• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.331-340
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• 2023

Predicting the survival time of a person in water (PIW) in maritime search and rescue (SAR) operations is an important concern. Although there have been many studies on survival models in marine-developed countries, it is difficult to apply them to Koreans in Korea's oceans because they were developed using marine distress data from the United Kingdom, United States, and Canada. Data on the survival time of a P IW were collected through interviews and surveys with a special rescue team from the Korea Coast Guard, SAR cases, press releases, and Korea Meteorological Administration data to address these issues. The maximum survival time (Korean) equation was developed by performing a regression analysis of this data, and the applicability to actual marine distress was reviewed and compared to the overseas survival model. By comprehensively using the maximum survival time (Korean), domestic SAR cases, and overseas survival models, guidelines for survival time and intensive and recommended search time were suggested. The study findings can contribute to decision-making, such as the input for search and rescue units. The findings can also help to determine the end of or reductions in SAR operations and explain policy decisions to the public and families of a PIW.

• Journal of the Korean Geotechnical Society
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• v.18 no.3
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• pp.51-59
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• 2002

This paper deals with development of a damage risk assessment system for adjacent buildings to under-passing tunnel face considering 3D-ground movement. The system consists of building and ground information module, monitoring data module, settlement evaluation module, and building damage risk assessment module. The major modules, settlement evaluation module and building damage assessment module, are based on settlement estimation model suggested by Attewell et al (1982) and the building damage assessment method by Mair et al. (1996). After estimating 3D-ground movements due to tunneling with settlement evaluation module, damage assessment far buildings is performed using building damage risk assessment module. The developed system has two major functions; 1) calculation of 3D-settlement with ground loss ($V_{s}$)or maximum settlement ($w_{max}$) and inflection point (i) using various empirical formulae, monitoring data, numerical results, and so on; 2) assessment of damage risk for adjacent buildings of arbitrary section with position change of tunnel face. The field data given by Boscadin and Cording (1989) leer the case of two-storied masonry building near the Metro tunnel in Washington D.C. was simulated to verify the applicability of the developed system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.4
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• pp.113-131
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• 2015

Decision effectiveness depends on type of knowledge within team members generated by decision making process. Thus, organization in accordance with teams' experience and capability ultimately achieve their desired outcome. However, previous research has not addressed a mediating role between different knowledge type in decision making and product competitive advantages(pioneering advantage and product quality superiority). Based on the knowledge-based view, we model how different knowledge characteristics in decision making affect to acquire each of knowledge in decision making effectively and then to apply acquired knowledge in decision making. Anchored in a source-position-performance (SPP) framework (Day and Wensley's, 1988), we shed light on the effects of three knowledge characteristics dimensions in decision making process on knowledge management competences in decision making for a new product project. We also examine the relationship between two dimensions of NPD knowledge management competences, and product competitive advantages which consist of market pioneering advantage and product quality superiority. To test the relationships, the empirical analyses are conducted using a sample of team managers who participated in NPD projects. This study suggest that managers should increase their acquirability and applicability of knowledge by integrating complexity of diverse and new knowledge, developing codifiability of well-documented knowledge, and creating the sharing common knowledge among NPD team members. Thus, they are able to outrun major competitors in terms of pioneering advantage and product quality superiority perspective.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.297-304
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• 2015

Recently, the demand for a PRT(Personal Rapid Transit) system based on autonomous navigation is increasing. Accordingly, the applicability investigations of the PRT system on rail tracks or roadways have been widely studied. In the case of unmanned vehicle operations without physical guideways on roadways, to monitor the position of the vehicle in real time is very important for stable, robust and reliable guidance of an autonomous vehicle. The Global Positioning System (GPS) has been commercially used for vehicle positioning. However, it cannot be applied in environments as tunnels or interiors of buildings. The PRT navigation system based on magnetic markers reference sensing that can overcome these environmental restrictions and the vehicle dynamics model for its H/W configuration are presented in this study. In addition, the design of a control S/W dedicated for unmanned operation of a PRT vehicle and its prototype implementation for experimental validation on a pilot network were successfully achieved.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.153-170
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• 2022

Waterjet has been comprehensively used in urban areas owing to a suitable technique for cutting concrete and rock, and low noise and vibration. Recently, the abrasive waterjet technique has been adopted and applied by the Korea Atomic Energy Research Institute to demolish concrete plugging without disturbing and damaging In-situ Demonstration of Engineered Barrier System in the disposal research tunnel. In this study, the use of abrasive waterjet in the tunnel was evaluated for practical applicability and the existing cutting model was compared with the experimental results. As a variable for waterjet cutting, multi-cutting, water flow rate, abrasive flow rate, and standoff distance were selected for the diversity of analysis. As regarding the practical application, the waterjet facilitated path selection for cutting the concrete plugging and prevented additional disturbances in the periphery. The pump's noise at idling was 64.9 dB which is satisfied with the noise regulatory standard, but it exceeded the standard at ejection to air and target concrete because the experiment was performed in the tunnel space. The experimental result showed that the error between the predicted and measured cutting volume was 12~13% for the first cut and 16% for second cut. The standoff distance had a significant influence on the cutting depth and width, and the error tended to decrease with decrement of standoff distance.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3A
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• pp.215-225
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• 2011

In general, the precast columns can obtain its homogeneous quality as they are produced in a factory with a hollow concrete block type by using high strength concrete, so that they can generate the reduction of dead load. Such a method of precast hollow concrete columns is already implemented in USA and Japan and used for connecting between blocks which use PC tendons. However, it is inevitable to have uneconomical construction with excessive cost in early stage when PC tendons are used. This study aims to develop an economical precast column with high quality and constructability which consists of only splice sleeve and general reinforcing bar without using PC tendons in order to reduce the construction period and cost. To achieve this goal, this study tested the performance of total 5 minimized models in the experiment with the variables such as hollowness, diameter of main reinforcement bar and cross-sectional size for the cross section of precast column by using grouting type splice sleeve which is a new type joint rebar. And it also verified the performance of column in the experiment for a large-sized model in order to overview its applicability by excluding large scale effect.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.6
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• pp.567-576
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• 2017

The object of this research is an establishment of BIM-based quantity takeoff(QTO) and cost estimation guidelines for reinforcement concrete structures focused on improvement of field applicability in transition period from 2D drawing-based environment to 3D BIM-based environment. Preliminary studies on existing guidelines and standards for BIM modeling, QTO and cost estimation of reinforcement concrete structures are performed, and then a standardization of BIM-based cost estimation process is proposed through comparative analysis between 2D drawing-based cost estimation process and 3D BIM-based. In addition, modeling, QTO, and cost estimation processes of cost-BIM model for RC structures are conducted. The contributions of this research and the guidelines suggested by this research are 1) lowering barriers to entity to the new BIM-based environment for small size companies, 2) reducing construction cost by a close estimate, 3) establishing the foundation for integrated management of informations through construction project life cycle, 4) and ultimately, developing the BIM ecosystems.