• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1881-1903
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• 2015

With the fast-developing of mobile terminals, positioning techniques based on fingerprinting method draws attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve its performance, we propose a radio map building and updating technique, which is able to customize the spatial and temporal dependency of radio maps. The method includes indoor propagation and penetration modeling and the analysis of human traffic. Based on the combination of Ray-Tracing Algorithm, Finite-Different Time-Domain and Rough Set Theory, the approach of indoor propagation modeling accurately represents the spatial dependency of the radio map. In terms of temporal dependency, we specifically study the factor of moving people in the interest area. With measurement and statistics, the factor of human traffic is introduced as the temporal updating component. We improve our existing indoor positioning system with the proposed building and updating method, and compare the localization accuracy. The results show that the enhanced system can conquer the influence caused by moving people, and maintain the confidence probability stable during week, which enhance the actual availability and robustness of fingerprinting-based indoor positioning system.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1499-1502
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• 2002

A method for acquiring an environmental map by integrating distance data obtained by sonars of a moving robot with web interface is proposed. Sonar data contains outliers in some cases such as ultrasonic beam is projected onto a corner of an object. Therefore, the influence of the outliers should be reduced by detecting outliers. In our method, the outliers are detected by two ways: (i) a method considering geometrical .elation among the observed surface and the projected ultrasonic beau, and (ii) a method considering consistency with data obtained by other sonars. By measurement by the sonar, the distance from the sonar to the obstacle is obtained. Assuming the two dimensional space we can know that the inside of the sector, whose renter coincide with the sonar and whose radius is equal to the obtained distance, is the free area, and a part of the arc of this sector is the obstacle area. The generation of the environmental map is done by integrating the free area and the obstacle area obtained by each measurement by the sonars. Before the integration, the outliers detection is done by two ways mentioned above. Experimental results show that obtained maps obtained by our methods with outliers defection are much better than those by a method without outliers detection.

• Journal of Distribution Science
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• v.20 no.8
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• pp.105-114
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• 2022

Purpose: The technology acceptance model (TAM) refers to a theory that maps the possibility or extent to which users can accept an innovative technology. The purpose of the current research is to investigate the mediating effect of TAM between online review and behavior intention for promoting golf app's distribution. Research design, data and methodology: In order to examine the relationship between app usage reviews, TAM, and behavioral intentions of golf app participants, the present author collected total 170 responses from South Korean participants based on web-based survey system. The main methodology which was selected by this study is mediation causality analysis that Baron and Kenny suggested. Results: The statistical findings definitely indicated that TAM mediating role exists between the positive emotion of golf app users regarding online reviews and positive behavior intention of golf app, which means that all three steps of mediation causality analysis were statistically significant. Conclusions: The present research concludes that the correct utilization of innovation in the design and implementation of the technology features translates into performance excellence. The model can be used to increase the online presence through innovation as a primary drive toward providing more convenience and accessibility to the users through mobile golf apps.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.4
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• pp.543-558
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• 2023

Currently, Japan is undertaking a nationwide project to measure and map radioactive contamination around Fukushima, as part of the efforts to restore normalcy following the nuclear accident. The Japan Atomic Energy Agency (JAEA) manages the Fukushima Environmental Safety Center, located approximately 20 km north of the Fukushima Daiichi nuclear power plant in Minamisōma City, Fukushima Prefecture. In collaboration with the JAEA, this study involved conducting comparison experiments and analyses with radiation detectors in high radiation environments, a challenging task in Korean environments. Environmental radiation surveys were conducted using three types of detectors: CZT, NaI(Tl), and LaBr₃(Ce), across two contaminated areas. Dose rate values were converted using dose rate conversion factors for each detector type, and dose rate maps were subsequently created and compared. The detectors yielded similar results, demonstrating their feasibility and reliability in high radiation environments. The findings of this study are expected to be a crucial reference for enhancing the verification and supplementation of procedures and methods in future radiation measurements and mobile surveys in high-radiation environments, using these three types of radiation instruments.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.267-274
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• 2023

