• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.4
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• pp.492-500
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• 2014

Objectives: The purpose of this study is to estimate the applicability of regional sample collection of environmental samples. The concentration of asbestos fibers were analyzed with two devices. One was an existing commercial air sampling pump that has been proved to be accurate and exact, and the other is a remodeled pump for sample collection which was made from an electric bubble generator originally designed for aquarium fish. Samples were collected with the two devices under the same environmental conditions and collection equipment. A comparative analysis of the concentration of ambient asbestos fiber was then performed. Methods: Based on previous research, six farmhouses with asbestos fiber slate roofs known to have high concentrations of asbestos fiber were selected. Using the existing commercial air sampling pump and the remodeled electric bubble generator, four to seven samples were collected each day one meter downwind from the edge of the slate roof at high volume (about 4 L/min) and low volume (about 1.4 L/min). The analyzer responsible for sample quality control of asbestos fibers counted the number of asbestos fibers with a phase microscope. Results: The rates of flow change of the existed sampler and the remodeled pump at high volume were 0.82% and 0.17%, respectively. The rates of flow change at low volume were 3.83% and 1.09%, but there was not significant difference. The rates of flow change are within the error range (${\pm}5%$) of OSHA analyzing methods. For the high volume sampler, the average asbestos fiber concentration in the air collected by the existed sampler is 6.270 fibers/L and for the remodeled one 5.527 fibers/L, not a significant difference. For the low volume sampler, the average asbestos fiber concentration in the air collected by the existed sampler is 7.755 fibers/L and for the remodeled one 7.706 fibers/L, not a significant difference. The total area of the slate roof of the targeted farmhouse has an effect on the concentration of asbestos fibers in the air from the existing pump and the remodeled one (p<0.01). Conclusions: The sampling function between the existing commercial pump and the remodeled one shows little difference. Therefore, the remodeled pump is considered a pump with a good availability for collecting ambient air asbestos samples.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.597-604
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• 2014

In this study, we investigate to show the limitations of current bus arrival time estimation model based on each bus route, and to propose a bus arrival time estimation model based on a bus stop to overcome these limitations. Using the characteristic of bus arrival time calculated on travel time between two bus stops, we develop a model to estimate bus arrival times with the data of all buses traveling the same section regardless of bus route numbers. In the proposed model, an estimated arrival time is calculated by weighted moving average method, and verification between observed value and estimated time is performed on the basis of RMSE. Error was reduced by up to 20% compared to the existing models and the data update period was reduced by more than half that is related to the accuracy of bus arrival time information. We expect to solve the following problems with the suggested method: sudden increase or decrease in arrival time of the bus, the difference of the expected arrival times at the same stop between two or more buses having different route numbers, and impossibility of offering information of a bus if the bus is not operated with the designated schedule.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.137-149
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• 2011

In this study, we analyzed the effects of seismic displacements due to the mega thrust earthquake occurred near Tohoku-Oki area on Mar. 11, 2011 with Mw 9.0 magnitude in the context of evaluation of position change by the earthquake on the Korean and Asian GPS permanent stations. For this, two weeks GPS data observed around the event of Tohoku-Oki earthquake (Mar. 4 ~ Mar. 18, 2011) were obtained from 22 GPS permanent stations in the vicinity of epicenter (Korea, Japan, Russia, China and Taiwan) and 284 IGS global stations. All available GPS data were processed and adjusted by GAMIT/GLOBK software to estimate the co-seismic horizontal displacements at each station. As the results of GPS analysis, the co-seismic displacements due to Tohoku-Oki earthquake were clearly revealed in the GPS stations of Asian region, Japan and its neighboring countries, and even to affect the horizontal position of GPS station (WUHN in China) which are located about 2,702km away from the epicenter. In conclusion, it was found that the Tohoku-Oki earthquake had resulted in the horizontal displacements ranging from 14.9 mm to 58.3 mm in Korea. So, these displacements can cause the position error of GPS geodetic survey up to 20 mm without updating the coordinates of Korean geodetic network.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.11
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• pp.51-62
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• 2003

