• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.59-69
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• 2021

Historical records of earthquakes are generally used as a basis to extrapolate the instrumental earthquake catalog in time and space during the probabilistic seismic hazard analysis (PSHA). However, the historical catalogs' input parameters determined through historical descriptions rather than any quantitative measurements are accompanied by considerable uncertainty in PSHA. Therefore, quantitative assessment to verify the historical earthquake parameters is essential for refining the reliability of PSHA. This study presents an approach and its application to constrain reliable ranges of the magnitude and corresponding epicenter of historical earthquakes. First, ranges rather than specific values of ground motion intensities are estimated at multiple locations with distances between each other for selected historical earthquakes by reviewing observed co-seismic natural phenomena, structural damage levels, or felt areas described in their historical records. Based on specific objective criteria, this study selects only one earthquake (July 24, 1643), which is potentially one of the largest historical earthquakes. Then, ground motion simulations are performed for sufficiently broadly distributed epicenters, with a regular grid to prevent one from relying on strong assumptions. Calculated peak ground accelerations and velocities in areas with the historical descriptions on corresponding earthquakes are converted to intensities with an empirical ground motion-intensity conversion equation to compare them with historical descriptions. For the ground motion simulation, ground motion prediction equations and a frequency-wavenumber method are used to consider the effects of possible source mechanisms and stress drop. From these quantitative calculations, reliable ranges of epicenters and magnitudes and the trade-off between them are inferred for the earthquake that can conservatively match the upper and lower boundaries of intensity values from historical descriptions.

• The Journal of Information Systems
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• v.30 no.4
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• pp.329-347
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• 2021

Purpose The purpose of this study is to examine the review of previous studies based on audiobooks, 1) using the IPA method, we examine the quality of audiobooks that listeners perceive as important and the quality of preferred audiobooks, 2) improvement directions for activating audio content and audiobook platform, to seek ways to establish strategic plans for the audiobook market. Design/methodology/approach This study conducted and experiment using three factors(usability, information and interaction) used in web service quality(WebQual). Usability consists of the availability of time, such as the use of audiobooks for hobbies and the ease of search. Information consists of the trends of the times, topics of converation and the latest trends. Interaction consists of the proportion and depth of celebrities. After that, grid analysis was performed using the traditional IPA method and the revised IPA method. It was judged that identifying and analyzing the preference for service provision and new content was more valuable than the Performance used in the existing method. Findings According to the results of the empirical analysis, it was found that the conversation material corresponding to informativity did not affect the audiobook during the online quality analysis. It is believed that improvements can be found through content production related to entertainment, webtoons, and general common sense that can increase listeners' interest. In addition, it was found that the advertising effect of celebrities corresponding to interactivity did not affect the results of this study, which should increase the concentration and immersion of listeners by increasing the technology for audiobooks.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.110-117
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• 2023

Reconstructing underwater geometry in real time with forward-looking sonar is critical for applications such as localization, mapping, and path planning. Geometrical data must be repeatedly calculated and overwritten in real time because the reliability of the acoustic data is affected by various factors. Moreover, scattering of signal data during the coordinate conversion process may lead to geometrical errors, which lowers the accuracy of the information obtained by the sensor system. In this study, we propose a three-step data processing method with low computational cost for real-time operation. First, the number of data points to be interpolated is determined with respect to the distance between each point and the size of the data grid in a Cartesian coordinate system. Then, the data are processed with a nonlinear interpolation so that they exhibit linear properties in the coordinate system. Finally, the data are transformed based on variations in the position and orientation of the sonar over time. The results of an evaluation of our proposed approach in a simulation show that the nonlinear interpolation operation constructed a continuous underwater geometry dataset with low geometrical error.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.71-71
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• 2021

