• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2523-2529
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• 2014

Location estimation service can be provided outdoors using various location estimation system based on GPS. However, location estimation system is based on existing indoor resources as GPS cannot be used because of insufficient visible satellites and weak signals. The fingerprinting technique that uses WLAN signal, in particular, is good to use indoors because it uses RSSI provided by AP to estimate location. However, its accuracy may vary depending on how accurate data the offline stage used where the fingerprinting map is built. The study sampled various data at the stage that builds the fingerprinting map and suggested a location estimation system that enhances its precision by saving the data of high frequency among them to improve this problem. The suggested location estimation system based on majority sampling data estimates location by filtering RSSI data of the highest frequency at the client and server to be saved at a map, building the map and measuring a similar distance. As a result of the test, the location estimation precision stood at minimum 87.5 % and maximum 90.4% with the margin of error at minimum 0.25 to 2.72m.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.585-596
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• 2022

This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.

• Journal of Radiation Protection and Research
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• v.33 no.1
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• pp.13-20
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• 2008

The flow rate measurements in a multi-phase flow pipeline were evaluated quantitatively by means of a clamp-on sealed radioisotope based on a cross correlation signal processing technique. The flow rates were calculated by a determination of the transit time between two sealed gamma sources by using a cross correlation function following FFT filtering, then corrected with vapor fraction in the pipeline which was measured by the ${\gamma}$-ray attenuation method. The pipeline model was manufactured by acrylic resin(ID. 8 cm, L=3.5 m, t=10 mm), and the multi-phase flow patterns were realized by an injection of compressed $N_2$ gas. Two sealed gamma sources of $^{137}Cs$ (E=0.662 MeV, ${\Gamma}$ $factor=0.326\;R{\cdot}h^{-1}{\cdot}m^2{\cdot}Ci^{-1}$) of 20 mCi and 17 mCi, and radiation detectors of $2"{\times}2"$ NaI(Tl) scintillation counter (Eberline, SP-3) were used for this study. Under the given conditions(the distance between two sources: 4D(D; inner diameter), N/S ratio: $0.12{\sim}0.15$, sampling time ${\Delta}t$: 4msec), the measured flow rates showed the maximum. relative error of 1.7 % when compared to the real ones through the vapor content corrections($6.1\;%{\sim}9.2\;%$). From a subsequent experiment, it was proven that the closer the distance between the two sealed sources is, the more precise the measured flow rates are. Provided additional studies related to the selection of radioisotopes their activity, and an optimization of the experimental geometry are carried out, it is anticipated that a radioisotope application for flow rate measurements can be used as an important tool for monitoring multi-phase facilities belonging to petrochemical and refinery industries and contributes economically in the light of maintenance and control of them.

• Geophysics and Geophysical Exploration
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• v.11 no.2
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• pp.127-136
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• 2008

To identify the potential area of gas hydrate in the Ulleung Basin, 2-D and 3-D seismic surveys using R/V Tamhae II were conducted in 2005 and 2006. Seismic survey equipment consisted of navigation system, recording system, streamer cable and air-gun source. For reliable velocity analysis in a deep sea area where water depths are mostly greater than 1,000 m and the target depth is up to about 500 msec interval below the seafloor, 3-km-long streamer and 1,035 $in^3$ tuned air-gun array were used. During the survey, a suite of quality control operations including source signature analysis, 2-D brute stack, RMS noise analysis and FK analysis were performed. The source signature was calculated to verify its conformity to quality specification and the gun dropout test was carried out to examine signature changes due to a single air gun's failure. From the online quality analysis, we could conclude that the overall data quality was very good even though some seismic data were affected by swell noise, parity error, spike noise and current rip noise. Especially, by checking the result of data quality enhancement using FK filtering and missing trace restoration technique for the 3-D seismic data inevitably contaminated with current rip noises, the acquired data were accepted and the field survey could be conducted continuously. Even in survey areas where the acquired data would be unsuitable for quality specification, the marine seismic survey efficiency could be improved by showing the possibility of noise suppression through onboard data processing.