• Journal of Soil and Groundwater Environment
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• v.25 no.2_spc
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• pp.86-100
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• 2020

Analyzing and monitoring environmental contaminants based on geophysical exploration techniques have become important and it is now widely applied to delineate spatial distribution geophysical characteristics in wide area. Among the techniques, induced polarization (IP) method, which measures polarization effects on electrical potential distribution, has drawn much attention as an effective tool for environmental monitoring since IP is sensitive to changes in biochemical reactions. However, various reactions stemming from the presence of multiple contaminants have greatly enhanced heterogeneity of polluted sites to result in highly variable electrical characteristics of the site. Those contaminants influence chemical and physical state of soil and groundwater to alter electrical double layer, which in turn influences polarization of the media. Since biochemical reactions between microbes and contaminants result in various IP effects, IP laboratory experiments were conducted to investigate IP responses of the contaminated soil samples under various conditions. Field IP surveys can delineate the spatial distribution of contamination, while providing additional information about electrical properties of a target medium, together with DC resistivity. Reviewing IP effects of contaminants as well as IP surveys can serve as a good starting point for the application of IP survey in site assessment for environmental remediation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1604-1611
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• 2020

Light field image captured by a microlens array-based camera has many limitations in practical use due to its low spatial resolution and angular resolution. High spatial resolution images can be easily acquired with a single image super-resolution technique that has been studied a lot recently. But there is a problem in that high angular resolution images are distorted in the process of using disparity information inherent among images, and thus it is difficult to obtain a high-quality angular resolution image. In this paper, we propose light field angular super-resolution that extracts an initial feature map using an dilated convolutional neural network in order to effectively extract the view difference information inherent among images and generates target image using a residual neural network. The proposed network showed superior performance in PSNR and subjective image quality compared to existing angular super-resolution networks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.249-256
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• 2022

In the case of Drone Photogrammetry, the "pixel to point tool" module of Metashape, Pix4D Mapper, ContextCapture, and Global MapperGIS, which is a simple software, are widely used. Each SW has its own logic for the analysis of aerial triangulation, but from the user's point of view, it is necessary to select a SW by comparative analysis of the coordinate values of geospatial information for the result. Taking aerial photos for drone photogrammetry, surveying GCP reference points through VRS-GPS Survey, processing the acquired basic data using each SW to construct ortho image and DSM, and GCPSurvey performance and acquisition from each SW The coordinates (X,Y) of the center point of the GCP target on the Ortho-Image and the height value (EL) of the GCP point by DSM were compared. According to the "Public Surveying Work Regulations", the results of each SW are all within the margin of error. It turned out that there is no problem with the regulations no matter which SW is included within the scope.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.355-362
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• 2017

Recently, UAV(Unmanned Aerial Vehicle) have been utilized in various fields, including surveys, mapping, and spatial analysis, depending on the increase in demand for spatial information and UAV is receiving a lot of attention due to rapid data acquisition and economic viability. In this study, the applicability of UAV image images was analyzed for urban disaster prevention. UAV images were acquired for the study area and digital surface model and ortho image were generated through data processing. Also, the process using PPK(Post Processed Kinematic) GNSS method is compared with existing method. Through the research, it was able to effectively deploy urban disaster prevention information about the target area, and displayed the effectiveness of the methods for efficient comparison with existing unmanned aerial photogrammetry. If the PPK technique is applied to thethe disaster prevention field, it is expected that the work flow in the field of rapid data acquisition and disaster prevention data construction can be greatly improved.

• Journal of Aerospace System Engineering
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• v.17 no.6
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• pp.46-53
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• 2023

As urban traffic congestion and environmental pollution are becoming significant issues in major cities, Urban Air Mobility (UAM) is gaining attention as an efficient solution. In this study, we conducted a geographic information system (GIS)-based spatial analysis and clustering algorithm considering the actual data of the terrain and infrastructure in the Busan area, through which we were able to select the location of vertiports and corridors (flight routes) for the UAM operation. Based on the Gimhae International Airport, which is expected to be the center of the UAM infrastructure system in the Busan region, we judged that three vertiport locations in the target area were suitable. Subsequently, we used the A^* (A-star) algorithm considering Ground Risk to select a flight path that minimized both risk and distance. Through this, we confirmed a risk reduction effect of 80.168% compared to the minimum distance route.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.518-540
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• 2024