LiDAR, one of the most important sensing methods used in mobile robots and cars with assistive/autonomous driving functions, is used to locate surrounding obstacles or to build maps. For real-time path generation, the detection of potholes or puddles on the driving surface is crucial. To achieve this, we used the coordinates of the reflection points provided by LiDAR as well as the intensity information to classify water areas, which was achieved by applying a linear regression method to the intensity distribution. The rationale for using the LiDAR index as an input variable for linear regression is presented, and we demonstrated that it is not affected by errors in the distance measurement value. Because of LiDAR vertical scanning, if the reflective surface is not uniform, it is divided into different groups according to the intensity distribution, and a mathematical basis for this is presented. Through experiments in an outdoor driving area, we could distinguish between flat ground, potholes, and puddles, and kinematic analysis was performed to calculate the maximum width that could be crossed for a given vehicle body size and wheel radius.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.155-166
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• 2019

Road infrastructure has increased explosively due to economic development after industrialization and at present road infrastructure is being changed and increased by construction of new roads and maintenance and expansion of existing roads. Such road infrastructure should support safe driving. Road management plays an important role in safe driving. The purpose of this dissertation is to verify predictability of dangerous sections by analyzing road geometrical structure such as surface irregularity and superelevation for some sections in Central Inland Expressway by MMS and present ways of managing roads using MMS. Having analyzed surface irregularity of roads by using MMS, it was found that over 50 percent of all eight sections, targets of this study need betterments and for superelevation, over 50 percent of two sections goes against superelevation standard. Targets of this study are sections that accidents occurred frequently based on history of past accidents and predictability of dangerous sections can be verified through analysis of road geometrical structure using MMS. Using MMS data created by construction of high definition maps which are being undergone for all roads and methods proposed by this study will help investigate dangerous sections efficiently according to road environment. A result of MMS can be used for maintenance of road furniture.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.637-645
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• 2019

Due to the transformation to the intelligent information society, the rapid change of our life and environment is expected. The Ministry of Science and ICT (MSIT) and the National Research Council of Science and Technology (NST) introduced a five-year government supported research institution's planning and evaluation based on the mid-to long-term perspective. This study collects international benchmarking information including industry, academia, and research fields by collecting mid- and long-term strategy reports from public research institutes, surveys by experts from abroad universities and research institutes, and analyzing overseas market information reports. The British Geological Survey (BGS), the U.S. Geological Survey (USGS) and the japanese geological survey related institutes (AIST-GSJ) plans for three-dimensional national geological information, predictions of geological environmental disasters, and development of important metals and material in the low carbon economic transformation and in the era of the Fourth Industrial Revolution. The mid- and long-term program emphasizes basic and public research on geological information through abroad experts survey such as the IPGP-CNRS etc. The market analysis of the mining automation and digital map sectors has been able to derive the fields in which the role of public research institutes by the market is expected such as data collection on land and in the air, mobile or three-dimensional information production, smooth/fast/real-time maps, custom map design, mapping support to various platforms, geological environmental risk assessment and disaster management information and maps.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.483-491
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• 2013

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.192-199
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• 2016

This article examines the location strategies of coffee franchises in Gyeonggi province. Due to its large population, broad area, and diverse industrial structure, Gyeonggi province is an ideal dataset for empirical testing of the location strategies. We collect the addresses of five major coffee franchises stores, convert them into geographic coordinates using Google Maps Geocoding API, and compute Haversine distances both between stores of the same franchise and between stores of different franchises. This novel approach leads to three discoveries. First, coffee-consuming age population is positively related to the number of stores and more strongly for commercial areas with a large floating population. Second, one third of Starbucks stores have another Starbucks store within a radius of 300m, which empirically confirms the 'Focused Destroy Strategy' of Starbucks that has multiple stores in central business districts. Third, for 80% of Starbucks stores, we can find Ediya stores within 500m, which supports Ediya's 'Next-to-Starbucks Strategy'. Our research methods can be efficiently applied to the analyses of other retail businesses such as convenience stores, fast food restaurants, and mobile phone shops.