• Korean Journal of Construction Engineering and Management
• /
• v.19 no.3
• /
• pp.23-32
• /
• 2018

As the size of the construction industry becomes larger, there are many tries to change the construction education for growing the pre-engineering who has many directly experience. However, architectural construction education, which requires a lot of practical understanding, is still limited to educational textbooks and field trips. Therefore, this study proposed the development of educational contents which can indirectly experience by combining smart media such as time-lapse, video and animation. Also, we verified the effectiveness of contents developed based on usability, practicality(fitness of education), necessity, possibility of commercialization. We will contribute to enhance the effectiveness of education through building construction education contents that take advantage of contents based on this.

• Atmosphere
• /
• v.31 no.3
• /
• pp.341-359
• /
• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• International Journal of Computer Science & Network Security
• /
• v.22 no.8
• /
• pp.299-303
• /
• 2022

The article examines the stage of verification of a crime report from the standpoint of the need for its legislative regulation. Moreover, it investigates the international experience in this field. The existing procedural models are described in detail on the example of the neighboring and faraway countries. An analysis of the provisions of the current criminal procedure law of Russia and foreign experience allowed the authors to identify existing problems in the implementation of departmental control and prosecutorial supervision at the stage of verifying a crime report. The aim of the study is to develop theoretical provisions and recommendations regarding the implementation of departmental procedural control and prosecutorial supervision over the activities of the investigator during the verification of reports of crimes, based on the study of experience, both in Russia and in a number of countries of the near and far abroad, which could find their reflection in law enforcement practice, as well as aimed at improving the current criminal procedure legislation. The authors substantiated the theory that a detailed examination of the foreign procedural foundations of checking a crime report will allow us to form the most suitable model for checking a crime report for our state, taking into account all possible features and successfully implement it into the current criminal procedural law of the Russian Federation.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.1
• /
• pp.23-35
• /
• 2023

Korea Augmentation Satellite System (KASS) is a satellite-based augmentation system (SBAS) that provides approach procedure with vertical guidance-I (APV-I) level corrections and integrity information to Korea territory. KASS is used to monitor navigation performance in real-time, and this paper introduces the design, implementation, and verification process of mission monitoring (MIMO) in KASS. MIMO was developed in compliance with the Minimum Operational Performance Standards of the Radio Technical Commission for Aeronautics for Global Positioning System (GPS)/SBAS airborne equipment. In this study, the MIMO system was verified by comparing and analyzing the outputs of reference tools. Additionally, the definition and derivation method of accuracy, integrity, continuity, and availability subject to MIMO were examined. The internal and external interfaces and functions were then designed and implemented. The GPS data pre-processing was minimized during the implementation to evaluate the navigation performance experienced by general users. Subsequently, tests and verification methods were used to compare the obtained results based on reference tools. The test was performed using the KASS dataset, which included GPS and SBAS observations. The decoding performance of the developed MIMO was identical to that of the reference tools. Additionally, the navigation performance was verified by confirming the similarity in trends. As MIMO is a component of KASS used for real-time monitoring of the navigation performance of SBAS, the KASS operator can identify whether an abnormality exists in the navigation performance in real-time. Moreover, the preliminary identification of the abnormal point during the post-processing of data can improve operational efficiency.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.4
• /
• pp.511-523
• /
• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.39 no.9
• /
• pp.735-742
• /
• 2015

In this paper, we compare and verify the prediction accuracy and feasibility for wind resources on a wind farm using the Weather Research and Forecasting (WRF) model, which is a numerical weather-prediction model. This model is not only able to simulate local weather phenomena, but also does not require automatic weather station (AWS), satellite, or meteorological mast data. To verify the feasibility of WRF to predict the wind resources required from a wind farm pre-feasibility study, we compare and verify measured wind data and the results predicted by WAsP. To do this, we use the Pyeongdae and Udo sites, which are located on the northeastern part of Jeju island. Together with the measured data, we use the results of annual and monthly mean wind speed, the Weibull distribution, the annual energy production (AEP), and a wind rose. The WRF results are shown to have a higher accuracy than the WAsP results. We therefore confirmed that WRF wind resources can be used in wind farm pre-feasibility studies.