This paper presents a novel survivor memeory management and decoding techniques with sequential backward state transition control in the trace back Viterbi decoder. The Viterbi algorithm is an maximum likelihood decoding scheme to estimate the likelihood of encoder state for channel error detection and correction. This scheme is applied to a broad range of digital communication such as intersymbol interference removing and channel equalization. In order to achieve the area-efficiency VLSI chip design with high throughput in the Viterbi decoder in which recursive operation is implied, more research is required to obtain a simple systematic parallel ACS architecture and surviver memory management. As a method of solution to the problem, this paper addresses a progressive decoding algorithm with sequential backward state transition control in the trace back Viterbi decoder. Compared to the conventional trace back decoding techniques, the required total memory can be greatly reduced in the proposed method. Furthermore, the proposed method can be implemented with a simple pipelined structure with systolic array type architecture. The implementation of the peripheral logic circuit for the control of memory access is not required, and memory access bandwidth can be reduced Therefore, the proposed method has characteristics of high area-efficiency and low power consumption with high throughput. Finally, the examples of decoding results for the received data with channel noise and application result are provided to evaluate the efficiency of the proposed method.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.545-555
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• 2020

A feasibility modeling for potential hydroelectric dam site selection was suggested using 1 sec ASTGTM (ASTER Global Digital Elevation Model) and Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) derived land use (MCD12Q1) data. The modeling includes DEM pre-processing of peak, sink, and flat, river network generation, watershed delineation and segmentation, terrain analysis of stream cross section and reservoir storage, and estimation of submerged area for compensation. The modeling algorithms were developed using Python and as an open source GIS. When a user-defined stream point is selected, the model evaluates potential hydroelectric head, reservoir surface area and storage capacity curve, watershed time of concentration from DEM, and compensation area from land use data. The model was tested for 4 locations of already constructed Buhang, BohyunMountain, Sungdeok, and Yeongju dams. The modeling results obtained maximum possible heads of 37.0, 67.0, 73.0, 42.0 m, surface areas of 1.81, 2.4, 2.8, 8.8 ㎢, storages of 35.9, 68.0, 91.3, 168.3×10⁶ ㎥ respectively. BohyunMountain and Sungdeok show validity but in case of Buhang and Yeongju dams have maximum head errors. These errors came from the stream generation error due to ASTGTM. So, wrong dam watershed boundary limit the head. This study showed a possibility to estimate potential hydroelectric dam sites before field investigation especially for overseas project.

• Journal of Biomedical Engineering Research
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• v.41 no.2
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• pp.107-119
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• 2020

The deep neural networks (DNN) that can replicate the behavior of the human expert who recognizes the characteristics of ECG waveform have been developed and studied to analyze ECG. However, although the existing DNNs can not provide the explanations for their decisions, those trials have attempted to determine whether patients have certain diseases or not and those decisions could not be accepted because of the absence of relating theoretical basis. In addition, these DNNs required a lot of training data to obtain sufficient accuracy in spite of the difficulty in the acquisition of relating clinical data. In this study, a small-sized continuous data processing DNN (C-DNN) was suggested to determine the simple characteristics of ECG wave that were not required additional explanations about its decisions and the C-DNN can be easily trained with small training data. Although it can analyze small input data that was selected in narrow region on whole ECG, it can continuously scan all ECG data and find important points such as start and end points of P, QRS and T waves within a short time. The star and end points of ECG waves determined by the C-DNNs were compared with the results performed by human experts to estimate the accuracies of the C-DNNs. The C-DNN has 150 inputs, 51 outputs, two hidden layers and one output layer. To find the start and end points, two C-DNNs were trained through deep learning technology and applied to a parameter acquisition algorithms. 12 lead ECG data measured in four patients and obtained through PhysioNet was processed to make training data by human experts. The accuracy of the C-DNNs were evaluated with extra data that were not used at deep learning by comparing the results between C-DNNs and human experts. The averages of the time differences between the C-DNNs and experts were 0.1 msec and 13.5 msec respectively and those standard deviations were 17.6 msec and 15.7 msec. The final step combining the results of C-DNN through the waveforms of 12 leads was successfully determined all 33 waves without error that the time differences of human experts decision were over 20 msec. The reliable decision of the ECG wave's start and end points benefits the acquisition of accurate ECG parameters such as the wave lengths, amplitudes and intervals of P, QRS and T waves.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.2
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• pp.43-55
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• 2015

