108
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• 2024

In the initial stages of urban air mobility (UAM) operations, compliance with existing visual flight rules and instrument flight regulations for conventional human-crewed aircraft is crucial. Additionally, voice communication between the on board pilot and relevant UAM stakeholders, including vertiports, is essential. Consequently, very high frequency (VHF) aviation voice communication must be consistently provided throughout all phases of UAM operations. This paper presents the results of the VHF communication coverage analysis for the initial UAM demonstration areas, encompassing the Hangang River and Incheon Ara-Canal corridors, as well as potential vertiport candidate locations. By considering the influence of terrain and buildings through the utilization of a digital surface model (DSM), communication quality prediction results are obtained for the analysis areas. The three-dimensional coverage analysis results indicate that stable coverage can be achieved within altitude corridors ranging from 300 m to 600 m. However, there are shaded areas in the low-altitude vertiport regions due to the impact of high-rise buildings. Therefore, additional research to ensure stable coverage around vertiports in the lower altitude areas is required.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.1-20
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• 2023

This study attempted to identify the relationship between the investment determinants of accelerators and investment performance through empirical analysis. Through literature review, four dimensions and 12 measurement items were extracted for investment determinants, which are independent variables, and investment performance was adjusted to the cumulative amount of subsequent investment based on previous studies. Performance data from 594 companies selected by TIPS from 2017 to 2019, which are relatively reliable and easy to secure data, were collected, and the subsequent investment cumulative attraction amount, which is a dependent variable, was hypothesized through multiple regression analysis three years after the investment. As a result of the study, 'industrial experience years' in the characteristics of founders, 'market size', 'market growth', 'competitive strength', and 'number of patents' in the characteristics of products and services had a significant positive (+) effect. The impact of independent variables on dependent variables was most influenced by the competitive strength of market characteristics, followed by the number of years of industrial experience, the number of patents, the size of the market, and market growth. This was different from the results of previous studies conducted mainly on qualitative research methods, and in most previous studies, the characteristics of founders were the most important, but the empirical analysis results were market characteristics. As a sub-factor, the intensity of competition, which was the subordinate to the importance of previous studies, had the greatest influence in empirical analysis. The academic significance of this study is that it presented a specific methodology to collect and build 594 empirical samples in the absence of empirical research on accelerator investment determinants, and created an opportunity to expand the theoretical discussion of investment determinants through causal research. In practice, the information asymmetry and uncertainty of startups that accelerators have can help them make effective investment decisions by establishing a systematic model of experience-dependent investment determinants.

• Science of Emotion and Sensibility
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• v.15 no.3
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• pp.389-398
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• 2012

Virtual worlds have pursued reality as if they actually exist. In order to evaluate the sense of reality in the computer-simulated worlds, several subjective questionnaires, which include specific independent variables, have been proposed in the literature. However, the questionnaires lack reliability and validity necessary for defining and measuring the virtual realization. Few studies have been conducted to investigate the effect of visual factors on the sense of reality experienced by exposing to a virtual environment. Therefore, this study was aimed at reinvestigating the variables and proposing a more reliable and advisable questionnaire for evaluating the virtual realization, focusing on visual factors. Twenty-one questions were gleaned from the literature and subjective interviews with focused groups. Exploratory factor analysis with oblique rotation was performed on the data obtained from 200 participants(females: 100) after exposing to a virtual character image described in an extreme way. After removing poorly loading items, remained subsets were subjected to confirmatory factor analysis on the data obtained from the same participants. As a result, 3 significant factors were determined to efficiently measure the virtual realization. The determined factors included visual presence(3 subset items), visual immersion(7 subset items), and visual interactivity(4 subset items). The proposed factors were verified by conducting a subjective evaluation in which participants were asked to evaluate a 3D virtual eyeball model based on the visual presence. The results implicated that the measurement method was suitable for evaluating the degree of the virtual realization. The proposed method is expected to reasonably measure the degree of the virtual realization.