• Bulletin of the Society of Naval Architects of Korea
• /
• v.54 no.4
• /
• pp.39-46
• /
• 2017

Jack-up drilling rigs are widely used in offshore oil and gas exploration industry. It is originally designed for use in the shallow waters less than 60m of water depth; there is growing demand for their use in deeper water depth over 150m and harsher environmental conditions. In this study, global in-place analysis of jack-up rig leg for North-sea oil well is performed through numerical analysis. Firstly, environmental conditions and seabed characteristics at the North-sea are collected and investigated measurements from survey report. Based on these data, design specifications are established and the overall basic design is performed. Dynamic characteristics of the jack-up rig for North-sea are considered in the global in-place analysis both leg and hull and the basic stability against overturning moment is also analyzed. The structural integrity of the jack-up rig leg/hull is verified through the code checks and the adequate safety margin is observed. The uncertainty in jack-up behaviour is greatly influenced by the uncertainties in the soil characteristics that determine the resistance of the foundation to the forces imposed by the jack-up structure. Among the risks above mentioned, the punch-through during pre-loading is the most frequently encountered foundation problem for jack-up rigs. The objective of this paper is to clarify the detailed structure and installation engineering matters for prove the structural safety of jack-up rigs during operation. With this intention the following items are addressed; - Characteristics of structural behavior considering soil effect against environmental loads - Modes of failure and related pre-loading procedure and parameters - Typical results of structural engineering and verification by actual measurement.

• Journal of Korean Academy of Nursing
• /
• v.45 no.2
• /
• pp.221-230
• /
• 2015

Purpose: This study was done to examine how laughter therapy impacts serotonin levels, QOL and depression in middle-aged women and to perform a path analysis for verification of the effects. Methods: A quasi-experimental study employing a nonequivalent control group and pre-post design was conducted. Participants were 64 middle-aged women (control=14 and experimental=50 in 3 groups according to level of depression). The intervention was conducted five times a week for a period of 2 weeks and the data analysis was conducted using repeated measures ANOVA, ANCOVA and LISREL. Results: Results showed that pre serotonin and QOL in women with severe depression were the lowest. Serotonin in the experimental groups increased after the 10th intervention (p=.006) and the rise was the highest in the group with severe depression (p=.001). Depression in all groups decreased after the 5th intervention (p=.022) and the biggest decline was observed in group with severe depression (p=.007). QOL of the moderate and severe groups increased after the 10th intervention (p=.049), and the increase rate was highest in group with severe depression (p<.006). Path analysis revealed that laughter therapy did not directly affect depression, but its effect was indirectly meditated through serotonin variation (p<.001). Conclusion: Results indicate that serotonin activation through laughter therapy can help middle-aged women by lessening depression and providing important grounds for depression control.

• Journal of the Korea society of information convergence
• /
• v.6 no.2
• /
• pp.15-22
• /
• 2013

Applying iterative learning utilizing SNS & Community to the class for pre-kindergarten teachers, the effectiveness of teaching satisfaction, self-efficacy, and curriculum understanding was verified. A iterative learning model utilizing SNS & Community in teachers leading traditional off-line teaching at college education field was applied separately into thinking to one-self by advance organizer, thinking together by presentation in the beginning of the class, and sharing the thoughts by community activities after the class. Iterative learning begins by being sent SNS to students from teachers before the class, but learners for themselves subsequently start to proceed self-directed learning activities. As a result, class satisfaction and understanding of pedagogy have been increased, and it had a positive influence on self-efficacy. Thus, it is to suggest utilizable SNS of professors and a teaching method utilizing Community to college students who need basic learning skills.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.4
• /
• pp.526-533
• /
• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.