Mobile technologies have enabled marketers to target consumers anywhere and anytime. However, as consumers react and respond differently depending on what situation they are in, there is an apparent need to determine when, where, and what kind of advertisement is most relevant to the consumer. This paper proposes a holistic approach to examine the response of consumers when faced with two types of contextual factors (environmental/spatial and social contexts) through the lens of the Mobile Advertising Effectiveness Framework. We focus on the contextual effects of perceived distance from the offline store and the effect of popularity cue indication. A scenario-based survey is conducted to investigate the effects of perceived distance and popularity cue on the users' attitudes, and ultimately on their response intentions, upon receipt of mobile ads. Results of the study confirm the hypotheses: first, mobile ads sent when users perceive the physical store to be in close proximity tend to evoke more positive attitudes and elicit better responses compared to when users perceive the store to be farther away. Additionally, ad messages indicating high popularity were found to be more appealing than those with low popularity. These empirical results underscore the pivotal role of context, encompassing both spatial context (proximity to offline stores) and social context (popularity cues), in shaping consumer attitudes and response intentions in mobile advertising. The findings of the study offer theoretical insights that underline the significance of holistic context-based approaches that in turn, marketers may use to design more effective mobile ad campaigns that may elicit better responses from consumers.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.101-107
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• 2020

We propose a realistic seeing through visualization methods in mixed reality environment. When a user wants to see specific location beyond a wall in indoor environment. The proposed system recognizes and registers the selected area using environment modelling and feature-based tracking. Then the selected area is diminished and the specific location is visualized in real-time. With the proposed seeing through methods, a user can understand spatial relationship of the building and can easily find the target location. We conducted a user study comparing the seeing through method to conventional indoor navigation service in order to investigate the potential of the proposed seeing through method. The proposed seeing through method was evaluated in navigation time in comparison with conventional approach. The proposed method enable users to navigate target locations 30% faster than the conventional approach.

• Smart Structures and Systems
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• v.20 no.3
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• pp.385-396
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• 2017

The recent advancements in sensing technologies allow us to record measurements from target structures at multiple locations and with relatively high spatial resolution. Such measurements can be used to develop data-driven methodologies for condition assessment, control, and health monitoring of target structures. One of the state-of-the-art technologies, Fiber Optic Strain Sensors (FOSS), is developed at NASA Armstrong Flight Research Center, and is based on Fiber Bragg Grating (FBG) sensors. These strain sensors are accurate, lightweight, and can provide almost continuous strain-field measurements along the length of the fiber. The strain measurements can then be used for real-time shape-sensing and operational load-estimation of complex structural systems. While several works have demonstrated the successful implementation of FOSS on large-scale real-life aerospace structures (i.e., airplane wings), there is paucity of studies in the literature that have investigated the potential of extending the application of FOSS into civil structures (e.g., tall buildings, bridges, etc.). This work assesses the feasibility of using FOSS to predict operational loads (e.g., wind loads) on chain-like structures. A thorough investigation is performed using analytical, computational, and experimental models of a 4-story steel building test specimen, developed at the University of Southern California. This study provides guidelines on the implementation of the FOSS technology on building-like structures, addresses the associated technical challenges, and suggests potential modifications to a load-estimation algorithm, to achieve a robust methodology for predicting operational loads using strain-field measurements.

• Journal of Cadastre & Land InformatiX
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• v.44 no.1
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• pp.127-138
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• 2014

It can be effective to extract and apply the updated information from the newly updated map data for updating road data of topographic map. In this study, update target data and update reference data are overlaid and the update objects are explored using network matching technique. And the network objects are classified into five matching and update cases and the update processes for each case are applied to the test data. For this study, road centerline data of digital topographic map is used as an update target data and road data of Korean Address Information System is used as an update reference data. The buffer-based network matching method is applied to the two data and the matching and update cases are classified after calculating the overlaid ratio of length. The newly updated road centerline data of digital topographic map is generated from the application of update process for each case. As a result, the update information can be extracted from the different map dataset and applied to the road network data updating.

• Journal of The Korean Astronomical Society
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• v.49 no.6
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• pp.255-259
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• 2016

We estimate the fractal dimension of the ${\rho}$ Ophiuchus Molecular Cloud Complex, associated with star forming regions. We selected a cube (${\upsilon}$, l, b) database, obtained with J = 1-0 transition lines of $^{12}CO$ and $^{13}CO$ at a resolution of 22" using a multibeam receiver system on the 14-m telescope of the Five College Radio Astronomy Observatory. Using a code developed within IRAF, we identified slice-clouds with two threshold temperatures to estimate the fractal dimension. With threshold temperatures of 2.25 K ($3{\sigma}$) and 3.75 K ($5{\sigma}$), the fractal dimension of the target cloud is estimated to be D = 1.52-1.54, where $P{\propto}A^{D/2}$, which is larger than previous results. We suggest that the sampling rate (spatial resolution) of observed data must be an important parameter when estimating the fractal dimension, and that narrower or wider dispersion around an arbitrary fit line and the intercepts at NP = 100 should be checked whether they relate to firms noise level or characteristic structure of the target cloud. This issue could be investigated by analysing several high resolution databases with different quality (low or moderate sensitivity).