• The Journal of Korean Academy of Prosthodontics
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• v.49 no.4
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• pp.324-332
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• 2011

Purpose: The purpose of this study was to compare the stress distribution of teeth and jaw on load by differentiating property of materials according to each layer of widely used mouthguard. Materials and methods: A Korean adult having normal cranium and mandible was selected to examine. A customized mouthguard was constructed by use of DRUFOMAT plate and DRUFOMAT-TE/-SQ of Dreve Co. according to Signature Mouthguard system. The cranium was scanned by means of computed tomography with 1mm interval. It was modeled with CANTIBio BIONIX/Body Builder program and simulated and interpreted using Alter HyperMesh program. The mouthguard was classified as follows according to the layers. (1) soft guard (Bioplast)(SG) (2) hard guard (Duran)(HG) (3) medium guard (Drufomat)(MG) (4) soft layer + hard layer (SG + HG) (5) hard layer + soft layer (HG + SG) (6) soft layer + hard layer + soft layer (SG + HG + SG) (7) hard layer + soft layer + hard layer (HG + SG + HG) The impact locations on mandible were gnathion, the center of inferior border, and the anterior edge of gonial angle. And the impact directions were oblique ($45^{\circ}$). The impact load was 800 N for 0.1 sec. The stress distribution was measured at maxillary teeth, TMJ and maxilla. The statistics were conducted using Repeated ANOVA and in case of difference, Duncan test was used as post analysis. Results: In teeth and maxilla, the mouthguard contacting soft layer of mandibular teeth presented lowest stress measure and, in contrast, in condyle, the mouthguard contacting hard layer of mandibular teeth presented lowest stress measure. Conclusion: For all impact directions, soft layer + hard layer + soft layer, the mouthguard with three layers which the hard layer is sandwiched between two soft layers, showed relatively even distribution of stress in impact.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.107-118
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• 2016

The advent of 5G mobile communications, which is expected in 2020, will provide many services such as Internet of Things (IoT) and vehicle-to-infra/vehicle/nomadic (V2X) communication. There are many requirements to realizing these services: reduced latency, high data rate and reliability, and real-time service. In particular, a high level of reliability and delay sensitivity with an increased data rate are very important for M2M, IoT, and Factory 4.0. Around the world, 5G standardization organizations have considered these services and grouped them to finally derive the technical requirements and service scenarios. The first scenario is broadcast services that use a high data rate for multiple cases of sporting events or emergencies. The second scenario is as support for e-Health, car reliability, etc.; the third scenario is related to VR games with delay sensitivity and real-time techniques. Recently, these groups have been forming agreements on the requirements for such scenarios and the target level. Various techniques are being studied to satisfy such requirements and are being discussed in the context of software-defined networking (SDN) as the next-generation network architecture. SDN is being used to standardize ONF and basically refers to a structure that separates signals for the control plane from the packets for the data plane. One of the best examples for low latency and high reliability is an intelligent traffic system (ITS) using V2X. Because a car passes a small cell of the 5G network very rapidly, the messages to be delivered in the event of an emergency have to be transported in a very short time. This is a typical example requiring high delay sensitivity. 5G has to support a high reliability and delay sensitivity requirements for V2X in the field of traffic control. For these reasons, V2X is a major application of critical delay. V2X (vehicle-to-infra/vehicle/nomadic) represents all types of communication methods applicable to road and vehicles. It refers to a connected or networked vehicle. V2X can be divided into three kinds of communications. First is the communication between a vehicle and infrastructure (vehicle-to-infrastructure; V2I). Second is the communication between a vehicle and another vehicle (vehicle-to-vehicle; V2V). Third is the communication between a vehicle and mobile equipment (vehicle-to-nomadic devices; V2N). This will be added in the future in various fields. Because the SDN structure is under consideration as the next-generation network architecture, the SDN architecture is significant. However, the centralized architecture of SDN can be considered as an unfavorable structure for delay-sensitive services because a centralized architecture is needed to communicate with many nodes and provide processing power. Therefore, in the case of emergency V2X communications, delay-related control functions require a tree supporting structure. For such a scenario, the architecture of the network processing the vehicle information is a major variable affecting delay. Because it is difficult to meet the desired level of delay sensitivity with a typical fully centralized SDN structure, research on the optimal size of an SDN for processing information is needed. This study examined the SDN architecture considering the V2X emergency delay requirements of a 5G network in the worst-case scenario and performed a system-level simulation on the speed of the car, radius, and cell tier to derive a range of cells for information transfer in SDN network. In the simulation, because 5G provides a sufficiently high data rate, the information for neighboring vehicle support to the car was assumed to be without errors. Furthermore, the 5G small cell was assumed to have a cell radius of 50-100 m, and the maximum speed of the vehicle was considered to be 30-200 km/h in order to examine the network architecture to minimize the delay.