현재 국내에서 사용되고 있는 지자체 단위 위험도 평가 기법들은 자연재난과 사회재난으로부터 유발되는 여러 위험성들을 함께 고려하여 평가에 반영하고 있다. 또한, 지자체 내에서 홍수위험에 노출될 수 있는 대상만을 선별하여 분석한 것이 아닌 지자체별 단순 통계값으로 평가가 이루어지기 때문에 홍수위험에 대한 정확한 평가가 어렵다는 한계를 가지고 있다. 따라서 본 연구에서는 Indicator Based Approach(IBA)에서 제시하는 평가 항목인 Hazard, Exposure, Vulnerability, Capacity 중 Exposure에 해당하는 건축물수를 대상으로 홍수위험지도와 중첩되는 건축물들을 선별하여 홍수위험도 평가를 수행하였다. 지자체별 건축물수 산정은 2018년 11월 기준 경기도 31개 시군별 도로명주소 전자지도(건물)와 500m × 500m 건축물수 격자자료를 사용하였다. 건축물수 격자자료는 도로명주소 전자지도의 건물 폴리곤 자료 대비 분석이 간편하다는 장점을 가지고 있다. 비교 분석을 통해 공간분석자료의 유형에 따라 발생하는 통계값의 차이는 격자자료에 보정계수를 적용하여 보완하였다. 보정된 경기도 지자체별 건축물수 격자자료로 세부지표 지수를 산정한 결과 단순히 자지체별 건축물수를 사용했을 때에는 화성시, 용인시, 평택시 순으로 지수가 크게 산정되었다, 하지만 홍수위험지도와 중첩된 건축물수를 사용했을 때에는 고양시, 광명시, 김포시 순으로 지수가 크게 산정되었다. 본 연구를 통해서 건축물수 격자자료와 홍수위험지도를 사용하여 위험도 평가를 수행했을 때 기존 방법론 대비 합리적인 평가결과를 얻을 수 있었다.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.74-82
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• 2019

The revolution of industry 4.0 is enabling us to build an intelligent connection society called smart cities. The use of renewable energy in particular solar energy is extremely important for modern society due to the growing power demand in smart cities, but its difficult to monitor and manage in each buildings since need to be deploy low energy sensors and information need to be transfer via wireless sensor network (WSN). The Internet of Things (IoT) / low-power wide-area (LPWA) is an emerging WSN technology, to collect and monitor data about environmental and physical electrical / electronics devices conditions in real time. However, providing power to IoT sensor end devices and other public electrical loads such as street lights, etc is an important challenging role because the sensor are usually battery powered and have a limited life time. In this paper, we proposes an efficient solar energy-based power management scheme for smart city based on IoT technology using LoRa wide-area network (LoRaWAN). This approach facilitates to maintain and prevent errors of solar panel based energy systems. The proposed solution maximizing output the power generated from solar panels system to distribute the power to the load and the grid. In this paper, we proved the efficiency of the proposed system with Simulink based system modeling and real-time emulation.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1223-1232
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• 2017

This paper presents an approach to detect changes caused by urban development over a large area using KOMPSAT optical images. In order to minimize the radiometric dissimilarities between the images acquired at different times, we apply the grid-based rough radiometric correction as a preprocessing to detect changes in a large area. To improve the accuracy of the change detection results for urban development, we mask-out non-interest areas such as water and forest regions by the use of land-cover map provided by the Ministry of Environment. The Change Vector Analysis(CVA) technique is applied to detect changes caused by urban development. To confirm the effectiveness of the proposed approach, a total of three study sites from Sejong City is constructed by combining KOMPSAT-2 images acquired on May 2007 and May 2016 and a KOMPSAT-3 image acquired on March 2014. As a result of the change detection accuracy evaluation for the study site generated from the KOMPSAT-2 image acquired on May 2007 and the KOMPSAT-3 image acquired on March 2014, the overall accuracy of change detection was about 91.00%. It is demonstrated that the proposed method is able to effectively detect urban development changes in a large area.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.17-35
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• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.93-102
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• 2018

This paper reports a Surge Protective Device (SPD) that is used to protect an automatic metering interface (AMI) power supplies of communication equipment on a low-voltage distribution system from a lightning current. The surge protective device (SPD) can be classified as one-port SPDs and two-port SPDs with decoupling elements depending on the connection type. The protection of internal systems against the lightning current may require a systematic approach consisting of coordinated SPDs. To deal with this, the definition of a lightning protection zone (LPZ) was studied and interpreted through a theoretical review. Because the lightning current resulting from a lightning surge is considerably high, there is limited protection from one SPD; therefore, coordinated cascaded MOV-based SPDs are installed to solve this problem. Regarding the power grid mentioned in this paper, a class II SPD for the low-voltage distribution system installed on the border of LPZ1 and LPZ2, which establish a protection coordination with the Arrester (LA, SA) that corresponds to the LPZO installed on the MOF stage connected to one system were designed to protect various communication (control) equipment, including the automatic meter reading system inside the branch-type electric supply panel of a building, not the incoming side of one system. In addition, performance-related tests were done by a comparison with the existing method through testing, and the optimal design was achieved for the 1-port SPD that uses a series connection and can bleed load current without any decoupling element.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.81-99
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• 2015

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.