In the case of satellite navigation positioning, the shielding of satellite signals is determined by the environment of the region at which a user is located, and the navigation performance is determined accordingly. The accuracy of user position determination varies depending on the dilution of precision (DOP) which is a measuring index for the geometric characteristics of visible satellites; and if the minimum visible satellites are not secured, position determination is impossible. Currently, the GLObal NAvigation Satellite system (GLONASS) of Russia is used to supplement the navigation performance of the Global Positioning System (GPS) in regions where GPS cannot be used. In addition, the European Satellite Navigation System (Galileo) of the European Union, the Chinese Satellite Navigation System (BeiDou) of China, the Quasi-Zenith Satellite System (QZSS) of Japan, and the Indian Regional Navigation Satellite System (IRNSS) of India are aimed to achieve the full operational capability (FOC) operation of the navigation system. Thus, the number of satellites available for navigation would rapidly increase, particularly in the Asian region; and when integrated navigation is performed, the improvement of navigation performance is expected to be much larger than that in other regions. To secure a stable and prompt position solution, GPS-GLONASS integrated navigation is generally performed at present. However, as available satellite navigation systems have been diversified, finding the minimum satellite constellation combination to obtain the best navigation performance has recently become an issue. For this purpose, it is necessary to examine and predict the navigation performance that could be obtained by the addition of the third satellite navigation system in addition to GPS-GLONASS. In this study, the current status of the integrated navigation performance for various satellite constellation combinations was analyzed based on 2014, and the navigation performance in 2020 was predicted based on the FOC plan of the satellite navigation system for each country. For this prediction, the orbital elements and nominal almanac data of satellite navigation systems that can be observed in the Korean Peninsula were organized, and the minimum elevation angle expecting signal shielding was established based on Matlab and the performance was predicted in terms of DOP. In the case of integrated navigation, a time offset determination algorithm needs to be considered in order to estimate the clock error between navigation systems, and it was analyzed using two kinds of methods: a satellite navigation message based estimation method and a receiver based method where a user directly performs estimation. This simulation is expected to be used as an index for the establishment of the minimum satellite constellation for obtaining the best navigation performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.25-31
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• 2016

Parasitic capacitance and resistance of FinFET transistors are the important components that determine the frequency performance of the circuit. Therefore, the researchers in our group developed more accurate parasitic capacitance and resistance for FinFETs than BSIM-CMG. To verify the RF performance, proposed model was applied to design an LNA that has $S_{21}$ more than 10dB and center frequency more than 60GHz using HSPICE. To verify the accuracy of the proposed model, mixed-mode capability of 3D TCAD simulator Sentaurus was used. $S_{21}$ of LNA was chosen as a reference to estimate the error. $S_{21}$ of proposed model showed 87.5% accuracy compared to that of Sentaurus in 10GHz~100GHz frequency range. The $S_{21}$ accuracy of BSIM-CMG model was 56.5%, so by using the proposed model, the accuracy of the circuit simulator improved by 31%. This results validates the accuracy of the proposed model in RF domain and show that the accuracies of the parasitic capacitance and resistance are critical in accurately predicting the LNA performance.

• Journal of Wetlands Research
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• v.22 no.1
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• pp.15-23
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• 2020

The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.