• ETRI Journal
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• v.40 no.1
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.507-517
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• 2012

The main purpose of this study is to address flooding resilient land use management strategy based on the distributional characteristics of storm damage areas in Gyeonggi-do. The employed methods are 1) Exploratory Spatial Data Analysis (ESDA) to understand the spatial patterns of storm damage areas occurred from 2005 to 2009, 2) Local Indicator of Spatial Association (LISA) to examine spatial autocorrelation existed in storm damage areas for the year of 2009. The results show that 1) crop land damage is very sensitive to heavy precipitation, 2) damaged buildings are found in all over the Gyeonggi areas, but relatively more damages are in the regions closed to the City of Seoul, 3) damaged roads-bridges, streams, and reaches are found in mostly rural areas, 4) building and crop land damage occurs mostly in lowlands with different spatial patterns. These findings imply that 1) it will be useful to consider the average distances and slopes of damaged building and crop lands from streams for the decision making of land use management strategy, 2) further management efforts are required in the north, east, and south regions of Gyeonggi areas to prevent roads-bridge, stream, and reach damages, 3) the present land use pattern needs to be carefully investigated by considering the damage clustered areas for the year of 2009 based on watershed and municipality boundaries.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.86.2-86.2
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• 2017

Blue stragglers (BSs) are the objects that are brighter and bluer than the stars at main-sequence turn-off point. In this study, we present the Ca-by and VI photometry for Galactic dwarf spheroidal galaxies using Subaru/Suprime-Cam and investigate the spatial distribution characteristics of BS stars using the hk index as a photometric metallicity indicator. We compare the cumulative radial distribution of the BS stars with those of two groups of red-giant-branch (RGB) stars divided by the hk-index strength, and find that the spatial distribution of all BS stars is closer to that of hk-weak (i.e. metal-poor) RGB stars. We also find that the hk-strong BS stars are more centrally concentrated than the hk-weak ones. We will discuss the use of hk-index as a metallicity indicator for the hot BS stars and suggest possible explanations for the results in terms of the origin of BS stars in the dwarf gal.

• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.5-11
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• 1991

Articulation test is a good predictor of spatial speech transmission quality. Like many other languages, articulation testing method using Korean language was proposed in 1989, and which was proved as a valid indicator in rooms with static background noise. In this paper, the testing method is examined in variable noise conditions. According to the experiment performed in 26 classrooms with variable background noise, the proposed articulation testing method using Korean Language is still in variable conditions.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.359-364
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• 2003

In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit), GPS(Global Positioning System), and DMI (Distance Measurement Indicator) is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40㎝ in curve trajectory and 27㎝ in linear trajectory, which are definitely depending on vehicular dynamics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.198-207
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• 2020

Smart City operates with the purpose of solving urban problems. The important thing in smart city operation is that spatial information must be managed at a high level. In addition, it has the characteristics of being managed by one platform. This study presented the core value dimension of smart city based on analysis of various domestic and overseas smart city operation cases. Smart cities are basically operated based on spatial information, and the higher the level of spatial information, the more smart city services can be connected and managed in an integrated manner. The performance dimension of smart city core values presented in the study includes prosperity, personalization, convenience, accuracy, sustainability, safety, environment, integration, etc., and there is a connectivity dimension, a concept that can be managed in an integrated manner. This study will be useful for empirical research on smart city performance dimension design and surveys based on case studies. It will also help field managers who develop, operate, and manage smart cities when quantifying performance dimensions.

• Journal of the Korean Geotechnical Society
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• v.28 no.1
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• pp.17-27
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• 2012

Since geologic data are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations but also to assess the uncertainty attached to the prediction. In this study the assessment of the local uncertainty of prediction for the thickness of the consolidation layer was performed by using the indicator approach. A conditional cumulative distribution function (ccdf) was first modeled, and then E-type estimates and the conditional variance were computed for the spatial distribution of the thickness of the consolidation layer. These results could be used to estimate the spatial distribution of secondary compression and to assess the local uncertainty of secondary compression for Songdo New City.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.399-401
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• 2003

This paper presents a geostatistical contextual classifier for the classification of remote sensing data. To obtain accurate spatial/contextual information, a simple indicator kriging algorithm with local means that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is applied. To illustrate the proposed scheme, supervised classification of multi-sensor remote sensing data is carried out. Analysis of the results indicates that the proposed method improved the classification accuracy, compared to the method based on the spectral information only.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.301-312
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• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

• 지반과기술
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• v.3 no.3
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• pp.15-22
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• 2006

The optimum blasting pattern to excavate a quarry efficiently and economically can be determined based on the minimum production cost, which is generally estimated according to rock fragmentation. Therefore, it is a critical problem to predict fragment size distribution of blasted rocks over an entire quarry. By comparing various prediction models, it can be ascertained that the result obtained from Kuz-Ram model relatively coincides with that of field measurements. Kuz-Ram model uses the concept of rock factor to signify conditions of rock mass such as block size, rock jointing, strength and others. For the evaluation of total production cost, it is imperative to estimate 3-D spatial distribution of rock factor for the entire quarry. In this study, a sequential indicator simulation technique is adopted for estimation of spatial distribution of rock factor due to its higher reproducibility of spatial variability and distribution models than Kriging methods. Further, this can reduce the uncertainty of predictor using distribution information of sample data. The entire quarry is classified into three types of rock mass and optimum blasting pattern is proposed for each type based on 3-D spatial distribution of rock factor. In addition, plane maps of rock factor distribution for each ground level are provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.